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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2006.04183 | Murat Sensoy | Murat Sensoy, Lance Kaplan, Federico Cerutti, Maryam Saleki | Uncertainty-Aware Deep Classifiers using Generative Models | This is a post-referred version of a conference paper published in
AAAI 2020 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks are often ignorant about what they do not know and
overconfident when they make uninformed predictions. Some recent approaches
quantify classification uncertainty directly by training the model to output
high uncertainty for the data samples close to class boundaries or from the
outside of the training distribution. These approaches use an auxiliary data
set during training to represent out-of-distribution samples. However,
selection or creation of such an auxiliary data set is non-trivial, especially
for high dimensional data such as images. In this work we develop a novel
neural network model that is able to express both aleatoric and epistemic
uncertainty to distinguish decision boundary and out-of-distribution regions of
the feature space. To this end, variational autoencoders and generative
adversarial networks are incorporated to automatically generate
out-of-distribution exemplars for training. Through extensive analysis, we
demonstrate that the proposed approach provides better estimates of uncertainty
for in- and out-of-distribution samples, and adversarial examples on well-known
data sets against state-of-the-art approaches including recent Bayesian
approaches for neural networks and anomaly detection methods.
| [
{
"created": "Sun, 7 Jun 2020 15:38:35 GMT",
"version": "v1"
}
] | 2020-06-09 | [
[
"Sensoy",
"Murat",
""
],
[
"Kaplan",
"Lance",
""
],
[
"Cerutti",
"Federico",
""
],
[
"Saleki",
"Maryam",
""
]
] | Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods. |
2212.05611 | Mustafa Taha Ko\c{c}yi\u{g}it | Mustafa Taha Ko\c{c}yi\u{g}it, Timothy M. Hospedales, Hakan Bilen | Accelerating Self-Supervised Learning via Efficient Training Strategies | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Recently the focus of the computer vision community has shifted from
expensive supervised learning towards self-supervised learning of visual
representations. While the performance gap between supervised and
self-supervised has been narrowing, the time for training self-supervised deep
networks remains an order of magnitude larger than its supervised counterparts,
which hinders progress, imposes carbon cost, and limits societal benefits to
institutions with substantial resources. Motivated by these issues, this paper
investigates reducing the training time of recent self-supervised methods by
various model-agnostic strategies that have not been used for this problem. In
particular, we study three strategies: an extendable cyclic learning rate
schedule, a matching progressive augmentation magnitude and image resolutions
schedule, and a hard positive mining strategy based on augmentation difficulty.
We show that all three methods combined lead up to 2.7 times speed-up in the
training time of several self-supervised methods while retaining comparable
performance to the standard self-supervised learning setting.
| [
{
"created": "Sun, 11 Dec 2022 21:49:39 GMT",
"version": "v1"
}
] | 2022-12-13 | [
[
"Koçyiğit",
"Mustafa Taha",
""
],
[
"Hospedales",
"Timothy M.",
""
],
[
"Bilen",
"Hakan",
""
]
] | Recently the focus of the computer vision community has shifted from expensive supervised learning towards self-supervised learning of visual representations. While the performance gap between supervised and self-supervised has been narrowing, the time for training self-supervised deep networks remains an order of magnitude larger than its supervised counterparts, which hinders progress, imposes carbon cost, and limits societal benefits to institutions with substantial resources. Motivated by these issues, this paper investigates reducing the training time of recent self-supervised methods by various model-agnostic strategies that have not been used for this problem. In particular, we study three strategies: an extendable cyclic learning rate schedule, a matching progressive augmentation magnitude and image resolutions schedule, and a hard positive mining strategy based on augmentation difficulty. We show that all three methods combined lead up to 2.7 times speed-up in the training time of several self-supervised methods while retaining comparable performance to the standard self-supervised learning setting. |
2403.13793 | Matthew Rahtz | Mary Phuong, Matthew Aitchison, Elliot Catt, Sarah Cogan, Alexandre
Kaskasoli, Victoria Krakovna, David Lindner, Matthew Rahtz, Yannis Assael,
Sarah Hodkinson, Heidi Howard, Tom Lieberum, Ramana Kumar, Maria Abi Raad,
Albert Webson, Lewis Ho, Sharon Lin, Sebastian Farquhar, Marcus Hutter,
Gregoire Deletang, Anian Ruoss, Seliem El-Sayed, Sasha Brown, Anca Dragan,
Rohin Shah, Allan Dafoe, Toby Shevlane | Evaluating Frontier Models for Dangerous Capabilities | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | To understand the risks posed by a new AI system, we must understand what it
can and cannot do. Building on prior work, we introduce a programme of new
"dangerous capability" evaluations and pilot them on Gemini 1.0 models. Our
evaluations cover four areas: (1) persuasion and deception; (2) cyber-security;
(3) self-proliferation; and (4) self-reasoning. We do not find evidence of
strong dangerous capabilities in the models we evaluated, but we flag early
warning signs. Our goal is to help advance a rigorous science of dangerous
capability evaluation, in preparation for future models.
| [
{
"created": "Wed, 20 Mar 2024 17:54:26 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Apr 2024 12:26:11 GMT",
"version": "v2"
}
] | 2024-04-08 | [
[
"Phuong",
"Mary",
""
],
[
"Aitchison",
"Matthew",
""
],
[
"Catt",
"Elliot",
""
],
[
"Cogan",
"Sarah",
""
],
[
"Kaskasoli",
"Alexandre",
""
],
[
"Krakovna",
"Victoria",
""
],
[
"Lindner",
"David",
""
],
[
"Rahtz",
"Matthew",
""
],
[
"Assael",
"Yannis",
""
],
[
"Hodkinson",
"Sarah",
""
],
[
"Howard",
"Heidi",
""
],
[
"Lieberum",
"Tom",
""
],
[
"Kumar",
"Ramana",
""
],
[
"Raad",
"Maria Abi",
""
],
[
"Webson",
"Albert",
""
],
[
"Ho",
"Lewis",
""
],
[
"Lin",
"Sharon",
""
],
[
"Farquhar",
"Sebastian",
""
],
[
"Hutter",
"Marcus",
""
],
[
"Deletang",
"Gregoire",
""
],
[
"Ruoss",
"Anian",
""
],
[
"El-Sayed",
"Seliem",
""
],
[
"Brown",
"Sasha",
""
],
[
"Dragan",
"Anca",
""
],
[
"Shah",
"Rohin",
""
],
[
"Dafoe",
"Allan",
""
],
[
"Shevlane",
"Toby",
""
]
] | To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new "dangerous capability" evaluations and pilot them on Gemini 1.0 models. Our evaluations cover four areas: (1) persuasion and deception; (2) cyber-security; (3) self-proliferation; and (4) self-reasoning. We do not find evidence of strong dangerous capabilities in the models we evaluated, but we flag early warning signs. Our goal is to help advance a rigorous science of dangerous capability evaluation, in preparation for future models. |
1504.05498 | Marc Torrellas | Marc Torrellas, Adrian Agustin and Josep Vidal | DoF-Delay Trade-Off for the $K$-user MIMO Interference Channel With
Delayed CSIT | 31 pages, 10 figures, 3 tables, submitted to the IEEE Transactions on
Information Theory | null | 10.1109/TIT.2016.2616348 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The degrees of freedom (DoF) of the $K$-user multiple-input multiple-output
(MIMO) interference channel are studied when perfect, but delayed channel state
information is available at the transmitter side (delayed CSIT). Recent works
have proposed schemes that achieve increasing DoF values, but at the cost of
long communication delays. This work proposes three linear precoding
strategies, formulated in such a way that the achievable DoF can be derived as
a function of the transmission delay, thus elucidating its achievable DoF-delay
trade-off. All strategies are based on the concept of interference alignment,
and built upon three main ingredients: delayed CSIT precoding, user scheduling,
and redundancy transmission. In this respect, the interference alignment is
realized by exploiting delayed CSIT in order to align the interference at the
non-intended receivers along the space-time domain. Finally, the latter part of
this work settles that all the proposed strategies work also for constant
channels, except for SISO. In such a case, the schemes can be made feasible by
resorting to asymmetric complex signaling concepts. This conclusion removes the
time-varying channels assumption unnecessarily common along all the literature
on delayed CSIT.
| [
{
"created": "Tue, 21 Apr 2015 16:14:23 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Jun 2015 19:51:55 GMT",
"version": "v2"
}
] | 2016-11-18 | [
[
"Torrellas",
"Marc",
""
],
[
"Agustin",
"Adrian",
""
],
[
"Vidal",
"Josep",
""
]
] | The degrees of freedom (DoF) of the $K$-user multiple-input multiple-output (MIMO) interference channel are studied when perfect, but delayed channel state information is available at the transmitter side (delayed CSIT). Recent works have proposed schemes that achieve increasing DoF values, but at the cost of long communication delays. This work proposes three linear precoding strategies, formulated in such a way that the achievable DoF can be derived as a function of the transmission delay, thus elucidating its achievable DoF-delay trade-off. All strategies are based on the concept of interference alignment, and built upon three main ingredients: delayed CSIT precoding, user scheduling, and redundancy transmission. In this respect, the interference alignment is realized by exploiting delayed CSIT in order to align the interference at the non-intended receivers along the space-time domain. Finally, the latter part of this work settles that all the proposed strategies work also for constant channels, except for SISO. In such a case, the schemes can be made feasible by resorting to asymmetric complex signaling concepts. This conclusion removes the time-varying channels assumption unnecessarily common along all the literature on delayed CSIT. |
2210.00094 | Amin Ghiasi | Amin Ghiasi, Ali Shafahi, Reza Ardekani | Improving Robustness with Adaptive Weight Decay | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose adaptive weight decay, which automatically tunes the
hyper-parameter for weight decay during each training iteration. For
classification problems, we propose changing the value of the weight decay
hyper-parameter on the fly based on the strength of updates from the
classification loss (i.e., gradient of cross-entropy), and the regularization
loss (i.e., $\ell_2$-norm of the weights). We show that this simple
modification can result in large improvements in adversarial robustness -- an
area which suffers from robust overfitting -- without requiring extra data
across various datasets and architecture choices. For example, our
reformulation results in $20\%$ relative robustness improvement for CIFAR-100,
and $10\%$ relative robustness improvement on CIFAR-10 comparing to the best
tuned hyper-parameters of traditional weight decay resulting in models that
have comparable performance to SOTA robustness methods. In addition, this
method has other desirable properties, such as less sensitivity to learning
rate, and smaller weight norms, which the latter contributes to robustness to
overfitting to label noise, and pruning.
| [
{
"created": "Fri, 30 Sep 2022 21:13:00 GMT",
"version": "v1"
},
{
"created": "Sat, 2 Dec 2023 01:27:27 GMT",
"version": "v2"
}
] | 2023-12-05 | [
[
"Ghiasi",
"Amin",
""
],
[
"Shafahi",
"Ali",
""
],
[
"Ardekani",
"Reza",
""
]
] | We propose adaptive weight decay, which automatically tunes the hyper-parameter for weight decay during each training iteration. For classification problems, we propose changing the value of the weight decay hyper-parameter on the fly based on the strength of updates from the classification loss (i.e., gradient of cross-entropy), and the regularization loss (i.e., $\ell_2$-norm of the weights). We show that this simple modification can result in large improvements in adversarial robustness -- an area which suffers from robust overfitting -- without requiring extra data across various datasets and architecture choices. For example, our reformulation results in $20\%$ relative robustness improvement for CIFAR-100, and $10\%$ relative robustness improvement on CIFAR-10 comparing to the best tuned hyper-parameters of traditional weight decay resulting in models that have comparable performance to SOTA robustness methods. In addition, this method has other desirable properties, such as less sensitivity to learning rate, and smaller weight norms, which the latter contributes to robustness to overfitting to label noise, and pruning. |
cs/0205028 | Steven Bird | Edward Loper and Steven Bird | NLTK: The Natural Language Toolkit | 8 pages, 1 figure, Proceedings of the ACL Workshop on Effective Tools
and Methodologies for Teaching Natural Language Processing and Computational
Linguistics, Philadelphia, July 2002, Association for Computational
Linguistics | null | null | null | cs.CL | null | NLTK, the Natural Language Toolkit, is a suite of open source program
modules, tutorials and problem sets, providing ready-to-use computational
linguistics courseware. NLTK covers symbolic and statistical natural language
processing, and is interfaced to annotated corpora. Students augment and
replace existing components, learn structured programming by example, and
manipulate sophisticated models from the outset.
| [
{
"created": "Fri, 17 May 2002 12:51:00 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Loper",
"Edward",
""
],
[
"Bird",
"Steven",
""
]
] | NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset. |
2008.03553 | Morteza Babaie | Aditya Sriram, Shivam Kalra, Morteza Babaie, Brady Kieffer, Waddah Al
Drobi, Shahryar Rahnamayan, Hany Kashani, Hamid R. Tizhoosh | Forming Local Intersections of Projections for Classifying and Searching
Histopathology Images | To appear in International Conference on AI in Medicine (AIME 2020) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a novel image descriptor called Forming Local
Intersections of Projections (FLIP) and its multi-resolution version (mFLIP)
for representing histopathology images. The descriptor is based on the Radon
transform wherein we apply parallel projections in small local neighborhoods of
gray-level images. Using equidistant projection directions in each window, we
extract unique and invariant characteristics of the neighborhood by taking the
intersection of adjacent projections. Thereafter, we construct a histogram for
each image, which we call the FLIP histogram. Various resolutions provide
different FLIP histograms which are then concatenated to form the mFLIP
descriptor. Our experiments included training common networks from scratch and
fine-tuning pre-trained networks to benchmark our proposed descriptor.
Experiments are conducted on the publicly available dataset KIMIA Path24 and
KIMIA Path960. For both of these datasets, FLIP and mFLIP descriptors show
promising results in all experiments.Using KIMIA Path24 data, FLIP outperformed
non-fine-tuned Inception-v3 and fine-tuned VGG16 and mFLIP outperformed
fine-tuned Inception-v3 in feature extracting.
| [
{
"created": "Sat, 8 Aug 2020 16:32:04 GMT",
"version": "v1"
}
] | 2020-08-11 | [
[
"Sriram",
"Aditya",
""
],
[
"Kalra",
"Shivam",
""
],
[
"Babaie",
"Morteza",
""
],
[
"Kieffer",
"Brady",
""
],
[
"Drobi",
"Waddah Al",
""
],
[
"Rahnamayan",
"Shahryar",
""
],
[
"Kashani",
"Hany",
""
],
[
"Tizhoosh",
"Hamid R.",
""
]
] | In this paper, we propose a novel image descriptor called Forming Local Intersections of Projections (FLIP) and its multi-resolution version (mFLIP) for representing histopathology images. The descriptor is based on the Radon transform wherein we apply parallel projections in small local neighborhoods of gray-level images. Using equidistant projection directions in each window, we extract unique and invariant characteristics of the neighborhood by taking the intersection of adjacent projections. Thereafter, we construct a histogram for each image, which we call the FLIP histogram. Various resolutions provide different FLIP histograms which are then concatenated to form the mFLIP descriptor. Our experiments included training common networks from scratch and fine-tuning pre-trained networks to benchmark our proposed descriptor. Experiments are conducted on the publicly available dataset KIMIA Path24 and KIMIA Path960. For both of these datasets, FLIP and mFLIP descriptors show promising results in all experiments.Using KIMIA Path24 data, FLIP outperformed non-fine-tuned Inception-v3 and fine-tuned VGG16 and mFLIP outperformed fine-tuned Inception-v3 in feature extracting. |
1704.04394 | Namhoon Lee | Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B. Choy, Philip H.
S. Torr and Manmohan Chandraker | DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting
Agents | Accepted at CVPR 2017 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a Deep Stochastic IOC RNN Encoderdecoder framework, DESIRE, for
the task of future predictions of multiple interacting agents in dynamic
scenes. DESIRE effectively predicts future locations of objects in multiple
scenes by 1) accounting for the multi-modal nature of the future prediction
(i.e., given the same context, future may vary), 2) foreseeing the potential
future outcomes and make a strategic prediction based on that, and 3) reasoning
not only from the past motion history, but also from the scene context as well
as the interactions among the agents. DESIRE achieves these in a single
end-to-end trainable neural network model, while being computationally
efficient. The model first obtains a diverse set of hypothetical future
prediction samples employing a conditional variational autoencoder, which are
ranked and refined by the following RNN scoring-regression module. Samples are
scored by accounting for accumulated future rewards, which enables better
long-term strategic decisions similar to IOC frameworks. An RNN scene context
fusion module jointly captures past motion histories, the semantic scene
context and interactions among multiple agents. A feedback mechanism iterates
over the ranking and refinement to further boost the prediction accuracy. We
evaluate our model on two publicly available datasets: KITTI and Stanford Drone
Dataset. Our experiments show that the proposed model significantly improves
the prediction accuracy compared to other baseline methods.
| [
{
"created": "Fri, 14 Apr 2017 11:15:44 GMT",
"version": "v1"
}
] | 2017-04-17 | [
[
"Lee",
"Namhoon",
""
],
[
"Choi",
"Wongun",
""
],
[
"Vernaza",
"Paul",
""
],
[
"Choy",
"Christopher B.",
""
],
[
"Torr",
"Philip H. S.",
""
],
[
"Chandraker",
"Manmohan",
""
]
] | We introduce a Deep Stochastic IOC RNN Encoderdecoder framework, DESIRE, for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE effectively predicts future locations of objects in multiple scenes by 1) accounting for the multi-modal nature of the future prediction (i.e., given the same context, future may vary), 2) foreseeing the potential future outcomes and make a strategic prediction based on that, and 3) reasoning not only from the past motion history, but also from the scene context as well as the interactions among the agents. DESIRE achieves these in a single end-to-end trainable neural network model, while being computationally efficient. The model first obtains a diverse set of hypothetical future prediction samples employing a conditional variational autoencoder, which are ranked and refined by the following RNN scoring-regression module. Samples are scored by accounting for accumulated future rewards, which enables better long-term strategic decisions similar to IOC frameworks. An RNN scene context fusion module jointly captures past motion histories, the semantic scene context and interactions among multiple agents. A feedback mechanism iterates over the ranking and refinement to further boost the prediction accuracy. We evaluate our model on two publicly available datasets: KITTI and Stanford Drone Dataset. Our experiments show that the proposed model significantly improves the prediction accuracy compared to other baseline methods. |
2011.11636 | Chun Yui Wong | Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Andrew B. Duncan,
Geoffrey Parks | Blade Envelopes Part I: Concept and Methodology | null | null | null | null | cs.CE stat.AP | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Blades manufactured through flank and point milling will likely exhibit
geometric variability. Gauging the aerodynamic repercussions of such
variability, prior to manufacturing a component, is challenging enough, let
alone trying to predict what the amplified impact of any in-service degradation
will be. While rules of thumb that govern the tolerance band can be devised
based on expected boundary layer characteristics at known regions and levels of
degradation, it remains a challenge to translate these insights into
quantitative bounds for manufacturing. In this work, we tackle this challenge
by leveraging ideas from dimension reduction to construct low-dimensional
representations of aerodynamic performance metrics. These low-dimensional
models can identify a subspace which contains designs that are invariant in
performance -- the inactive subspace. By sampling within this subspace, we
design techniques for drafting manufacturing tolerances and for quantifying
whether a scanned component should be used or scrapped. We introduce the blade
envelope as a computational manufacturing guide for a blade that is also
amenable to qualitative visualizations. In this paper, the first of two parts,
we discuss its underlying concept and detail its computational methodology,
assuming one is interested only in the single objective of ensuring that the
loss of all manufactured blades remains constant. To demonstrate the utility of
our ideas we devise a series of computational experiments with the Von Karman
Institute's LS89 turbine blade.
| [
{
"created": "Sun, 22 Nov 2020 11:34:59 GMT",
"version": "v1"
},
{
"created": "Mon, 21 Dec 2020 17:21:51 GMT",
"version": "v2"
},
{
"created": "Fri, 25 Dec 2020 14:39:52 GMT",
"version": "v3"
},
{
"created": "Tue, 5 Jan 2021 15:03:16 GMT",
"version": "v4"
},
{
"created": "Fri, 31 Dec 2021 11:15:20 GMT",
"version": "v5"
}
] | 2022-01-03 | [
[
"Wong",
"Chun Yui",
""
],
[
"Seshadri",
"Pranay",
""
],
[
"Scillitoe",
"Ashley",
""
],
[
"Duncan",
"Andrew B.",
""
],
[
"Parks",
"Geoffrey",
""
]
] | Blades manufactured through flank and point milling will likely exhibit geometric variability. Gauging the aerodynamic repercussions of such variability, prior to manufacturing a component, is challenging enough, let alone trying to predict what the amplified impact of any in-service degradation will be. While rules of thumb that govern the tolerance band can be devised based on expected boundary layer characteristics at known regions and levels of degradation, it remains a challenge to translate these insights into quantitative bounds for manufacturing. In this work, we tackle this challenge by leveraging ideas from dimension reduction to construct low-dimensional representations of aerodynamic performance metrics. These low-dimensional models can identify a subspace which contains designs that are invariant in performance -- the inactive subspace. By sampling within this subspace, we design techniques for drafting manufacturing tolerances and for quantifying whether a scanned component should be used or scrapped. We introduce the blade envelope as a computational manufacturing guide for a blade that is also amenable to qualitative visualizations. In this paper, the first of two parts, we discuss its underlying concept and detail its computational methodology, assuming one is interested only in the single objective of ensuring that the loss of all manufactured blades remains constant. To demonstrate the utility of our ideas we devise a series of computational experiments with the Von Karman Institute's LS89 turbine blade. |
2408.07137 | Yutong Hu | Yutong Hu and Kangcheng Luo and Yansong Feng | ELLA: Empowering LLMs for Interpretable, Accurate and Informative Legal
Advice | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Despite remarkable performance in legal consultation exhibited by legal Large
Language Models(LLMs) combined with legal article retrieval components, there
are still cases when the advice given is incorrect or baseless. To alleviate
these problems, we propose {\bf ELLA}, a tool for {\bf E}mpowering {\bf L}LMs
for interpretable, accurate, and informative {\bf L}egal {\bf A}dvice. ELLA
visually presents the correlation between legal articles and LLM's response by
calculating their similarities, providing users with an intuitive legal basis
for the responses. Besides, based on the users' queries, ELLA retrieves
relevant legal articles and displays them to users. Users can interactively
select legal articles for LLM to generate more accurate responses. ELLA also
retrieves relevant legal cases for user reference. Our user study shows that
presenting the legal basis for the response helps users understand better. The
accuracy of LLM's responses also improves when users intervene in selecting
legal articles for LLM. Providing relevant legal cases also aids individuals in
obtaining comprehensive information.
| [
{
"created": "Tue, 13 Aug 2024 18:12:00 GMT",
"version": "v1"
}
] | 2024-08-15 | [
[
"Hu",
"Yutong",
""
],
[
"Luo",
"Kangcheng",
""
],
[
"Feng",
"Yansong",
""
]
] | Despite remarkable performance in legal consultation exhibited by legal Large Language Models(LLMs) combined with legal article retrieval components, there are still cases when the advice given is incorrect or baseless. To alleviate these problems, we propose {\bf ELLA}, a tool for {\bf E}mpowering {\bf L}LMs for interpretable, accurate, and informative {\bf L}egal {\bf A}dvice. ELLA visually presents the correlation between legal articles and LLM's response by calculating their similarities, providing users with an intuitive legal basis for the responses. Besides, based on the users' queries, ELLA retrieves relevant legal articles and displays them to users. Users can interactively select legal articles for LLM to generate more accurate responses. ELLA also retrieves relevant legal cases for user reference. Our user study shows that presenting the legal basis for the response helps users understand better. The accuracy of LLM's responses also improves when users intervene in selecting legal articles for LLM. Providing relevant legal cases also aids individuals in obtaining comprehensive information. |
2006.09184 | Amir Ahmad | Amir Ahmada, Sunita Garhwal, Santosh Kumar Ray, Gagan Kumar, Sharaf J.
Malebary, Omar Mohammed Omar Barukab | The Number of Confirmed Cases of Covid-19 by using Machine Learning:
Methods and Challenges | null | null | null | null | cs.SI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Covid-19 is one of the biggest health challenges that the world has ever
faced. Public health policy makers need the reliable prediction of the
confirmed cases in future to plan medical facilities. Machine learning methods
learn from the historical data and make a prediction about the event. Machine
learning methods have been used to predict the number of confirmed cases of
Covid-19. In this paper, we present a detailed review of these research papers.
We present a taxonomy that groups them in four categories. We further present
the challenges in this field. We provide suggestions to the machine learning
practitioners to improve the performance of machine learning methods for the
prediction of confirmed cases of Covid-19.
| [
{
"created": "Thu, 11 Jun 2020 15:34:59 GMT",
"version": "v1"
}
] | 2020-06-17 | [
[
"Ahmada",
"Amir",
""
],
[
"Garhwal",
"Sunita",
""
],
[
"Ray",
"Santosh Kumar",
""
],
[
"Kumar",
"Gagan",
""
],
[
"Malebary",
"Sharaf J.",
""
],
[
"Barukab",
"Omar Mohammed Omar",
""
]
] | Covid-19 is one of the biggest health challenges that the world has ever faced. Public health policy makers need the reliable prediction of the confirmed cases in future to plan medical facilities. Machine learning methods learn from the historical data and make a prediction about the event. Machine learning methods have been used to predict the number of confirmed cases of Covid-19. In this paper, we present a detailed review of these research papers. We present a taxonomy that groups them in four categories. We further present the challenges in this field. We provide suggestions to the machine learning practitioners to improve the performance of machine learning methods for the prediction of confirmed cases of Covid-19. |
2202.00866 | Qimeng Wang | Yan Gao and Qimeng Wang and Xu Tang and Haochen Wang and Fei Ding and
Jing Li and Yao Hu | Decoupled IoU Regression for Object Detection | ACMMM 2021 Poster | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Non-maximum suppression (NMS) is widely used in object detection pipelines
for removing duplicated bounding boxes. The inconsistency between the
confidence for NMS and the real localization confidence seriously affects
detection performance. Prior works propose to predict Intersection-over-Union
(IoU) between bounding boxes and corresponding ground-truths to improve NMS,
while accurately predicting IoU is still a challenging problem. We argue that
the complex definition of IoU and feature misalignment make it difficult to
predict IoU accurately. In this paper, we propose a novel Decoupled IoU
Regression (DIR) model to handle these problems. The proposed DIR decouples the
traditional localization confidence metric IoU into two new metrics, Purity and
Integrity. Purity reflects the proportion of the object area in the detected
bounding box, and Integrity refers to the completeness of the detected object
area. Separately predicting Purity and Integrity can divide the complex mapping
between the bounding box and its IoU into two clearer mappings and model them
independently. In addition, a simple but effective feature realignment approach
is also introduced to make the IoU regressor work in a hindsight manner, which
can make the target mapping more stable. The proposed DIR can be conveniently
integrated with existing two-stage detectors and significantly improve their
performance. Through a simple implementation of DIR with HTC, we obtain 51.3%
AP on MS COCO benchmark, which outperforms previous methods and achieves
state-of-the-art.
| [
{
"created": "Wed, 2 Feb 2022 04:01:11 GMT",
"version": "v1"
}
] | 2022-02-03 | [
[
"Gao",
"Yan",
""
],
[
"Wang",
"Qimeng",
""
],
[
"Tang",
"Xu",
""
],
[
"Wang",
"Haochen",
""
],
[
"Ding",
"Fei",
""
],
[
"Li",
"Jing",
""
],
[
"Hu",
"Yao",
""
]
] | Non-maximum suppression (NMS) is widely used in object detection pipelines for removing duplicated bounding boxes. The inconsistency between the confidence for NMS and the real localization confidence seriously affects detection performance. Prior works propose to predict Intersection-over-Union (IoU) between bounding boxes and corresponding ground-truths to improve NMS, while accurately predicting IoU is still a challenging problem. We argue that the complex definition of IoU and feature misalignment make it difficult to predict IoU accurately. In this paper, we propose a novel Decoupled IoU Regression (DIR) model to handle these problems. The proposed DIR decouples the traditional localization confidence metric IoU into two new metrics, Purity and Integrity. Purity reflects the proportion of the object area in the detected bounding box, and Integrity refers to the completeness of the detected object area. Separately predicting Purity and Integrity can divide the complex mapping between the bounding box and its IoU into two clearer mappings and model them independently. In addition, a simple but effective feature realignment approach is also introduced to make the IoU regressor work in a hindsight manner, which can make the target mapping more stable. The proposed DIR can be conveniently integrated with existing two-stage detectors and significantly improve their performance. Through a simple implementation of DIR with HTC, we obtain 51.3% AP on MS COCO benchmark, which outperforms previous methods and achieves state-of-the-art. |
2111.12056 | Jinu Gong | Jinu Gong, Osvaldo Simeone, Rahif Kassab, and Joonhyuk Kang | Forget-SVGD: Particle-Based Bayesian Federated Unlearning | submitted for conference publication | null | null | null | cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Variational particle-based Bayesian learning methods have the advantage of
not being limited by the bias affecting more conventional parametric
techniques. This paper proposes to leverage the flexibility of non-parametric
Bayesian approximate inference to develop a novel Bayesian federated unlearning
method, referred to as Forget-Stein Variational Gradient Descent (Forget-SVGD).
Forget-SVGD builds on SVGD - a particle-based approximate Bayesian inference
scheme using gradient-based deterministic updates - and on its distributed
(federated) extension known as Distributed SVGD (DSVGD). Upon the completion of
federated learning, as one or more participating agents request for their data
to be "forgotten", Forget-SVGD carries out local SVGD updates at the agents
whose data need to be "unlearned", which are interleaved with communication
rounds with a parameter server. The proposed method is validated via
performance comparisons with non-parametric schemes that train from scratch by
excluding data to be forgotten, as well as with existing parametric Bayesian
unlearning methods.
| [
{
"created": "Tue, 23 Nov 2021 18:15:50 GMT",
"version": "v1"
}
] | 2021-11-24 | [
[
"Gong",
"Jinu",
""
],
[
"Simeone",
"Osvaldo",
""
],
[
"Kassab",
"Rahif",
""
],
[
"Kang",
"Joonhyuk",
""
]
] | Variational particle-based Bayesian learning methods have the advantage of not being limited by the bias affecting more conventional parametric techniques. This paper proposes to leverage the flexibility of non-parametric Bayesian approximate inference to develop a novel Bayesian federated unlearning method, referred to as Forget-Stein Variational Gradient Descent (Forget-SVGD). Forget-SVGD builds on SVGD - a particle-based approximate Bayesian inference scheme using gradient-based deterministic updates - and on its distributed (federated) extension known as Distributed SVGD (DSVGD). Upon the completion of federated learning, as one or more participating agents request for their data to be "forgotten", Forget-SVGD carries out local SVGD updates at the agents whose data need to be "unlearned", which are interleaved with communication rounds with a parameter server. The proposed method is validated via performance comparisons with non-parametric schemes that train from scratch by excluding data to be forgotten, as well as with existing parametric Bayesian unlearning methods. |
2010.11334 | Chiyu Zhang | Muhammad Abdul-Mageed, Chiyu Zhang, Houda Bouamor and Nizar Habash | NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task | Accepted in The Fifth Arabic Natural Language Processing Workshop
(WANLP 2020) | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present the results and findings of the First Nuanced Arabic Dialect
Identification Shared Task (NADI). This Shared Task includes two subtasks:
country-level dialect identification (Subtask 1) and province-level sub-dialect
identification (Subtask 2). The data for the shared task covers a total of 100
provinces from 21 Arab countries and are collected from the Twitter domain. As
such, NADI is the first shared task to target naturally-occurring fine-grained
dialectal text at the sub-country level. A total of 61 teams from 25 countries
registered to participate in the tasks, thus reflecting the interest of the
community in this area. We received 47 submissions for Subtask 1 from 18 teams
and 9 submissions for Subtask 2 from 9 teams.
| [
{
"created": "Wed, 21 Oct 2020 22:14:28 GMT",
"version": "v1"
},
{
"created": "Sun, 1 Nov 2020 04:53:19 GMT",
"version": "v2"
},
{
"created": "Mon, 9 Nov 2020 19:18:33 GMT",
"version": "v3"
}
] | 2020-11-11 | [
[
"Abdul-Mageed",
"Muhammad",
""
],
[
"Zhang",
"Chiyu",
""
],
[
"Bouamor",
"Houda",
""
],
[
"Habash",
"Nizar",
""
]
] | We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI). This Shared Task includes two subtasks: country-level dialect identification (Subtask 1) and province-level sub-dialect identification (Subtask 2). The data for the shared task covers a total of 100 provinces from 21 Arab countries and are collected from the Twitter domain. As such, NADI is the first shared task to target naturally-occurring fine-grained dialectal text at the sub-country level. A total of 61 teams from 25 countries registered to participate in the tasks, thus reflecting the interest of the community in this area. We received 47 submissions for Subtask 1 from 18 teams and 9 submissions for Subtask 2 from 9 teams. |
1912.01146 | Basilis Mamalis | Basilis Mamalis | Prolonging Network Lifetime in Wireless Sensor Networks with
Path-Constrained Mobile Sink | 10 pages | International Journal of Advanced Computer Science and
Applications, Vol. 5, No. 10, 2014 | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many studies in recent years have considered the use of mobile sinks (MS) for
data gathering in wireless sensor networks (WSN), so as to reduce the need for
data forwarding among the sensor nodes (SN) and thereby prolong the network
lifetime. Moreover, in practice, often the MS tour length has to be kept below
a threshold, usually due to timeliness constraints on the sensors data
(delay-critical applications). This paper presents a modified clustering and
data forwarding protocol combined with a MS solution for efficient data
gathering in wireless sensor networks (WSNs) with delay constraints. The
adopted cluster formation method is based in the 'residual energy' of the SNs
and it is appropriately modified in order to fit properly to the requirement of
length-constrained MS tour, which involves, among else, the need for
inter-cluster communication and increased data forwarding. In addition, a
suitable data gathering protocol is designed, based on an approximated TSP
route that satisfies the given length constraint, whereas the proper
application of reclustering phases guarantees the effective handling of the
'energy holes' caused around the CHs involved in the MS route. Extended
simulation experiments show the stable and energy-efficient behavior of the
proposed scheme (thus leading to increased network lifetime) as well as its
higher performance in comparison to other competent approaches from the
literature.
| [
{
"created": "Tue, 3 Dec 2019 01:30:44 GMT",
"version": "v1"
}
] | 2019-12-04 | [
[
"Mamalis",
"Basilis",
""
]
] | Many studies in recent years have considered the use of mobile sinks (MS) for data gathering in wireless sensor networks (WSN), so as to reduce the need for data forwarding among the sensor nodes (SN) and thereby prolong the network lifetime. Moreover, in practice, often the MS tour length has to be kept below a threshold, usually due to timeliness constraints on the sensors data (delay-critical applications). This paper presents a modified clustering and data forwarding protocol combined with a MS solution for efficient data gathering in wireless sensor networks (WSNs) with delay constraints. The adopted cluster formation method is based in the 'residual energy' of the SNs and it is appropriately modified in order to fit properly to the requirement of length-constrained MS tour, which involves, among else, the need for inter-cluster communication and increased data forwarding. In addition, a suitable data gathering protocol is designed, based on an approximated TSP route that satisfies the given length constraint, whereas the proper application of reclustering phases guarantees the effective handling of the 'energy holes' caused around the CHs involved in the MS route. Extended simulation experiments show the stable and energy-efficient behavior of the proposed scheme (thus leading to increased network lifetime) as well as its higher performance in comparison to other competent approaches from the literature. |
2003.00406 | Xiao Wang | Sulan Zhai, Shunqiang Liu, Xiao Wang, Jin Tang | FMT:Fusing Multi-task Convolutional Neural Network for Person Search | Published on Multimedia Tools and Applications | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Person search is to detect all persons and identify the query persons from
detected persons in the image without proposals and bounding boxes, which is
different from person re-identification. In this paper, we propose a fusing
multi-task convolutional neural network(FMT-CNN) to tackle the correlation and
heterogeneity of detection and re-identification with a single convolutional
neural network. We focus on how the interplay of person detection and person
re-identification affects the overall performance. We employ person labels in
region proposal network to produce features for person re-identification and
person detection network, which can improve the accuracy of detection and
re-identification simultaneously. We also use a multiple loss to train our
re-identification network. Experiment results on CUHK-SYSU Person Search
dataset show that the performance of our proposed method is superior to
state-of-the-art approaches in both mAP and top-1.
| [
{
"created": "Sun, 1 Mar 2020 05:20:47 GMT",
"version": "v1"
}
] | 2020-03-03 | [
[
"Zhai",
"Sulan",
""
],
[
"Liu",
"Shunqiang",
""
],
[
"Wang",
"Xiao",
""
],
[
"Tang",
"Jin",
""
]
] | Person search is to detect all persons and identify the query persons from detected persons in the image without proposals and bounding boxes, which is different from person re-identification. In this paper, we propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification with a single convolutional neural network. We focus on how the interplay of person detection and person re-identification affects the overall performance. We employ person labels in region proposal network to produce features for person re-identification and person detection network, which can improve the accuracy of detection and re-identification simultaneously. We also use a multiple loss to train our re-identification network. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches in both mAP and top-1. |
2012.05054 | Huansheng Ning Prof | Huansheng Ning and Feifei Shi | Could robots be regarded as humans in future? | 4 pages, 1 table | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the overwhelming advances in Artificial Intelligence (AI), brain science
and neuroscience, robots are developing towards a direction of much more
human-like and human-friendly. We can't help but wonder whether robots could be
regarded as humans in future? In this article, we propose a novel perspective
to analyze the essential difference between humans and robots, that is based on
their respective living spaces, particularly the independent and intrinsic
thinking space. We finally come to the conclusion that, only when robots own
the independent and intrinsic thinking space as humans, could they have the
prerequisites to be regarded as humans.
| [
{
"created": "Tue, 1 Dec 2020 13:04:08 GMT",
"version": "v1"
}
] | 2020-12-10 | [
[
"Ning",
"Huansheng",
""
],
[
"Shi",
"Feifei",
""
]
] | With the overwhelming advances in Artificial Intelligence (AI), brain science and neuroscience, robots are developing towards a direction of much more human-like and human-friendly. We can't help but wonder whether robots could be regarded as humans in future? In this article, we propose a novel perspective to analyze the essential difference between humans and robots, that is based on their respective living spaces, particularly the independent and intrinsic thinking space. We finally come to the conclusion that, only when robots own the independent and intrinsic thinking space as humans, could they have the prerequisites to be regarded as humans. |
1210.2195 | J\"org P\"uhrer | Marina De Vos, Do\u{g}a Gizem K{\i}za, Johannes Oetsch, J\"org
P\"uhrer, Hans Tompits | Annotating Answer-Set Programs in LANA? | null | null | null | null | cs.SE cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While past research in answer-set programming (ASP) mainly focused on theory,
ASP solver technology, and applications, the present work situates itself in
the context of a quite recent research trend: development support for ASP. In
particular, we propose to augment answer-set programs with additional
meta-information formulated in a dedicated annotation language, called LANA.
This language allows the grouping of rules into coherent blocks and to specify
language signatures, types, pre- and postconditions, as well as unit tests for
such blocks. While these annotations are invisible to an ASP solver, as they
take the form of program comments, they can be interpreted by tools for
documentation, testing, and verification purposes, as well as to eliminate
sources of common programming errors by realising syntax checking or code
completion features. To demonstrate its versatility, we introduce two such
tools, viz. (i) ASPDOC, for generating an HTML documentation for a program
based on the annotated information, and (ii) ASPUNIT, for running and
monitoring unit tests on program blocks. LANA is also exploited in the SeaLion
system, an integrated development environment for ASP based on Eclipse. To
appear in Theory and Practice of Logic Programming
| [
{
"created": "Mon, 8 Oct 2012 09:26:15 GMT",
"version": "v1"
}
] | 2012-10-09 | [
[
"De Vos",
"Marina",
""
],
[
"Kıza",
"Doğa Gizem",
""
],
[
"Oetsch",
"Johannes",
""
],
[
"Pührer",
"Jörg",
""
],
[
"Tompits",
"Hans",
""
]
] | While past research in answer-set programming (ASP) mainly focused on theory, ASP solver technology, and applications, the present work situates itself in the context of a quite recent research trend: development support for ASP. In particular, we propose to augment answer-set programs with additional meta-information formulated in a dedicated annotation language, called LANA. This language allows the grouping of rules into coherent blocks and to specify language signatures, types, pre- and postconditions, as well as unit tests for such blocks. While these annotations are invisible to an ASP solver, as they take the form of program comments, they can be interpreted by tools for documentation, testing, and verification purposes, as well as to eliminate sources of common programming errors by realising syntax checking or code completion features. To demonstrate its versatility, we introduce two such tools, viz. (i) ASPDOC, for generating an HTML documentation for a program based on the annotated information, and (ii) ASPUNIT, for running and monitoring unit tests on program blocks. LANA is also exploited in the SeaLion system, an integrated development environment for ASP based on Eclipse. To appear in Theory and Practice of Logic Programming |
0909.5278 | Yngve Villanger | Fedor V. Fomin and Yngve Villanger | Finding Induced Subgraphs via Minimal Triangulations | 14 pages | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Potential maximal cliques and minimal separators are combinatorial objects
which were introduced and studied in the realm of minimal triangulations
problems including Minimum Fill-in and Treewidth. We discover unexpected
applications of these notions to the field of moderate exponential algorithms.
In particular, we show that given an n-vertex graph G together with its set of
potential maximal cliques Pi_G, and an integer t, it is possible in time |Pi_G|
* n^(O(t)) to find a maximum induced subgraph of treewidth t in G; and for a
given graph F of treewidth t, to decide if G contains an induced subgraph
isomorphic to F. Combined with an improved algorithm enumerating all potential
maximal cliques in time O(1.734601^n), this yields that both problems are
solvable in time 1.734601^n * n^(O(t)).
| [
{
"created": "Tue, 29 Sep 2009 07:13:39 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Dec 2009 09:55:02 GMT",
"version": "v2"
}
] | 2009-12-22 | [
[
"Fomin",
"Fedor V.",
""
],
[
"Villanger",
"Yngve",
""
]
] | Potential maximal cliques and minimal separators are combinatorial objects which were introduced and studied in the realm of minimal triangulations problems including Minimum Fill-in and Treewidth. We discover unexpected applications of these notions to the field of moderate exponential algorithms. In particular, we show that given an n-vertex graph G together with its set of potential maximal cliques Pi_G, and an integer t, it is possible in time |Pi_G| * n^(O(t)) to find a maximum induced subgraph of treewidth t in G; and for a given graph F of treewidth t, to decide if G contains an induced subgraph isomorphic to F. Combined with an improved algorithm enumerating all potential maximal cliques in time O(1.734601^n), this yields that both problems are solvable in time 1.734601^n * n^(O(t)). |
2305.16532 | Amir Samad | Amir Samadi, Konstantinos Koufos, Kurt Debattista and Mehrdad Dianati | Counterfactual Explainer Framework for Deep Reinforcement Learning
Models Using Policy Distillation | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Deep Reinforcement Learning (DRL) has demonstrated promising capability in
solving complex control problems. However, DRL applications in safety-critical
systems are hindered by the inherent lack of robust verification techniques to
assure their performance in such applications. One of the key requirements of
the verification process is the development of effective techniques to explain
the system functionality, i.e., why the system produces specific results in
given circumstances. Recently, interpretation methods based on the
Counterfactual (CF) explanation approach have been proposed to address the
problem of explanation in DRLs. This paper proposes a novel CF explanation
framework to explain the decisions made by a black-box DRL. To evaluate the
efficacy of the proposed explanation framework, we carried out several
experiments in the domains of automated driving systems and Atari Pong game.
Our analysis demonstrates that the proposed framework generates plausible and
meaningful explanations for various decisions made by deep underlying DRLs.
Source codes are available at:
\url{https://github.com/Amir-Samadi/Counterfactual-Explanation}
| [
{
"created": "Thu, 25 May 2023 23:30:48 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Jun 2023 12:20:53 GMT",
"version": "v2"
},
{
"created": "Sat, 7 Oct 2023 11:34:25 GMT",
"version": "v3"
}
] | 2023-10-10 | [
[
"Samadi",
"Amir",
""
],
[
"Koufos",
"Konstantinos",
""
],
[
"Debattista",
"Kurt",
""
],
[
"Dianati",
"Mehrdad",
""
]
] | Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems. However, DRL applications in safety-critical systems are hindered by the inherent lack of robust verification techniques to assure their performance in such applications. One of the key requirements of the verification process is the development of effective techniques to explain the system functionality, i.e., why the system produces specific results in given circumstances. Recently, interpretation methods based on the Counterfactual (CF) explanation approach have been proposed to address the problem of explanation in DRLs. This paper proposes a novel CF explanation framework to explain the decisions made by a black-box DRL. To evaluate the efficacy of the proposed explanation framework, we carried out several experiments in the domains of automated driving systems and Atari Pong game. Our analysis demonstrates that the proposed framework generates plausible and meaningful explanations for various decisions made by deep underlying DRLs. Source codes are available at: \url{https://github.com/Amir-Samadi/Counterfactual-Explanation} |
2004.14571 | Sadasivam Aadhavan | Aadhavan Sadasivam, Kausic Gunasekar, Hasan Davulcu, Yezhou Yang | memeBot: Towards Automatic Image Meme Generation | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Image memes have become a widespread tool used by people for interacting and
exchanging ideas over social media, blogs, and open messengers. This work
proposes to treat automatic image meme generation as a translation process, and
further present an end to end neural and probabilistic approach to generate an
image-based meme for any given sentence using an encoder-decoder architecture.
For a given input sentence, an image meme is generated by combining a meme
template image and a text caption where the meme template image is selected
from a set of popular candidates using a selection module, and the meme caption
is generated by an encoder-decoder model. An encoder is used to map the
selected meme template and the input sentence into a meme embedding and a
decoder is used to decode the meme caption from the meme embedding. The
generated natural language meme caption is conditioned on the input sentence
and the selected meme template. The model learns the dependencies between the
meme captions and the meme template images and generates new memes using the
learned dependencies. The quality of the generated captions and the generated
memes is evaluated through both automated and human evaluation. An experiment
is designed to score how well the generated memes can represent the tweets from
Twitter conversations. Experiments on Twitter data show the efficacy of the
model in generating memes for sentences in online social interaction.
| [
{
"created": "Thu, 30 Apr 2020 03:48:14 GMT",
"version": "v1"
}
] | 2020-05-01 | [
[
"Sadasivam",
"Aadhavan",
""
],
[
"Gunasekar",
"Kausic",
""
],
[
"Davulcu",
"Hasan",
""
],
[
"Yang",
"Yezhou",
""
]
] | Image memes have become a widespread tool used by people for interacting and exchanging ideas over social media, blogs, and open messengers. This work proposes to treat automatic image meme generation as a translation process, and further present an end to end neural and probabilistic approach to generate an image-based meme for any given sentence using an encoder-decoder architecture. For a given input sentence, an image meme is generated by combining a meme template image and a text caption where the meme template image is selected from a set of popular candidates using a selection module, and the meme caption is generated by an encoder-decoder model. An encoder is used to map the selected meme template and the input sentence into a meme embedding and a decoder is used to decode the meme caption from the meme embedding. The generated natural language meme caption is conditioned on the input sentence and the selected meme template. The model learns the dependencies between the meme captions and the meme template images and generates new memes using the learned dependencies. The quality of the generated captions and the generated memes is evaluated through both automated and human evaluation. An experiment is designed to score how well the generated memes can represent the tweets from Twitter conversations. Experiments on Twitter data show the efficacy of the model in generating memes for sentences in online social interaction. |
2311.01599 | Bogdan Chornomaz | Zachary Chase, Bogdan Chornomaz, Shay Moran, Amir Yehudayoff | Local Borsuk-Ulam, Stability, and Replicability | null | null | null | null | cs.LG cs.DS | http://creativecommons.org/licenses/by/4.0/ | We use and adapt the Borsuk-Ulam Theorem from topology to derive limitations
on list-replicable and globally stable learning algorithms. We further
demonstrate the applicability of our methods in combinatorics and topology.
We show that, besides trivial cases, both list-replicable and globally stable
learning are impossible in the agnostic PAC setting. This is in contrast with
the realizable case where it is known that any class with a finite Littlestone
dimension can be learned by such algorithms. In the realizable PAC setting, we
sharpen previous impossibility results and broaden their scope. Specifically,
we establish optimal bounds for list replicability and global stability numbers
in finite classes. This provides an exponential improvement over previous works
and implies an exponential separation from the Littlestone dimension. We
further introduce lower bounds for weak learners, i.e., learners that are only
marginally better than random guessing. Lower bounds from previous works apply
only to stronger learners.
To offer a broader and more comprehensive view of our topological approach,
we prove a local variant of the Borsuk-Ulam theorem in topology and a result in
combinatorics concerning Kneser colorings. In combinatorics, we prove that if
$c$ is a coloring of all non-empty subsets of $[n]$ such that disjoint sets
have different colors, then there is a chain of subsets that receives at least
$1+ \lfloor n/2\rfloor$ colors (this bound is sharp). In topology, we prove
e.g. that for any open antipodal-free cover of the $d$-dimensional sphere,
there is a point $x$ that belongs to at least $t=\lceil\frac{d+3}{2}\rceil$
sets.
| [
{
"created": "Thu, 2 Nov 2023 21:10:16 GMT",
"version": "v1"
}
] | 2023-11-06 | [
[
"Chase",
"Zachary",
""
],
[
"Chornomaz",
"Bogdan",
""
],
[
"Moran",
"Shay",
""
],
[
"Yehudayoff",
"Amir",
""
]
] | We use and adapt the Borsuk-Ulam Theorem from topology to derive limitations on list-replicable and globally stable learning algorithms. We further demonstrate the applicability of our methods in combinatorics and topology. We show that, besides trivial cases, both list-replicable and globally stable learning are impossible in the agnostic PAC setting. This is in contrast with the realizable case where it is known that any class with a finite Littlestone dimension can be learned by such algorithms. In the realizable PAC setting, we sharpen previous impossibility results and broaden their scope. Specifically, we establish optimal bounds for list replicability and global stability numbers in finite classes. This provides an exponential improvement over previous works and implies an exponential separation from the Littlestone dimension. We further introduce lower bounds for weak learners, i.e., learners that are only marginally better than random guessing. Lower bounds from previous works apply only to stronger learners. To offer a broader and more comprehensive view of our topological approach, we prove a local variant of the Borsuk-Ulam theorem in topology and a result in combinatorics concerning Kneser colorings. In combinatorics, we prove that if $c$ is a coloring of all non-empty subsets of $[n]$ such that disjoint sets have different colors, then there is a chain of subsets that receives at least $1+ \lfloor n/2\rfloor$ colors (this bound is sharp). In topology, we prove e.g. that for any open antipodal-free cover of the $d$-dimensional sphere, there is a point $x$ that belongs to at least $t=\lceil\frac{d+3}{2}\rceil$ sets. |
2006.03722 | Michael Fauss | Michael Fau{\ss}, Alex Dysto, H. Vincent Poor | MMSE Bounds Under Kullback-Leibler Divergence Constraints on the Joint
Input-Output Distribution | Submitted for publication in the IEEE Transactions on Signal
Processing | null | null | null | cs.IT math.IT stat.OT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes a new family of lower and upper bounds on the minimum
mean squared error (MMSE). The key idea is to minimize/maximize the MMSE
subject to the constraint that the joint distribution of the input-output
statistics lies in a Kullback-Leibler divergence ball centered at some Gaussian
reference distribution. Both bounds are tight and are attained by Gaussian
distributions whose mean is identical to that of the reference distribution and
whose covariance matrix is determined by a scalar parameter that can be
obtained by finding the root of a monotonic function. The upper bound
corresponds to a minimax optimal estimator and provides performance guarantees
under distributional uncertainty. The lower bound provides an alternative to
well-known inequalities in estimation theory, such as the Cram\'er-Rao bound,
that is potentially tighter and defined for a larger class of distributions.
Examples of applications in signal processing and information theory illustrate
the usefulness of the proposed bounds in practice.
| [
{
"created": "Fri, 5 Jun 2020 22:30:59 GMT",
"version": "v1"
}
] | 2020-06-09 | [
[
"Fauß",
"Michael",
""
],
[
"Dysto",
"Alex",
""
],
[
"Poor",
"H. Vincent",
""
]
] | This paper proposes a new family of lower and upper bounds on the minimum mean squared error (MMSE). The key idea is to minimize/maximize the MMSE subject to the constraint that the joint distribution of the input-output statistics lies in a Kullback-Leibler divergence ball centered at some Gaussian reference distribution. Both bounds are tight and are attained by Gaussian distributions whose mean is identical to that of the reference distribution and whose covariance matrix is determined by a scalar parameter that can be obtained by finding the root of a monotonic function. The upper bound corresponds to a minimax optimal estimator and provides performance guarantees under distributional uncertainty. The lower bound provides an alternative to well-known inequalities in estimation theory, such as the Cram\'er-Rao bound, that is potentially tighter and defined for a larger class of distributions. Examples of applications in signal processing and information theory illustrate the usefulness of the proposed bounds in practice. |
1712.03866 | Iason Oikonomidis | Paschalis Panteleris, Iason Oikonomidis, Antonis Argyros | Using a single RGB frame for real time 3D hand pose estimation in the
wild | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method for the real-time estimation of the full 3D pose of one
or more human hands using a single commodity RGB camera. Recent work in the
area has displayed impressive progress using RGBD input. However, since the
introduction of RGBD sensors, there has been little progress for the case of
monocular color input. We capitalize on the latest advancements of deep
learning, combining them with the power of generative hand pose estimation
techniques to achieve real-time monocular 3D hand pose estimation in
unrestricted scenarios. More specifically, given an RGB image and the relevant
camera calibration information, we employ a state-of-the-art detector to
localize hands. Given a crop of a hand in the image, we run the pretrained
network of OpenPose for hands to estimate the 2D location of hand joints.
Finally, non-linear least-squares minimization fits a 3D model of the hand to
the estimated 2D joint positions, recovering the 3D hand pose. Extensive
experimental results provide comparison to the state of the art as well as
qualitative assessment of the method in the wild.
| [
{
"created": "Mon, 11 Dec 2017 16:16:05 GMT",
"version": "v1"
}
] | 2017-12-12 | [
[
"Panteleris",
"Paschalis",
""
],
[
"Oikonomidis",
"Iason",
""
],
[
"Argyros",
"Antonis",
""
]
] | We present a method for the real-time estimation of the full 3D pose of one or more human hands using a single commodity RGB camera. Recent work in the area has displayed impressive progress using RGBD input. However, since the introduction of RGBD sensors, there has been little progress for the case of monocular color input. We capitalize on the latest advancements of deep learning, combining them with the power of generative hand pose estimation techniques to achieve real-time monocular 3D hand pose estimation in unrestricted scenarios. More specifically, given an RGB image and the relevant camera calibration information, we employ a state-of-the-art detector to localize hands. Given a crop of a hand in the image, we run the pretrained network of OpenPose for hands to estimate the 2D location of hand joints. Finally, non-linear least-squares minimization fits a 3D model of the hand to the estimated 2D joint positions, recovering the 3D hand pose. Extensive experimental results provide comparison to the state of the art as well as qualitative assessment of the method in the wild. |
1911.11018 | Xiaowei Gu | Xiaowei Gu, Plamen P Angelov and Eduardo Almeida Soares | A Self-Adaptive Synthetic Over-Sampling Technique for Imbalanced
Classification | This paper has been submitted to International Journal of Intelligent
Systems for publication | null | 10.1002/int.22230 | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditionally, in supervised machine learning, (a significant) part of the
available data (usually 50% to 80%) is used for training and the rest for
validation. In many problems, however, the data is highly imbalanced in regard
to different classes or does not have good coverage of the feasible data space
which, in turn, creates problems in validation and usage phase. In this paper,
we propose a technique for synthesising feasible and likely data to help
balance the classes as well as to boost the performance in terms of confusion
matrix as well as overall. The idea, in a nutshell, is to synthesise data
samples in close vicinity to the actual data samples specifically for the less
represented (minority) classes. This has also implications to the so-called
fairness of machine learning. In this paper, we propose a specific method for
synthesising data in a way to balance the classes and boost the performance,
especially of the minority classes. It is generic and can be applied to
different base algorithms, e.g. support vector machine, k-nearest neighbour,
deep networks, rule-based classifiers, decision trees, etc. The results
demonstrated that: i) a significantly more balanced (and fair) classification
results can be achieved; ii) that the overall performance as well as the
performance per class measured by confusion matrix can be boosted. In addition,
this approach can be very valuable for the cases when the number of actual
available labelled data is small which itself is one of the problems of the
contemporary machine learning.
| [
{
"created": "Mon, 25 Nov 2019 16:08:32 GMT",
"version": "v1"
}
] | 2020-04-21 | [
[
"Gu",
"Xiaowei",
""
],
[
"Angelov",
"Plamen P",
""
],
[
"Soares",
"Eduardo Almeida",
""
]
] | Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50% to 80%) is used for training and the rest for validation. In many problems, however, the data is highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesising feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesise data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesising data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, e.g. support vector machine, k-nearest neighbour, deep networks, rule-based classifiers, decision trees, etc. The results demonstrated that: i) a significantly more balanced (and fair) classification results can be achieved; ii) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning. |
2206.14666 | Anthony Coache | Anthony Coache, Sebastian Jaimungal, \'Alvaro Cartea | Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement
Learning | 41 pages, 7 figures | null | null | null | cs.LG q-fin.CP q-fin.PM q-fin.RM q-fin.TR | http://creativecommons.org/licenses/by-sa/4.0/ | We propose a novel framework to solve risk-sensitive reinforcement learning
(RL) problems where the agent optimises time-consistent dynamic spectral risk
measures. Based on the notion of conditional elicitability, our methodology
constructs (strictly consistent) scoring functions that are used as penalizers
in the estimation procedure. Our contribution is threefold: we (i) devise an
efficient approach to estimate a class of dynamic spectral risk measures with
deep neural networks, (ii) prove that these dynamic spectral risk measures may
be approximated to any arbitrary accuracy using deep neural networks, and (iii)
develop a risk-sensitive actor-critic algorithm that uses full episodes and
does not require any additional nested transitions. We compare our conceptually
improved reinforcement learning algorithm with the nested simulation approach
and illustrate its performance in two settings: statistical arbitrage and
portfolio allocation on both simulated and real data.
| [
{
"created": "Wed, 29 Jun 2022 14:11:15 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Oct 2022 18:31:01 GMT",
"version": "v2"
},
{
"created": "Mon, 1 May 2023 15:16:41 GMT",
"version": "v3"
}
] | 2023-05-02 | [
[
"Coache",
"Anthony",
""
],
[
"Jaimungal",
"Sebastian",
""
],
[
"Cartea",
"Álvaro",
""
]
] | We propose a novel framework to solve risk-sensitive reinforcement learning (RL) problems where the agent optimises time-consistent dynamic spectral risk measures. Based on the notion of conditional elicitability, our methodology constructs (strictly consistent) scoring functions that are used as penalizers in the estimation procedure. Our contribution is threefold: we (i) devise an efficient approach to estimate a class of dynamic spectral risk measures with deep neural networks, (ii) prove that these dynamic spectral risk measures may be approximated to any arbitrary accuracy using deep neural networks, and (iii) develop a risk-sensitive actor-critic algorithm that uses full episodes and does not require any additional nested transitions. We compare our conceptually improved reinforcement learning algorithm with the nested simulation approach and illustrate its performance in two settings: statistical arbitrage and portfolio allocation on both simulated and real data. |
2004.13138 | Jinghui Lu | Jinghui Lu and Brian MacNamee | Investigating the Effectiveness of Representations Based on Pretrained
Transformer-based Language Models in Active Learning for Labelling Text
Datasets | arXiv admin note: substantial text overlap with arXiv:1910.03505 | null | null | null | cs.IR cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Active learning has been shown to be an effective way to alleviate some of
the effort required in utilising large collections of unlabelled data for
machine learning tasks without needing to fully label them. The representation
mechanism used to represent text documents when performing active learning,
however, has a significant influence on how effective the process will be.
While simple vector representations such as bag-of-words and embedding-based
representations based on techniques such as word2vec have been shown to be an
effective way to represent documents during active learning, the emergence of
representation mechanisms based on the pre-trained transformer-based neural
network models popular in natural language processing research (e.g. BERT)
offer a promising, and as yet not fully explored, alternative. This paper
describes a comprehensive evaluation of the effectiveness of representations
based on pre-trained transformer-based language models for active learning.
This evaluation shows that transformer-based models, especially BERT-like
models, that have not yet been widely used in active learning, achieve a
significant improvement over more commonly used vector representations like
bag-of-words or other classical word embeddings like word2vec. This paper also
investigates the effectiveness of representations based on variants of BERT
such as Roberta, Albert as well as comparing the effectiveness of the [CLS]
token representation and the aggregated representation that can be generated
using BERT-like models. Finally, we propose an approach Adaptive Tuning Active
Learning. Our experiments show that the limited label information acquired in
active learning can not only be used for training a classifier but can also
adaptively improve the embeddings generated by the BERT-like language models as
well.
| [
{
"created": "Tue, 21 Apr 2020 02:37:44 GMT",
"version": "v1"
}
] | 2020-04-29 | [
[
"Lu",
"Jinghui",
""
],
[
"MacNamee",
"Brian",
""
]
] | Active learning has been shown to be an effective way to alleviate some of the effort required in utilising large collections of unlabelled data for machine learning tasks without needing to fully label them. The representation mechanism used to represent text documents when performing active learning, however, has a significant influence on how effective the process will be. While simple vector representations such as bag-of-words and embedding-based representations based on techniques such as word2vec have been shown to be an effective way to represent documents during active learning, the emergence of representation mechanisms based on the pre-trained transformer-based neural network models popular in natural language processing research (e.g. BERT) offer a promising, and as yet not fully explored, alternative. This paper describes a comprehensive evaluation of the effectiveness of representations based on pre-trained transformer-based language models for active learning. This evaluation shows that transformer-based models, especially BERT-like models, that have not yet been widely used in active learning, achieve a significant improvement over more commonly used vector representations like bag-of-words or other classical word embeddings like word2vec. This paper also investigates the effectiveness of representations based on variants of BERT such as Roberta, Albert as well as comparing the effectiveness of the [CLS] token representation and the aggregated representation that can be generated using BERT-like models. Finally, we propose an approach Adaptive Tuning Active Learning. Our experiments show that the limited label information acquired in active learning can not only be used for training a classifier but can also adaptively improve the embeddings generated by the BERT-like language models as well. |
2405.20902 | Rongwu Xu | Rongwu Xu, Zehan Qi, Wei Xu | Preemptive Answer "Attacks" on Chain-of-Thought Reasoning | Accepted to ACL'24 (Findings). Camera-ready version | null | null | null | cs.CL cs.AI cs.CR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Large language models (LLMs) showcase impressive reasoning capabilities when
coupled with Chain-of-Thought (CoT) prompting. However, the robustness of this
approach warrants further investigation. In this paper, we introduce a novel
scenario termed preemptive answers, where the LLM obtains an answer before
engaging in reasoning. This situation can arise inadvertently or induced by
malicious users by prompt injection attacks. Experiments reveal that preemptive
answers significantly impair the model's reasoning capability across various
CoT methods and a broad spectrum of datasets. To bolster the robustness of
reasoning, we propose two measures aimed at mitigating this issue to some
extent.
| [
{
"created": "Fri, 31 May 2024 15:15:04 GMT",
"version": "v1"
}
] | 2024-06-03 | [
[
"Xu",
"Rongwu",
""
],
[
"Qi",
"Zehan",
""
],
[
"Xu",
"Wei",
""
]
] | Large language models (LLMs) showcase impressive reasoning capabilities when coupled with Chain-of-Thought (CoT) prompting. However, the robustness of this approach warrants further investigation. In this paper, we introduce a novel scenario termed preemptive answers, where the LLM obtains an answer before engaging in reasoning. This situation can arise inadvertently or induced by malicious users by prompt injection attacks. Experiments reveal that preemptive answers significantly impair the model's reasoning capability across various CoT methods and a broad spectrum of datasets. To bolster the robustness of reasoning, we propose two measures aimed at mitigating this issue to some extent. |
2103.02650 | Kiant\'e Brantley | Kiant\'e Brantley, Soroush Mehri, Geoffrey J. Gordon | Successor Feature Sets: Generalizing Successor Representations Across
Policies | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Successor-style representations have many advantages for reinforcement
learning: for example, they can help an agent generalize from past experience
to new goals, and they have been proposed as explanations of behavioral and
neural data from human and animal learners. They also form a natural bridge
between model-based and model-free RL methods: like the former they make
predictions about future experiences, and like the latter they allow efficient
prediction of total discounted rewards. However, successor-style
representations are not optimized to generalize across policies: typically, we
maintain a limited-length list of policies, and share information among them by
representation learning or GPI. Successor-style representations also typically
make no provision for gathering information or reasoning about latent
variables. To address these limitations, we bring together ideas from
predictive state representations, belief space value iteration, successor
features, and convex analysis: we develop a new, general successor-style
representation, together with a Bellman equation that connects multiple sources
of information within this representation, including different latent states,
policies, and reward functions. The new representation is highly expressive:
for example, it lets us efficiently read off an optimal policy for a new reward
function, or a policy that imitates a new demonstration. For this paper, we
focus on exact computation of the new representation in small, known
environments, since even this restricted setting offers plenty of interesting
questions. Our implementation does not scale to large, unknown environments --
nor would we expect it to, since it generalizes POMDP value iteration, which is
difficult to scale. However, we believe that future work will allow us to
extend our ideas to approximate reasoning in large, unknown environments.
| [
{
"created": "Wed, 3 Mar 2021 19:36:44 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Mar 2021 22:09:04 GMT",
"version": "v2"
}
] | 2021-03-17 | [
[
"Brantley",
"Kianté",
""
],
[
"Mehri",
"Soroush",
""
],
[
"Gordon",
"Geoffrey J.",
""
]
] | Successor-style representations have many advantages for reinforcement learning: for example, they can help an agent generalize from past experience to new goals, and they have been proposed as explanations of behavioral and neural data from human and animal learners. They also form a natural bridge between model-based and model-free RL methods: like the former they make predictions about future experiences, and like the latter they allow efficient prediction of total discounted rewards. However, successor-style representations are not optimized to generalize across policies: typically, we maintain a limited-length list of policies, and share information among them by representation learning or GPI. Successor-style representations also typically make no provision for gathering information or reasoning about latent variables. To address these limitations, we bring together ideas from predictive state representations, belief space value iteration, successor features, and convex analysis: we develop a new, general successor-style representation, together with a Bellman equation that connects multiple sources of information within this representation, including different latent states, policies, and reward functions. The new representation is highly expressive: for example, it lets us efficiently read off an optimal policy for a new reward function, or a policy that imitates a new demonstration. For this paper, we focus on exact computation of the new representation in small, known environments, since even this restricted setting offers plenty of interesting questions. Our implementation does not scale to large, unknown environments -- nor would we expect it to, since it generalizes POMDP value iteration, which is difficult to scale. However, we believe that future work will allow us to extend our ideas to approximate reasoning in large, unknown environments. |
2305.11854 | Hiroki Furuta | Hiroki Furuta, Kuang-Huei Lee, Ofir Nachum, Yutaka Matsuo, Aleksandra
Faust, Shixiang Shane Gu, Izzeddin Gur | Multimodal Web Navigation with Instruction-Finetuned Foundation Models | Accepted to ICLR 2024. Website:
https://sites.google.com/view/mm-webnav/ | null | null | null | cs.LG cs.AI stat.ML | http://creativecommons.org/licenses/by/4.0/ | The progress of autonomous web navigation has been hindered by the dependence
on billions of exploratory interactions via online reinforcement learning, and
domain-specific model designs that make it difficult to leverage generalization
from rich out-of-domain data. In this work, we study data-driven offline
training for web agents with vision-language foundation models. We propose an
instruction-following multimodal agent, WebGUM, that observes both webpage
screenshots and HTML pages and outputs web navigation actions, such as click
and type. WebGUM is trained by jointly finetuning an instruction-finetuned
language model and a vision encoder with temporal and local perception on a
large corpus of demonstrations. We empirically demonstrate this recipe improves
the agent's ability of grounded multimodal perception, HTML comprehension, and
multi-step reasoning, outperforming prior works by a significant margin. On the
MiniWoB, we improve over the previous best offline methods by more than 45.8%,
even outperforming online-finetuned SoTA, humans, and GPT-4-based agent. On the
WebShop benchmark, our 3-billion-parameter model achieves superior performance
to the existing SoTA, PaLM-540B. Furthermore, WebGUM exhibits strong positive
transfer to the real-world planning tasks on the Mind2Web. We also collect 347K
high-quality demonstrations using our trained models, 38 times larger than
prior work, and make them available to promote future research in this
direction.
| [
{
"created": "Fri, 19 May 2023 17:44:34 GMT",
"version": "v1"
},
{
"created": "Sun, 1 Oct 2023 10:15:01 GMT",
"version": "v2"
},
{
"created": "Mon, 19 Feb 2024 01:56:54 GMT",
"version": "v3"
},
{
"created": "Sun, 25 Feb 2024 16:21:00 GMT",
"version": "v4"
}
] | 2024-02-27 | [
[
"Furuta",
"Hiroki",
""
],
[
"Lee",
"Kuang-Huei",
""
],
[
"Nachum",
"Ofir",
""
],
[
"Matsuo",
"Yutaka",
""
],
[
"Faust",
"Aleksandra",
""
],
[
"Gu",
"Shixiang Shane",
""
],
[
"Gur",
"Izzeddin",
""
]
] | The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model designs that make it difficult to leverage generalization from rich out-of-domain data. In this work, we study data-driven offline training for web agents with vision-language foundation models. We propose an instruction-following multimodal agent, WebGUM, that observes both webpage screenshots and HTML pages and outputs web navigation actions, such as click and type. WebGUM is trained by jointly finetuning an instruction-finetuned language model and a vision encoder with temporal and local perception on a large corpus of demonstrations. We empirically demonstrate this recipe improves the agent's ability of grounded multimodal perception, HTML comprehension, and multi-step reasoning, outperforming prior works by a significant margin. On the MiniWoB, we improve over the previous best offline methods by more than 45.8%, even outperforming online-finetuned SoTA, humans, and GPT-4-based agent. On the WebShop benchmark, our 3-billion-parameter model achieves superior performance to the existing SoTA, PaLM-540B. Furthermore, WebGUM exhibits strong positive transfer to the real-world planning tasks on the Mind2Web. We also collect 347K high-quality demonstrations using our trained models, 38 times larger than prior work, and make them available to promote future research in this direction. |
2403.12384 | Yifan Liu | Yifan Liu, Kangning Zhang, Xiangyuan Ren, Yanhua Huang, Jiarui Jin,
Yingjie Qin, Ruilong Su, Ruiwen Xu, Yong Yu, Weinan Zhang | AlignRec: Aligning and Training in Multimodal Recommendations | 9 page paper, 2 page appendix. Accepted by CIKM24 | null | null | null | cs.IR cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | With the development of multimedia systems, multimodal recommendations are
playing an essential role, as they can leverage rich contexts beyond
interactions. Existing methods mainly regard multimodal information as an
auxiliary, using them to help learn ID features; However, there exist semantic
gaps among multimodal content features and ID-based features, for which
directly using multimodal information as an auxiliary would lead to
misalignment in representations of users and items. In this paper, we first
systematically investigate the misalignment issue in multimodal
recommendations, and propose a solution named AlignRec. In AlignRec, the
recommendation objective is decomposed into three alignments, namely alignment
within contents, alignment between content and categorical ID, and alignment
between users and items. Each alignment is characterized by a specific
objective function and is integrated into our multimodal recommendation
framework. To effectively train AlignRec, we propose starting from pre-training
the first alignment to obtain unified multimodal features and subsequently
training the following two alignments together with these features as input. As
it is essential to analyze whether each multimodal feature helps in training
and accelerate the iteration cycle of recommendation models, we design three
new classes of metrics to evaluate intermediate performance. Our extensive
experiments on three real-world datasets consistently verify the superiority of
AlignRec compared to nine baselines. We also find that the multimodal features
generated by AlignRec are better than currently used ones, which are to be
open-sourced in our repository https://github.com/sjtulyf123/AlignRec_CIKM24.
| [
{
"created": "Tue, 19 Mar 2024 02:49:32 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Mar 2024 02:50:36 GMT",
"version": "v2"
},
{
"created": "Tue, 21 May 2024 11:51:03 GMT",
"version": "v3"
},
{
"created": "Thu, 1 Aug 2024 03:32:49 GMT",
"version": "v4"
}
] | 2024-08-02 | [
[
"Liu",
"Yifan",
""
],
[
"Zhang",
"Kangning",
""
],
[
"Ren",
"Xiangyuan",
""
],
[
"Huang",
"Yanhua",
""
],
[
"Jin",
"Jiarui",
""
],
[
"Qin",
"Yingjie",
""
],
[
"Su",
"Ruilong",
""
],
[
"Xu",
"Ruiwen",
""
],
[
"Yu",
"Yong",
""
],
[
"Zhang",
"Weinan",
""
]
] | With the development of multimedia systems, multimodal recommendations are playing an essential role, as they can leverage rich contexts beyond interactions. Existing methods mainly regard multimodal information as an auxiliary, using them to help learn ID features; However, there exist semantic gaps among multimodal content features and ID-based features, for which directly using multimodal information as an auxiliary would lead to misalignment in representations of users and items. In this paper, we first systematically investigate the misalignment issue in multimodal recommendations, and propose a solution named AlignRec. In AlignRec, the recommendation objective is decomposed into three alignments, namely alignment within contents, alignment between content and categorical ID, and alignment between users and items. Each alignment is characterized by a specific objective function and is integrated into our multimodal recommendation framework. To effectively train AlignRec, we propose starting from pre-training the first alignment to obtain unified multimodal features and subsequently training the following two alignments together with these features as input. As it is essential to analyze whether each multimodal feature helps in training and accelerate the iteration cycle of recommendation models, we design three new classes of metrics to evaluate intermediate performance. Our extensive experiments on three real-world datasets consistently verify the superiority of AlignRec compared to nine baselines. We also find that the multimodal features generated by AlignRec are better than currently used ones, which are to be open-sourced in our repository https://github.com/sjtulyf123/AlignRec_CIKM24. |
2402.08249 | Ying Jin | Ying Jin and Jiaqi Wang and Dahua Lin | SepRep-Net: Multi-source Free Domain Adaptation via Model Separation And
Reparameterization | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | We consider multi-source free domain adaptation, the problem of adapting
multiple existing models to a new domain without accessing the source data.
Among existing approaches, methods based on model ensemble are effective in
both the source and target domains, but incur significantly increased
computational costs. Towards this dilemma, in this work, we propose a novel
framework called SepRep-Net, which tackles multi-source free domain adaptation
via model Separation and Reparameterization.Concretely, SepRep-Net reassembled
multiple existing models to a unified network, while maintaining separate
pathways (Separation). During training, separate pathways are optimized in
parallel with the information exchange regularly performed via an additional
feature merging unit. With our specific design, these pathways can be further
reparameterized into a single one to facilitate inference (Reparameterization).
SepRep-Net is characterized by 1) effectiveness: competitive performance on the
target domain, 2) efficiency: low computational costs, and 3) generalizability:
maintaining more source knowledge than existing solutions. As a general
approach, SepRep-Net can be seamlessly plugged into various methods. Extensive
experiments validate the performance of SepRep-Net on mainstream benchmarks.
| [
{
"created": "Tue, 13 Feb 2024 06:35:00 GMT",
"version": "v1"
},
{
"created": "Fri, 17 May 2024 08:24:44 GMT",
"version": "v2"
}
] | 2024-05-20 | [
[
"Jin",
"Ying",
""
],
[
"Wang",
"Jiaqi",
""
],
[
"Lin",
"Dahua",
""
]
] | We consider multi-source free domain adaptation, the problem of adapting multiple existing models to a new domain without accessing the source data. Among existing approaches, methods based on model ensemble are effective in both the source and target domains, but incur significantly increased computational costs. Towards this dilemma, in this work, we propose a novel framework called SepRep-Net, which tackles multi-source free domain adaptation via model Separation and Reparameterization.Concretely, SepRep-Net reassembled multiple existing models to a unified network, while maintaining separate pathways (Separation). During training, separate pathways are optimized in parallel with the information exchange regularly performed via an additional feature merging unit. With our specific design, these pathways can be further reparameterized into a single one to facilitate inference (Reparameterization). SepRep-Net is characterized by 1) effectiveness: competitive performance on the target domain, 2) efficiency: low computational costs, and 3) generalizability: maintaining more source knowledge than existing solutions. As a general approach, SepRep-Net can be seamlessly plugged into various methods. Extensive experiments validate the performance of SepRep-Net on mainstream benchmarks. |
2201.11192 | Gyri Reiersen | Gyri Reiersen, David Dao, Bj\"orn L\"utjens, Konstantin Klemmer, Kenza
Amara, Attila Steinegger, Ce Zhang, Xiaoxiang Zhu | ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with
Deep Learning and Aerial Imagery | Accepted paper for the AI for Social Impact Track at the AAAI 2022 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Forest biomass is a key influence for future climate, and the world urgently
needs highly scalable financing schemes, such as carbon offsetting
certifications, to protect and restore forests. Current manual forest carbon
stock inventory methods of measuring single trees by hand are time, labour, and
cost-intensive and have been shown to be subjective. They can lead to
substantial overestimation of the carbon stock and ultimately distrust in
forest financing. The potential for impact and scale of leveraging advancements
in machine learning and remote sensing technologies is promising but needs to
be of high quality in order to replace the current forest stock protocols for
certifications.
In this paper, we present ReforesTree, a benchmark dataset of forest carbon
stock in six agro-forestry carbon offsetting sites in Ecuador. Furthermore, we
show that a deep learning-based end-to-end model using individual tree
detection from low cost RGB-only drone imagery is accurately estimating forest
carbon stock within official carbon offsetting certification standards.
Additionally, our baseline CNN model outperforms state-of-the-art
satellite-based forest biomass and carbon stock estimates for this type of
small-scale, tropical agro-forestry sites. We present this dataset to encourage
machine learning research in this area to increase accountability and
transparency of monitoring, verification and reporting (MVR) in carbon
offsetting projects, as well as scaling global reforestation financing through
accurate remote sensing.
| [
{
"created": "Wed, 26 Jan 2022 21:27:57 GMT",
"version": "v1"
}
] | 2022-01-28 | [
[
"Reiersen",
"Gyri",
""
],
[
"Dao",
"David",
""
],
[
"Lütjens",
"Björn",
""
],
[
"Klemmer",
"Konstantin",
""
],
[
"Amara",
"Kenza",
""
],
[
"Steinegger",
"Attila",
""
],
[
"Zhang",
"Ce",
""
],
[
"Zhu",
"Xiaoxiang",
""
]
] | Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and restore forests. Current manual forest carbon stock inventory methods of measuring single trees by hand are time, labour, and cost-intensive and have been shown to be subjective. They can lead to substantial overestimation of the carbon stock and ultimately distrust in forest financing. The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications. In this paper, we present ReforesTree, a benchmark dataset of forest carbon stock in six agro-forestry carbon offsetting sites in Ecuador. Furthermore, we show that a deep learning-based end-to-end model using individual tree detection from low cost RGB-only drone imagery is accurately estimating forest carbon stock within official carbon offsetting certification standards. Additionally, our baseline CNN model outperforms state-of-the-art satellite-based forest biomass and carbon stock estimates for this type of small-scale, tropical agro-forestry sites. We present this dataset to encourage machine learning research in this area to increase accountability and transparency of monitoring, verification and reporting (MVR) in carbon offsetting projects, as well as scaling global reforestation financing through accurate remote sensing. |
2209.11324 | Kostas Peppas P | Dimitrios G. Selimis, Mar Francis De Guzman, Fotis I. Lazarakis,
Kyriakos N. Manganaris, Kostas P. Peppas and Katsuyuki Haneda | Path Loss, Angular Spread and Channel Sparsity Modeling for Indoor and
Outdoor Environments at the sub-THz Band | null | null | null | null | cs.IT math.IT | http://creativecommons.org/publicdomain/zero/1.0/ | In this paper, we present new measurement results to model large-scale path
loss, angular spread and channel sparsity at the sub-THz (141-145 GHz) band,
for both indoor and outdoor scenarios. Extensive measurement campaigns have
been carried out, taking into account both line-of-sight (LoS) and non
line-of-sight (NLoS) propagation. For all considered propagation scenarios,
omni-directional and directional path loss models have been developed, based on
the so-called close-in (CI) free-space reference distance model. Moreover, path
loss modeling has been applied for the 2nd and 3rd strongest multipath
components (MPCs), based on which path loss exponent and large-scale shadow
fading estimates have been derived. A power angular spread analysis is further
presented, using up to the 3rd strongest MPC. Finally, results on the sparsity
of the wireless channel have also been presented by employing the so-called
Gini index.
| [
{
"created": "Thu, 22 Sep 2022 21:32:51 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Nov 2022 17:21:45 GMT",
"version": "v2"
},
{
"created": "Wed, 26 Apr 2023 09:33:24 GMT",
"version": "v3"
}
] | 2023-04-27 | [
[
"Selimis",
"Dimitrios G.",
""
],
[
"De Guzman",
"Mar Francis",
""
],
[
"Lazarakis",
"Fotis I.",
""
],
[
"Manganaris",
"Kyriakos N.",
""
],
[
"Peppas",
"Kostas P.",
""
],
[
"Haneda",
"Katsuyuki",
""
]
] | In this paper, we present new measurement results to model large-scale path loss, angular spread and channel sparsity at the sub-THz (141-145 GHz) band, for both indoor and outdoor scenarios. Extensive measurement campaigns have been carried out, taking into account both line-of-sight (LoS) and non line-of-sight (NLoS) propagation. For all considered propagation scenarios, omni-directional and directional path loss models have been developed, based on the so-called close-in (CI) free-space reference distance model. Moreover, path loss modeling has been applied for the 2nd and 3rd strongest multipath components (MPCs), based on which path loss exponent and large-scale shadow fading estimates have been derived. A power angular spread analysis is further presented, using up to the 3rd strongest MPC. Finally, results on the sparsity of the wireless channel have also been presented by employing the so-called Gini index. |
1802.08176 | George K. Thiruvathukal | Ahmed S. Kaseb, Bo Fu, Anup Mohan, Yung-Hsiang Lu, Amy Reibman, George
K. Thiruvathukal | Analyzing Real-Time Multimedia Content From Network Cameras Using CPUs
and GPUs in the Cloud | Accepted at MIPR 2018 | null | null | null | cs.DC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Millions of network cameras are streaming real-time multimedia content
(images or videos) for various environments (e.g., highways and malls) and can
be used for a variety of applications. Analyzing the content from many network
cameras requires significant amounts of computing resources. Cloud vendors
offer resources in the form of cloud instances with different capabilities and
hourly costs. Some instances include GPUs that can accelerate analysis
programs. Doing so incurs additional monetary cost because instances with GPUs
are more expensive. It is a challenging problem to reduce the overall monetary
cost of using the cloud to analyze the real-time multimedia content from
network cameras while meeting the desired analysis frame rates. This paper
describes a cloud resource manager that solves this problem by estimating the
resource requirements of executing analysis programs using CPU or GPU,
formulating the resource allocation problem as a multiple-choice vector bin
packing problem, and solving it using an existing algorithm. The experiments
show that the manager can reduce up to 61\% of the cost compared with other
allocation strategies.
| [
{
"created": "Wed, 21 Feb 2018 14:46:27 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Mar 2018 14:30:56 GMT",
"version": "v2"
}
] | 2018-03-22 | [
[
"Kaseb",
"Ahmed S.",
""
],
[
"Fu",
"Bo",
""
],
[
"Mohan",
"Anup",
""
],
[
"Lu",
"Yung-Hsiang",
""
],
[
"Reibman",
"Amy",
""
],
[
"Thiruvathukal",
"George K.",
""
]
] | Millions of network cameras are streaming real-time multimedia content (images or videos) for various environments (e.g., highways and malls) and can be used for a variety of applications. Analyzing the content from many network cameras requires significant amounts of computing resources. Cloud vendors offer resources in the form of cloud instances with different capabilities and hourly costs. Some instances include GPUs that can accelerate analysis programs. Doing so incurs additional monetary cost because instances with GPUs are more expensive. It is a challenging problem to reduce the overall monetary cost of using the cloud to analyze the real-time multimedia content from network cameras while meeting the desired analysis frame rates. This paper describes a cloud resource manager that solves this problem by estimating the resource requirements of executing analysis programs using CPU or GPU, formulating the resource allocation problem as a multiple-choice vector bin packing problem, and solving it using an existing algorithm. The experiments show that the manager can reduce up to 61\% of the cost compared with other allocation strategies. |
2407.17786 | Chi-Han Peng | Chia-Chia Chen and Chi-Han Peng | Topology-Preserving Downsampling of Binary Images | Accepted to The 18th European Conference on Computer Vision (ECCV)
2024 | null | null | null | cs.CV cs.GR | http://creativecommons.org/licenses/by/4.0/ | We present a novel discrete optimization-based approach to generate
downsampled versions of binary images that are guaranteed to have the same
topology as the original, measured by the zeroth and first Betti numbers of the
black regions, while having good similarity to the original image as measured
by IoU and Dice scores. To our best knowledge, all existing binary image
downsampling methods do not have such topology-preserving guarantees. We also
implemented a baseline morphological operation (dilation)-based approach that
always generates topologically correct results. However, we found the
similarity scores to be much worse. We demonstrate several applications of our
approach. First, generating smaller versions of medical image segmentation
masks for easier human inspection. Second, improving the efficiency of binary
image operations, including persistent homology computation and shortest path
computation, by substituting the original images with smaller ones. In
particular, the latter is a novel application that is made feasible only by the
full topology-preservation guarantee of our method.
| [
{
"created": "Thu, 25 Jul 2024 05:30:09 GMT",
"version": "v1"
}
] | 2024-07-26 | [
[
"Chen",
"Chia-Chia",
""
],
[
"Peng",
"Chi-Han",
""
]
] | We present a novel discrete optimization-based approach to generate downsampled versions of binary images that are guaranteed to have the same topology as the original, measured by the zeroth and first Betti numbers of the black regions, while having good similarity to the original image as measured by IoU and Dice scores. To our best knowledge, all existing binary image downsampling methods do not have such topology-preserving guarantees. We also implemented a baseline morphological operation (dilation)-based approach that always generates topologically correct results. However, we found the similarity scores to be much worse. We demonstrate several applications of our approach. First, generating smaller versions of medical image segmentation masks for easier human inspection. Second, improving the efficiency of binary image operations, including persistent homology computation and shortest path computation, by substituting the original images with smaller ones. In particular, the latter is a novel application that is made feasible only by the full topology-preservation guarantee of our method. |
2405.02653 | Qianli Zhou | Qianli Zhou and Tianxiang Zhan and Yong Deng | Isopignistic Canonical Decomposition via Belief Evolution Network | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Developing a general information processing model in uncertain environments
is fundamental for the advancement of explainable artificial intelligence.
Dempster-Shafer theory of evidence is a well-known and effective reasoning
method for representing epistemic uncertainty, which is closely related to
subjective probability theory and possibility theory. Although they can be
transformed to each other under some particular belief structures, there
remains a lack of a clear and interpretable transformation process, as well as
a unified approach for information processing. In this paper, we aim to address
these issues from the perspectives of isopignistic belief functions and the
hyper-cautious transferable belief model. Firstly, we propose an isopignistic
transformation based on the belief evolution network. This transformation
allows for the adjustment of the information granule while retaining the
potential decision outcome. The isopignistic transformation is integrated with
a hyper-cautious transferable belief model to establish a new canonical
decomposition. This decomposition offers a reverse path between the possibility
distribution and its isopignistic mass functions. The result of the canonical
decomposition, called isopignistic function, is an identical information
content distribution to reflect the propensity and relative commitment degree
of the BPA. Furthermore, this paper introduces a method to reconstruct the
basic belief assignment by adjusting the isopignistic function. It explores the
advantages of this approach in modeling and handling uncertainty within the
hyper-cautious transferable belief model. More general, this paper establishes
a theoretical basis for building general models of artificial intelligence
based on probability theory, Dempster-Shafer theory, and possibility theory.
| [
{
"created": "Sat, 4 May 2024 12:39:15 GMT",
"version": "v1"
}
] | 2024-05-07 | [
[
"Zhou",
"Qianli",
""
],
[
"Zhan",
"Tianxiang",
""
],
[
"Deng",
"Yong",
""
]
] | Developing a general information processing model in uncertain environments is fundamental for the advancement of explainable artificial intelligence. Dempster-Shafer theory of evidence is a well-known and effective reasoning method for representing epistemic uncertainty, which is closely related to subjective probability theory and possibility theory. Although they can be transformed to each other under some particular belief structures, there remains a lack of a clear and interpretable transformation process, as well as a unified approach for information processing. In this paper, we aim to address these issues from the perspectives of isopignistic belief functions and the hyper-cautious transferable belief model. Firstly, we propose an isopignistic transformation based on the belief evolution network. This transformation allows for the adjustment of the information granule while retaining the potential decision outcome. The isopignistic transformation is integrated with a hyper-cautious transferable belief model to establish a new canonical decomposition. This decomposition offers a reverse path between the possibility distribution and its isopignistic mass functions. The result of the canonical decomposition, called isopignistic function, is an identical information content distribution to reflect the propensity and relative commitment degree of the BPA. Furthermore, this paper introduces a method to reconstruct the basic belief assignment by adjusting the isopignistic function. It explores the advantages of this approach in modeling and handling uncertainty within the hyper-cautious transferable belief model. More general, this paper establishes a theoretical basis for building general models of artificial intelligence based on probability theory, Dempster-Shafer theory, and possibility theory. |
2303.03616 | Van-Thach Do | Van-Thach Do and Quang-Cuong Pham | Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D
Surfaces | 8 pages, 8 figures | IEEE ROBOTICS AND AUTOMATION LETTERS, VOL. 8, NO. 9, SEPTEMBER
2023 | 10.1109/LRA.2023.3296943 | null | cs.RO cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new approach to obtaining nearly complete coverage
paths (CP) with low overlapping on 3D general surfaces using mesh models. The
CP is obtained by segmenting the mesh model into a given number of clusters
using constrained centroidal Voronoi tessellation (CCVT) and finding the
shortest path from cluster centroids using the geodesic metric efficiently. We
introduce a new cost function to harmoniously achieve uniform areas of the
obtained clusters and a restriction on the variation of triangle normals during
the construction of CCVTs. The obtained clusters can be used to construct
high-quality viewpoints (VP) for visual coverage tasks. Here, we utilize the
planned VPs as cleaning configurations to perform residual powder removal in
additive manufacturing using manipulator robots. The self-occlusion of VPs and
ensuring collision-free robot configurations are addressed by integrating a
proposed optimization-based strategy to find a set of candidate rays for each
VP into the motion planning phase. CP planning benchmarks and physical
experiments are conducted to demonstrate the effectiveness of the proposed
approach. We show that our approach can compute the CPs and VPs of various mesh
models with a massive number of triangles within a reasonable time.
| [
{
"created": "Tue, 7 Mar 2023 03:01:26 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Jun 2023 00:55:58 GMT",
"version": "v2"
},
{
"created": "Thu, 8 Jun 2023 01:43:29 GMT",
"version": "v3"
}
] | 2023-10-24 | [
[
"Do",
"Van-Thach",
""
],
[
"Pham",
"Quang-Cuong",
""
]
] | This paper presents a new approach to obtaining nearly complete coverage paths (CP) with low overlapping on 3D general surfaces using mesh models. The CP is obtained by segmenting the mesh model into a given number of clusters using constrained centroidal Voronoi tessellation (CCVT) and finding the shortest path from cluster centroids using the geodesic metric efficiently. We introduce a new cost function to harmoniously achieve uniform areas of the obtained clusters and a restriction on the variation of triangle normals during the construction of CCVTs. The obtained clusters can be used to construct high-quality viewpoints (VP) for visual coverage tasks. Here, we utilize the planned VPs as cleaning configurations to perform residual powder removal in additive manufacturing using manipulator robots. The self-occlusion of VPs and ensuring collision-free robot configurations are addressed by integrating a proposed optimization-based strategy to find a set of candidate rays for each VP into the motion planning phase. CP planning benchmarks and physical experiments are conducted to demonstrate the effectiveness of the proposed approach. We show that our approach can compute the CPs and VPs of various mesh models with a massive number of triangles within a reasonable time. |
2303.00154 | Elena Trunz | Elena Trunz, Jonathan Klein, Jan M\"uller, Lukas Bode, Ralf Sarlette,
Michael Weinmann, Reinhard Klein | Neural inverse procedural modeling of knitting yarns from images | 23 pages, 16 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We investigate the capabilities of neural inverse procedural modeling to
infer high-quality procedural yarn models with fiber-level details from single
images of depicted yarn samples. While directly inferring all parameters of the
underlying yarn model based on a single neural network may seem an intuitive
choice, we show that the complexity of yarn structures in terms of twisting and
migration characteristics of the involved fibers can be better encountered in
terms of ensembles of networks that focus on individual characteristics. We
analyze the effect of different loss functions including a parameter loss to
penalize the deviation of inferred parameters to ground truth annotations, a
reconstruction loss to enforce similar statistics of the image generated for
the estimated parameters in comparison to training images as well as an
additional regularization term to explicitly penalize deviations between latent
codes of synthetic images and the average latent code of real images in the
latent space of the encoder. We demonstrate that the combination of a carefully
designed parametric, procedural yarn model with respective network ensembles as
well as loss functions even allows robust parameter inference when solely
trained on synthetic data. Since our approach relies on the availability of a
yarn database with parameter annotations and we are not aware of such a
respectively available dataset, we additionally provide, to the best of our
knowledge, the first dataset of yarn images with annotations regarding the
respective yarn parameters. For this purpose, we use a novel yarn generator
that improves the realism of the produced results over previous approaches.
| [
{
"created": "Wed, 1 Mar 2023 00:56:39 GMT",
"version": "v1"
}
] | 2023-03-02 | [
[
"Trunz",
"Elena",
""
],
[
"Klein",
"Jonathan",
""
],
[
"Müller",
"Jan",
""
],
[
"Bode",
"Lukas",
""
],
[
"Sarlette",
"Ralf",
""
],
[
"Weinmann",
"Michael",
""
],
[
"Klein",
"Reinhard",
""
]
] | We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples. While directly inferring all parameters of the underlying yarn model based on a single neural network may seem an intuitive choice, we show that the complexity of yarn structures in terms of twisting and migration characteristics of the involved fibers can be better encountered in terms of ensembles of networks that focus on individual characteristics. We analyze the effect of different loss functions including a parameter loss to penalize the deviation of inferred parameters to ground truth annotations, a reconstruction loss to enforce similar statistics of the image generated for the estimated parameters in comparison to training images as well as an additional regularization term to explicitly penalize deviations between latent codes of synthetic images and the average latent code of real images in the latent space of the encoder. We demonstrate that the combination of a carefully designed parametric, procedural yarn model with respective network ensembles as well as loss functions even allows robust parameter inference when solely trained on synthetic data. Since our approach relies on the availability of a yarn database with parameter annotations and we are not aware of such a respectively available dataset, we additionally provide, to the best of our knowledge, the first dataset of yarn images with annotations regarding the respective yarn parameters. For this purpose, we use a novel yarn generator that improves the realism of the produced results over previous approaches. |
2001.06350 | Maira Gatti de Bayser | Maira Gatti de Bayser, Melina Alberio Guerra, Paulo Cavalin, Claudio
Pinhanez | A Hybrid Solution to Learn Turn-Taking in Multi-Party Service-based Chat
Groups | arXiv admin note: text overlap with arXiv:1907.02090 | null | null | null | cs.CL cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To predict the next most likely participant to interact in a multi-party
conversation is a difficult problem. In a text-based chat group, the only
information available is the sender, the content of the text and the dialogue
history. In this paper we present our study on how these information can be
used on the prediction task through a corpus and architecture that integrates
turn-taking classifiers based on Maximum Likelihood Expectation (MLE),
Convolutional Neural Networks (CNN) and Finite State Automata (FSA). The corpus
is a synthetic adaptation of the Multi-Domain Wizard-of-Oz dataset (MultiWOZ)
to a multiple travel service-based bots scenario with dialogue errors and was
created to simulate user's interaction and evaluate the architecture. We
present experimental results which show that the CNN approach achieves better
performance than the baseline with an accuracy of 92.34%, but the integrated
solution with MLE, CNN and FSA achieves performance even better, with 95.65%.
| [
{
"created": "Tue, 14 Jan 2020 22:37:21 GMT",
"version": "v1"
}
] | 2020-01-20 | [
[
"de Bayser",
"Maira Gatti",
""
],
[
"Guerra",
"Melina Alberio",
""
],
[
"Cavalin",
"Paulo",
""
],
[
"Pinhanez",
"Claudio",
""
]
] | To predict the next most likely participant to interact in a multi-party conversation is a difficult problem. In a text-based chat group, the only information available is the sender, the content of the text and the dialogue history. In this paper we present our study on how these information can be used on the prediction task through a corpus and architecture that integrates turn-taking classifiers based on Maximum Likelihood Expectation (MLE), Convolutional Neural Networks (CNN) and Finite State Automata (FSA). The corpus is a synthetic adaptation of the Multi-Domain Wizard-of-Oz dataset (MultiWOZ) to a multiple travel service-based bots scenario with dialogue errors and was created to simulate user's interaction and evaluate the architecture. We present experimental results which show that the CNN approach achieves better performance than the baseline with an accuracy of 92.34%, but the integrated solution with MLE, CNN and FSA achieves performance even better, with 95.65%. |
1607.02037 | Parthe Pandit | Parthe Pandit and Ankur A. Kulkarni | Refinement of the Equilibrium of Public Goods Games over Networks:
Efficiency and Effort of Specialized Equilibria | null | Journal of Mathematical Economics, Available online 16 April 2018 | 10.1016/j.jmateco.2018.04.002 | null | cs.GT math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently Bramoulle and Kranton presented a model for the provision of public
goods over a network and showed the existence of a class of Nash equilibria
called specialized equilibria wherein some agents exert maximum effort while
other agents free ride. We examine the efficiency, effort and cost of
specialized equilibria in comparison to other equilibria. Our main results show
that the welfare of a particular specialized equilibrium approaches the maximum
welfare amongst all equilibria as the concavity of the benefit function tends
to unity. For forest networks a similar result also holds as the concavity
approaches zero. Moreover, without any such concavity conditions, there exists
for any network a specialized equilibrium that requires the maximum weighted
effort amongst all equilibria. When the network is a forest, a specialized
equilibrium also incurs the minimum total cost amongst all equilibria. For
well-covered forest networks we show that all welfare maximizing equilibria are
specialized and all equilibria incur the same total cost. Thus we argue that
specialized equilibria may be considered as a refinement of the equilibrium of
the public goods game. We show several results on the structure and efficiency
of equilibria that highlight the role of dependants in the network.
| [
{
"created": "Thu, 7 Jul 2016 14:51:33 GMT",
"version": "v1"
},
{
"created": "Mon, 24 Jan 2022 04:52:46 GMT",
"version": "v2"
}
] | 2022-01-25 | [
[
"Pandit",
"Parthe",
""
],
[
"Kulkarni",
"Ankur A.",
""
]
] | Recently Bramoulle and Kranton presented a model for the provision of public goods over a network and showed the existence of a class of Nash equilibria called specialized equilibria wherein some agents exert maximum effort while other agents free ride. We examine the efficiency, effort and cost of specialized equilibria in comparison to other equilibria. Our main results show that the welfare of a particular specialized equilibrium approaches the maximum welfare amongst all equilibria as the concavity of the benefit function tends to unity. For forest networks a similar result also holds as the concavity approaches zero. Moreover, without any such concavity conditions, there exists for any network a specialized equilibrium that requires the maximum weighted effort amongst all equilibria. When the network is a forest, a specialized equilibrium also incurs the minimum total cost amongst all equilibria. For well-covered forest networks we show that all welfare maximizing equilibria are specialized and all equilibria incur the same total cost. Thus we argue that specialized equilibria may be considered as a refinement of the equilibrium of the public goods game. We show several results on the structure and efficiency of equilibria that highlight the role of dependants in the network. |
2010.08781 | Nick Brown | Ludovic A.R. Capelli, Zhenjiang Hu, Timothy A.K. Zakian, Nick Brown,
J. Mark Bull | iPregel: Vertex-centric programmability vs memory efficiency and
performance, why choose? | null | In Parallel Computing. 2019 Aug 1;86:45-56 | 10.1016/j.parco.2019.04.005 | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | The vertex-centric programming model, designed to improve the programmability
in graph processing application writing, has attracted great attention over the
years. However, shared memory frameworks that implement the vertex-centric
interface all expose a common tradeoff: programmability against memory
efficiency and performance.
Our approach, iPregel, preserves vertex-centric programmability, while
implementing optimisations for performance, and designing these so they are
transparent to a user's application code, hence not impacting programmability.
In this paper, we evaluate iPregel against FemtoGraph, whose characteristics
are identical, an asynchronous counterpart GraphChi and the
vertex-subset-centric framework Ligra. Our experiments include three of the
most popular vertex-centric benchmark applications over 4 real-world publicly
accessible graphs, which cover orders of magnitude between a million to a
billion edges, measuring execution time and peak memory usage. Finally, we
evaluate the programmability of each framework by comparing against Google's
original Pregel framework.
Experiments demonstrate that iPregel, like FemtoGraph, does not sacrifice
vertex-centric programmability for additional performance and memory efficiency
optimisations, which contrasts against GraphChi and Ligra. Sacrificing
vertex-centric programmability allowed the latter to benefit from substantial
performance and memory efficiency gains. We demonstrate that iPregel is up to
2300 times faster than FemtoGraph, as well as generating a memory footprint up
to 100 times smaller. Ligra and GraphChi are up to 17000 and 700 times faster
than FemtoGraph but, when comparing against iPregel, this maximum speed-up
drops to 10. Furthermore, with PageRank, iPregel is the fastest overall. For
memory efficiency, iPregel provides the same memory efficiency as Ligra and 3
to 6 times lighter than GraphChi on average.
| [
{
"created": "Sat, 17 Oct 2020 12:40:52 GMT",
"version": "v1"
}
] | 2020-10-20 | [
[
"Capelli",
"Ludovic A. R.",
""
],
[
"Hu",
"Zhenjiang",
""
],
[
"Zakian",
"Timothy A. K.",
""
],
[
"Brown",
"Nick",
""
],
[
"Bull",
"J. Mark",
""
]
] | The vertex-centric programming model, designed to improve the programmability in graph processing application writing, has attracted great attention over the years. However, shared memory frameworks that implement the vertex-centric interface all expose a common tradeoff: programmability against memory efficiency and performance. Our approach, iPregel, preserves vertex-centric programmability, while implementing optimisations for performance, and designing these so they are transparent to a user's application code, hence not impacting programmability. In this paper, we evaluate iPregel against FemtoGraph, whose characteristics are identical, an asynchronous counterpart GraphChi and the vertex-subset-centric framework Ligra. Our experiments include three of the most popular vertex-centric benchmark applications over 4 real-world publicly accessible graphs, which cover orders of magnitude between a million to a billion edges, measuring execution time and peak memory usage. Finally, we evaluate the programmability of each framework by comparing against Google's original Pregel framework. Experiments demonstrate that iPregel, like FemtoGraph, does not sacrifice vertex-centric programmability for additional performance and memory efficiency optimisations, which contrasts against GraphChi and Ligra. Sacrificing vertex-centric programmability allowed the latter to benefit from substantial performance and memory efficiency gains. We demonstrate that iPregel is up to 2300 times faster than FemtoGraph, as well as generating a memory footprint up to 100 times smaller. Ligra and GraphChi are up to 17000 and 700 times faster than FemtoGraph but, when comparing against iPregel, this maximum speed-up drops to 10. Furthermore, with PageRank, iPregel is the fastest overall. For memory efficiency, iPregel provides the same memory efficiency as Ligra and 3 to 6 times lighter than GraphChi on average. |
2205.15918 | Pierre Erbacher | Pierre Erbacher, Ludovic Denoyer, Laure Soulier | Interactive Query Clarification and Refinement via User Simulation | null | null | null | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | When users initiate search sessions, their queries are often unclear or might
lack of context; this resulting in inefficient document ranking. Multiple
approaches have been proposed by the Information Retrieval community to add
context and retrieve documents aligned with users' intents. While some work
focus on query disambiguation using users' browsing history, a recent line of
work proposes to interact with users by asking clarification questions or/and
proposing clarification panels. However, these approaches count either a
limited number (i.e., 1) of interactions with user or log-based interactions.
In this paper, we propose and evaluate a fully simulated query clarification
framework allowing multi-turn interactions between IR systems and user agents.
| [
{
"created": "Tue, 31 May 2022 16:08:41 GMT",
"version": "v1"
}
] | 2022-06-01 | [
[
"Erbacher",
"Pierre",
""
],
[
"Denoyer",
"Ludovic",
""
],
[
"Soulier",
"Laure",
""
]
] | When users initiate search sessions, their queries are often unclear or might lack of context; this resulting in inefficient document ranking. Multiple approaches have been proposed by the Information Retrieval community to add context and retrieve documents aligned with users' intents. While some work focus on query disambiguation using users' browsing history, a recent line of work proposes to interact with users by asking clarification questions or/and proposing clarification panels. However, these approaches count either a limited number (i.e., 1) of interactions with user or log-based interactions. In this paper, we propose and evaluate a fully simulated query clarification framework allowing multi-turn interactions between IR systems and user agents. |
2403.02628 | Biqing Qi | Biqing Qi, Xingquan Chen, Junqi Gao, Dong Li, Jianxing Liu, Ligang Wu
and Bowen Zhou | Interactive Continual Learning: Fast and Slow Thinking | Accepted to CVPR 2024 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advanced life forms, sustained by the synergistic interaction of neural
cognitive mechanisms, continually acquire and transfer knowledge throughout
their lifespan. In contrast, contemporary machine learning paradigms exhibit
limitations in emulating the facets of continual learning (CL). Nonetheless,
the emergence of large language models (LLMs) presents promising avenues for
realizing CL via interactions with these models. Drawing on Complementary
Learning System theory, this paper presents a novel Interactive Continual
Learning (ICL) framework, enabled by collaborative interactions among models of
various sizes. Specifically, we assign the ViT model as System1 and multimodal
LLM as System2. To enable the memory module to deduce tasks from class
information and enhance Set2Set retrieval, we propose the Class-Knowledge-Task
Multi-Head Attention (CKT-MHA). Additionally, to improve memory retrieval in
System1 through enhanced geometric representation, we introduce the CL-vMF
mechanism, based on the von Mises-Fisher (vMF) distribution. Meanwhile, we
introduce the von Mises-Fisher Outlier Detection and Interaction (vMF-ODI)
strategy to identify hard examples, thus enhancing collaboration between
System1 and System2 for complex reasoning realization. Comprehensive evaluation
of our proposed ICL demonstrates significant resistance to forgetting and
superior performance relative to existing methods. Code is available at
github.com/ICL.
| [
{
"created": "Tue, 5 Mar 2024 03:37:28 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Mar 2024 02:19:52 GMT",
"version": "v2"
}
] | 2024-03-20 | [
[
"Qi",
"Biqing",
""
],
[
"Chen",
"Xingquan",
""
],
[
"Gao",
"Junqi",
""
],
[
"Li",
"Dong",
""
],
[
"Liu",
"Jianxing",
""
],
[
"Wu",
"Ligang",
""
],
[
"Zhou",
"Bowen",
""
]
] | Advanced life forms, sustained by the synergistic interaction of neural cognitive mechanisms, continually acquire and transfer knowledge throughout their lifespan. In contrast, contemporary machine learning paradigms exhibit limitations in emulating the facets of continual learning (CL). Nonetheless, the emergence of large language models (LLMs) presents promising avenues for realizing CL via interactions with these models. Drawing on Complementary Learning System theory, this paper presents a novel Interactive Continual Learning (ICL) framework, enabled by collaborative interactions among models of various sizes. Specifically, we assign the ViT model as System1 and multimodal LLM as System2. To enable the memory module to deduce tasks from class information and enhance Set2Set retrieval, we propose the Class-Knowledge-Task Multi-Head Attention (CKT-MHA). Additionally, to improve memory retrieval in System1 through enhanced geometric representation, we introduce the CL-vMF mechanism, based on the von Mises-Fisher (vMF) distribution. Meanwhile, we introduce the von Mises-Fisher Outlier Detection and Interaction (vMF-ODI) strategy to identify hard examples, thus enhancing collaboration between System1 and System2 for complex reasoning realization. Comprehensive evaluation of our proposed ICL demonstrates significant resistance to forgetting and superior performance relative to existing methods. Code is available at github.com/ICL. |
1408.7035 | Elad Michael Schiller (PhD) | Oscar Morales-Ponce and Elad M. Schiller and Paolo Falcone | Cooperation with Disagreement Correction in the Presence of
Communication Failures | Extended version of the paper with the same name that appears in 17th
International IEEE Conference on Intelligent Transportation Systems | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vehicle-to-vehicle communication is a fundamental requirement in cooperative
vehicular systems to achieve high performance while keeping high safety
standards. Vehicles periodically exchange critical information with nearby
vehicles to determine their maneuvers according to the information quality and
established strategies. However, wireless communication is prone to failures.
Thus, participants can be unaware that other participants have not received the
information on time resulting in conflicting trajectories that may not be safe.
We present a deterministic solution that allows all participants to use a
default strategy when other participants have not received on time the complete
information. We base our solution on a timed distributed protocol that adapts
its output according to the effect of message omission failures so that the
disagreement period occurs for no longer than a constant time (of the order of
milliseconds) that only depends on the message delay. We formally show the
correctness and perform experiments to corroborate its efficiency. We explain
how the proposed solution can be used on vehicular platooning to attain high
performance and still guarantee high safety standards despite communication
failures. We believe that this work can facilitate the implementation of
cooperative driving systems that have to deal with inherent (communication)
uncertainties.
| [
{
"created": "Fri, 29 Aug 2014 14:34:00 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Feb 2015 23:00:57 GMT",
"version": "v2"
}
] | 2015-03-02 | [
[
"Morales-Ponce",
"Oscar",
""
],
[
"Schiller",
"Elad M.",
""
],
[
"Falcone",
"Paolo",
""
]
] | Vehicle-to-vehicle communication is a fundamental requirement in cooperative vehicular systems to achieve high performance while keeping high safety standards. Vehicles periodically exchange critical information with nearby vehicles to determine their maneuvers according to the information quality and established strategies. However, wireless communication is prone to failures. Thus, participants can be unaware that other participants have not received the information on time resulting in conflicting trajectories that may not be safe. We present a deterministic solution that allows all participants to use a default strategy when other participants have not received on time the complete information. We base our solution on a timed distributed protocol that adapts its output according to the effect of message omission failures so that the disagreement period occurs for no longer than a constant time (of the order of milliseconds) that only depends on the message delay. We formally show the correctness and perform experiments to corroborate its efficiency. We explain how the proposed solution can be used on vehicular platooning to attain high performance and still guarantee high safety standards despite communication failures. We believe that this work can facilitate the implementation of cooperative driving systems that have to deal with inherent (communication) uncertainties. |
2006.04489 | Hichem Sahbi | Ahmed Mazari and Hichem Sahbi | Action Recognition with Deep Multiple Aggregation Networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most of the current action recognition algorithms are based on deep networks
which stack multiple convolutional, pooling and fully connected layers. While
convolutional and fully connected operations have been widely studied in the
literature, the design of pooling operations that handle action recognition,
with different sources of temporal granularity in action categories, has
comparatively received less attention, and existing solutions rely mainly on
max or averaging operations. The latter are clearly powerless to fully exhibit
the actual temporal granularity of action categories and thereby constitute a
bottleneck in classification performances. In this paper, we introduce a novel
hierarchical pooling design that captures different levels of temporal
granularity in action recognition. Our design principle is coarse-to-fine and
achieved using a tree-structured network; as we traverse this network top-down,
pooling operations are getting less invariant but timely more resolute and well
localized. Learning the combination of operations in this network -- which best
fits a given ground-truth -- is obtained by solving a constrained minimization
problem whose solution corresponds to the distribution of weights that capture
the contribution of each level (and thereby temporal granularity) in the global
hierarchical pooling process. Besides being principled and well grounded, the
proposed hierarchical pooling is also video-length and resolution agnostic.
Extensive experiments conducted on the challenging UCF-101, HMDB-51 and
JHMDB-21 databases corroborate all these statements.
| [
{
"created": "Mon, 8 Jun 2020 11:37:38 GMT",
"version": "v1"
}
] | 2020-06-09 | [
[
"Mazari",
"Ahmed",
""
],
[
"Sahbi",
"Hichem",
""
]
] | Most of the current action recognition algorithms are based on deep networks which stack multiple convolutional, pooling and fully connected layers. While convolutional and fully connected operations have been widely studied in the literature, the design of pooling operations that handle action recognition, with different sources of temporal granularity in action categories, has comparatively received less attention, and existing solutions rely mainly on max or averaging operations. The latter are clearly powerless to fully exhibit the actual temporal granularity of action categories and thereby constitute a bottleneck in classification performances. In this paper, we introduce a novel hierarchical pooling design that captures different levels of temporal granularity in action recognition. Our design principle is coarse-to-fine and achieved using a tree-structured network; as we traverse this network top-down, pooling operations are getting less invariant but timely more resolute and well localized. Learning the combination of operations in this network -- which best fits a given ground-truth -- is obtained by solving a constrained minimization problem whose solution corresponds to the distribution of weights that capture the contribution of each level (and thereby temporal granularity) in the global hierarchical pooling process. Besides being principled and well grounded, the proposed hierarchical pooling is also video-length and resolution agnostic. Extensive experiments conducted on the challenging UCF-101, HMDB-51 and JHMDB-21 databases corroborate all these statements. |
1911.07260 | Yunming Zhang | Yunming Zhang, Ajay Brahmakshatriya, Xinyi Chen, Laxman Dhulipala,
Shoaib Kamil, Saman Amarasinghe, Julian Shun | Optimizing Ordered Graph Algorithms with GraphIt | null | CGO 2020 | null | null | cs.PL cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many graph problems can be solved using ordered parallel graph algorithms
that achieve significant speedup over their unordered counterparts by reducing
redundant work. This paper introduces a new priority-based extension to
GraphIt, a domain-specific language for writing graph applications, to simplify
writing high-performance parallel ordered graph algorithms. The extension
enables vertices to be processed in a dynamic order while hiding low-level
implementation details from the user. We extend the compiler with new program
analyses, transformations, and code generation to produce fast implementations
of ordered parallel graph algorithms. We also introduce bucket fusion, a new
performance optimization that fuses together different rounds of ordered
algorithms to reduce synchronization overhead, resulting in
$1.2\times$--3$\times$ speedup over the fastest existing ordered algorithm
implementations on road networks with large diameters. With the extension,
GraphIt achieves up to 3$\times$ speedup on six ordered graph algorithms over
state-of-the-art frameworks and hand-optimized implementations (Julienne,
Galois, and GAPBS) that support ordered algorithms.
| [
{
"created": "Sun, 17 Nov 2019 15:51:02 GMT",
"version": "v1"
},
{
"created": "Sun, 26 Jan 2020 23:37:14 GMT",
"version": "v2"
}
] | 2020-01-28 | [
[
"Zhang",
"Yunming",
""
],
[
"Brahmakshatriya",
"Ajay",
""
],
[
"Chen",
"Xinyi",
""
],
[
"Dhulipala",
"Laxman",
""
],
[
"Kamil",
"Shoaib",
""
],
[
"Amarasinghe",
"Saman",
""
],
[
"Shun",
"Julian",
""
]
] | Many graph problems can be solved using ordered parallel graph algorithms that achieve significant speedup over their unordered counterparts by reducing redundant work. This paper introduces a new priority-based extension to GraphIt, a domain-specific language for writing graph applications, to simplify writing high-performance parallel ordered graph algorithms. The extension enables vertices to be processed in a dynamic order while hiding low-level implementation details from the user. We extend the compiler with new program analyses, transformations, and code generation to produce fast implementations of ordered parallel graph algorithms. We also introduce bucket fusion, a new performance optimization that fuses together different rounds of ordered algorithms to reduce synchronization overhead, resulting in $1.2\times$--3$\times$ speedup over the fastest existing ordered algorithm implementations on road networks with large diameters. With the extension, GraphIt achieves up to 3$\times$ speedup on six ordered graph algorithms over state-of-the-art frameworks and hand-optimized implementations (Julienne, Galois, and GAPBS) that support ordered algorithms. |
2407.00706 | Man Shun Ang | Andersen Ang, Waqas Bin Hamed, Hans De Sterck | Sum-of-norms regularized Nonnegative Matrix Factorization | 22 pages, 12 figures | null | null | null | cs.LG math.OC stat.ML | http://creativecommons.org/licenses/by/4.0/ | When applying nonnegative matrix factorization (NMF), generally the rank
parameter is unknown. Such rank in NMF, called the nonnegative rank, is usually
estimated heuristically since computing the exact value of it is NP-hard. In
this work, we propose an approximation method to estimate such rank while
solving NMF on-the-fly. We use sum-of-norm (SON), a group-lasso structure that
encourages pairwise similarity, to reduce the rank of a factor matrix where the
rank is overestimated at the beginning. On various datasets, SON-NMF is able to
reveal the correct nonnegative rank of the data without any prior knowledge nor
tuning.
SON-NMF is a nonconvx nonsmmoth non-separable non-proximable problem, solving
it is nontrivial. First, as rank estimation in NMF is NP-hard, the proposed
approach does not enjoy a lower computational complexity. Using a
graph-theoretic argument, we prove that the complexity of the SON-NMF is almost
irreducible. Second, the per-iteration cost of any algorithm solving SON-NMF is
possibly high, which motivated us to propose a first-order BCD algorithm to
approximately solve SON-NMF with a low per-iteration cost, in which we do so by
the proximal average operator. Lastly, we propose a simple greedy method for
post-processing.
SON-NMF exhibits favourable features for applications. Beside the ability to
automatically estimate the rank from data, SON-NMF can deal with rank-deficient
data matrix, can detect weak component with small energy. Furthermore, on the
application of hyperspectral imaging, SON-NMF handle the issue of spectral
variability naturally.
| [
{
"created": "Sun, 30 Jun 2024 14:16:27 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Ang",
"Andersen",
""
],
[
"Hamed",
"Waqas Bin",
""
],
[
"De Sterck",
"Hans",
""
]
] | When applying nonnegative matrix factorization (NMF), generally the rank parameter is unknown. Such rank in NMF, called the nonnegative rank, is usually estimated heuristically since computing the exact value of it is NP-hard. In this work, we propose an approximation method to estimate such rank while solving NMF on-the-fly. We use sum-of-norm (SON), a group-lasso structure that encourages pairwise similarity, to reduce the rank of a factor matrix where the rank is overestimated at the beginning. On various datasets, SON-NMF is able to reveal the correct nonnegative rank of the data without any prior knowledge nor tuning. SON-NMF is a nonconvx nonsmmoth non-separable non-proximable problem, solving it is nontrivial. First, as rank estimation in NMF is NP-hard, the proposed approach does not enjoy a lower computational complexity. Using a graph-theoretic argument, we prove that the complexity of the SON-NMF is almost irreducible. Second, the per-iteration cost of any algorithm solving SON-NMF is possibly high, which motivated us to propose a first-order BCD algorithm to approximately solve SON-NMF with a low per-iteration cost, in which we do so by the proximal average operator. Lastly, we propose a simple greedy method for post-processing. SON-NMF exhibits favourable features for applications. Beside the ability to automatically estimate the rank from data, SON-NMF can deal with rank-deficient data matrix, can detect weak component with small energy. Furthermore, on the application of hyperspectral imaging, SON-NMF handle the issue of spectral variability naturally. |
2312.09230 | Arthur Conmy | Rhys Gould, Euan Ong, George Ogden, Arthur Conmy | Successor Heads: Recurring, Interpretable Attention Heads In The Wild | 12 main text pages, with appendix | null | null | null | cs.LG cs.AI cs.CL | http://creativecommons.org/licenses/by/4.0/ | In this work we present successor heads: attention heads that increment
tokens with a natural ordering, such as numbers, months, and days. For example,
successor heads increment 'Monday' into 'Tuesday'. We explain the successor
head behavior with an approach rooted in mechanistic interpretability, the
field that aims to explain how models complete tasks in human-understandable
terms. Existing research in this area has found interpretable language model
components in small toy models. However, results in toy models have not yet led
to insights that explain the internals of frontier models and little is
currently understood about the internal operations of large language models. In
this paper, we analyze the behavior of successor heads in large language models
(LLMs) and find that they implement abstract representations that are common to
different architectures. They form in LLMs with as few as 31 million
parameters, and at least as many as 12 billion parameters, such as GPT-2,
Pythia, and Llama-2. We find a set of 'mod-10 features' that underlie how
successor heads increment in LLMs across different architectures and sizes. We
perform vector arithmetic with these features to edit head behavior and provide
insights into numeric representations within LLMs. Additionally, we study the
behavior of successor heads on natural language data, identifying interpretable
polysemanticity in a Pythia successor head.
| [
{
"created": "Thu, 14 Dec 2023 18:55:47 GMT",
"version": "v1"
}
] | 2023-12-15 | [
[
"Gould",
"Rhys",
""
],
[
"Ong",
"Euan",
""
],
[
"Ogden",
"George",
""
],
[
"Conmy",
"Arthur",
""
]
] | In this work we present successor heads: attention heads that increment tokens with a natural ordering, such as numbers, months, and days. For example, successor heads increment 'Monday' into 'Tuesday'. We explain the successor head behavior with an approach rooted in mechanistic interpretability, the field that aims to explain how models complete tasks in human-understandable terms. Existing research in this area has found interpretable language model components in small toy models. However, results in toy models have not yet led to insights that explain the internals of frontier models and little is currently understood about the internal operations of large language models. In this paper, we analyze the behavior of successor heads in large language models (LLMs) and find that they implement abstract representations that are common to different architectures. They form in LLMs with as few as 31 million parameters, and at least as many as 12 billion parameters, such as GPT-2, Pythia, and Llama-2. We find a set of 'mod-10 features' that underlie how successor heads increment in LLMs across different architectures and sizes. We perform vector arithmetic with these features to edit head behavior and provide insights into numeric representations within LLMs. Additionally, we study the behavior of successor heads on natural language data, identifying interpretable polysemanticity in a Pythia successor head. |
2110.01717 | Zhao Xu | Zhao Xu, Youzhi Luo, Xuan Zhang, Xinyi Xu, Yaochen Xie, Meng Liu,
Kaleb Dickerson, Cheng Deng, Maho Nakata, Shuiwang Ji | Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular
Graphs | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph neural networks are emerging as promising methods for modeling
molecular graphs, in which nodes and edges correspond to atoms and chemical
bonds, respectively. Recent studies show that when 3D molecular geometries,
such as bond lengths and angles, are available, molecular property prediction
tasks can be made more accurate. However, computing of 3D molecular geometries
requires quantum calculations that are computationally prohibitive. For
example, accurate calculation of 3D geometries of a small molecule requires
hours of computing time using density functional theory (DFT). Here, we propose
to predict the ground-state 3D geometries from molecular graphs using machine
learning methods. To make this feasible, we develop a benchmark, known as
Molecule3D, that includes a dataset with precise ground-state geometries of
approximately 4 million molecules derived from DFT. We also provide a set of
software tools for data processing, splitting, training, and evaluation, etc.
Specifically, we propose to assess the error and validity of predicted
geometries using four metrics. We implement two baseline methods that either
predict the pairwise distance between atoms or atom coordinates in 3D space.
Experimental results show that, compared with generating 3D geometries with
RDKit, our method can achieve comparable prediction accuracy but with much
smaller computational costs. Our Molecule3D is available as a module of the
MoleculeX software library (https://github.com/divelab/MoleculeX).
| [
{
"created": "Thu, 30 Sep 2021 22:09:28 GMT",
"version": "v1"
}
] | 2021-10-06 | [
[
"Xu",
"Zhao",
""
],
[
"Luo",
"Youzhi",
""
],
[
"Zhang",
"Xuan",
""
],
[
"Xu",
"Xinyi",
""
],
[
"Xie",
"Yaochen",
""
],
[
"Liu",
"Meng",
""
],
[
"Dickerson",
"Kaleb",
""
],
[
"Deng",
"Cheng",
""
],
[
"Nakata",
"Maho",
""
],
[
"Ji",
"Shuiwang",
""
]
] | Graph neural networks are emerging as promising methods for modeling molecular graphs, in which nodes and edges correspond to atoms and chemical bonds, respectively. Recent studies show that when 3D molecular geometries, such as bond lengths and angles, are available, molecular property prediction tasks can be made more accurate. However, computing of 3D molecular geometries requires quantum calculations that are computationally prohibitive. For example, accurate calculation of 3D geometries of a small molecule requires hours of computing time using density functional theory (DFT). Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods. To make this feasible, we develop a benchmark, known as Molecule3D, that includes a dataset with precise ground-state geometries of approximately 4 million molecules derived from DFT. We also provide a set of software tools for data processing, splitting, training, and evaluation, etc. Specifically, we propose to assess the error and validity of predicted geometries using four metrics. We implement two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space. Experimental results show that, compared with generating 3D geometries with RDKit, our method can achieve comparable prediction accuracy but with much smaller computational costs. Our Molecule3D is available as a module of the MoleculeX software library (https://github.com/divelab/MoleculeX). |
1709.05665 | Kevis-Kokitsi Maninis | Thomas Probst, Kevis-Kokitsi Maninis, Ajad Chhatkuli, Mouloud Ourak,
Emmanuel Vander Poorten, Luc Van Gool | Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted
Retinal Surgery | Accepted in Robotics and Automation Letters (RA-L). Project page:
http://www.vision.ee.ethz.ch/~kmaninis/keypoints2stereo/index.html | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computer vision and robotics are being increasingly applied in medical
interventions. Especially in interventions where extreme precision is required
they could make a difference. One such application is robot-assisted retinal
microsurgery. In recent works, such interventions are conducted under a
stereo-microscope, and with a robot-controlled surgical tool. The
complementarity of computer vision and robotics has however not yet been fully
exploited. In order to improve the robot control we are interested in 3D
reconstruction of the anatomy and in automatic tool localization using a stereo
microscope. In this paper, we solve this problem for the first time using a
single pipeline, starting from uncalibrated cameras to reach metric 3D
reconstruction and registration, in retinal microsurgery. The key ingredients
of our method are: (a) surgical tool landmark detection, and (b) 3D
reconstruction with the stereo microscope, using the detected landmarks. To
address the former, we propose a novel deep learning method that detects and
recognizes keypoints in high definition images at higher than real-time speed.
We use the detected 2D keypoints along with their corresponding 3D coordinates
obtained from the robot sensors to calibrate the stereo microscope using an
affine projection model. We design an online 3D reconstruction pipeline that
makes use of smoothness constraints and performs robot-to-camera registration.
The entire pipeline is extensively validated on open-sky porcine eye sequences.
Quantitative and qualitative results are presented for all steps.
| [
{
"created": "Sun, 17 Sep 2017 14:00:26 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Nov 2017 17:59:08 GMT",
"version": "v2"
}
] | 2017-11-22 | [
[
"Probst",
"Thomas",
""
],
[
"Maninis",
"Kevis-Kokitsi",
""
],
[
"Chhatkuli",
"Ajad",
""
],
[
"Ourak",
"Mouloud",
""
],
[
"Poorten",
"Emmanuel Vander",
""
],
[
"Van Gool",
"Luc",
""
]
] | Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference. One such application is robot-assisted retinal microsurgery. In recent works, such interventions are conducted under a stereo-microscope, and with a robot-controlled surgical tool. The complementarity of computer vision and robotics has however not yet been fully exploited. In order to improve the robot control we are interested in 3D reconstruction of the anatomy and in automatic tool localization using a stereo microscope. In this paper, we solve this problem for the first time using a single pipeline, starting from uncalibrated cameras to reach metric 3D reconstruction and registration, in retinal microsurgery. The key ingredients of our method are: (a) surgical tool landmark detection, and (b) 3D reconstruction with the stereo microscope, using the detected landmarks. To address the former, we propose a novel deep learning method that detects and recognizes keypoints in high definition images at higher than real-time speed. We use the detected 2D keypoints along with their corresponding 3D coordinates obtained from the robot sensors to calibrate the stereo microscope using an affine projection model. We design an online 3D reconstruction pipeline that makes use of smoothness constraints and performs robot-to-camera registration. The entire pipeline is extensively validated on open-sky porcine eye sequences. Quantitative and qualitative results are presented for all steps. |
1707.09217 | Gerhard Gossen | Gerhard Gossen and Elena Demidova and Thomas Risse | Extracting Event-Centric Document Collections from Large-Scale Web
Archives | To be published in the proceedings of the Conference on Theory and
Practice of Digital Libraries (TPDL) 2017 | null | null | null | cs.DL cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Web archives are typically very broad in scope and extremely large in scale.
This makes data analysis appear daunting, especially for non-computer
scientists. These collections constitute an increasingly important source for
researchers in the social sciences, the historical sciences and journalists
interested in studying past events. However, there are currently no access
methods that help users to efficiently access information, in particular about
specific events, beyond the retrieval of individual disconnected documents.
Therefore we propose a novel method to extract event-centric document
collections from large scale Web archives. This method relies on a specialized
focused extraction algorithm. Our experiments on the German Web archive
(covering a time period of 19 years) demonstrate that our method enables the
extraction of event-centric collections for different event types.
| [
{
"created": "Fri, 28 Jul 2017 13:18:01 GMT",
"version": "v1"
}
] | 2017-07-31 | [
[
"Gossen",
"Gerhard",
""
],
[
"Demidova",
"Elena",
""
],
[
"Risse",
"Thomas",
""
]
] | Web archives are typically very broad in scope and extremely large in scale. This makes data analysis appear daunting, especially for non-computer scientists. These collections constitute an increasingly important source for researchers in the social sciences, the historical sciences and journalists interested in studying past events. However, there are currently no access methods that help users to efficiently access information, in particular about specific events, beyond the retrieval of individual disconnected documents. Therefore we propose a novel method to extract event-centric document collections from large scale Web archives. This method relies on a specialized focused extraction algorithm. Our experiments on the German Web archive (covering a time period of 19 years) demonstrate that our method enables the extraction of event-centric collections for different event types. |
2207.09865 | Haipeng Xiong Mr. | Haipeng Xiong and Angela Yao | Discrete-Constrained Regression for Local Counting Models | Accepted by ECCV 2022 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Local counts, or the number of objects in a local area, is a continuous value
by nature. Yet recent state-of-the-art methods show that formulating counting
as a classification task performs better than regression. Through a series of
experiments on carefully controlled synthetic data, we show that this
counter-intuitive result is caused by imprecise ground truth local counts.
Factors such as biased dot annotations and incorrectly matched Gaussian kernels
used to generate ground truth counts introduce deviations from the true local
counts. Standard continuous regression is highly sensitive to these errors,
explaining the performance gap between classification and regression. To
mitigate the sensitivity, we loosen the regression formulation from a
continuous scale to a discrete ordering and propose a novel
discrete-constrained (DC) regression. Applied to crowd counting, DC-regression
is more accurate than both classification and standard regression on three
public benchmarks. A similar advantage also holds for the age estimation task,
verifying the overall effectiveness of DC-regression.
| [
{
"created": "Wed, 20 Jul 2022 12:54:23 GMT",
"version": "v1"
}
] | 2022-07-21 | [
[
"Xiong",
"Haipeng",
""
],
[
"Yao",
"Angela",
""
]
] | Local counts, or the number of objects in a local area, is a continuous value by nature. Yet recent state-of-the-art methods show that formulating counting as a classification task performs better than regression. Through a series of experiments on carefully controlled synthetic data, we show that this counter-intuitive result is caused by imprecise ground truth local counts. Factors such as biased dot annotations and incorrectly matched Gaussian kernels used to generate ground truth counts introduce deviations from the true local counts. Standard continuous regression is highly sensitive to these errors, explaining the performance gap between classification and regression. To mitigate the sensitivity, we loosen the regression formulation from a continuous scale to a discrete ordering and propose a novel discrete-constrained (DC) regression. Applied to crowd counting, DC-regression is more accurate than both classification and standard regression on three public benchmarks. A similar advantage also holds for the age estimation task, verifying the overall effectiveness of DC-regression. |
2106.00943 | Xinyi Zhang | Xinyi Zhang, Keisuke Koyama, Yukiyasu Domae, Weiwei Wan and Kensuke
Harada | A Topological Solution of Entanglement for Complex-shaped Parts in
Robotic Bin-picking | 7 pages | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the problem of picking up only one object at a time
avoiding any entanglement in bin-picking. To cope with a difficult case where
the complex-shaped objects are heavily entangled together, we propose a
topology-based method that can generate non-tangle grasp positions on a single
depth image. The core technique is entanglement map, which is a feature map to
measure the entanglement possibilities obtained from the input image. We use
the entanglement map to select probable regions containing graspable objects.
The optimum grasping pose is detected from the selected regions considering the
collision between robot hand and objects. Experimental results show that our
analytic method provides a more comprehensive and intuitive observation of
entanglement and exceeds previous learning-based work in success rates.
Especially, our topology-based method does not rely on any object models or
time-consuming training process, so that it can be easily adapted to more
complex bin-picking scenes.
| [
{
"created": "Wed, 2 Jun 2021 05:09:09 GMT",
"version": "v1"
},
{
"created": "Tue, 1 Mar 2022 02:33:02 GMT",
"version": "v2"
}
] | 2022-03-02 | [
[
"Zhang",
"Xinyi",
""
],
[
"Koyama",
"Keisuke",
""
],
[
"Domae",
"Yukiyasu",
""
],
[
"Wan",
"Weiwei",
""
],
[
"Harada",
"Kensuke",
""
]
] | This paper addresses the problem of picking up only one object at a time avoiding any entanglement in bin-picking. To cope with a difficult case where the complex-shaped objects are heavily entangled together, we propose a topology-based method that can generate non-tangle grasp positions on a single depth image. The core technique is entanglement map, which is a feature map to measure the entanglement possibilities obtained from the input image. We use the entanglement map to select probable regions containing graspable objects. The optimum grasping pose is detected from the selected regions considering the collision between robot hand and objects. Experimental results show that our analytic method provides a more comprehensive and intuitive observation of entanglement and exceeds previous learning-based work in success rates. Especially, our topology-based method does not rely on any object models or time-consuming training process, so that it can be easily adapted to more complex bin-picking scenes. |
2406.13983 | Juan Luque | Juan Luque, Sharmila Duppala, John Dickerson, Aravind Srinivasan | Barter Exchange with Shared Item Valuations | A previous version of this work appeared in the proceedings of WWW
'24 | null | 10.1145/3589334.3645632 | null | cs.DS | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In barter exchanges agents enter seeking to swap their items for other items
on their wishlist. We consider a centralized barter exchange with a set of
agents and items where each item has a positive value. The goal is to compute a
(re)allocation of items maximizing the agents' collective utility subject to
each agent's total received value being comparable to their total given value.
Many such centralized barter exchanges exist and serve crucial roles; e.g.,
kidney exchange programs, which are often formulated as variants of directed
cycle packing. We show finding a reallocation where each agent's total given
and total received values are equal is NP-hard. On the other hand, we develop a
randomized algorithm that achieves optimal utility in expectation and where, i)
for any agent, with probability 1 their received value is at least their given
value minus $v^*$ where $v^*$ is said agent's most valuable owned and
wished-for item, and ii) each agent's given and received values are equal in
expectation.
| [
{
"created": "Thu, 20 Jun 2024 04:23:53 GMT",
"version": "v1"
}
] | 2024-06-21 | [
[
"Luque",
"Juan",
""
],
[
"Duppala",
"Sharmila",
""
],
[
"Dickerson",
"John",
""
],
[
"Srinivasan",
"Aravind",
""
]
] | In barter exchanges agents enter seeking to swap their items for other items on their wishlist. We consider a centralized barter exchange with a set of agents and items where each item has a positive value. The goal is to compute a (re)allocation of items maximizing the agents' collective utility subject to each agent's total received value being comparable to their total given value. Many such centralized barter exchanges exist and serve crucial roles; e.g., kidney exchange programs, which are often formulated as variants of directed cycle packing. We show finding a reallocation where each agent's total given and total received values are equal is NP-hard. On the other hand, we develop a randomized algorithm that achieves optimal utility in expectation and where, i) for any agent, with probability 1 their received value is at least their given value minus $v^*$ where $v^*$ is said agent's most valuable owned and wished-for item, and ii) each agent's given and received values are equal in expectation. |
2308.10610 | Yubiao Yue | Yubiao Yue, Xinyu Zeng, Xiaoqiang Shi, Meiping Zhang, Fan Zhang,
Yunxin Liang, Yan Liu, Zhenzhang Li, Yang Li | Ear-Keeper: Real-time Diagnosis of Ear Lesions Utilizing
Ultralight-Ultrafast ConvNet and Large-scale Ear Endoscopic Dataset | 18 pages,8 figures | null | null | null | cs.CV cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning-based ear disease diagnosis technology has proven effective and
affordable. However, due to the lack of ear endoscope datasets with diversity,
the practical potential of the deep learning model has not been thoroughly
studied. Moreover, existing research failed to achieve a good trade-off between
model inference speed and parameter size, rendering models inapplicable in
real-world settings. To address these challenges, we constructed the first
large-scale ear endoscopic dataset comprising eight types of ear diseases and
disease-free samples from two institutions. Inspired by ShuffleNetV2, we
proposed Best-EarNet, an ultrafast and ultralight network enabling real-time
ear disease diagnosis. Best-EarNet incorporates a novel Local-Global Spatial
Feature Fusion Module and multi-scale supervision strategy, which facilitates
the model focusing on global-local information within feature maps at various
levels. Utilizing transfer learning, the accuracy of Best-EarNet with only
0.77M parameters achieves 95.23% (internal 22,581 images) and 92.14% (external
1,652 images), respectively. In particular, it achieves an average frame per
second of 80 on the CPU. From the perspective of model practicality, the
proposed Best-EarNet is superior to state-of-the-art backbone models in ear
lesion detection tasks. Most importantly, Ear-keeper, an intelligent diagnosis
system based Best-EarNet, was developed successfully and deployed on common
electronic devices (smartphone, tablet computer and personal computer). In the
future, Ear-Keeper has the potential to assist the public and healthcare
providers in performing comprehensive scanning and diagnosis of the ear canal
in real-time video, thereby promptly detecting ear lesions.
| [
{
"created": "Mon, 21 Aug 2023 10:20:46 GMT",
"version": "v1"
},
{
"created": "Sun, 21 Jan 2024 13:23:10 GMT",
"version": "v2"
},
{
"created": "Tue, 23 Jan 2024 01:48:20 GMT",
"version": "v3"
},
{
"created": "Wed, 10 Apr 2024 08:16:18 GMT",
"version": "v4"
}
] | 2024-04-11 | [
[
"Yue",
"Yubiao",
""
],
[
"Zeng",
"Xinyu",
""
],
[
"Shi",
"Xiaoqiang",
""
],
[
"Zhang",
"Meiping",
""
],
[
"Zhang",
"Fan",
""
],
[
"Liang",
"Yunxin",
""
],
[
"Liu",
"Yan",
""
],
[
"Li",
"Zhenzhang",
""
],
[
"Li",
"Yang",
""
]
] | Deep learning-based ear disease diagnosis technology has proven effective and affordable. However, due to the lack of ear endoscope datasets with diversity, the practical potential of the deep learning model has not been thoroughly studied. Moreover, existing research failed to achieve a good trade-off between model inference speed and parameter size, rendering models inapplicable in real-world settings. To address these challenges, we constructed the first large-scale ear endoscopic dataset comprising eight types of ear diseases and disease-free samples from two institutions. Inspired by ShuffleNetV2, we proposed Best-EarNet, an ultrafast and ultralight network enabling real-time ear disease diagnosis. Best-EarNet incorporates a novel Local-Global Spatial Feature Fusion Module and multi-scale supervision strategy, which facilitates the model focusing on global-local information within feature maps at various levels. Utilizing transfer learning, the accuracy of Best-EarNet with only 0.77M parameters achieves 95.23% (internal 22,581 images) and 92.14% (external 1,652 images), respectively. In particular, it achieves an average frame per second of 80 on the CPU. From the perspective of model practicality, the proposed Best-EarNet is superior to state-of-the-art backbone models in ear lesion detection tasks. Most importantly, Ear-keeper, an intelligent diagnosis system based Best-EarNet, was developed successfully and deployed on common electronic devices (smartphone, tablet computer and personal computer). In the future, Ear-Keeper has the potential to assist the public and healthcare providers in performing comprehensive scanning and diagnosis of the ear canal in real-time video, thereby promptly detecting ear lesions. |
2211.01703 | Ke Sun | Ke Sun, Samir M. Perlaza, and Alain Jean-Marie | $2 \times 2$ Zero-Sum Games with Commitments and Noisy Observations | Accepted by 2023 IEEE Int. Symp. on Information Theory (ISIT) | null | null | null | cs.GT cs.IT cs.LG math.IT math.ST stat.ML stat.TH | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, $2\times2$ zero-sum games are studied under the following
assumptions: $(1)$ One of the players (the leader) commits to choose its
actions by sampling a given probability measure (strategy); $(2)$ The leader
announces its action, which is observed by its opponent (the follower) through
a binary channel; and $(3)$ the follower chooses its strategy based on the
knowledge of the leader's strategy and the noisy observation of the leader's
action. Under these conditions, the equilibrium is shown to always exist.
Interestingly, even subject to noise, observing the actions of the leader is
shown to be either beneficial or immaterial for the follower. More
specifically, the payoff at the equilibrium of this game is upper bounded by
the payoff at the Stackelberg equilibrium (SE) in pure strategies; and lower
bounded by the payoff at the Nash equilibrium, which is equivalent to the SE in
mixed strategies.Finally, necessary and sufficient conditions for observing the
payoff at equilibrium to be equal to its lower bound are presented. Sufficient
conditions for the payoff at equilibrium to be equal to its upper bound are
also presented.
| [
{
"created": "Thu, 3 Nov 2022 10:56:00 GMT",
"version": "v1"
},
{
"created": "Wed, 10 May 2023 00:50:30 GMT",
"version": "v2"
},
{
"created": "Thu, 11 May 2023 08:29:01 GMT",
"version": "v3"
}
] | 2023-05-12 | [
[
"Sun",
"Ke",
""
],
[
"Perlaza",
"Samir M.",
""
],
[
"Jean-Marie",
"Alain",
""
]
] | In this paper, $2\times2$ zero-sum games are studied under the following assumptions: $(1)$ One of the players (the leader) commits to choose its actions by sampling a given probability measure (strategy); $(2)$ The leader announces its action, which is observed by its opponent (the follower) through a binary channel; and $(3)$ the follower chooses its strategy based on the knowledge of the leader's strategy and the noisy observation of the leader's action. Under these conditions, the equilibrium is shown to always exist. Interestingly, even subject to noise, observing the actions of the leader is shown to be either beneficial or immaterial for the follower. More specifically, the payoff at the equilibrium of this game is upper bounded by the payoff at the Stackelberg equilibrium (SE) in pure strategies; and lower bounded by the payoff at the Nash equilibrium, which is equivalent to the SE in mixed strategies.Finally, necessary and sufficient conditions for observing the payoff at equilibrium to be equal to its lower bound are presented. Sufficient conditions for the payoff at equilibrium to be equal to its upper bound are also presented. |
1703.08100 | Jiongqian Liang | Jiongqian Liang, Peter Jacobs, Jiankai Sun, Srinivasan Parthasarathy | Semi-supervised Embedding in Attributed Networks with Outliers | in Proceedings of SIAM International Conference on Data Mining
(SDM'18) | null | null | null | cs.SI cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.
| [
{
"created": "Thu, 23 Mar 2017 15:15:53 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Oct 2017 20:51:05 GMT",
"version": "v2"
},
{
"created": "Mon, 5 Mar 2018 19:14:14 GMT",
"version": "v3"
},
{
"created": "Thu, 26 Apr 2018 21:54:52 GMT",
"version": "v4"
}
] | 2018-04-30 | [
[
"Liang",
"Jiongqian",
""
],
[
"Jacobs",
"Peter",
""
],
[
"Sun",
"Jiankai",
""
],
[
"Parthasarathy",
"Srinivasan",
""
]
] | In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO is interpretable as outlier score and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting -- flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task. |
1304.3083 | Norman C. Dalkey | Norman C. Dalkey | Models vs. Inductive Inference for Dealing With Probabilistic Knowledge | Appears in Proceedings of the Second Conference on Uncertainty in
Artificial Intelligence (UAI1986) | null | null | UAI-P-1986-PG-63-70 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two different approaches to dealing with probabilistic knowledge are examined
-models and inductive inference. Examples of the first are: influence diagrams
[1], Bayesian networks [2], log-linear models [3, 4]. Examples of the second
are: games-against nature [5, 6] varieties of maximum-entropy methods [7, 8,
9], and the author's min-score induction [10]. In the modeling approach, the
basic issue is manageability, with respect to data elicitation and computation.
Thus, it is assumed that the pertinent set of users in some sense knows the
relevant probabilities, and the problem is to format that knowledge in a way
that is convenient to input and store and that allows computation of the
answers to current questions in an expeditious fashion. The basic issue for the
inductive approach appears at first sight to be very different. In this
approach it is presumed that the relevant probabilities are only partially
known, and the problem is to extend that incomplete information in a reasonable
way to answer current questions. Clearly, this approach requires that some form
of induction be invoked. Of course, manageability is an important additional
concern. Despite their seeming differences, the two approaches have a fair
amount in common, especially with respect to the structural framework they
employ. Roughly speaking, this framework involves identifying clusters of
variables which strongly interact, establishing marginal probability
distributions on the clusters, and extending the subdistributions to a more
complete distribution, usually via a product formalism. The product extension
is justified on the modeling approach in terms of assumed conditional
independence; in the inductive approach the product form arises from an
inductive rule.
| [
{
"created": "Wed, 27 Mar 2013 19:51:44 GMT",
"version": "v1"
}
] | 2013-04-12 | [
[
"Dalkey",
"Norman C.",
""
]
] | Two different approaches to dealing with probabilistic knowledge are examined -models and inductive inference. Examples of the first are: influence diagrams [1], Bayesian networks [2], log-linear models [3, 4]. Examples of the second are: games-against nature [5, 6] varieties of maximum-entropy methods [7, 8, 9], and the author's min-score induction [10]. In the modeling approach, the basic issue is manageability, with respect to data elicitation and computation. Thus, it is assumed that the pertinent set of users in some sense knows the relevant probabilities, and the problem is to format that knowledge in a way that is convenient to input and store and that allows computation of the answers to current questions in an expeditious fashion. The basic issue for the inductive approach appears at first sight to be very different. In this approach it is presumed that the relevant probabilities are only partially known, and the problem is to extend that incomplete information in a reasonable way to answer current questions. Clearly, this approach requires that some form of induction be invoked. Of course, manageability is an important additional concern. Despite their seeming differences, the two approaches have a fair amount in common, especially with respect to the structural framework they employ. Roughly speaking, this framework involves identifying clusters of variables which strongly interact, establishing marginal probability distributions on the clusters, and extending the subdistributions to a more complete distribution, usually via a product formalism. The product extension is justified on the modeling approach in terms of assumed conditional independence; in the inductive approach the product form arises from an inductive rule. |
1307.6649 | Dr. Rajesh Kumar Tiwari | Kumar Gunjan, R. K. Tiwari, G. Sahoo | Towards Securing APIs in Cloud Computing | International Journal of Computer Engineering and Applications, June
2013. arXiv admin note: text overlap with arXiv:0901.0131 by other authors | null | null | null | cs.DC cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Every organisation today wants to adopt cloud computing paradigm and leverage
its various advantages. Today everyone is aware of its characteristics which
have made it so popular and how it can help the organisations focus on their
core activities leaving all IT services development and maintenance to the
cloud service providers. Application Programming Interfaces (APIs) act as the
interface between the CSPs and the consumers. This paper proposes an improved
access control mechanism for securing the Cloud APIs.
| [
{
"created": "Thu, 25 Jul 2013 07:41:44 GMT",
"version": "v1"
}
] | 2013-07-26 | [
[
"Gunjan",
"Kumar",
""
],
[
"Tiwari",
"R. K.",
""
],
[
"Sahoo",
"G.",
""
]
] | Every organisation today wants to adopt cloud computing paradigm and leverage its various advantages. Today everyone is aware of its characteristics which have made it so popular and how it can help the organisations focus on their core activities leaving all IT services development and maintenance to the cloud service providers. Application Programming Interfaces (APIs) act as the interface between the CSPs and the consumers. This paper proposes an improved access control mechanism for securing the Cloud APIs. |
1903.09465 | Yuan Cao | Zhuotao Liu and Yuan Cao and Xuewu Zhang and Changping Zhu and Fan
Zhang | Managing Recurrent Virtual Network Updates in Multi-Tenant Datacenters:
A System Perspective | null | null | null | null | cs.CR cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the advent of software-defined networking, network configuration through
programmable interfaces becomes practical, leading to various on-demand
opportunities for network routing update in multi-tenant datacenters, where
tenants have diverse requirements on network routings such as short latency,
low path inflation, large bandwidth, high reliability, etc. Conventional
solutions that rely on topology search coupled with an objective function
https:// www.overleaf.com/project/5beb742041ab9c0e3caec84f to find desired
routings have at least two shortcomings: (i) they run into scalability issues
when handling consistent and frequent routing updates and (ii) they restrict
the flexibility and capability to satisfy various routing requirements. To
address these issues, this paper proposes a novel search and optimization
decoupled design, which not only saves considerable topology search costs via
search result reuse, but also avoids possible sub-optimality in greedy routing
search algorithms by making decisions based on the global view of all possible
routings. We implement a prototype of our proposed system, OpReduce, and
perform extensive evaluations to validate its design goals.
| [
{
"created": "Fri, 22 Mar 2019 12:04:02 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Mar 2019 07:45:34 GMT",
"version": "v2"
},
{
"created": "Fri, 29 Mar 2019 11:17:54 GMT",
"version": "v3"
},
{
"created": "Sun, 7 Apr 2019 06:45:38 GMT",
"version": "v4"
},
{
"created": "Thu, 18 Jun 2020 05:22:28 GMT",
"version": "v5"
}
] | 2020-06-19 | [
[
"Liu",
"Zhuotao",
""
],
[
"Cao",
"Yuan",
""
],
[
"Zhang",
"Xuewu",
""
],
[
"Zhu",
"Changping",
""
],
[
"Zhang",
"Fan",
""
]
] | With the advent of software-defined networking, network configuration through programmable interfaces becomes practical, leading to various on-demand opportunities for network routing update in multi-tenant datacenters, where tenants have diverse requirements on network routings such as short latency, low path inflation, large bandwidth, high reliability, etc. Conventional solutions that rely on topology search coupled with an objective function https:// www.overleaf.com/project/5beb742041ab9c0e3caec84f to find desired routings have at least two shortcomings: (i) they run into scalability issues when handling consistent and frequent routing updates and (ii) they restrict the flexibility and capability to satisfy various routing requirements. To address these issues, this paper proposes a novel search and optimization decoupled design, which not only saves considerable topology search costs via search result reuse, but also avoids possible sub-optimality in greedy routing search algorithms by making decisions based on the global view of all possible routings. We implement a prototype of our proposed system, OpReduce, and perform extensive evaluations to validate its design goals. |
2306.13631 | Ayca Takmaz | Ay\c{c}a Takmaz, Elisabetta Fedele, Robert W. Sumner, Marc Pollefeys,
Federico Tombari, Francis Engelmann | OpenMask3D: Open-Vocabulary 3D Instance Segmentation | NeurIPS 2023. Project page: https://openmask3d.github.io/ | NeurIPS 2023 | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the task of open-vocabulary 3D instance segmentation. Current
approaches for 3D instance segmentation can typically only recognize object
categories from a pre-defined closed set of classes that are annotated in the
training datasets. This results in important limitations for real-world
applications where one might need to perform tasks guided by novel,
open-vocabulary queries related to a wide variety of objects. Recently,
open-vocabulary 3D scene understanding methods have emerged to address this
problem by learning queryable features for each point in the scene. While such
a representation can be directly employed to perform semantic segmentation,
existing methods cannot separate multiple object instances. In this work, we
address this limitation, and propose OpenMask3D, which is a zero-shot approach
for open-vocabulary 3D instance segmentation. Guided by predicted
class-agnostic 3D instance masks, our model aggregates per-mask features via
multi-view fusion of CLIP-based image embeddings. Experiments and ablation
studies on ScanNet200 and Replica show that OpenMask3D outperforms other
open-vocabulary methods, especially on the long-tail distribution. Qualitative
experiments further showcase OpenMask3D's ability to segment object properties
based on free-form queries describing geometry, affordances, and materials.
| [
{
"created": "Fri, 23 Jun 2023 17:36:44 GMT",
"version": "v1"
},
{
"created": "Sun, 29 Oct 2023 14:04:25 GMT",
"version": "v2"
}
] | 2023-10-31 | [
[
"Takmaz",
"Ayça",
""
],
[
"Fedele",
"Elisabetta",
""
],
[
"Sumner",
"Robert W.",
""
],
[
"Pollefeys",
"Marc",
""
],
[
"Tombari",
"Federico",
""
],
[
"Engelmann",
"Francis",
""
]
] | We introduce the task of open-vocabulary 3D instance segmentation. Current approaches for 3D instance segmentation can typically only recognize object categories from a pre-defined closed set of classes that are annotated in the training datasets. This results in important limitations for real-world applications where one might need to perform tasks guided by novel, open-vocabulary queries related to a wide variety of objects. Recently, open-vocabulary 3D scene understanding methods have emerged to address this problem by learning queryable features for each point in the scene. While such a representation can be directly employed to perform semantic segmentation, existing methods cannot separate multiple object instances. In this work, we address this limitation, and propose OpenMask3D, which is a zero-shot approach for open-vocabulary 3D instance segmentation. Guided by predicted class-agnostic 3D instance masks, our model aggregates per-mask features via multi-view fusion of CLIP-based image embeddings. Experiments and ablation studies on ScanNet200 and Replica show that OpenMask3D outperforms other open-vocabulary methods, especially on the long-tail distribution. Qualitative experiments further showcase OpenMask3D's ability to segment object properties based on free-form queries describing geometry, affordances, and materials. |
2312.11774 | Yuze He | Yuze He, Yushi Bai, Matthieu Lin, Jenny Sheng, Yubin Hu, Qi Wang,
Yu-Hui Wen, Yong-Jin Liu | Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | By lifting the pre-trained 2D diffusion models into Neural Radiance Fields
(NeRFs), text-to-3D generation methods have made great progress. Many
state-of-the-art approaches usually apply score distillation sampling (SDS) to
optimize the NeRF representations, which supervises the NeRF optimization with
pre-trained text-conditioned 2D diffusion models such as Imagen. However, the
supervision signal provided by such pre-trained diffusion models only depends
on text prompts and does not constrain the multi-view consistency. To inject
the cross-view consistency into diffusion priors, some recent works finetune
the 2D diffusion model with multi-view data, but still lack fine-grained view
coherence. To tackle this challenge, we incorporate multi-view image conditions
into the supervision signal of NeRF optimization, which explicitly enforces
fine-grained view consistency. With such stronger supervision, our proposed
text-to-3D method effectively mitigates the generation of floaters (due to
excessive densities) and completely empty spaces (due to insufficient
densities). Our quantitative evaluations on the T$^3$Bench dataset demonstrate
that our method achieves state-of-the-art performance over existing text-to-3D
methods. We will make the code publicly available.
| [
{
"created": "Tue, 19 Dec 2023 01:09:49 GMT",
"version": "v1"
}
] | 2023-12-20 | [
[
"He",
"Yuze",
""
],
[
"Bai",
"Yushi",
""
],
[
"Lin",
"Matthieu",
""
],
[
"Sheng",
"Jenny",
""
],
[
"Hu",
"Yubin",
""
],
[
"Wang",
"Qi",
""
],
[
"Wen",
"Yu-Hui",
""
],
[
"Liu",
"Yong-Jin",
""
]
] | By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress. Many state-of-the-art approaches usually apply score distillation sampling (SDS) to optimize the NeRF representations, which supervises the NeRF optimization with pre-trained text-conditioned 2D diffusion models such as Imagen. However, the supervision signal provided by such pre-trained diffusion models only depends on text prompts and does not constrain the multi-view consistency. To inject the cross-view consistency into diffusion priors, some recent works finetune the 2D diffusion model with multi-view data, but still lack fine-grained view coherence. To tackle this challenge, we incorporate multi-view image conditions into the supervision signal of NeRF optimization, which explicitly enforces fine-grained view consistency. With such stronger supervision, our proposed text-to-3D method effectively mitigates the generation of floaters (due to excessive densities) and completely empty spaces (due to insufficient densities). Our quantitative evaluations on the T$^3$Bench dataset demonstrate that our method achieves state-of-the-art performance over existing text-to-3D methods. We will make the code publicly available. |
2206.02597 | Alvari Sepp\"anen | Alvari Sepp\"anen, Eerik Alamikkotervo, Risto Ojala, Giacomo Dario,
Kari Tammi | No GPU? No problem: an ultra fast 3D detection of road users with a
simple proposal generator and energy-based out-of-distribution PointNets | null | null | 10.1186/s40537-023-00859-5 | null | cs.RO cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel architecture for point cloud road user detection,
which is based on a classical point cloud proposal generator approach, that
utilizes simple geometrical rules. New methods are coupled with this technique
to achieve extremely small computational requirement, and mAP that is
comparable to the state-of-the-art. The idea is to specifically exploit
geometrical rules in hopes of faster performance. The typical downsides of this
approach, e.g. global context loss, are tackled in this paper, and solutions
are presented. This approach allows real-time performance on a single core CPU,
which is not the case with end-to-end solutions presented in the
state-of-the-art. We have evaluated the performance of the method with the
public KITTI dataset, and with our own annotated dataset collected with a small
mobile robot platform. Moreover, we also present a novel ground segmentation
method, which is evaluated with the public SemanticKITTI dataset.
| [
{
"created": "Mon, 6 Jun 2022 13:08:22 GMT",
"version": "v1"
}
] | 2024-01-04 | [
[
"Seppänen",
"Alvari",
""
],
[
"Alamikkotervo",
"Eerik",
""
],
[
"Ojala",
"Risto",
""
],
[
"Dario",
"Giacomo",
""
],
[
"Tammi",
"Kari",
""
]
] | This paper presents a novel architecture for point cloud road user detection, which is based on a classical point cloud proposal generator approach, that utilizes simple geometrical rules. New methods are coupled with this technique to achieve extremely small computational requirement, and mAP that is comparable to the state-of-the-art. The idea is to specifically exploit geometrical rules in hopes of faster performance. The typical downsides of this approach, e.g. global context loss, are tackled in this paper, and solutions are presented. This approach allows real-time performance on a single core CPU, which is not the case with end-to-end solutions presented in the state-of-the-art. We have evaluated the performance of the method with the public KITTI dataset, and with our own annotated dataset collected with a small mobile robot platform. Moreover, we also present a novel ground segmentation method, which is evaluated with the public SemanticKITTI dataset. |
2008.03808 | Mohammed Alqahtani | Mohammed Alqahtani, Susan Gauch, Omar Salman, Mohammed Ibrahim, Reem
Al-Saffar | Diverse Group Formation Based on Multiple Demographic Features | null | KDIR, 169-178, 2020 | null | null | cs.IR cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of group formation is to build a team to accomplish a specific task.
Algorithms are employed to improve the effectiveness of the team so formed and
the efficiency of the group selection process. However, there is concern that
team formation algorithms could be biased against minorities due to the
algorithms themselves or the data on which they are trained. Hence, it is
essential to build fair team formation systems that incorporate demographic
information into the process of building the group. Although there has been
extensive work on modeling individuals expertise for expert recommendation and
or team formation, there has been relatively little prior work on modeling
demographics and incorporating demographics into the group formation process.
We propose a novel method to represent experts demographic profiles based on
multidimensional demographic features. Moreover, we introduce two diversity
ranking algorithms that form a group by considering demographic features along
with the minimum required skills. Unlike many ranking algorithms that consider
one Boolean demographic feature (e.g., gender or race), our diversity ranking
algorithms consider multiple multivalued demographic attributes simultaneously.
We evaluate our proposed algorithms using a real dataset based on members of a
computer science program committee. The result shows that our algorithms form a
program committee that is more diverse with an acceptable loss in utility.
| [
{
"created": "Sun, 9 Aug 2020 20:15:04 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Dec 2020 06:11:10 GMT",
"version": "v2"
}
] | 2020-12-04 | [
[
"Alqahtani",
"Mohammed",
""
],
[
"Gauch",
"Susan",
""
],
[
"Salman",
"Omar",
""
],
[
"Ibrahim",
"Mohammed",
""
],
[
"Al-Saffar",
"Reem",
""
]
] | The goal of group formation is to build a team to accomplish a specific task. Algorithms are employed to improve the effectiveness of the team so formed and the efficiency of the group selection process. However, there is concern that team formation algorithms could be biased against minorities due to the algorithms themselves or the data on which they are trained. Hence, it is essential to build fair team formation systems that incorporate demographic information into the process of building the group. Although there has been extensive work on modeling individuals expertise for expert recommendation and or team formation, there has been relatively little prior work on modeling demographics and incorporating demographics into the group formation process. We propose a novel method to represent experts demographic profiles based on multidimensional demographic features. Moreover, we introduce two diversity ranking algorithms that form a group by considering demographic features along with the minimum required skills. Unlike many ranking algorithms that consider one Boolean demographic feature (e.g., gender or race), our diversity ranking algorithms consider multiple multivalued demographic attributes simultaneously. We evaluate our proposed algorithms using a real dataset based on members of a computer science program committee. The result shows that our algorithms form a program committee that is more diverse with an acceptable loss in utility. |
2210.00704 | Yingming Pu | Yingming Pu | Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic
Prediction | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The continuous expansion of the urban construction scale has recently
contributed to the demand for the dynamics of traffic intersections that are
managed, making adaptive modellings become a hot topic. Existing deep learning
methods are powerful to fit complex heterogeneous graphs. However, they still
have drawbacks, which can be roughly classified into two categories, 1)
spatiotemporal async-modelling approaches separately consider temporal and
spatial dependencies, resulting in weak generalization and large instability
while aggregating; 2) spatiotemporal sync-modelling is hard to capture
long-term temporal dependencies because of the local receptive field. In order
to overcome above challenges, a \textbf{C}ombined \textbf{D}ynamic
\textbf{V}irtual spatiotemporal \textbf{G}raph \textbf{M}apping
\textbf{(CDVGM)} is proposed in this work. The contributions are the following:
1) a dynamic virtual graph Laplacian ($DVGL$) is designed, which considers both
the spatial signal passing and the temporal features simultaneously; 2) the
Long-term Temporal Strengthen model ($LT^2S$) for improving the stability of
time series forecasting; Extensive experiments demonstrate that CDVGM has
excellent performances of fast convergence speed and low resource consumption
and achieves the current SOTA effect in terms of both accuracy and
generalization. The code is available at
\hyperlink{https://github.com/Dandelionym/CDVGM.}{https://github.com/Dandelionym/CDVGM.}
| [
{
"created": "Mon, 3 Oct 2022 04:11:21 GMT",
"version": "v1"
}
] | 2022-10-04 | [
[
"Pu",
"Yingming",
""
]
] | The continuous expansion of the urban construction scale has recently contributed to the demand for the dynamics of traffic intersections that are managed, making adaptive modellings become a hot topic. Existing deep learning methods are powerful to fit complex heterogeneous graphs. However, they still have drawbacks, which can be roughly classified into two categories, 1) spatiotemporal async-modelling approaches separately consider temporal and spatial dependencies, resulting in weak generalization and large instability while aggregating; 2) spatiotemporal sync-modelling is hard to capture long-term temporal dependencies because of the local receptive field. In order to overcome above challenges, a \textbf{C}ombined \textbf{D}ynamic \textbf{V}irtual spatiotemporal \textbf{G}raph \textbf{M}apping \textbf{(CDVGM)} is proposed in this work. The contributions are the following: 1) a dynamic virtual graph Laplacian ($DVGL$) is designed, which considers both the spatial signal passing and the temporal features simultaneously; 2) the Long-term Temporal Strengthen model ($LT^2S$) for improving the stability of time series forecasting; Extensive experiments demonstrate that CDVGM has excellent performances of fast convergence speed and low resource consumption and achieves the current SOTA effect in terms of both accuracy and generalization. The code is available at \hyperlink{https://github.com/Dandelionym/CDVGM.}{https://github.com/Dandelionym/CDVGM.} |
1811.07442 | Armon Shariati | Armon Shariati, Bernd Pfrommer, Camillo J. Taylor | Predictive and Semantic Layout Estimation for Robotic Applications in
Manhattan Worlds | null | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes an approach to automatically extracting floor plans from
the kinds of incomplete measurements that could be acquired by an autonomous
mobile robot. The approach proceeds by reasoning about extended structural
layout surfaces which are automatically extracted from the available data. The
scheme can be run in an online manner to build water tight representations of
the environment. The system effectively speculates about room boundaries and
free space regions which provides useful guidance to subsequent motion planning
systems. Experimental results are presented on multiple data sets.
| [
{
"created": "Mon, 19 Nov 2018 00:49:54 GMT",
"version": "v1"
}
] | 2018-11-20 | [
[
"Shariati",
"Armon",
""
],
[
"Pfrommer",
"Bernd",
""
],
[
"Taylor",
"Camillo J.",
""
]
] | This paper describes an approach to automatically extracting floor plans from the kinds of incomplete measurements that could be acquired by an autonomous mobile robot. The approach proceeds by reasoning about extended structural layout surfaces which are automatically extracted from the available data. The scheme can be run in an online manner to build water tight representations of the environment. The system effectively speculates about room boundaries and free space regions which provides useful guidance to subsequent motion planning systems. Experimental results are presented on multiple data sets. |
1808.08429 | Ines Dutra | Carla Silva, In\^es Dutra, Marcus S. Dahlem | Driven tabu search: a quantum inherent optimisation | 6 pages | null | null | null | cs.ET quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantum computers are different from binary digital electronic computers
based on transistors. Common digital computing encodes the data into binary
digits (bits), each of which is always in one of two definite states (0 or 1),
quantum computation uses quantum bits (qubits). A circuit-based qubit quantum
computer exists and is available for experiments via cloud, the IBM quantum
experience project. We implemented a Quantum Tabu Search in order to obtain a
quantum combinatorial optimisation, suggesting that an
entanglement-metaheuristic can display optimal solutions and accelerate the
optimisation process by using entangled states. We show by building optimal
coupling maps that the distribution of our results gave similar shape as shown
previous results in an existing teleport circuit. Our research aims to find
which graph of coupling better matches a quantum circuit.
| [
{
"created": "Sat, 25 Aug 2018 13:49:27 GMT",
"version": "v1"
}
] | 2018-08-28 | [
[
"Silva",
"Carla",
""
],
[
"Dutra",
"Inês",
""
],
[
"Dahlem",
"Marcus S.",
""
]
] | Quantum computers are different from binary digital electronic computers based on transistors. Common digital computing encodes the data into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses quantum bits (qubits). A circuit-based qubit quantum computer exists and is available for experiments via cloud, the IBM quantum experience project. We implemented a Quantum Tabu Search in order to obtain a quantum combinatorial optimisation, suggesting that an entanglement-metaheuristic can display optimal solutions and accelerate the optimisation process by using entangled states. We show by building optimal coupling maps that the distribution of our results gave similar shape as shown previous results in an existing teleport circuit. Our research aims to find which graph of coupling better matches a quantum circuit. |
2402.13973 | Jiahao Zhang | Jiahao Zhang, Rui Xue, Wenqi Fan, Xin Xu, Qing Li, Jian Pei, Xiaorui
Liu | Linear-Time Graph Neural Networks for Scalable Recommendations | 12 pages, 5 figures, accepted by The Web Conference 2024 | null | null | null | cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In an era of information explosion, recommender systems are vital tools to
deliver personalized recommendations for users. The key of recommender systems
is to forecast users' future behaviors based on previous user-item
interactions. Due to their strong expressive power of capturing high-order
connectivities in user-item interaction data, recent years have witnessed a
rising interest in leveraging Graph Neural Networks (GNNs) to boost the
prediction performance of recommender systems. Nonetheless, classic Matrix
Factorization (MF) and Deep Neural Network (DNN) approaches still play an
important role in real-world large-scale recommender systems due to their
scalability advantages. Despite the existence of GNN-acceleration solutions, it
remains an open question whether GNN-based recommender systems can scale as
efficiently as classic MF and DNN methods. In this paper, we propose a
Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender
systems to achieve comparable scalability as classic MF approaches while
maintaining GNNs' powerful expressiveness for superior prediction accuracy.
Extensive experiments and ablation studies are presented to validate the
effectiveness and scalability of the proposed algorithm. Our implementation
based on PyTorch is available.
| [
{
"created": "Wed, 21 Feb 2024 17:58:10 GMT",
"version": "v1"
}
] | 2024-02-22 | [
[
"Zhang",
"Jiahao",
""
],
[
"Xue",
"Rui",
""
],
[
"Fan",
"Wenqi",
""
],
[
"Xu",
"Xin",
""
],
[
"Li",
"Qing",
""
],
[
"Pei",
"Jian",
""
],
[
"Liu",
"Xiaorui",
""
]
] | In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Due to their strong expressive power of capturing high-order connectivities in user-item interaction data, recent years have witnessed a rising interest in leveraging Graph Neural Networks (GNNs) to boost the prediction performance of recommender systems. Nonetheless, classic Matrix Factorization (MF) and Deep Neural Network (DNN) approaches still play an important role in real-world large-scale recommender systems due to their scalability advantages. Despite the existence of GNN-acceleration solutions, it remains an open question whether GNN-based recommender systems can scale as efficiently as classic MF and DNN methods. In this paper, we propose a Linear-Time Graph Neural Network (LTGNN) to scale up GNN-based recommender systems to achieve comparable scalability as classic MF approaches while maintaining GNNs' powerful expressiveness for superior prediction accuracy. Extensive experiments and ablation studies are presented to validate the effectiveness and scalability of the proposed algorithm. Our implementation based on PyTorch is available. |
2212.06436 | Thomas Werthenbach | Thomas Werthenbach and Johan Pouwelse | Survey on social reputation mechanisms: Someone told me I can trust you | 10 pages, 3 figures, 1 table | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | Nowadays, most business and social interactions have moved to the internet,
highlighting the relevance of creating online trust. One way to obtain a
measure of trust is through reputation mechanisms, which record one's past
performance and interactions to generate a reputational value. We observe that
numerous existing reputation mechanisms share similarities with actual social
phenomena; we call such mechanisms 'social reputation mechanisms'. The aim of
this paper is to discuss several social phenomena and map these to existing
social reputation mechanisms in a variety of scopes. First, we focus on
reputation mechanisms in the individual scope, in which everyone is responsible
for their own reputation. Subjective reputational values may be communicated to
different entities in the form of recommendations. Secondly, we discuss social
reputation mechanisms in the acquaintances scope, where one's reputation can be
tied to another through vouching or invite-only networks. Finally, we present
existing social reputation mechanisms in the neighbourhood scope. In such
systems, one's reputation can heavily be affected by the behaviour of others in
their neighbourhood or social group.
| [
{
"created": "Tue, 13 Dec 2022 08:57:42 GMT",
"version": "v1"
}
] | 2022-12-14 | [
[
"Werthenbach",
"Thomas",
""
],
[
"Pouwelse",
"Johan",
""
]
] | Nowadays, most business and social interactions have moved to the internet, highlighting the relevance of creating online trust. One way to obtain a measure of trust is through reputation mechanisms, which record one's past performance and interactions to generate a reputational value. We observe that numerous existing reputation mechanisms share similarities with actual social phenomena; we call such mechanisms 'social reputation mechanisms'. The aim of this paper is to discuss several social phenomena and map these to existing social reputation mechanisms in a variety of scopes. First, we focus on reputation mechanisms in the individual scope, in which everyone is responsible for their own reputation. Subjective reputational values may be communicated to different entities in the form of recommendations. Secondly, we discuss social reputation mechanisms in the acquaintances scope, where one's reputation can be tied to another through vouching or invite-only networks. Finally, we present existing social reputation mechanisms in the neighbourhood scope. In such systems, one's reputation can heavily be affected by the behaviour of others in their neighbourhood or social group. |
1911.07486 | Max Maass | Mikhail Fomichev, Max Maass, Matthias Hollick | Zero-Interaction Security -- Towards Sound Experimental Validation | 6 Pages. Companion article to arXiv:1901.07255 [cs.CR], dataset at
https://zenodo.org/record/2537721 | ACM GetMobile, Vol. 23 Issue 2 (June 2019), p. 16-21 | 10.1145/3372300.3372304 | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reproducibility and realistic datasets are crucial for advancing research.
Unfortunately, they are often neglected as valid scientific contributions in
many young disciplines, with computer science being no exception. In this
article, we show the challenges encountered when reproducing the work of
others, collecting realistic data in the wild, and ensuring that our own work
is reproducible in turn. The presented findings are based on our study
investigating the limits of zero-interaction security (ZIS) -- a novel concept,
leveraging sensor data collected by Internet of Things (IoT) devices to pair or
authenticate devices. In particular, we share our experiences in reproducing
five state-of-the-art ZIS schemes, collecting a comprehensive dataset of sensor
data from the real world, evaluating these schemes on the collected data, and
releasing the data, code, and documentation to facilitate reproducibility of
our results. In our discussion, we outline general considerations when
conducting similar studies and give specific examples of technical and
methodological issues that we experienced. We hope that our findings will raise
awareness about the importance of reproducibility and realistic datasets in
computer science and inform future research.
| [
{
"created": "Mon, 18 Nov 2019 08:46:49 GMT",
"version": "v1"
}
] | 2019-11-19 | [
[
"Fomichev",
"Mikhail",
""
],
[
"Maass",
"Max",
""
],
[
"Hollick",
"Matthias",
""
]
] | Reproducibility and realistic datasets are crucial for advancing research. Unfortunately, they are often neglected as valid scientific contributions in many young disciplines, with computer science being no exception. In this article, we show the challenges encountered when reproducing the work of others, collecting realistic data in the wild, and ensuring that our own work is reproducible in turn. The presented findings are based on our study investigating the limits of zero-interaction security (ZIS) -- a novel concept, leveraging sensor data collected by Internet of Things (IoT) devices to pair or authenticate devices. In particular, we share our experiences in reproducing five state-of-the-art ZIS schemes, collecting a comprehensive dataset of sensor data from the real world, evaluating these schemes on the collected data, and releasing the data, code, and documentation to facilitate reproducibility of our results. In our discussion, we outline general considerations when conducting similar studies and give specific examples of technical and methodological issues that we experienced. We hope that our findings will raise awareness about the importance of reproducibility and realistic datasets in computer science and inform future research. |
1807.05987 | Svitlana Lytvynova H | Svitlana Lytvynova and Oksana Melnyk | Professional Development of Teachers Using Cloud Services During
Non-formal Education | null | null | null | null | cs.CY | http://creativecommons.org/publicdomain/zero/1.0/ | The rapid development of cloud services and their implementation in secondary
education require an increase in the IC-competence of teachers during
non-formal education. The implementation of cloud services will make it
possible to create some conditions for learning mobility of all participants of
teaching and learning activities. The rapid development of cloud services and
their implementation in secondary education require an increase in the
IC-competence of teachers during non-formal education. The implementation of
cloud services will make it possible to create some conditions for learning
mobility of all participants of teaching and learning activities. The paper
analyzes the main forms of the organization of teachers learning (workshops,
trainings and summer schools). The special features of the non-formal teachers
ICT training include the availability of high-speed Internet and some computer
equipment. The obtained basic and additional services allow teachers to make
the extensive use of cloud services for different activities, namely the
organization of students group work and inverted learning, team-work on
projects, assistance during homework, preparation for contests, conducting
web-quests.
| [
{
"created": "Mon, 16 Jul 2018 17:39:01 GMT",
"version": "v1"
}
] | 2018-07-17 | [
[
"Lytvynova",
"Svitlana",
""
],
[
"Melnyk",
"Oksana",
""
]
] | The rapid development of cloud services and their implementation in secondary education require an increase in the IC-competence of teachers during non-formal education. The implementation of cloud services will make it possible to create some conditions for learning mobility of all participants of teaching and learning activities. The rapid development of cloud services and their implementation in secondary education require an increase in the IC-competence of teachers during non-formal education. The implementation of cloud services will make it possible to create some conditions for learning mobility of all participants of teaching and learning activities. The paper analyzes the main forms of the organization of teachers learning (workshops, trainings and summer schools). The special features of the non-formal teachers ICT training include the availability of high-speed Internet and some computer equipment. The obtained basic and additional services allow teachers to make the extensive use of cloud services for different activities, namely the organization of students group work and inverted learning, team-work on projects, assistance during homework, preparation for contests, conducting web-quests. |
1505.05958 | Zhenyu Shen | Jingyu Hua, Zhenyu Shen, Sheng Zhong | We Can Track You If You Take the Metro: Tracking Metro Riders Using
Accelerometers on Smartphones | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motion sensors (e.g., accelerometers) on smartphones have been demonstrated
to be a powerful side channel for attackers to spy on users' inputs on
touchscreen. In this paper, we reveal another motion accelerometer-based attack
which is particularly serious: when a person takes the metro, a malicious
application on her smartphone can easily use accelerator readings to trace her.
We first propose a basic attack that can automatically extract metro-related
data from a large amount of mixed accelerator readings, and then use an
ensemble interval classier built from supervised learning to infer the riding
intervals of the user. While this attack is very effective, the supervised
learning part requires the attacker to collect labeled training data for each
station interval, which is a significant amount of effort. To improve the
efficiency of our attack, we further propose a semi-supervised learning
approach, which only requires the attacker to collect labeled data for a very
small number of station intervals with obvious characteristics. We conduct real
experiments on a metro line in a major city. The results show that the
inferring accuracy could reach 89\% and 92\% if the user takes the metro for 4
and 6 stations, respectively.
| [
{
"created": "Fri, 22 May 2015 05:59:55 GMT",
"version": "v1"
}
] | 2015-05-25 | [
[
"Hua",
"Jingyu",
""
],
[
"Shen",
"Zhenyu",
""
],
[
"Zhong",
"Sheng",
""
]
] | Motion sensors (e.g., accelerometers) on smartphones have been demonstrated to be a powerful side channel for attackers to spy on users' inputs on touchscreen. In this paper, we reveal another motion accelerometer-based attack which is particularly serious: when a person takes the metro, a malicious application on her smartphone can easily use accelerator readings to trace her. We first propose a basic attack that can automatically extract metro-related data from a large amount of mixed accelerator readings, and then use an ensemble interval classier built from supervised learning to infer the riding intervals of the user. While this attack is very effective, the supervised learning part requires the attacker to collect labeled training data for each station interval, which is a significant amount of effort. To improve the efficiency of our attack, we further propose a semi-supervised learning approach, which only requires the attacker to collect labeled data for a very small number of station intervals with obvious characteristics. We conduct real experiments on a metro line in a major city. The results show that the inferring accuracy could reach 89\% and 92\% if the user takes the metro for 4 and 6 stations, respectively. |
2105.07444 | Ramakrishnan Raman | Ramakrishnan Raman, Meenakshi D'Souza | Knowledge Value Stream Framework For Complex Product Design Decisions | 31 pages, 25 Figures. A preliminary version of this work was
presented in 2017 IEEE Technology & Engineering Management Conference
(TEMSCON), which took place on June 8-10, 2017 in Santa Clara County, CA,
USA. Subsequently, the work has significantly evolved, expanded in scope and
progressed to its present form in this submission | null | null | null | cs.SI | http://creativecommons.org/licenses/by/4.0/ | Product Development value stream includes all the activities, value added and
non value added, for designing and developing a product. It is characterized by
flow of knowledge that drives the decisions, and deals with how the product is
conceptualized, architected and designed by the design teams. The intangible
flow of knowledge is determined by knowledge value stream, which shapes how the
raw concepts and ideas flow into mature knowledge, how the knowledge is
socialized, internalized, and how the knowledge impels the decisions in the
product development value stream. For complex products, the design teams
encounter tough challenges, such as uncertainty and variability, while making
design decisions. This paper proposes a framework for knowledge value stream
for complex product design decisions. The framework encompasses knowledge
cadence and learning cycles as its core elements and incorporates rudiments for
complex product design such as uncertainty, variability and perceptions. It
helps in managing uncertainty and variability and evolving the required
knowledge to drive optimal decisions during the design of complex products. It
advocates a phased model for framework deployment towards establishing and
progressively maturing the knowledge value stream.
| [
{
"created": "Sun, 16 May 2021 14:38:12 GMT",
"version": "v1"
}
] | 2021-05-18 | [
[
"Raman",
"Ramakrishnan",
""
],
[
"D'Souza",
"Meenakshi",
""
]
] | Product Development value stream includes all the activities, value added and non value added, for designing and developing a product. It is characterized by flow of knowledge that drives the decisions, and deals with how the product is conceptualized, architected and designed by the design teams. The intangible flow of knowledge is determined by knowledge value stream, which shapes how the raw concepts and ideas flow into mature knowledge, how the knowledge is socialized, internalized, and how the knowledge impels the decisions in the product development value stream. For complex products, the design teams encounter tough challenges, such as uncertainty and variability, while making design decisions. This paper proposes a framework for knowledge value stream for complex product design decisions. The framework encompasses knowledge cadence and learning cycles as its core elements and incorporates rudiments for complex product design such as uncertainty, variability and perceptions. It helps in managing uncertainty and variability and evolving the required knowledge to drive optimal decisions during the design of complex products. It advocates a phased model for framework deployment towards establishing and progressively maturing the knowledge value stream. |
2208.05315 | YiSong Yu | Yisong Yu, Beihong Jin, Jiageng Song, Beibei Li, Yiyuan Zheng, and Wei
Zhu | Improving Micro-video Recommendation by Controlling Position Bias | accepted by ECML PKDD2022 | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the micro-video apps become popular, the numbers of micro-videos and users
increase rapidly, which highlights the importance of micro-video
recommendation. Although the micro-video recommendation can be naturally
treated as the sequential recommendation, the previous sequential
recommendation models do not fully consider the characteristics of micro-video
apps, and in their inductive biases, the role of positions is not in accord
with the reality in the micro-video scenario. Therefore, in the paper, we
present a model named PDMRec (Position Decoupled Micro-video Recommendation).
PDMRec applies separate self-attention modules to model micro-video information
and the positional information and then aggregate them together, avoid the
noisy correlations between micro-video semantics and positional information
being encoded into the sequence embeddings. Moreover, PDMRec proposes
contrastive learning strategies which closely match with the characteristics of
the micro-video scenario, thus reducing the interference from micro-video
positions in sequences. We conduct the extensive experiments on two real-world
datasets. The experimental results shows that PDMRec outperforms existing
multiple state-of-the-art models and achieves significant performance
improvements.
| [
{
"created": "Tue, 9 Aug 2022 11:58:57 GMT",
"version": "v1"
}
] | 2022-08-11 | [
[
"Yu",
"Yisong",
""
],
[
"Jin",
"Beihong",
""
],
[
"Song",
"Jiageng",
""
],
[
"Li",
"Beibei",
""
],
[
"Zheng",
"Yiyuan",
""
],
[
"Zhu",
"Wei",
""
]
] | As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario. Therefore, in the paper, we present a model named PDMRec (Position Decoupled Micro-video Recommendation). PDMRec applies separate self-attention modules to model micro-video information and the positional information and then aggregate them together, avoid the noisy correlations between micro-video semantics and positional information being encoded into the sequence embeddings. Moreover, PDMRec proposes contrastive learning strategies which closely match with the characteristics of the micro-video scenario, thus reducing the interference from micro-video positions in sequences. We conduct the extensive experiments on two real-world datasets. The experimental results shows that PDMRec outperforms existing multiple state-of-the-art models and achieves significant performance improvements. |
2212.08397 | Tulasimohan Molli | Prahladh Harsha, Tulasi mohan Molli, Ashutosh Shankar | Criticality of $\text{AC}^0$ formulae | null | null | null | null | cs.CC | http://creativecommons.org/licenses/by/4.0/ | Rossman [In $\textit{Proc. $34$th Comput. Complexity Conf.}$, 2019]
introduced the notion of $\textit{criticality}$. The criticality of a Boolean
function $f : \{0,1\}^n \to \{0,1\}$ is the minimum $\lambda \geq 1$ such that
for all positive integers $t$, \[ \Pr_{\rho \sim
\mathcal{R}_p}\left[\text{DT}_{\text{depth}}(f|_{\rho}) \geq t\right] \leq
(p\lambda)^t. \] H\"astad's celebrated switching lemma shows that the
criticality of any $k$-DNF is at most $O(k)$. Subsequent improvements to
correlation bounds of $\text{AC}^0$-circuits against parity showed that the
criticality of any $\text{AC}^0$-$\textit{circuit}$ of size $S$ and depth $d+1$
is at most $O(\log S)^d$ and any $\textit{regular}$
$\text{AC}^0$-$\textit{formula}$ of size $S$ and depth $d+1$ is at most
$O\left(\frac1d \cdot \log S\right)^d$. We strengthen these results by showing
that the criticality of $\textit{any}$ $\text{AC}^0$-formula (not necessarily
regular) of size $S$ and depth $d+1$ is at most $O\left(\frac1d\cdot {\log
S}\right)^d$, resolving a conjecture due to Rossman.
This result also implies Rossman's optimal lower bound on the size of any
depth-$d$ $\text{AC}^0$-formula computing parity [$\textit{Comput. Complexity,
27(2):209--223, 2018.}$]. Our result implies tight correlation bounds against
parity, tight Fourier concentration results and improved $\#$SAT algorithm for
$\text{AC}^0$-formulae.
| [
{
"created": "Fri, 16 Dec 2022 10:41:04 GMT",
"version": "v1"
},
{
"created": "Wed, 21 Dec 2022 05:52:30 GMT",
"version": "v2"
},
{
"created": "Thu, 5 Jan 2023 04:38:50 GMT",
"version": "v3"
}
] | 2023-01-06 | [
[
"Harsha",
"Prahladh",
""
],
[
"Molli",
"Tulasi mohan",
""
],
[
"Shankar",
"Ashutosh",
""
]
] | Rossman [In $\textit{Proc. $34$th Comput. Complexity Conf.}$, 2019] introduced the notion of $\textit{criticality}$. The criticality of a Boolean function $f : \{0,1\}^n \to \{0,1\}$ is the minimum $\lambda \geq 1$ such that for all positive integers $t$, \[ \Pr_{\rho \sim \mathcal{R}_p}\left[\text{DT}_{\text{depth}}(f|_{\rho}) \geq t\right] \leq (p\lambda)^t. \] H\"astad's celebrated switching lemma shows that the criticality of any $k$-DNF is at most $O(k)$. Subsequent improvements to correlation bounds of $\text{AC}^0$-circuits against parity showed that the criticality of any $\text{AC}^0$-$\textit{circuit}$ of size $S$ and depth $d+1$ is at most $O(\log S)^d$ and any $\textit{regular}$ $\text{AC}^0$-$\textit{formula}$ of size $S$ and depth $d+1$ is at most $O\left(\frac1d \cdot \log S\right)^d$. We strengthen these results by showing that the criticality of $\textit{any}$ $\text{AC}^0$-formula (not necessarily regular) of size $S$ and depth $d+1$ is at most $O\left(\frac1d\cdot {\log S}\right)^d$, resolving a conjecture due to Rossman. This result also implies Rossman's optimal lower bound on the size of any depth-$d$ $\text{AC}^0$-formula computing parity [$\textit{Comput. Complexity, 27(2):209--223, 2018.}$]. Our result implies tight correlation bounds against parity, tight Fourier concentration results and improved $\#$SAT algorithm for $\text{AC}^0$-formulae. |
1902.03439 | Toke H{\o}iland-J{\o}rgensen | Toke H{\o}iland-J{\o}rgensen, Per Hurtig and Anna Brunstrom | PoliFi: Airtime Policy Enforcement for WiFi | null | null | null | null | cs.NI | http://creativecommons.org/licenses/by-sa/4.0/ | As WiFi grows ever more popular, airtime contention becomes an increasing
problem. One way to alleviate this is through network policy enforcement.
Unfortunately, WiFi lacks protocol support for configuring policies for its
usage, and since network-wide coordination cannot generally be ensured,
enforcing policy is challenging. However, as we have shown in previous work, an
access point can influence the behaviour of connected devices by changing its
scheduling of transmission opportunities, which can be used to achieve airtime
fairness. In this work, we show that this mechanism can be extended to
successfully enforce airtime usage policies in WiFi networks. We implement this
as an extension our previous airtime fairness work, and present PoliFi, the
resulting policy enforcement system. Our evaluation shows that PoliFi makes it
possible to express a range of useful policies. These include prioritisation of
specific devices; balancing groups of devices for sharing between different
logical networks or network slices; and limiting groups of devices to implement
guest networks or other low-priority services. We also show how these can be
used to improve the performance of a real-world DASH video streaming
application.
| [
{
"created": "Sat, 9 Feb 2019 16:29:22 GMT",
"version": "v1"
}
] | 2019-02-12 | [
[
"Høiland-Jørgensen",
"Toke",
""
],
[
"Hurtig",
"Per",
""
],
[
"Brunstrom",
"Anna",
""
]
] | As WiFi grows ever more popular, airtime contention becomes an increasing problem. One way to alleviate this is through network policy enforcement. Unfortunately, WiFi lacks protocol support for configuring policies for its usage, and since network-wide coordination cannot generally be ensured, enforcing policy is challenging. However, as we have shown in previous work, an access point can influence the behaviour of connected devices by changing its scheduling of transmission opportunities, which can be used to achieve airtime fairness. In this work, we show that this mechanism can be extended to successfully enforce airtime usage policies in WiFi networks. We implement this as an extension our previous airtime fairness work, and present PoliFi, the resulting policy enforcement system. Our evaluation shows that PoliFi makes it possible to express a range of useful policies. These include prioritisation of specific devices; balancing groups of devices for sharing between different logical networks or network slices; and limiting groups of devices to implement guest networks or other low-priority services. We also show how these can be used to improve the performance of a real-world DASH video streaming application. |
1110.0550 | Sunil Khatri | Pey-Chang Kent Lin, Ayan Mandal, Sunil P Khatri | Boolean Satisfiability using Noise Based Logic | 6 pages, 1 figure | null | null | null | cs.CC cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a novel algorithm to solve the Boolean
Satisfiability (SAT) problem, using noise-based logic (NBL). Contrary to what
the name may suggest, NBL is not a random/fuzzy logic system. In fact, it is a
completely deterministic logic system. A key property of NBL is that it allows
us to apply a superposition of many input vectors to a SAT instance at the same
time, circumventing a key restriction and assumption in the traditional
approach to solving SAT. By exploiting the superposition property of NBL, our
NBL-based SAT algorithm can determine whether an instance is SAT or not in a
single operation. A satisfying solution can be found by iteratively performing
SAT check operations up to n times, where n is the number of variables in the
SAT instance. Although this paper does not focus on the realization of an
NBL-based SAT engine, such an engine can be conceived using analog circuits
(wide-band amplifiers, adders and multipliers), FPGAs or ASICs. Additionally,
we also discus scalability of our approach, which can apply to NBL in general.
The NBL-based SAT engine described in this paper has been simulated in software
for validation purposes.
| [
{
"created": "Tue, 4 Oct 2011 00:32:40 GMT",
"version": "v1"
}
] | 2011-10-05 | [
[
"Lin",
"Pey-Chang Kent",
""
],
[
"Mandal",
"Ayan",
""
],
[
"Khatri",
"Sunil P",
""
]
] | In this paper, we present a novel algorithm to solve the Boolean Satisfiability (SAT) problem, using noise-based logic (NBL). Contrary to what the name may suggest, NBL is not a random/fuzzy logic system. In fact, it is a completely deterministic logic system. A key property of NBL is that it allows us to apply a superposition of many input vectors to a SAT instance at the same time, circumventing a key restriction and assumption in the traditional approach to solving SAT. By exploiting the superposition property of NBL, our NBL-based SAT algorithm can determine whether an instance is SAT or not in a single operation. A satisfying solution can be found by iteratively performing SAT check operations up to n times, where n is the number of variables in the SAT instance. Although this paper does not focus on the realization of an NBL-based SAT engine, such an engine can be conceived using analog circuits (wide-band amplifiers, adders and multipliers), FPGAs or ASICs. Additionally, we also discus scalability of our approach, which can apply to NBL in general. The NBL-based SAT engine described in this paper has been simulated in software for validation purposes. |
2310.20327 | Guoliang Lin | Guoliang Lin, Hanjiang Lai, Yan Pan, Jian Yin | Improving Entropy-Based Test-Time Adaptation from a Clustering View | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Domain shift is a common problem in the realistic world, where training data
and test data follow different data distributions. To deal with this problem,
fully test-time adaptation (TTA) leverages the unlabeled data encountered
during test time to adapt the model. In particular, entropy-based TTA (EBTTA)
methods, which minimize the prediction's entropy on test samples, have shown
great success. In this paper, we introduce a new clustering perspective on the
EBTTA. It is an iterative algorithm: 1) in the assignment step, the forward
process of the EBTTA models is the assignment of labels for these test samples,
and 2) in the updating step, the backward process is the update of the model
via the assigned samples. This new perspective allows us to explore how entropy
minimization influences test-time adaptation. Accordingly, this observation can
guide us to put forward the improvement of EBTTA. We propose to improve EBTTA
from the assignment step and the updating step, where robust label assignment,
similarity-preserving constraint, sample selection, and gradient accumulation
are proposed to explicitly utilize more information. Experimental results
demonstrate that our method can achieve consistent improvements on various
datasets. Code is provided in the supplementary material.
| [
{
"created": "Tue, 31 Oct 2023 10:10:48 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Nov 2023 14:47:30 GMT",
"version": "v2"
},
{
"created": "Tue, 7 Nov 2023 04:03:13 GMT",
"version": "v3"
},
{
"created": "Sat, 18 Nov 2023 06:14:05 GMT",
"version": "v4"
},
{
"created": "Tue, 9 Apr 2024 13:22:43 GMT",
"version": "v5"
},
{
"created": "Fri, 26 Apr 2024 03:11:42 GMT",
"version": "v6"
}
] | 2024-04-29 | [
[
"Lin",
"Guoliang",
""
],
[
"Lai",
"Hanjiang",
""
],
[
"Pan",
"Yan",
""
],
[
"Yin",
"Jian",
""
]
] | Domain shift is a common problem in the realistic world, where training data and test data follow different data distributions. To deal with this problem, fully test-time adaptation (TTA) leverages the unlabeled data encountered during test time to adapt the model. In particular, entropy-based TTA (EBTTA) methods, which minimize the prediction's entropy on test samples, have shown great success. In this paper, we introduce a new clustering perspective on the EBTTA. It is an iterative algorithm: 1) in the assignment step, the forward process of the EBTTA models is the assignment of labels for these test samples, and 2) in the updating step, the backward process is the update of the model via the assigned samples. This new perspective allows us to explore how entropy minimization influences test-time adaptation. Accordingly, this observation can guide us to put forward the improvement of EBTTA. We propose to improve EBTTA from the assignment step and the updating step, where robust label assignment, similarity-preserving constraint, sample selection, and gradient accumulation are proposed to explicitly utilize more information. Experimental results demonstrate that our method can achieve consistent improvements on various datasets. Code is provided in the supplementary material. |
1311.6883 | Chaitanya Swamy | Hadi Minooei and Chaitanya Swamy | Near-Optimal and Robust Mechanism Design for Covering Problems with
Correlated Players | Major changes compared to the previous version. Please consult this
version | null | null | null | cs.GT cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of designing incentive-compatible, ex-post
individually rational (IR) mechanisms for covering problems in the Bayesian
setting, where players' types are drawn from an underlying distribution and may
be correlated, and the goal is to minimize the expected total payment made by
the mechanism. We formulate a notion of incentive compatibility (IC) that we
call {\em support-based IC} that is substantially more robust than Bayesian IC,
and develop black-box reductions from support-based-IC mechanism design to
algorithm design. For single-dimensional settings, this black-box reduction
applies even when we only have an LP-relative {\em approximation algorithm} for
the algorithmic problem. Thus, we obtain near-optimal mechanisms for various
covering settings including single-dimensional covering problems, multi-item
procurement auctions, and multidimensional facility location.
| [
{
"created": "Wed, 27 Nov 2013 07:20:32 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Sep 2016 04:57:27 GMT",
"version": "v2"
}
] | 2016-09-30 | [
[
"Minooei",
"Hadi",
""
],
[
"Swamy",
"Chaitanya",
""
]
] | We consider the problem of designing incentive-compatible, ex-post individually rational (IR) mechanisms for covering problems in the Bayesian setting, where players' types are drawn from an underlying distribution and may be correlated, and the goal is to minimize the expected total payment made by the mechanism. We formulate a notion of incentive compatibility (IC) that we call {\em support-based IC} that is substantially more robust than Bayesian IC, and develop black-box reductions from support-based-IC mechanism design to algorithm design. For single-dimensional settings, this black-box reduction applies even when we only have an LP-relative {\em approximation algorithm} for the algorithmic problem. Thus, we obtain near-optimal mechanisms for various covering settings including single-dimensional covering problems, multi-item procurement auctions, and multidimensional facility location. |
2006.11569 | Haiping Huang | Jianwen Zhou, and Haiping Huang | Weakly-correlated synapses promote dimension reduction in deep neural
networks | 21 pages, 8 figures | Phys. Rev. E 103, 012315 (2021) | 10.1103/PhysRevE.103.012315 | null | cs.LG cond-mat.dis-nn cond-mat.stat-mech q-bio.NC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | By controlling synaptic and neural correlations, deep learning has achieved
empirical successes in improving classification performances. How synaptic
correlations affect neural correlations to produce disentangled hidden
representations remains elusive. Here we propose a simplified model of
dimension reduction, taking into account pairwise correlations among synapses,
to reveal the mechanism underlying how the synaptic correlations affect
dimension reduction. Our theory determines the synaptic-correlation scaling
form requiring only mathematical self-consistency, for both binary and
continuous synapses. The theory also predicts that weakly-correlated synapses
encourage dimension reduction compared to their orthogonal counterparts. In
addition, these synapses slow down the decorrelation process along the network
depth. These two computational roles are explained by the proposed mean-field
equation. The theoretical predictions are in excellent agreement with numerical
simulations, and the key features are also captured by a deep learning with
Hebbian rules.
| [
{
"created": "Sat, 20 Jun 2020 13:11:37 GMT",
"version": "v1"
}
] | 2021-02-02 | [
[
"Zhou",
"Jianwen",
""
],
[
"Huang",
"Haiping",
""
]
] | By controlling synaptic and neural correlations, deep learning has achieved empirical successes in improving classification performances. How synaptic correlations affect neural correlations to produce disentangled hidden representations remains elusive. Here we propose a simplified model of dimension reduction, taking into account pairwise correlations among synapses, to reveal the mechanism underlying how the synaptic correlations affect dimension reduction. Our theory determines the synaptic-correlation scaling form requiring only mathematical self-consistency, for both binary and continuous synapses. The theory also predicts that weakly-correlated synapses encourage dimension reduction compared to their orthogonal counterparts. In addition, these synapses slow down the decorrelation process along the network depth. These two computational roles are explained by the proposed mean-field equation. The theoretical predictions are in excellent agreement with numerical simulations, and the key features are also captured by a deep learning with Hebbian rules. |
2303.10325 | Guandong Li | Guandong Li, Xian Yang | Smartbanner: Intelligent banner design framework that strikes a balance
between creative freedom and design rules | null | Published 23 November 2022 Art Multimedia Tools and Applications | 10.1007/s11042-022-14138-7 | null | cs.HC cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Companies use banners extensively to promote their products, and the
intelligent automatic synthesis of banners is a challenging event. Under the
premise of inputting only a small amount of information such as product, text
and size, it can synthesize styles with high freedom and richness, but at the
same time, it must satisfy the design specifications of advertisers for
advertising and scenes. We propose an intelligent banner design framework that
strikes a balance between creative freedom and design rules, called
smartbanner. Smartbanner consists of planner, actuator, adjuster and generator.
The banner is synthesized through the combined framework, which fully liberates
the designer and reduces the threshold and cost of design. It increases the
click-through rate by 30%, improves the human efficiency of designers by 500%
under the condition of ensuring the quality of creation, and synthesizes
hundreds of millions of pictures in batches throughout the year.
| [
{
"created": "Sat, 18 Mar 2023 04:01:53 GMT",
"version": "v1"
}
] | 2023-03-21 | [
[
"Li",
"Guandong",
""
],
[
"Yang",
"Xian",
""
]
] | Companies use banners extensively to promote their products, and the intelligent automatic synthesis of banners is a challenging event. Under the premise of inputting only a small amount of information such as product, text and size, it can synthesize styles with high freedom and richness, but at the same time, it must satisfy the design specifications of advertisers for advertising and scenes. We propose an intelligent banner design framework that strikes a balance between creative freedom and design rules, called smartbanner. Smartbanner consists of planner, actuator, adjuster and generator. The banner is synthesized through the combined framework, which fully liberates the designer and reduces the threshold and cost of design. It increases the click-through rate by 30%, improves the human efficiency of designers by 500% under the condition of ensuring the quality of creation, and synthesizes hundreds of millions of pictures in batches throughout the year. |
2112.02082 | Zheng Dong | Zheng Dong, Ke Xu, Ziheng Duan, Hujun Bao, Weiwei Xu, Rynson W.H. Lau | Geometry-aware Two-scale PIFu Representation for Human Reconstruction | Accepted by NeurIPS 2022. 20 pages, 20 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although PIFu-based 3D human reconstruction methods are popular, the quality
of recovered details is still unsatisfactory. In a sparse (e.g., 3 RGBD
sensors) capture setting, the depth noise is typically amplified in the PIFu
representation, resulting in flat facial surfaces and geometry-fallible bodies.
In this paper, we propose a novel geometry-aware two-scale PIFu for 3D human
reconstruction from sparse, noisy inputs. Our key idea is to exploit the
complementary properties of depth denoising and 3D reconstruction, for learning
a two-scale PIFu representation to reconstruct high-frequency facial details
and consistent bodies separately. To this end, we first formulate depth
denoising and 3D reconstruction as a multi-task learning problem. The depth
denoising process enriches the local geometry information of the reconstruction
features, while the reconstruction process enhances depth denoising with global
topology information. We then propose to learn the two-scale PIFu
representation using two MLPs based on the denoised depth and geometry-aware
features. Extensive experiments demonstrate the effectiveness of our approach
in reconstructing facial details and bodies of different poses and its
superiority over state-of-the-art methods.
| [
{
"created": "Fri, 3 Dec 2021 18:46:49 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Sep 2022 08:30:11 GMT",
"version": "v2"
}
] | 2022-09-28 | [
[
"Dong",
"Zheng",
""
],
[
"Xu",
"Ke",
""
],
[
"Duan",
"Ziheng",
""
],
[
"Bao",
"Hujun",
""
],
[
"Xu",
"Weiwei",
""
],
[
"Lau",
"Rynson W. H.",
""
]
] | Although PIFu-based 3D human reconstruction methods are popular, the quality of recovered details is still unsatisfactory. In a sparse (e.g., 3 RGBD sensors) capture setting, the depth noise is typically amplified in the PIFu representation, resulting in flat facial surfaces and geometry-fallible bodies. In this paper, we propose a novel geometry-aware two-scale PIFu for 3D human reconstruction from sparse, noisy inputs. Our key idea is to exploit the complementary properties of depth denoising and 3D reconstruction, for learning a two-scale PIFu representation to reconstruct high-frequency facial details and consistent bodies separately. To this end, we first formulate depth denoising and 3D reconstruction as a multi-task learning problem. The depth denoising process enriches the local geometry information of the reconstruction features, while the reconstruction process enhances depth denoising with global topology information. We then propose to learn the two-scale PIFu representation using two MLPs based on the denoised depth and geometry-aware features. Extensive experiments demonstrate the effectiveness of our approach in reconstructing facial details and bodies of different poses and its superiority over state-of-the-art methods. |
2402.14269 | Jihyeok Jung | Jihyeok Jung, Chan-Oi Song, Deok-Joo Lee, Kiho Yoon | Optimal Mechanism in a Dynamic Stochastic Knapsack Environment | 8 pages, 1 figures, presented in AAAI 38th conference on Artificial
Intelligence | null | null | null | cs.GT econ.GN q-fin.EC | http://creativecommons.org/licenses/by/4.0/ | This study introduces an optimal mechanism in a dynamic stochastic knapsack
environment. The model features a single seller who has a fixed quantity of a
perfectly divisible item. Impatient buyers with a piece-wise linear utility
function arrive randomly and they report the two-dimensional private
information: marginal value and demanded quantity. We derive a
revenue-maximizing dynamic mechanism in a finite discrete time framework that
satisfies incentive compatibility, individual rationality, and feasibility
conditions. It is achieved by characterizing buyers' utility and deriving the
Bellman equation. Moreover, we propose the essential penalty scheme for
incentive compatibility, as well as the allocation and payment policies.
Lastly, we propose algorithms to approximate the optimal policy, based on the
Monte Carlo simulation-based regression method and reinforcement learning.
| [
{
"created": "Thu, 22 Feb 2024 04:08:31 GMT",
"version": "v1"
}
] | 2024-02-23 | [
[
"Jung",
"Jihyeok",
""
],
[
"Song",
"Chan-Oi",
""
],
[
"Lee",
"Deok-Joo",
""
],
[
"Yoon",
"Kiho",
""
]
] | This study introduces an optimal mechanism in a dynamic stochastic knapsack environment. The model features a single seller who has a fixed quantity of a perfectly divisible item. Impatient buyers with a piece-wise linear utility function arrive randomly and they report the two-dimensional private information: marginal value and demanded quantity. We derive a revenue-maximizing dynamic mechanism in a finite discrete time framework that satisfies incentive compatibility, individual rationality, and feasibility conditions. It is achieved by characterizing buyers' utility and deriving the Bellman equation. Moreover, we propose the essential penalty scheme for incentive compatibility, as well as the allocation and payment policies. Lastly, we propose algorithms to approximate the optimal policy, based on the Monte Carlo simulation-based regression method and reinforcement learning. |
1806.01353 | Scott Lee | Scott Lee | Natural Language Generation for Electronic Health Records | null | null | 10.1038/s41746-018-0070-0 | null | cs.CL cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A variety of methods existing for generating synthetic electronic health
records (EHRs), but they are not capable of generating unstructured text, like
emergency department (ED) chief complaints, history of present illness or
progress notes. Here, we use the encoder-decoder model, a deep learning
algorithm that features in many contemporary machine translation systems, to
generate synthetic chief complaints from discrete variables in EHRs, like age
group, gender, and discharge diagnosis. After being trained end-to-end on
authentic records, the model can generate realistic chief complaint text that
preserves much of the epidemiological information in the original data. As a
side effect of the model's optimization goal, these synthetic chief complaints
are also free of relatively uncommon abbreviation and misspellings, and they
include none of the personally-identifiable information (PII) that was in the
training data, suggesting it may be used to support the de-identification of
text in EHRs. When combined with algorithms like generative adversarial
networks (GANs), our model could be used to generate fully-synthetic EHRs,
facilitating data sharing between healthcare providers and researchers and
improving our ability to develop machine learning methods tailored to the
information in healthcare data.
| [
{
"created": "Fri, 1 Jun 2018 12:01:48 GMT",
"version": "v1"
}
] | 2018-12-18 | [
[
"Lee",
"Scott",
""
]
] | A variety of methods existing for generating synthetic electronic health records (EHRs), but they are not capable of generating unstructured text, like emergency department (ED) chief complaints, history of present illness or progress notes. Here, we use the encoder-decoder model, a deep learning algorithm that features in many contemporary machine translation systems, to generate synthetic chief complaints from discrete variables in EHRs, like age group, gender, and discharge diagnosis. After being trained end-to-end on authentic records, the model can generate realistic chief complaint text that preserves much of the epidemiological information in the original data. As a side effect of the model's optimization goal, these synthetic chief complaints are also free of relatively uncommon abbreviation and misspellings, and they include none of the personally-identifiable information (PII) that was in the training data, suggesting it may be used to support the de-identification of text in EHRs. When combined with algorithms like generative adversarial networks (GANs), our model could be used to generate fully-synthetic EHRs, facilitating data sharing between healthcare providers and researchers and improving our ability to develop machine learning methods tailored to the information in healthcare data. |
1412.6564 | Chris J. Maddison | Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver | Move Evaluation in Go Using Deep Convolutional Neural Networks | Minor edits and included captures in Figure 2 | null | null | null | cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The game of Go is more challenging than other board games, due to the
difficulty of constructing a position or move evaluation function. In this
paper we investigate whether deep convolutional networks can be used to
directly represent and learn this knowledge. We train a large 12-layer
convolutional neural network by supervised learning from a database of human
professional games. The network correctly predicts the expert move in 55% of
positions, equalling the accuracy of a 6 dan human player. When the trained
convolutional network was used directly to play games of Go, without any
search, it beat the traditional search program GnuGo in 97% of games, and
matched the performance of a state-of-the-art Monte-Carlo tree search that
simulates a million positions per move.
| [
{
"created": "Sat, 20 Dec 2014 00:31:30 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Apr 2015 19:03:34 GMT",
"version": "v2"
}
] | 2015-04-13 | [
[
"Maddison",
"Chris J.",
""
],
[
"Huang",
"Aja",
""
],
[
"Sutskever",
"Ilya",
""
],
[
"Silver",
"David",
""
]
] | The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates a million positions per move. |
2108.06608 | Simon Bultmann | Simon Bultmann, Jan Quenzel and Sven Behnke | Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles | Accepted for: 10th European Conference on Mobile Robots (ECMR), Bonn,
Germany, September 2021 | null | null | null | cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors
have tremendous potential for fast autonomous or remote-controlled semantic
scene analysis, e.g., for disaster examination. In this work, we propose a UAV
system for real-time semantic inference and fusion of multiple sensor
modalities. Semantic segmentation of LiDAR scans and RGB images, as well as
object detection on RGB and thermal images, run online onboard the UAV computer
using lightweight CNN architectures and embedded inference accelerators. We
follow a late fusion approach where semantic information from multiple
modalities augments 3D point clouds and image segmentation masks while also
generating an allocentric semantic map. Our system provides augmented semantic
images and point clouds with $\approx\,$9$\,$Hz. We evaluate the integrated
system in real-world experiments in an urban environment.
| [
{
"created": "Sat, 14 Aug 2021 20:16:08 GMT",
"version": "v1"
}
] | 2021-08-17 | [
[
"Bultmann",
"Simon",
""
],
[
"Quenzel",
"Jan",
""
],
[
"Behnke",
"Sven",
""
]
] | Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors have tremendous potential for fast autonomous or remote-controlled semantic scene analysis, e.g., for disaster examination. In this work, we propose a UAV system for real-time semantic inference and fusion of multiple sensor modalities. Semantic segmentation of LiDAR scans and RGB images, as well as object detection on RGB and thermal images, run online onboard the UAV computer using lightweight CNN architectures and embedded inference accelerators. We follow a late fusion approach where semantic information from multiple modalities augments 3D point clouds and image segmentation masks while also generating an allocentric semantic map. Our system provides augmented semantic images and point clouds with $\approx\,$9$\,$Hz. We evaluate the integrated system in real-world experiments in an urban environment. |
1710.08679 | Takuma Yamaguchi | Takuma Yamaguchi (1), Kohei Fujita (1 and 2), Tsuyoshi Ichimura (1 and
2), Muneo Hori (1 and 2), Maddegedara Lalith (1 and 2) and Kengo Nakajima (3)
((1) Earthquake Research Institute and Department of Civil Engineering, The
University of Tokyo, (2) Advanced Institute for Computational Science, RIKEN,
(3) Information Technology Center, The University of Tokyo) | Implicit Low-Order Unstructured Finite-Element Multiple Simulation
Enhanced by Dense Computation using OpenACC | 18 pages, 10 figures, accepted for WACCPD2017 | null | null | null | cs.DC cs.MS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we develop a low-order three-dimensional finite-element solver
for fast multiple-case crust deformation analysis on GPU-based systems. Based
on a high-performance solver designed for massively parallel CPU based systems,
we modify the algorithm to reduce random data access, and then insert OpenACC
directives. The developed solver on ten Reedbush-H nodes (20 P100 GPUs)
attained speedup of 14.2 times from 20 K computer nodes, which is high
considering the peak memory bandwidth ratio of 11.4 between the two systems. On
the newest Volta generation V100 GPUs, the solver attained a further 2.45 times
speedup from P100 GPUs. As a demonstrative example, we computed 368 cases of
crustal deformation analyses of northeast Japan with 400 million degrees of
freedom. The total procedure of algorithm modification and porting
implementation took only two weeks; we can see that high performance
improvement was achieved with low development cost. With the developed solver,
we can expect improvement in reliability of crust-deformation analyses by
many-case analyses on a wide range of GPU-based systems.
| [
{
"created": "Tue, 24 Oct 2017 09:51:31 GMT",
"version": "v1"
}
] | 2017-10-25 | [
[
"Yamaguchi",
"Takuma",
"",
"1 and 2"
],
[
"Fujita",
"Kohei",
"",
"1 and 2"
],
[
"Ichimura",
"Tsuyoshi",
"",
"1 and\n 2"
],
[
"Hori",
"Muneo",
"",
"1 and 2"
],
[
"Lalith",
"Maddegedara",
"",
"1 and 2"
],
[
"Nakajima",
"Kengo",
""
]
] | In this paper, we develop a low-order three-dimensional finite-element solver for fast multiple-case crust deformation analysis on GPU-based systems. Based on a high-performance solver designed for massively parallel CPU based systems, we modify the algorithm to reduce random data access, and then insert OpenACC directives. The developed solver on ten Reedbush-H nodes (20 P100 GPUs) attained speedup of 14.2 times from 20 K computer nodes, which is high considering the peak memory bandwidth ratio of 11.4 between the two systems. On the newest Volta generation V100 GPUs, the solver attained a further 2.45 times speedup from P100 GPUs. As a demonstrative example, we computed 368 cases of crustal deformation analyses of northeast Japan with 400 million degrees of freedom. The total procedure of algorithm modification and porting implementation took only two weeks; we can see that high performance improvement was achieved with low development cost. With the developed solver, we can expect improvement in reliability of crust-deformation analyses by many-case analyses on a wide range of GPU-based systems. |
2310.04863 | Yangze Li | Yangze Li, Fan Yu, Yuhao Liang, Pengcheng Guo, Mohan Shi, Zhihao Du,
Shiliang Zhang, Lei Xie | SA-Paraformer: Non-autoregressive End-to-End Speaker-Attributed ASR | null | null | null | null | cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Joint modeling of multi-speaker ASR and speaker diarization has recently
shown promising results in speaker-attributed automatic speech recognition
(SA-ASR).Although being able to obtain state-of-the-art (SOTA) performance,
most of the studies are based on an autoregressive (AR) decoder which generates
tokens one-by-one and results in a large real-time factor (RTF). To speed up
inference, we introduce a recently proposed non-autoregressive model Paraformer
as an acoustic model in the SA-ASR model.Paraformer uses a single-step decoder
to enable parallel generation, obtaining comparable performance to the SOTA AR
transformer models. Besides, we propose a speaker-filling strategy to reduce
speaker identification errors and adopt an inter-CTC strategy to enhance the
encoder's ability in acoustic modeling. Experiments on the AliMeeting corpus
show that our model outperforms the cascaded SA-ASR model by a 6.1% relative
speaker-dependent character error rate (SD-CER) reduction on the test set.
Moreover, our model achieves a comparable SD-CER of 34.8% with only 1/10 RTF
compared with the SOTA joint AR SA-ASR model.
| [
{
"created": "Sat, 7 Oct 2023 16:07:42 GMT",
"version": "v1"
}
] | 2023-10-10 | [
[
"Li",
"Yangze",
""
],
[
"Yu",
"Fan",
""
],
[
"Liang",
"Yuhao",
""
],
[
"Guo",
"Pengcheng",
""
],
[
"Shi",
"Mohan",
""
],
[
"Du",
"Zhihao",
""
],
[
"Zhang",
"Shiliang",
""
],
[
"Xie",
"Lei",
""
]
] | Joint modeling of multi-speaker ASR and speaker diarization has recently shown promising results in speaker-attributed automatic speech recognition (SA-ASR).Although being able to obtain state-of-the-art (SOTA) performance, most of the studies are based on an autoregressive (AR) decoder which generates tokens one-by-one and results in a large real-time factor (RTF). To speed up inference, we introduce a recently proposed non-autoregressive model Paraformer as an acoustic model in the SA-ASR model.Paraformer uses a single-step decoder to enable parallel generation, obtaining comparable performance to the SOTA AR transformer models. Besides, we propose a speaker-filling strategy to reduce speaker identification errors and adopt an inter-CTC strategy to enhance the encoder's ability in acoustic modeling. Experiments on the AliMeeting corpus show that our model outperforms the cascaded SA-ASR model by a 6.1% relative speaker-dependent character error rate (SD-CER) reduction on the test set. Moreover, our model achieves a comparable SD-CER of 34.8% with only 1/10 RTF compared with the SOTA joint AR SA-ASR model. |
2407.12818 | Dishank Aggarwal | Dishank Aggarwal, Pushpak Bhattacharyya, Bhaskaran Raman | "I understand why I got this grade": Automatic Short Answer Grading with
Feedback | null | null | null | null | cs.CL cs.AI cs.CY | http://creativecommons.org/licenses/by/4.0/ | The demand for efficient and accurate assessment methods has intensified as
education systems transition to digital platforms. Providing feedback is
essential in educational settings and goes beyond simply conveying marks as it
justifies the assigned marks. In this context, we present a significant
advancement in automated grading by introducing Engineering Short Answer
Feedback (EngSAF) -- a dataset of 5.8k student answers accompanied by reference
answers and questions for the Automatic Short Answer Grading (ASAG) task. The
EngSAF dataset is meticulously curated to cover a diverse range of subjects,
questions, and answer patterns from multiple engineering domains. We leverage
state-of-the-art large language models' (LLMs) generative capabilities with our
Label-Aware Synthetic Feedback Generation (LASFG) strategy to include feedback
in our dataset. This paper underscores the importance of enhanced feedback in
practical educational settings, outlines dataset annotation and feedback
generation processes, conducts a thorough EngSAF analysis, and provides
different LLMs-based zero-shot and finetuned baselines for future comparison.
Additionally, we demonstrate the efficiency and effectiveness of the ASAG
system through its deployment in a real-world end-semester exam at the Indian
Institute of Technology Bombay (IITB), showcasing its practical viability and
potential for broader implementation in educational institutions.
| [
{
"created": "Sun, 30 Jun 2024 15:42:18 GMT",
"version": "v1"
}
] | 2024-07-19 | [
[
"Aggarwal",
"Dishank",
""
],
[
"Bhattacharyya",
"Pushpak",
""
],
[
"Raman",
"Bhaskaran",
""
]
] | The demand for efficient and accurate assessment methods has intensified as education systems transition to digital platforms. Providing feedback is essential in educational settings and goes beyond simply conveying marks as it justifies the assigned marks. In this context, we present a significant advancement in automated grading by introducing Engineering Short Answer Feedback (EngSAF) -- a dataset of 5.8k student answers accompanied by reference answers and questions for the Automatic Short Answer Grading (ASAG) task. The EngSAF dataset is meticulously curated to cover a diverse range of subjects, questions, and answer patterns from multiple engineering domains. We leverage state-of-the-art large language models' (LLMs) generative capabilities with our Label-Aware Synthetic Feedback Generation (LASFG) strategy to include feedback in our dataset. This paper underscores the importance of enhanced feedback in practical educational settings, outlines dataset annotation and feedback generation processes, conducts a thorough EngSAF analysis, and provides different LLMs-based zero-shot and finetuned baselines for future comparison. Additionally, we demonstrate the efficiency and effectiveness of the ASAG system through its deployment in a real-world end-semester exam at the Indian Institute of Technology Bombay (IITB), showcasing its practical viability and potential for broader implementation in educational institutions. |
2312.03420 | Yingyan Xu | Yingyan Xu, Prashanth Chandran, Sebastian Weiss, Markus Gross, Gaspard
Zoss, Derek Bradley | Artist-Friendly Relightable and Animatable Neural Heads | null | null | null | null | cs.CV cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An increasingly common approach for creating photo-realistic digital avatars
is through the use of volumetric neural fields. The original neural radiance
field (NeRF) allowed for impressive novel view synthesis of static heads when
trained on a set of multi-view images, and follow up methods showed that these
neural representations can be extended to dynamic avatars. Recently, new
variants also surpassed the usual drawback of baked-in illumination in neural
representations, showing that static neural avatars can be relit in any
environment. In this work we simultaneously tackle both the motion and
illumination problem, proposing a new method for relightable and animatable
neural heads. Our method builds on a proven dynamic avatar approach based on a
mixture of volumetric primitives, combined with a recently-proposed lightweight
hardware setup for relightable neural fields, and includes a novel architecture
that allows relighting dynamic neural avatars performing unseen expressions in
any environment, even with nearfield illumination and viewpoints.
| [
{
"created": "Wed, 6 Dec 2023 11:06:46 GMT",
"version": "v1"
}
] | 2023-12-07 | [
[
"Xu",
"Yingyan",
""
],
[
"Chandran",
"Prashanth",
""
],
[
"Weiss",
"Sebastian",
""
],
[
"Gross",
"Markus",
""
],
[
"Zoss",
"Gaspard",
""
],
[
"Bradley",
"Derek",
""
]
] | An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields. The original neural radiance field (NeRF) allowed for impressive novel view synthesis of static heads when trained on a set of multi-view images, and follow up methods showed that these neural representations can be extended to dynamic avatars. Recently, new variants also surpassed the usual drawback of baked-in illumination in neural representations, showing that static neural avatars can be relit in any environment. In this work we simultaneously tackle both the motion and illumination problem, proposing a new method for relightable and animatable neural heads. Our method builds on a proven dynamic avatar approach based on a mixture of volumetric primitives, combined with a recently-proposed lightweight hardware setup for relightable neural fields, and includes a novel architecture that allows relighting dynamic neural avatars performing unseen expressions in any environment, even with nearfield illumination and viewpoints. |
2205.06773 | Daniel Casini | Daniel Casini, Paolo Pazzaglia, Alessandro Biondi, Marco Di Natale | Optimized Partitioning and Priority Assignment of Real-Time Applications
on Heterogeneous Platforms with Hardware Acceleration | null | Journal of Systems Architecture, Volume 124, March 2022, 102416 | 10.1016/j.sysarc.2022.102416 | null | cs.DC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Hardware accelerators, such as those based on GPUs and FPGAs, offer an
excellent opportunity to efficiently parallelize functionalities. Recently,
modern embedded platforms started being equipped with such accelerators,
resulting in a compelling choice for emerging, highly computational intensive
workloads, like those required by next-generation autonomous driving systems.
Alongside the need for computational efficiency, such workloads are commonly
characterized by real-time requirements, which need to be satisfied to
guarantee the safe and correct behavior of the system. To this end, this paper
proposes a holistic framework to help designers partition real-time
applications on heterogeneous platforms with hardware accelerators. The
proposed model is inspired by a realistic setup of an advanced driving
assistance system presented in the WATERS 2019 Challenge by Bosch, further
generalized to encompass a broader range of heterogeneous architectures. The
resulting analysis is linearized and used to encode an optimization problem
that jointly (i) guarantees timing constraints, (ii) finds a suitable
task-to-core mapping, (iii) assigns a priority to each task, and (iv) selects
which computations to accelerate, seeking for the most convenient trade-off
between the smaller worst-case execution time provided by accelerators and
synchronization and queuing delays.
| [
{
"created": "Tue, 19 Apr 2022 15:26:52 GMT",
"version": "v1"
}
] | 2022-05-16 | [
[
"Casini",
"Daniel",
""
],
[
"Pazzaglia",
"Paolo",
""
],
[
"Biondi",
"Alessandro",
""
],
[
"Di Natale",
"Marco",
""
]
] | Hardware accelerators, such as those based on GPUs and FPGAs, offer an excellent opportunity to efficiently parallelize functionalities. Recently, modern embedded platforms started being equipped with such accelerators, resulting in a compelling choice for emerging, highly computational intensive workloads, like those required by next-generation autonomous driving systems. Alongside the need for computational efficiency, such workloads are commonly characterized by real-time requirements, which need to be satisfied to guarantee the safe and correct behavior of the system. To this end, this paper proposes a holistic framework to help designers partition real-time applications on heterogeneous platforms with hardware accelerators. The proposed model is inspired by a realistic setup of an advanced driving assistance system presented in the WATERS 2019 Challenge by Bosch, further generalized to encompass a broader range of heterogeneous architectures. The resulting analysis is linearized and used to encode an optimization problem that jointly (i) guarantees timing constraints, (ii) finds a suitable task-to-core mapping, (iii) assigns a priority to each task, and (iv) selects which computations to accelerate, seeking for the most convenient trade-off between the smaller worst-case execution time provided by accelerators and synchronization and queuing delays. |
2105.06270 | Chen-Chen Fan | Chen-Chen Fan, Haiqun Xie, Liang Peng, Hongjun Yang, Zhen-Liang Ni,
Guan'an Wang, Yan-Jie Zhou, Sheng Chen, Zhijie Fang, Shuyun Huang, Zeng-Guang
Hou | Group Feature Learning and Domain Adversarial Neural Network for aMCI
Diagnosis System Based on EEG | This paper has been accepted by 2021 International Conference on
Robotics and Automation (ICRA 2021) | null | null | null | cs.LG cs.RO eess.SP | http://creativecommons.org/licenses/by/4.0/ | Medical diagnostic robot systems have been paid more and more attention due
to its objectivity and accuracy. The diagnosis of mild cognitive impairment
(MCI) is considered an effective means to prevent Alzheimer's disease (AD).
Doctors diagnose MCI based on various clinical examinations, which are
expensive and the diagnosis results rely on the knowledge of doctors.
Therefore, it is necessary to develop a robot diagnostic system to eliminate
the influence of human factors and obtain a higher accuracy rate. In this
paper, we propose a novel Group Feature Domain Adversarial Neural Network
(GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important
modules. A Group Feature Extraction (GFE) module is proposed to reduce
individual differences by learning group-level features through adversarial
learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed
to reduce the distribution difference between the source and target domain in a
domain adaption way. On three types of data set, GF-DANN achieves the best
accuracy compared with classic machine learning and deep learning methods. On
the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the
sensitivity and specificity are 90% and 89%. In addition, by comparing three
EEG data collection paradigms, our results demonstrate that the DMS paradigm
has the potential to build an aMCI diagnose robot system.
| [
{
"created": "Wed, 28 Apr 2021 08:08:32 GMT",
"version": "v1"
}
] | 2021-05-14 | [
[
"Fan",
"Chen-Chen",
""
],
[
"Xie",
"Haiqun",
""
],
[
"Peng",
"Liang",
""
],
[
"Yang",
"Hongjun",
""
],
[
"Ni",
"Zhen-Liang",
""
],
[
"Wang",
"Guan'an",
""
],
[
"Zhou",
"Yan-Jie",
""
],
[
"Chen",
"Sheng",
""
],
[
"Fang",
"Zhijie",
""
],
[
"Huang",
"Shuyun",
""
],
[
"Hou",
"Zeng-Guang",
""
]
] | Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system. |
1301.1465 | Carlo Condo | Carlo Condo and Amer Baghdadi and Guido Masera | A joint communication and application simulator for NoC-based SoCs | Withdrawn, due to extended and revised version being published | null | null | null | cs.AR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | NoCs have become a widespread paradigm in the system-on-chip design world,
not only for multi-purpose SoCs, but also for application-specific ICs. The
common approach in the NoC design world is to separate the design of the
interconnection from the design of the processing elements: this is well suited
for a large number of developments, but the need for joint application and NoC
design is not uncommon, especially in the application specific case. The
correlation between processing and communication tasks can be strong, and
separate or trace-based simulations fall often short of the desired precision.
In this work, the OMNET++ based JANoCS simulator is presented: concurrent
simulation of processing and communication allow cycle-accurate evaluation of
the system. Two cases of study are presented, showing both the need for joint
simulations and the effectiveness of JANoCS.
| [
{
"created": "Tue, 8 Jan 2013 09:59:28 GMT",
"version": "v1"
},
{
"created": "Fri, 31 May 2013 11:09:16 GMT",
"version": "v2"
}
] | 2013-06-03 | [
[
"Condo",
"Carlo",
""
],
[
"Baghdadi",
"Amer",
""
],
[
"Masera",
"Guido",
""
]
] | NoCs have become a widespread paradigm in the system-on-chip design world, not only for multi-purpose SoCs, but also for application-specific ICs. The common approach in the NoC design world is to separate the design of the interconnection from the design of the processing elements: this is well suited for a large number of developments, but the need for joint application and NoC design is not uncommon, especially in the application specific case. The correlation between processing and communication tasks can be strong, and separate or trace-based simulations fall often short of the desired precision. In this work, the OMNET++ based JANoCS simulator is presented: concurrent simulation of processing and communication allow cycle-accurate evaluation of the system. Two cases of study are presented, showing both the need for joint simulations and the effectiveness of JANoCS. |
1505.01617 | Dengji Zhao | Dengji Zhao and Sarvapali D. Ramchurn and Nicholas R. Jennings | Incentive Design for Ridesharing with Uncertainty | 13 pages | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a ridesharing problem where there is uncertainty about the
completion of trips from both drivers and riders. Specifically, we study
ridesharing mechanisms that aim to incentivize commuters to reveal their
valuation for trips and their probability of undertaking their trips. Due to
the interdependence created by the uncertainty on commuters' valuations, we
show that the Groves mechanisms are not ex-post truthful even if there is only
one commuter whose valuation depends on the other commuters' uncertainty of
undertaking their trips. To circumvent this impossibility, we propose an
ex-post truthful mechanism, the best incentive we can design without
sacrificing social welfare in this setting. Our mechanism pays a commuter if
she undertakes her trip, otherwise she is penalized for not undertaking her
trip. Furthermore, we identify a sufficient and necessary condition under which
our mechanism is ex-post truthful.
| [
{
"created": "Thu, 7 May 2015 08:14:25 GMT",
"version": "v1"
}
] | 2015-05-08 | [
[
"Zhao",
"Dengji",
""
],
[
"Ramchurn",
"Sarvapali D.",
""
],
[
"Jennings",
"Nicholas R.",
""
]
] | We consider a ridesharing problem where there is uncertainty about the completion of trips from both drivers and riders. Specifically, we study ridesharing mechanisms that aim to incentivize commuters to reveal their valuation for trips and their probability of undertaking their trips. Due to the interdependence created by the uncertainty on commuters' valuations, we show that the Groves mechanisms are not ex-post truthful even if there is only one commuter whose valuation depends on the other commuters' uncertainty of undertaking their trips. To circumvent this impossibility, we propose an ex-post truthful mechanism, the best incentive we can design without sacrificing social welfare in this setting. Our mechanism pays a commuter if she undertakes her trip, otherwise she is penalized for not undertaking her trip. Furthermore, we identify a sufficient and necessary condition under which our mechanism is ex-post truthful. |
2304.08745 | Kasper Green Larsen | Kasper Green Larsen, Huacheng Yu | Super-Logarithmic Lower Bounds for Dynamic Graph Problems | null | null | null | null | cs.DS cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we prove a $\tilde{\Omega}(\lg^{3/2} n )$ unconditional lower
bound on the maximum of the query time and update time for dynamic data
structures supporting reachability queries in $n$-node directed acyclic graphs
under edge insertions. This is the first super-logarithmic lower bound for any
natural graph problem. In proving the lower bound, we also make novel
contributions to the state-of-the-art data structure lower bound techniques
that we hope may lead to further progress in proving lower bounds.
| [
{
"created": "Tue, 18 Apr 2023 05:46:47 GMT",
"version": "v1"
}
] | 2023-04-19 | [
[
"Larsen",
"Kasper Green",
""
],
[
"Yu",
"Huacheng",
""
]
] | In this work, we prove a $\tilde{\Omega}(\lg^{3/2} n )$ unconditional lower bound on the maximum of the query time and update time for dynamic data structures supporting reachability queries in $n$-node directed acyclic graphs under edge insertions. This is the first super-logarithmic lower bound for any natural graph problem. In proving the lower bound, we also make novel contributions to the state-of-the-art data structure lower bound techniques that we hope may lead to further progress in proving lower bounds. |
1804.00410 | Wen-Cheng Chen | Wen-Cheng Chen, Chien-Wen Chen, Min-Chun Hu | SyncGAN: Synchronize the Latent Space of Cross-modal Generative
Adversarial Networks | 9 pages, Part of this work is accepted by IEEE International
Conference on Multimedia Expo 2018 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generative adversarial network (GAN) has achieved impressive success on
cross-domain generation, but it faces difficulty in cross-modal generation due
to the lack of a common distribution between heterogeneous data. Most existing
methods of conditional based cross-modal GANs adopt the strategy of
one-directional transfer and have achieved preliminary success on text-to-image
transfer. Instead of learning the transfer between different modalities, we aim
to learn a synchronous latent space representing the cross-modal common
concept. A novel network component named synchronizer is proposed in this work
to judge whether the paired data is synchronous/corresponding or not, which can
constrain the latent space of generators in the GANs. Our GAN model, named as
SyncGAN, can successfully generate synchronous data (e.g., a pair of image and
sound) from identical random noise. For transforming data from one modality to
another, we recover the latent code by inverting the mappings of a generator
and use it to generate data of different modality. In addition, the proposed
model can achieve semi-supervised learning, which makes our model more flexible
for practical applications.
| [
{
"created": "Mon, 2 Apr 2018 06:27:50 GMT",
"version": "v1"
}
] | 2018-04-03 | [
[
"Chen",
"Wen-Cheng",
""
],
[
"Chen",
"Chien-Wen",
""
],
[
"Hu",
"Min-Chun",
""
]
] | Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one-directional transfer and have achieved preliminary success on text-to-image transfer. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain the latent space of generators in the GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve semi-supervised learning, which makes our model more flexible for practical applications. |
2408.00327 | Yun-Chih Chen | Yun-Chih Chen, Yuan-Hao Chang, Tei-Wei Kuo | Search-in-Memory (SiM): Reliable, Versatile, and Efficient Data Matching
in SSD's NAND Flash Memory Chip for Data Indexing Acceleration | This paper has been accepted for presentation at the The
International Conference on Hardware/Software Codesign and System Synthesis
(CODES+ISSS) in September, 2024. An extended abstract of this paper was
presented in Design, Automation & Test in Europe Conference & Exhibition
(DATE), 2024 | null | null | null | cs.AR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To index the increasing volume of data, modern data indexes are typically
stored on SSDs and cached in DRAM. However, searching such an index has
resulted in significant I/O traffic due to limited access locality and
inefficient cache utilization. At the heart of index searching is the operation
of filtering through vast data spans to isolate a small, relevant subset, which
involves basic equality tests rather than the complex arithmetic provided by
modern CPUs. This paper introduces the Search-in-Memory (SiM) chip, which
demonstrates the feasibility of performing data filtering directly within a
NAND flash memory chip, transmitting only relevant search results rather than
complete pages. Instead of adding complex circuits, we propose repurposing
existing circuitry for efficient and accurate bitwise parallel matching. We
demonstrate how different data structures can use our flexible SIMD command
interface to offload index searches. This strategy not only frees up the CPU
for more computationally demanding tasks, but it also optimizes DRAM usage for
write buffering, significantly lowering energy consumption associated with I/O
transmission between the CPU and DRAM. Extensive testing across a wide range of
workloads reveals up to a 9X speedup in write-heavy workloads and up to 45%
energy savings due to reduced read and write I/O. Furthermore, we achieve
significant reductions in median and tail read latencies of up to 89% and 85%
respectively.
| [
{
"created": "Thu, 1 Aug 2024 07:00:18 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Aug 2024 07:37:51 GMT",
"version": "v2"
}
] | 2024-08-05 | [
[
"Chen",
"Yun-Chih",
""
],
[
"Chang",
"Yuan-Hao",
""
],
[
"Kuo",
"Tei-Wei",
""
]
] | To index the increasing volume of data, modern data indexes are typically stored on SSDs and cached in DRAM. However, searching such an index has resulted in significant I/O traffic due to limited access locality and inefficient cache utilization. At the heart of index searching is the operation of filtering through vast data spans to isolate a small, relevant subset, which involves basic equality tests rather than the complex arithmetic provided by modern CPUs. This paper introduces the Search-in-Memory (SiM) chip, which demonstrates the feasibility of performing data filtering directly within a NAND flash memory chip, transmitting only relevant search results rather than complete pages. Instead of adding complex circuits, we propose repurposing existing circuitry for efficient and accurate bitwise parallel matching. We demonstrate how different data structures can use our flexible SIMD command interface to offload index searches. This strategy not only frees up the CPU for more computationally demanding tasks, but it also optimizes DRAM usage for write buffering, significantly lowering energy consumption associated with I/O transmission between the CPU and DRAM. Extensive testing across a wide range of workloads reveals up to a 9X speedup in write-heavy workloads and up to 45% energy savings due to reduced read and write I/O. Furthermore, we achieve significant reductions in median and tail read latencies of up to 89% and 85% respectively. |
2310.17471 | Zhiheng Guo | Xiang Chen, Zhiheng Guo, Xijun Wang, Howard H. Yang, Chenyuan Feng,
Junshen Su, Sihui Zheng, Tony Q. S. Quek | Foundation Model Based Native AI Framework in 6G with Cloud-Edge-End
Collaboration | 8 pages, 4 figures, 1 table | null | null | null | cs.IT cs.DC cs.LG cs.NI eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Future wireless communication networks are in a position to move beyond
data-centric, device-oriented connectivity and offer intelligent, immersive
experiences based on task-oriented connections, especially in the context of
the thriving development of pre-trained foundation models (PFM) and the
evolving vision of 6G native artificial intelligence (AI). Therefore,
redefining modes of collaboration between devices and servers and constructing
native intelligence libraries become critically important in 6G. In this paper,
we analyze the challenges of achieving 6G native AI from the perspectives of
data, intelligence, and networks. Then, we propose a 6G native AI framework
based on foundation models, provide a customization approach for intent-aware
PFM, present a construction of a task-oriented AI toolkit, and outline a novel
cloud-edge-end collaboration paradigm. As a practical use case, we apply this
framework for orchestration, achieving the maximum sum rate within a wireless
communication system, and presenting preliminary evaluation results. Finally,
we outline research directions for achieving native AI in 6G.
| [
{
"created": "Thu, 26 Oct 2023 15:19:40 GMT",
"version": "v1"
}
] | 2023-10-27 | [
[
"Chen",
"Xiang",
""
],
[
"Guo",
"Zhiheng",
""
],
[
"Wang",
"Xijun",
""
],
[
"Yang",
"Howard H.",
""
],
[
"Feng",
"Chenyuan",
""
],
[
"Su",
"Junshen",
""
],
[
"Zheng",
"Sihui",
""
],
[
"Quek",
"Tony Q. S.",
""
]
] | Future wireless communication networks are in a position to move beyond data-centric, device-oriented connectivity and offer intelligent, immersive experiences based on task-oriented connections, especially in the context of the thriving development of pre-trained foundation models (PFM) and the evolving vision of 6G native artificial intelligence (AI). Therefore, redefining modes of collaboration between devices and servers and constructing native intelligence libraries become critically important in 6G. In this paper, we analyze the challenges of achieving 6G native AI from the perspectives of data, intelligence, and networks. Then, we propose a 6G native AI framework based on foundation models, provide a customization approach for intent-aware PFM, present a construction of a task-oriented AI toolkit, and outline a novel cloud-edge-end collaboration paradigm. As a practical use case, we apply this framework for orchestration, achieving the maximum sum rate within a wireless communication system, and presenting preliminary evaluation results. Finally, we outline research directions for achieving native AI in 6G. |
1710.03774 | Hang Ma | Hang Ma, Sven Koenig | AI Buzzwords Explained: Multi-Agent Path Finding (MAPF) | null | null | 10.1145/3137574.3137579 | null | cs.AI cs.MA cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explanation of the hot topic "multi-agent path finding".
| [
{
"created": "Tue, 10 Oct 2017 18:24:34 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Oct 2017 00:21:44 GMT",
"version": "v2"
}
] | 2017-10-18 | [
[
"Ma",
"Hang",
""
],
[
"Koenig",
"Sven",
""
]
] | Explanation of the hot topic "multi-agent path finding". |
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