id stringlengths 9 10 | submitter stringlengths 1 64 ⌀ | authors stringlengths 4 20.7k | title stringlengths 4 246 | comments stringlengths 1 523 ⌀ | journal-ref stringlengths 4 404 ⌀ | doi stringlengths 11 153 ⌀ | report-no stringlengths 2 254 ⌀ | categories stringlengths 5 98 | license stringclasses 9 values | orig_abstract stringlengths 14 3.35k | versions listlengths 1 60 | update_date stringlengths 10 10 | authors_parsed listlengths 1 1.35k | abstract stringlengths 11 3.34k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1606.04202 | Avik Sengupta | Avik Sengupta and Ravi Tandon | Improved Approximation of Storage-Rate Tradeoff for Caching with
Multiple Demands | Extended version of a submission to IEEE Trans. on Communications | null | null | null | cs.IT cs.NI math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Caching at the network edge has emerged as a viable solution for alleviating
the severe capacity crunch in modern content centric wireless networks by
leveraging network load-balancing in the form of localized content storage and
delivery. In this work, we consider a cache-aided network where the cache
storage phase is assisted by a central server and users can demand multiple
files at each transmission interval. To service these demands, we consider two
delivery models - $(1)$ centralized content delivery where user demands at each
transmission interval are serviced by the central server via multicast
transmissions; and $(2)$ device-to-device (D2D) assisted distributed delivery
where users multicast to each other in order to service file demands. For such
cache-aided networks, we present new results on the fundamental cache storage
vs. transmission rate tradeoff. Specifically, we develop a new technique for
characterizing information theoretic lower bounds on the storage-rate tradeoff
and show that the new lower bounds are strictly tighter than cut-set bounds
from literature. Furthermore, using the new lower bounds, we establish the
optimal storage-rate tradeoff to within a constant multiplicative gap. We show
that, for multiple demands per user, achievable schemes based on repetition of
schemes for single demands are order-optimal under both delivery models.
| [
{
"created": "Tue, 14 Jun 2016 04:53:35 GMT",
"version": "v1"
}
] | 2016-06-15 | [
[
"Sengupta",
"Avik",
""
],
[
"Tandon",
"Ravi",
""
]
] | Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in modern content centric wireless networks by leveraging network load-balancing in the form of localized content storage and delivery. In this work, we consider a cache-aided network where the cache storage phase is assisted by a central server and users can demand multiple files at each transmission interval. To service these demands, we consider two delivery models - $(1)$ centralized content delivery where user demands at each transmission interval are serviced by the central server via multicast transmissions; and $(2)$ device-to-device (D2D) assisted distributed delivery where users multicast to each other in order to service file demands. For such cache-aided networks, we present new results on the fundamental cache storage vs. transmission rate tradeoff. Specifically, we develop a new technique for characterizing information theoretic lower bounds on the storage-rate tradeoff and show that the new lower bounds are strictly tighter than cut-set bounds from literature. Furthermore, using the new lower bounds, we establish the optimal storage-rate tradeoff to within a constant multiplicative gap. We show that, for multiple demands per user, achievable schemes based on repetition of schemes for single demands are order-optimal under both delivery models. |
2107.02757 | Zhibin Duan | Zhibin Duan, Dongsheng Wang, Bo Chen, Chaojie Wang, Wenchao Chen,
Yewen Li, Jie Ren, Mingyuan Zhou | Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network | null | null | null | null | cs.IR cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hierarchical topic models such as the gamma belief network (GBN) have
delivered promising results in mining multi-layer document representations and
discovering interpretable topic taxonomies. However, they often assume in the
prior that the topics at each layer are independently drawn from the Dirichlet
distribution, ignoring the dependencies between the topics both at the same
layer and across different layers. To relax this assumption, we propose
sawtooth factorial topic embedding guided GBN, a deep generative model of
documents that captures the dependencies and semantic similarities between the
topics in the embedding space. Specifically, both the words and topics are
represented as embedding vectors of the same dimension. The topic matrix at a
layer is factorized into the product of a factor loading matrix and a topic
embedding matrix, the transpose of which is set as the factor loading matrix of
the layer above. Repeating this particular type of factorization, which shares
components between adjacent layers, leads to a structure referred to as
sawtooth factorization. An auto-encoding variational inference network is
constructed to optimize the model parameter via stochastic gradient descent.
Experiments on big corpora show that our models outperform other neural topic
models on extracting deeper interpretable topics and deriving better document
representations.
| [
{
"created": "Wed, 30 Jun 2021 10:14:57 GMT",
"version": "v1"
}
] | 2021-07-07 | [
[
"Duan",
"Zhibin",
""
],
[
"Wang",
"Dongsheng",
""
],
[
"Chen",
"Bo",
""
],
[
"Wang",
"Chaojie",
""
],
[
"Chen",
"Wenchao",
""
],
[
"Li",
"Yewen",
""
],
[
"Ren",
"Jie",
""
],
[
"Zhou",
"Mingyuan",
""
]
] | Hierarchical topic models such as the gamma belief network (GBN) have delivered promising results in mining multi-layer document representations and discovering interpretable topic taxonomies. However, they often assume in the prior that the topics at each layer are independently drawn from the Dirichlet distribution, ignoring the dependencies between the topics both at the same layer and across different layers. To relax this assumption, we propose sawtooth factorial topic embedding guided GBN, a deep generative model of documents that captures the dependencies and semantic similarities between the topics in the embedding space. Specifically, both the words and topics are represented as embedding vectors of the same dimension. The topic matrix at a layer is factorized into the product of a factor loading matrix and a topic embedding matrix, the transpose of which is set as the factor loading matrix of the layer above. Repeating this particular type of factorization, which shares components between adjacent layers, leads to a structure referred to as sawtooth factorization. An auto-encoding variational inference network is constructed to optimize the model parameter via stochastic gradient descent. Experiments on big corpora show that our models outperform other neural topic models on extracting deeper interpretable topics and deriving better document representations. |
cs/0207007 | Denis Popel | Denis V. Popel and Nawar Al-Hakeem | Evolutionary Circuit Design: Information Theory Perspective on Signal
Propagation | 5 pages, 3 figures, 2 tables, ISSPIT'2001 | ISSPIT'2001 | null | null | cs.OH | null | This paper presents case-study results on the application of information
theoretic approach to gate-level evolutionary circuit design. We introduce
information measures to provide better estimates of synthesis criteria of
digital circuits. For example, the analysis of signal propagation during
evolving gate-level synthesis can be improved by using information theoretic
measures that will make it possible to find the most effective geometry and
therefore predict the cost of the final design solution. The problem is
considered from the information engine point of view. That is, the process of
evolutionary gate-level circuit design is presented via such measures as
entropy, logical work and information vitality. Some examples of geometry
driven synthesis are provided to prove the above idea.
| [
{
"created": "Wed, 3 Jul 2002 16:59:23 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Popel",
"Denis V.",
""
],
[
"Al-Hakeem",
"Nawar",
""
]
] | This paper presents case-study results on the application of information theoretic approach to gate-level evolutionary circuit design. We introduce information measures to provide better estimates of synthesis criteria of digital circuits. For example, the analysis of signal propagation during evolving gate-level synthesis can be improved by using information theoretic measures that will make it possible to find the most effective geometry and therefore predict the cost of the final design solution. The problem is considered from the information engine point of view. That is, the process of evolutionary gate-level circuit design is presented via such measures as entropy, logical work and information vitality. Some examples of geometry driven synthesis are provided to prove the above idea. |
2005.04042 | Stefan Hoffmann | Stefan Hoffmann | Computational Complexity of Synchronization under Regular Commutative
Constraints | Published in COCOON 2020 (The 26th International Computing and
Combinatorics Conference); 2nd version is update of the published version and
1st version; both contain a minor error, the assumption of maximality in the
NP-c and PSPACE-c results (propositions 5 & 6) is missing, and of
incomparability of the vectors in main theorem; fixed in this version. See
(new) discussion after main theorem | Computing and Combinatorics, 26th International Conference, COCOON
2020, Proceedings, pages 460-471 | 10.1007/978-3-030-58150-3_37 | null | cs.FL cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Here we study the computational complexity of the constrained synchronization
problem for the class of regular commutative constraint languages. Utilizing a
vector representation of regular commutative constraint languages, we give a
full classification of the computational complexity of the constraint
synchronization problem. Depending on the constraint language, our problem
becomes PSPACE-complete, NP-complete or polynomial time solvable. In addition,
we derive a polynomial time decision procedure for the complexity of the
constraint synchronization problem, given some constraint automaton accepting a
commutative language as input.
| [
{
"created": "Fri, 8 May 2020 13:43:23 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Sep 2020 20:12:21 GMT",
"version": "v2"
}
] | 2020-09-04 | [
[
"Hoffmann",
"Stefan",
""
]
] | Here we study the computational complexity of the constrained synchronization problem for the class of regular commutative constraint languages. Utilizing a vector representation of regular commutative constraint languages, we give a full classification of the computational complexity of the constraint synchronization problem. Depending on the constraint language, our problem becomes PSPACE-complete, NP-complete or polynomial time solvable. In addition, we derive a polynomial time decision procedure for the complexity of the constraint synchronization problem, given some constraint automaton accepting a commutative language as input. |
2003.01993 | Igor Buzhinsky | Igor Buzhinsky, Arseny Nerinovsky, Stavros Tripakis | Metrics and methods for robustness evaluation of neural networks with
generative models | 24 pages, 9 figures; data in Table 3 and Fig. 3 corrected (results
unchanged), several typos fixed, references updated | null | null | null | cs.LG cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent studies have shown that modern deep neural network classifiers are
easy to fool, assuming that an adversary is able to slightly modify their
inputs. Many papers have proposed adversarial attacks, defenses and methods to
measure robustness to such adversarial perturbations. However, most commonly
considered adversarial examples are based on $\ell_p$-bounded perturbations in
the input space of the neural network, which are unlikely to arise naturally.
Recently, especially in computer vision, researchers discovered "natural" or
"semantic" perturbations, such as rotations, changes of brightness, or more
high-level changes, but these perturbations have not yet been systematically
utilized to measure the performance of classifiers. In this paper, we propose
several metrics to measure robustness of classifiers to natural adversarial
examples, and methods to evaluate them. These metrics, called latent space
performance metrics, are based on the ability of generative models to capture
probability distributions, and are defined in their latent spaces. On three
image classification case studies, we evaluate the proposed metrics for several
classifiers, including ones trained in conventional and robust ways. We find
that the latent counterparts of adversarial robustness are associated with the
accuracy of the classifier rather than its conventional adversarial robustness,
but the latter is still reflected on the properties of found latent
perturbations. In addition, our novel method of finding latent adversarial
perturbations demonstrates that these perturbations are often perceptually
small.
| [
{
"created": "Wed, 4 Mar 2020 10:58:59 GMT",
"version": "v1"
},
{
"created": "Sun, 15 Mar 2020 15:55:23 GMT",
"version": "v2"
}
] | 2020-03-17 | [
[
"Buzhinsky",
"Igor",
""
],
[
"Nerinovsky",
"Arseny",
""
],
[
"Tripakis",
"Stavros",
""
]
] | Recent studies have shown that modern deep neural network classifiers are easy to fool, assuming that an adversary is able to slightly modify their inputs. Many papers have proposed adversarial attacks, defenses and methods to measure robustness to such adversarial perturbations. However, most commonly considered adversarial examples are based on $\ell_p$-bounded perturbations in the input space of the neural network, which are unlikely to arise naturally. Recently, especially in computer vision, researchers discovered "natural" or "semantic" perturbations, such as rotations, changes of brightness, or more high-level changes, but these perturbations have not yet been systematically utilized to measure the performance of classifiers. In this paper, we propose several metrics to measure robustness of classifiers to natural adversarial examples, and methods to evaluate them. These metrics, called latent space performance metrics, are based on the ability of generative models to capture probability distributions, and are defined in their latent spaces. On three image classification case studies, we evaluate the proposed metrics for several classifiers, including ones trained in conventional and robust ways. We find that the latent counterparts of adversarial robustness are associated with the accuracy of the classifier rather than its conventional adversarial robustness, but the latter is still reflected on the properties of found latent perturbations. In addition, our novel method of finding latent adversarial perturbations demonstrates that these perturbations are often perceptually small. |
1806.00264 | Ting-Ting Liang | Ting-Ting Liang, Satoshi Tsutsui, Liangcai Gao, Jing-Jing Lu and
Mengyan Sun | Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image
Semantic Segmentaion | 12 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the time-consuming routine work for a radiologist is to discern
anatomical structures from tomographic images. For assisting radiologists, this
paper develops an automatic segmentation method for pelvic magnetic resonance
(MR) images. The task has three major challenges 1) A pelvic organ can have
various sizes and shapes depending on the axial image, which requires local
contexts to segment correctly. 2) Different organs often have quite similar
appearance in MR images, which requires global context to segment. 3) The
number of available annotated images are very small to use the latest
segmentation algorithms. To address the challenges, we propose a novel
convolutional neural network called Attention-Pyramid network (APNet) that
effectively exploits both local and global contexts, in addition to a
data-augmentation technique that is particularly effective for MR images. In
order to evaluate our method, we construct fine-grained (50 pelvic organs) MR
image segmentation dataset, and experimentally confirm the superior performance
of our techniques over the state-of-the-art image segmentation methods.
| [
{
"created": "Fri, 1 Jun 2018 10:13:45 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Jun 2018 16:57:39 GMT",
"version": "v2"
}
] | 2018-06-29 | [
[
"Liang",
"Ting-Ting",
""
],
[
"Tsutsui",
"Satoshi",
""
],
[
"Gao",
"Liangcai",
""
],
[
"Lu",
"Jing-Jing",
""
],
[
"Sun",
"Mengyan",
""
]
] | One of the time-consuming routine work for a radiologist is to discern anatomical structures from tomographic images. For assisting radiologists, this paper develops an automatic segmentation method for pelvic magnetic resonance (MR) images. The task has three major challenges 1) A pelvic organ can have various sizes and shapes depending on the axial image, which requires local contexts to segment correctly. 2) Different organs often have quite similar appearance in MR images, which requires global context to segment. 3) The number of available annotated images are very small to use the latest segmentation algorithms. To address the challenges, we propose a novel convolutional neural network called Attention-Pyramid network (APNet) that effectively exploits both local and global contexts, in addition to a data-augmentation technique that is particularly effective for MR images. In order to evaluate our method, we construct fine-grained (50 pelvic organs) MR image segmentation dataset, and experimentally confirm the superior performance of our techniques over the state-of-the-art image segmentation methods. |
2402.15420 | Simon Holk | Simon Holk, Daniel Marta, Iolanda Leite | PREDILECT: Preferences Delineated with Zero-Shot Language-based
Reasoning in Reinforcement Learning | 8 pages, 8 Figures, 2 Tables | null | 10.1145/3610977.3634970 | null | cs.RO cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Preference-based reinforcement learning (RL) has emerged as a new field in
robot learning, where humans play a pivotal role in shaping robot behavior by
expressing preferences on different sequences of state-action pairs. However,
formulating realistic policies for robots demands responses from humans to an
extensive array of queries. In this work, we approach the sample-efficiency
challenge by expanding the information collected per query to contain both
preferences and optional text prompting. To accomplish this, we leverage the
zero-shot capabilities of a large language model (LLM) to reason from the text
provided by humans. To accommodate the additional query information, we
reformulate the reward learning objectives to contain flexible highlights --
state-action pairs that contain relatively high information and are related to
the features processed in a zero-shot fashion from a pretrained LLM. In both a
simulated scenario and a user study, we reveal the effectiveness of our work by
analyzing the feedback and its implications. Additionally, the collective
feedback collected serves to train a robot on socially compliant trajectories
in a simulated social navigation landscape. We provide video examples of the
trained policies at https://sites.google.com/view/rl-predilect
| [
{
"created": "Fri, 23 Feb 2024 16:30:05 GMT",
"version": "v1"
}
] | 2024-02-26 | [
[
"Holk",
"Simon",
""
],
[
"Marta",
"Daniel",
""
],
[
"Leite",
"Iolanda",
""
]
] | Preference-based reinforcement learning (RL) has emerged as a new field in robot learning, where humans play a pivotal role in shaping robot behavior by expressing preferences on different sequences of state-action pairs. However, formulating realistic policies for robots demands responses from humans to an extensive array of queries. In this work, we approach the sample-efficiency challenge by expanding the information collected per query to contain both preferences and optional text prompting. To accomplish this, we leverage the zero-shot capabilities of a large language model (LLM) to reason from the text provided by humans. To accommodate the additional query information, we reformulate the reward learning objectives to contain flexible highlights -- state-action pairs that contain relatively high information and are related to the features processed in a zero-shot fashion from a pretrained LLM. In both a simulated scenario and a user study, we reveal the effectiveness of our work by analyzing the feedback and its implications. Additionally, the collective feedback collected serves to train a robot on socially compliant trajectories in a simulated social navigation landscape. We provide video examples of the trained policies at https://sites.google.com/view/rl-predilect |
2305.14749 | Chaitanya K. Joshi | Chaitanya K. Joshi, Arian R. Jamasb, Ramon Vi\~nas, Charles Harris,
Simon V. Mathis, Alex Morehead, Rishabh Anand, Pietro Li\`o | gRNAde: Geometric Deep Learning for 3D RNA inverse design | Previously titled 'Multi-State RNA Design with Geometric Multi-Graph
Neural Networks', presented at ICML 2023 Computational Biology Workshop | null | null | null | cs.LG q-bio.BM q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational RNA design tasks are often posed as inverse problems, where
sequences are designed based on adopting a single desired secondary structure
without considering 3D geometry and conformational diversity. We introduce
gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design
sequences that explicitly account for structure and dynamics. Under the hood,
gRNAde is a multi-state Graph Neural Network that generates candidate RNA
sequences conditioned on one or more 3D backbone structures where the
identities of the bases are unknown. On a single-state fixed backbone re-design
benchmark of 14 RNA structures from the PDB identified by Das et al. [2010],
gRNAde obtains higher native sequence recovery rates (56% on average) compared
to Rosetta (45% on average), taking under a second to produce designs compared
to the reported hours for Rosetta. We further demonstrate the utility of gRNAde
on a new benchmark of multi-state design for structurally flexible RNAs, as
well as zero-shot ranking of mutational fitness landscapes in a retrospective
analysis of a recent RNA polymerase ribozyme structure. Open source code:
https://github.com/chaitjo/geometric-rna-design
| [
{
"created": "Wed, 24 May 2023 05:46:56 GMT",
"version": "v1"
},
{
"created": "Thu, 25 May 2023 14:53:11 GMT",
"version": "v2"
},
{
"created": "Sun, 28 May 2023 22:44:27 GMT",
"version": "v3"
},
{
"created": "Sun, 31 Mar 2024 10:03:17 GMT",
"version": "v4"
},
{
"created": "Sat, 25 May 2024 23:11:45 GMT",
"version": "v5"
}
] | 2024-05-28 | [
[
"Joshi",
"Chaitanya K.",
""
],
[
"Jamasb",
"Arian R.",
""
],
[
"Viñas",
"Ramon",
""
],
[
"Harris",
"Charles",
""
],
[
"Mathis",
"Simon V.",
""
],
[
"Morehead",
"Alex",
""
],
[
"Anand",
"Rishabh",
""
],
[
"Liò",
"Pietro",
""
]
] | Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. Under the hood, gRNAde is a multi-state Graph Neural Network that generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. [2010], gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent RNA polymerase ribozyme structure. Open source code: https://github.com/chaitjo/geometric-rna-design |
2006.09785 | Jathushan Rajasegaran | Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan,
Mubarak Shah | Self-supervised Knowledge Distillation for Few-shot Learning | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-world contains an overwhelmingly large number of object classes,
learning all of which at once is infeasible. Few shot learning is a promising
learning paradigm due to its ability to learn out of order distributions
quickly with only a few samples. Recent works [7, 41] show that simply learning
a good feature embedding can outperform more sophisticated meta-learning and
metric learning algorithms for few-shot learning. In this paper, we propose a
simple approach to improve the representation capacity of deep neural networks
for few-shot learning tasks. We follow a two-stage learning process: First, we
train a neural network to maximize the entropy of the feature embedding, thus
creating an optimal output manifold using a self-supervised auxiliary loss. In
the second stage, we minimize the entropy on feature embedding by bringing
self-supervised twins together, while constraining the manifold with
student-teacher distillation. Our experiments show that, even in the first
stage, self-supervision can outperform current state-of-the-art methods, with
further gains achieved by our second stage distillation process. Our codes are
available at: https://github.com/brjathu/SKD.
| [
{
"created": "Wed, 17 Jun 2020 11:27:00 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Aug 2020 05:22:39 GMT",
"version": "v2"
}
] | 2020-08-05 | [
[
"Rajasegaran",
"Jathushan",
""
],
[
"Khan",
"Salman",
""
],
[
"Hayat",
"Munawar",
""
],
[
"Khan",
"Fahad Shahbaz",
""
],
[
"Shah",
"Mubarak",
""
]
] | Real-world contains an overwhelmingly large number of object classes, learning all of which at once is infeasible. Few shot learning is a promising learning paradigm due to its ability to learn out of order distributions quickly with only a few samples. Recent works [7, 41] show that simply learning a good feature embedding can outperform more sophisticated meta-learning and metric learning algorithms for few-shot learning. In this paper, we propose a simple approach to improve the representation capacity of deep neural networks for few-shot learning tasks. We follow a two-stage learning process: First, we train a neural network to maximize the entropy of the feature embedding, thus creating an optimal output manifold using a self-supervised auxiliary loss. In the second stage, we minimize the entropy on feature embedding by bringing self-supervised twins together, while constraining the manifold with student-teacher distillation. Our experiments show that, even in the first stage, self-supervision can outperform current state-of-the-art methods, with further gains achieved by our second stage distillation process. Our codes are available at: https://github.com/brjathu/SKD. |
2008.02251 | Thomas K\"ustner | Thomas K\"ustner, Tobias Hepp, Marc Fischer, Martin Schwartz, Andreas
Fritsche, Hans-Ulrich H\"aring, Konstantin Nikolaou, Fabian Bamberg, Bin
Yang, Fritz Schick, Sergios Gatidis, J\"urgen Machann | Fully Automated and Standardized Segmentation of Adipose Tissue
Compartments by Deep Learning in Three-dimensional Whole-body MRI of
Epidemiological Cohort Studies | This manuscript has been accepted for publication in Radiology:
Artificial Intelligence (https://pubs.rsna.org/journal/ai), which is
published by the Radiological Society of North America (RSNA) | null | null | null | cs.CV eess.IV physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: To enable fast and reliable assessment of subcutaneous and visceral
adipose tissue compartments derived from whole-body MRI. Methods:
Quantification and localization of different adipose tissue compartments from
whole-body MR images is of high interest to examine metabolic conditions. For
correct identification and phenotyping of individuals at increased risk for
metabolic diseases, a reliable automatic segmentation of adipose tissue into
subcutaneous and visceral adipose tissue is required. In this work we propose a
3D convolutional neural network (DCNet) to provide a robust and objective
segmentation. In this retrospective study, we collected 1000 cases (66$\pm$ 13
years; 523 women) from the Tuebingen Family Study and from the German Center
for Diabetes research (TUEF/DZD), as well as 300 cases (53$\pm$ 11 years; 152
women) from the German National Cohort (NAKO) database for model training,
validation, and testing with a transfer learning between the cohorts. These
datasets had variable imaging sequences, imaging contrasts, receiver coil
arrangements, scanners and imaging field strengths. The proposed DCNet was
compared against a comparable 3D UNet segmentation in terms of sensitivity,
specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds)
and reliable adipose tissue segmentation can be obtained with high Dice overlap
(0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and
accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage
450x450x2000mm${}^3$). Segmentation masks and adipose tissue profiles are
automatically reported back to the referring physician. Conclusion: Automatic
adipose tissue segmentation is feasible in 3D whole-body MR data sets and is
generalizable to different epidemiological cohort studies with the proposed
DCNet.
| [
{
"created": "Wed, 5 Aug 2020 17:30:14 GMT",
"version": "v1"
}
] | 2020-08-06 | [
[
"Küstner",
"Thomas",
""
],
[
"Hepp",
"Tobias",
""
],
[
"Fischer",
"Marc",
""
],
[
"Schwartz",
"Martin",
""
],
[
"Fritsche",
"Andreas",
""
],
[
"Häring",
"Hans-Ulrich",
""
],
[
"Nikolaou",
"Konstantin",
""
],
[
"Bamberg",
"Fabian",
""
],
[
"Yang",
"Bin",
""
],
[
"Schick",
"Fritz",
""
],
[
"Gatidis",
"Sergios",
""
],
[
"Machann",
"Jürgen",
""
]
] | Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automatic segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work we propose a 3D convolutional neural network (DCNet) to provide a robust and objective segmentation. In this retrospective study, we collected 1000 cases (66$\pm$ 13 years; 523 women) from the Tuebingen Family Study and from the German Center for Diabetes research (TUEF/DZD), as well as 300 cases (53$\pm$ 11 years; 152 women) from the German National Cohort (NAKO) database for model training, validation, and testing with a transfer learning between the cohorts. These datasets had variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners and imaging field strengths. The proposed DCNet was compared against a comparable 3D UNet segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds) and reliable adipose tissue segmentation can be obtained with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage 450x450x2000mm${}^3$). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician. Conclusion: Automatic adipose tissue segmentation is feasible in 3D whole-body MR data sets and is generalizable to different epidemiological cohort studies with the proposed DCNet. |
2001.07615 | Stefan Ultes | Stefan Ultes | Improving Interaction Quality Estimation with BiLSTMs and the Impact on
Dialogue Policy Learning | Published at SIGDIAL 2019 | null | null | null | cs.CL cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning suitable and well-performing dialogue behaviour in statistical
spoken dialogue systems has been in the focus of research for many years. While
most work which is based on reinforcement learning employs an objective measure
like task success for modelling the reward signal, we use a reward based on
user satisfaction estimation. We propose a novel estimator and show that it
outperforms all previous estimators while learning temporal dependencies
implicitly. Furthermore, we apply this novel user satisfaction estimation model
live in simulated experiments where the satisfaction estimation model is
trained on one domain and applied in many other domains which cover a similar
task. We show that applying this model results in higher estimated
satisfaction, similar task success rates and a higher robustness to noise.
| [
{
"created": "Tue, 21 Jan 2020 15:39:12 GMT",
"version": "v1"
}
] | 2020-01-22 | [
[
"Ultes",
"Stefan",
""
]
] | Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work which is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we use a reward based on user satisfaction estimation. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. Furthermore, we apply this novel user satisfaction estimation model live in simulated experiments where the satisfaction estimation model is trained on one domain and applied in many other domains which cover a similar task. We show that applying this model results in higher estimated satisfaction, similar task success rates and a higher robustness to noise. |
2302.14294 | Aravindh Raman | Haris Bin Zia, Jiahui He, Aravindh Raman, Ignacio Castro, Nishanth
Sastry, Gareth Tyson | Flocking to Mastodon: Tracking the Great Twitter Migration | null | null | null | null | cs.SI | http://creativecommons.org/licenses/by/4.0/ | The acquisition of Twitter by Elon Musk has spurred controversy and
uncertainty among Twitter users. The move raised as many praises as concerns,
particularly regarding Musk's views on free speech. As a result, a large number
of Twitter users have looked for alternatives to Twitter. Mastodon, a
decentralized micro-blogging social network, has attracted the attention of
many users and the general media. In this paper, we track and analyze the
migration of 136,009 users from Twitter to Mastodon. Our analysis sheds light
on the user-driven pressure towards centralization in a decentralized ecosystem
and identifies the strong influence of the social network in platform
migration. We also characterize the activity of migrated users on both Twitter
and Mastodon.
| [
{
"created": "Tue, 28 Feb 2023 03:59:19 GMT",
"version": "v1"
}
] | 2023-03-01 | [
[
"Zia",
"Haris Bin",
""
],
[
"He",
"Jiahui",
""
],
[
"Raman",
"Aravindh",
""
],
[
"Castro",
"Ignacio",
""
],
[
"Sastry",
"Nishanth",
""
],
[
"Tyson",
"Gareth",
""
]
] | The acquisition of Twitter by Elon Musk has spurred controversy and uncertainty among Twitter users. The move raised as many praises as concerns, particularly regarding Musk's views on free speech. As a result, a large number of Twitter users have looked for alternatives to Twitter. Mastodon, a decentralized micro-blogging social network, has attracted the attention of many users and the general media. In this paper, we track and analyze the migration of 136,009 users from Twitter to Mastodon. Our analysis sheds light on the user-driven pressure towards centralization in a decentralized ecosystem and identifies the strong influence of the social network in platform migration. We also characterize the activity of migrated users on both Twitter and Mastodon. |
1905.02691 | Patrick M. Pilarski | Patrick M. Pilarski, Andrew Butcher, Michael Johanson, Matthew M.
Botvinick, Andrew Bolt, Adam S. R. Parker | Learned human-agent decision-making, communication and joint action in a
virtual reality environment | 5 pages, 3 figures. Accepted to The 4th Multidisciplinary Conference
on Reinforcement Learning and Decision Making, July 7-10, 2019, McGill
University, Montreal, Quebec, Canada | null | null | null | cs.AI cs.HC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans make decisions and act alongside other humans to pursue both
short-term and long-term goals. As a result of ongoing progress in areas such
as computing science and automation, humans now also interact with non-human
agents of varying complexity as part of their day-to-day activities;
substantial work is being done to integrate increasingly intelligent machine
agents into human work and play. With increases in the cognitive, sensory, and
motor capacity of these agents, intelligent machinery for human assistance can
now reasonably be considered to engage in joint action with humans---i.e., two
or more agents adapting their behaviour and their understanding of each other
so as to progress in shared objectives or goals. The mechanisms, conditions,
and opportunities for skillful joint action in human-machine partnerships is of
great interest to multiple communities. Despite this, human-machine joint
action is as yet under-explored, especially in cases where a human and an
intelligent machine interact in a persistent way during the course of
real-time, daily-life experience. In this work, we contribute a virtual reality
environment wherein a human and an agent can adapt their predictions, their
actions, and their communication so as to pursue a simple foraging task. In a
case study with a single participant, we provide an example of human-agent
coordination and decision-making involving prediction learning on the part of
the human and the machine agent, and control learning on the part of the
machine agent wherein audio communication signals are used to cue its human
partner in service of acquiring shared reward. These comparisons suggest the
utility of studying human-machine coordination in a virtual reality
environment, and identify further research that will expand our understanding
of persistent human-machine joint action.
| [
{
"created": "Tue, 7 May 2019 16:53:48 GMT",
"version": "v1"
}
] | 2019-05-08 | [
[
"Pilarski",
"Patrick M.",
""
],
[
"Butcher",
"Andrew",
""
],
[
"Johanson",
"Michael",
""
],
[
"Botvinick",
"Matthew M.",
""
],
[
"Bolt",
"Andrew",
""
],
[
"Parker",
"Adam S. R.",
""
]
] | Humans make decisions and act alongside other humans to pursue both short-term and long-term goals. As a result of ongoing progress in areas such as computing science and automation, humans now also interact with non-human agents of varying complexity as part of their day-to-day activities; substantial work is being done to integrate increasingly intelligent machine agents into human work and play. With increases in the cognitive, sensory, and motor capacity of these agents, intelligent machinery for human assistance can now reasonably be considered to engage in joint action with humans---i.e., two or more agents adapting their behaviour and their understanding of each other so as to progress in shared objectives or goals. The mechanisms, conditions, and opportunities for skillful joint action in human-machine partnerships is of great interest to multiple communities. Despite this, human-machine joint action is as yet under-explored, especially in cases where a human and an intelligent machine interact in a persistent way during the course of real-time, daily-life experience. In this work, we contribute a virtual reality environment wherein a human and an agent can adapt their predictions, their actions, and their communication so as to pursue a simple foraging task. In a case study with a single participant, we provide an example of human-agent coordination and decision-making involving prediction learning on the part of the human and the machine agent, and control learning on the part of the machine agent wherein audio communication signals are used to cue its human partner in service of acquiring shared reward. These comparisons suggest the utility of studying human-machine coordination in a virtual reality environment, and identify further research that will expand our understanding of persistent human-machine joint action. |
2207.05316 | Connor Parde | Connor J. Parde, Virginia E. Strehle, Vivekjyoti Banerjee, Ying Hu,
Jacqueline G. Cavazos, Carlos D. Castillo, Alice J. O'Toole | Twin identification over viewpoint change: A deep convolutional neural
network surpasses humans | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep convolutional neural networks (DCNNs) have achieved human-level accuracy
in face identification (Phillips et al., 2018), though it is unclear how
accurately they discriminate highly-similar faces. Here, humans and a DCNN
performed a challenging face-identity matching task that included identical
twins. Participants (N=87) viewed pairs of face images of three types:
same-identity, general imposter pairs (different identities from similar
demographic groups), and twin imposter pairs (identical twin siblings). The
task was to determine whether the pairs showed the same person or different
people. Identity comparisons were tested in three viewpoint-disparity
conditions: frontal to frontal, frontal to 45-degree profile, and frontal to
90-degree profile. Accuracy for discriminating matched-identity pairs from
twin-imposters and general imposters was assessed in each viewpoint-disparity
condition. Humans were more accurate for general-imposter pairs than
twin-imposter pairs, and accuracy declined with increased viewpoint disparity
between the images in a pair. A DCNN trained for face identification (Ranjan et
al., 2018) was tested on the same image pairs presented to humans. Machine
performance mirrored the pattern of human accuracy, but with performance at or
above all humans in all but one condition. Human and machine similarity scores
were compared across all image-pair types. This item-level analysis showed that
human and machine similarity ratings correlated significantly in six of nine
image-pair types [range r=0.38 to r=0.63], suggesting general accord between
the perception of face similarity by humans and the DCNN. These findings also
contribute to our understanding of DCNN performance for discriminating
high-resemblance faces, demonstrate that the DCNN performs at a level at or
above humans, and suggest a degree of parity between the features used by
humans and the DCNN.
| [
{
"created": "Tue, 12 Jul 2022 04:59:53 GMT",
"version": "v1"
}
] | 2022-07-13 | [
[
"Parde",
"Connor J.",
""
],
[
"Strehle",
"Virginia E.",
""
],
[
"Banerjee",
"Vivekjyoti",
""
],
[
"Hu",
"Ying",
""
],
[
"Cavazos",
"Jacqueline G.",
""
],
[
"Castillo",
"Carlos D.",
""
],
[
"O'Toole",
"Alice J.",
""
]
] | Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N=87) viewed pairs of face images of three types: same-identity, general imposter pairs (different identities from similar demographic groups), and twin imposter pairs (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45-degree profile, and frontal to 90-degree profile. Accuracy for discriminating matched-identity pairs from twin-imposters and general imposters was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r=0.38 to r=0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN. |
2008.03444 | Xinyi Xu Mr | Xinyi Xu and Tiancheng Huang and Pengfei Wei and Akshay Narayan and
Tze-Yun Leong | Hierarchical Reinforcement Learning in StarCraft II with Human Expertise
in Subgoals Selection | In Submission to AAMAS 2021 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work is inspired by recent advances in hierarchical reinforcement
learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in
learning efficiency from heuristic-based subgoal selection, experience replay
(Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning
(Bengio et al. 2009; Zaremba and Sutskever 2014). We propose a new method to
integrate HRL, experience replay and effective subgoal selection through an
implicit curriculum design based on human expertise to support sample-efficient
learning and enhance interpretability of the agent's behavior. Human expertise
remains indispensable in many areas such as medicine (Buch, Ahmed, and
Maruthappu 2018) and law (Cath 2018), where interpretability, explainability
and transparency are crucial in the decision making process, for ethical and
legal reasons. Our method simplifies the complex task sets for achieving the
overall objectives by decomposing them into subgoals at different levels of
abstraction. Incorporating relevant subjective knowledge also significantly
reduces the computational resources spent in exploration for RL, especially in
high speed, changing, and complex environments where the transition dynamics
cannot be effectively learned and modelled in a short time. Experimental
results in two StarCraft II (SC2) (Vinyals et al. 2017) minigames demonstrate
that our method can achieve better sample efficiency than flat and end-to-end
RL methods, and provides an effective method for explaining the agent's
performance.
| [
{
"created": "Sat, 8 Aug 2020 04:56:30 GMT",
"version": "v1"
},
{
"created": "Sat, 26 Sep 2020 00:15:12 GMT",
"version": "v2"
},
{
"created": "Tue, 29 Sep 2020 01:15:05 GMT",
"version": "v3"
}
] | 2020-09-30 | [
[
"Xu",
"Xinyi",
""
],
[
"Huang",
"Tiancheng",
""
],
[
"Wei",
"Pengfei",
""
],
[
"Narayan",
"Akshay",
""
],
[
"Leong",
"Tze-Yun",
""
]
] | This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014). We propose a new method to integrate HRL, experience replay and effective subgoal selection through an implicit curriculum design based on human expertise to support sample-efficient learning and enhance interpretability of the agent's behavior. Human expertise remains indispensable in many areas such as medicine (Buch, Ahmed, and Maruthappu 2018) and law (Cath 2018), where interpretability, explainability and transparency are crucial in the decision making process, for ethical and legal reasons. Our method simplifies the complex task sets for achieving the overall objectives by decomposing them into subgoals at different levels of abstraction. Incorporating relevant subjective knowledge also significantly reduces the computational resources spent in exploration for RL, especially in high speed, changing, and complex environments where the transition dynamics cannot be effectively learned and modelled in a short time. Experimental results in two StarCraft II (SC2) (Vinyals et al. 2017) minigames demonstrate that our method can achieve better sample efficiency than flat and end-to-end RL methods, and provides an effective method for explaining the agent's performance. |
0707.0568 | Gesualdo Scutari | Gesualdo Scutari, D.P. Palomar, S. Barbarossa | Optimal Linear Precoding Strategies for Wideband Non-Cooperative Systems
based on Game Theory-Part I: Nash Equilibria | Paper submitted to IEEE Transactions on Signal Processing, September
22, 2005. Revised March 14, 2007. Accepted June 5, 2007. To be published on
IEEE Transactions on Signal Processing, 2007. To appear on IEEE Transactions
on Signal Processing, 2007 | null | 10.1109/TSP.2007.907807 | null | cs.IT cs.GT math.IT | null | In this two-parts paper we propose a decentralized strategy, based on a
game-theoretic formulation, to find out the optimal precoding/multiplexing
matrices for a multipoint-to-multipoint communication system composed of a set
of wideband links sharing the same physical resources, i.e., time and
bandwidth. We assume, as optimality criterion, the achievement of a Nash
equilibrium and consider two alternative optimization problems: 1) the
competitive maximization of mutual information on each link, given constraints
on the transmit power and on the spectral mask imposed by the radio spectrum
regulatory bodies; and 2) the competitive maximization of the transmission
rate, using finite order constellations, under the same constraints as above,
plus a constraint on the average error probability. In Part I of the paper, we
start by showing that the solution set of both noncooperative games is always
nonempty and contains only pure strategies. Then, we prove that the optimal
precoding/multiplexing scheme for both games leads to a channel diagonalizing
structure, so that both matrix-valued problems can be recast in a simpler
unified vector power control game, with no performance penalty. Thus, we study
this simpler game and derive sufficient conditions ensuring the uniqueness of
the Nash equilibrium. Interestingly, although derived under stronger
constraints, incorporating for example spectral mask constraints, our
uniqueness conditions have broader validity than previously known conditions.
Finally, we assess the goodness of the proposed decentralized strategy by
comparing its performance with the performance of a Pareto-optimal centralized
scheme. To reach the Nash equilibria of the game, in Part II, we propose
alternative distributed algorithms, along with their convergence conditions.
| [
{
"created": "Wed, 4 Jul 2007 10:33:25 GMT",
"version": "v1"
}
] | 2009-11-13 | [
[
"Scutari",
"Gesualdo",
""
],
[
"Palomar",
"D. P.",
""
],
[
"Barbarossa",
"S.",
""
]
] | In this two-parts paper we propose a decentralized strategy, based on a game-theoretic formulation, to find out the optimal precoding/multiplexing matrices for a multipoint-to-multipoint communication system composed of a set of wideband links sharing the same physical resources, i.e., time and bandwidth. We assume, as optimality criterion, the achievement of a Nash equilibrium and consider two alternative optimization problems: 1) the competitive maximization of mutual information on each link, given constraints on the transmit power and on the spectral mask imposed by the radio spectrum regulatory bodies; and 2) the competitive maximization of the transmission rate, using finite order constellations, under the same constraints as above, plus a constraint on the average error probability. In Part I of the paper, we start by showing that the solution set of both noncooperative games is always nonempty and contains only pure strategies. Then, we prove that the optimal precoding/multiplexing scheme for both games leads to a channel diagonalizing structure, so that both matrix-valued problems can be recast in a simpler unified vector power control game, with no performance penalty. Thus, we study this simpler game and derive sufficient conditions ensuring the uniqueness of the Nash equilibrium. Interestingly, although derived under stronger constraints, incorporating for example spectral mask constraints, our uniqueness conditions have broader validity than previously known conditions. Finally, we assess the goodness of the proposed decentralized strategy by comparing its performance with the performance of a Pareto-optimal centralized scheme. To reach the Nash equilibria of the game, in Part II, we propose alternative distributed algorithms, along with their convergence conditions. |
1807.06614 | Ayonga Hereid | Ayonga Hereid, Omar Harib, Ross Hartley, Yukai Gong and Jessy W.
Grizzle | Rapid Trajectory Optimization Using C-FROST with Illustration on a
Cassie-Series Dynamic Walking Biped | null | null | null | null | cs.RO cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the big attractions of low-dimensional models for gait design has been
the ability to compute solutions rapidly, whereas one of their drawbacks has
been the difficulty in mapping the solutions back to the target robot. This
paper presents a set of tools for rapidly determining solutions for
``humanoids'' without removing or lumping degrees of freedom. The main tools
are (1) C-FROST, an open-source C++ interface for FROST, a direct collocation
optimization tool; and (2) multi-threading. The results will be illustrated on
a 20-DoF floating-base model for a Cassie-series bipedal robot through
numerical calculations and physical experiments.
| [
{
"created": "Tue, 17 Jul 2018 18:28:06 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Jul 2018 15:15:58 GMT",
"version": "v2"
},
{
"created": "Fri, 15 Mar 2019 16:39:06 GMT",
"version": "v3"
}
] | 2019-03-18 | [
[
"Hereid",
"Ayonga",
""
],
[
"Harib",
"Omar",
""
],
[
"Hartley",
"Ross",
""
],
[
"Gong",
"Yukai",
""
],
[
"Grizzle",
"Jessy W.",
""
]
] | One of the big attractions of low-dimensional models for gait design has been the ability to compute solutions rapidly, whereas one of their drawbacks has been the difficulty in mapping the solutions back to the target robot. This paper presents a set of tools for rapidly determining solutions for ``humanoids'' without removing or lumping degrees of freedom. The main tools are (1) C-FROST, an open-source C++ interface for FROST, a direct collocation optimization tool; and (2) multi-threading. The results will be illustrated on a 20-DoF floating-base model for a Cassie-series bipedal robot through numerical calculations and physical experiments. |
1410.6796 | Wojciech Mazurczyk | Wojciech Mazurczyk and Luca Caviglione | Steganography in Modern Smartphones and Mitigation Techniques | 25 pages, 8 figures, 6 tables | null | null | null | cs.MM cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | By offering sophisticated services and centralizing a huge volume of personal
data, modern smartphones changed the way we socialize, entertain and work. To
this aim, they rely upon complex hardware/software frameworks leading to a
number of vulnerabilities, attacks and hazards to profile individuals or gather
sensitive information. However, the majority of works evaluating the security
degree of smartphones neglects steganography, which can be mainly used to: i)
exfiltrate confidential data via camouflage methods, and ii) conceal valuable
or personal information into innocent looking carriers.
Therefore, this paper surveys the state of the art of steganographic
techniques for smartphones, with emphasis on methods developed over the period
2005 to the second quarter of 2014. The different approaches are grouped
according to the portion of the device used to hide information, leading to
three different covert channels, i.e., local, object and network. Also, it
reviews the relevant approaches used to detect and mitigate steganographic
attacks or threats. Lastly, it showcases the most popular software applications
to embed secret data into carriers, as well as possible future directions.
| [
{
"created": "Wed, 27 Aug 2014 08:46:05 GMT",
"version": "v1"
}
] | 2014-10-27 | [
[
"Mazurczyk",
"Wojciech",
""
],
[
"Caviglione",
"Luca",
""
]
] | By offering sophisticated services and centralizing a huge volume of personal data, modern smartphones changed the way we socialize, entertain and work. To this aim, they rely upon complex hardware/software frameworks leading to a number of vulnerabilities, attacks and hazards to profile individuals or gather sensitive information. However, the majority of works evaluating the security degree of smartphones neglects steganography, which can be mainly used to: i) exfiltrate confidential data via camouflage methods, and ii) conceal valuable or personal information into innocent looking carriers. Therefore, this paper surveys the state of the art of steganographic techniques for smartphones, with emphasis on methods developed over the period 2005 to the second quarter of 2014. The different approaches are grouped according to the portion of the device used to hide information, leading to three different covert channels, i.e., local, object and network. Also, it reviews the relevant approaches used to detect and mitigate steganographic attacks or threats. Lastly, it showcases the most popular software applications to embed secret data into carriers, as well as possible future directions. |
2006.08157 | Yunwen Lei | Yunwen Lei and Yiming Ying | Fine-Grained Analysis of Stability and Generalization for Stochastic
Gradient Descent | to appear in ICML 2020 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently there are a considerable amount of work devoted to the study of the
algorithmic stability and generalization for stochastic gradient descent (SGD).
However, the existing stability analysis requires to impose restrictive
assumptions on the boundedness of gradients, strong smoothness and convexity of
loss functions. In this paper, we provide a fine-grained analysis of stability
and generalization for SGD by substantially relaxing these assumptions.
Firstly, we establish stability and generalization for SGD by removing the
existing bounded gradient assumptions. The key idea is the introduction of a
new stability measure called on-average model stability, for which we develop
novel bounds controlled by the risks of SGD iterates. This yields
generalization bounds depending on the behavior of the best model, and leads to
the first-ever-known fast bounds in the low-noise setting using stability
approach. Secondly, the smoothness assumption is relaxed by considering loss
functions with Holder continuous (sub)gradients for which we show that optimal
bounds are still achieved by balancing computation and stability. To our best
knowledge, this gives the first-ever-known stability and generalization bounds
for SGD with even non-differentiable loss functions. Finally, we study learning
problems with (strongly) convex objectives but non-convex loss functions.
| [
{
"created": "Mon, 15 Jun 2020 06:30:19 GMT",
"version": "v1"
}
] | 2020-06-16 | [
[
"Lei",
"Yunwen",
""
],
[
"Ying",
"Yiming",
""
]
] | Recently there are a considerable amount of work devoted to the study of the algorithmic stability and generalization for stochastic gradient descent (SGD). However, the existing stability analysis requires to impose restrictive assumptions on the boundedness of gradients, strong smoothness and convexity of loss functions. In this paper, we provide a fine-grained analysis of stability and generalization for SGD by substantially relaxing these assumptions. Firstly, we establish stability and generalization for SGD by removing the existing bounded gradient assumptions. The key idea is the introduction of a new stability measure called on-average model stability, for which we develop novel bounds controlled by the risks of SGD iterates. This yields generalization bounds depending on the behavior of the best model, and leads to the first-ever-known fast bounds in the low-noise setting using stability approach. Secondly, the smoothness assumption is relaxed by considering loss functions with Holder continuous (sub)gradients for which we show that optimal bounds are still achieved by balancing computation and stability. To our best knowledge, this gives the first-ever-known stability and generalization bounds for SGD with even non-differentiable loss functions. Finally, we study learning problems with (strongly) convex objectives but non-convex loss functions. |
2002.08347 | Florian Tram\`er | Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry | On Adaptive Attacks to Adversarial Example Defenses | NeurIPS 2020 | null | null | null | cs.LG cs.CR stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adaptive attacks have (rightfully) become the de facto standard for
evaluating defenses to adversarial examples. We find, however, that typical
adaptive evaluations are incomplete. We demonstrate that thirteen defenses
recently published at ICLR, ICML and NeurIPS---and chosen for illustrative and
pedagogical purposes---can be circumvented despite attempting to perform
evaluations using adaptive attacks. While prior evaluation papers focused
mainly on the end result---showing that a defense was ineffective---this paper
focuses on laying out the methodology and the approach necessary to perform an
adaptive attack. We hope that these analyses will serve as guidance on how to
properly perform adaptive attacks against defenses to adversarial examples, and
thus will allow the community to make further progress in building more robust
models.
| [
{
"created": "Wed, 19 Feb 2020 18:50:29 GMT",
"version": "v1"
},
{
"created": "Fri, 23 Oct 2020 12:07:41 GMT",
"version": "v2"
}
] | 2020-10-26 | [
[
"Tramer",
"Florian",
""
],
[
"Carlini",
"Nicholas",
""
],
[
"Brendel",
"Wieland",
""
],
[
"Madry",
"Aleksander",
""
]
] | Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to adversarial examples. We find, however, that typical adaptive evaluations are incomplete. We demonstrate that thirteen defenses recently published at ICLR, ICML and NeurIPS---and chosen for illustrative and pedagogical purposes---can be circumvented despite attempting to perform evaluations using adaptive attacks. While prior evaluation papers focused mainly on the end result---showing that a defense was ineffective---this paper focuses on laying out the methodology and the approach necessary to perform an adaptive attack. We hope that these analyses will serve as guidance on how to properly perform adaptive attacks against defenses to adversarial examples, and thus will allow the community to make further progress in building more robust models. |
2104.04748 | Zhengxu Hou | Zhengxu Hou, Bang Liu, Ruihui Zhao, Zijing Ou, Yafei Liu, Xi Chen,
Yefeng Zheng | Imperfect also Deserves Reward: Multi-Level and Sequential Reward
Modeling for Better Dialog Management | 9 pages | NAACL 2021 | null | null | cs.CL cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For task-oriented dialog systems, training a Reinforcement Learning (RL)
based Dialog Management module suffers from low sample efficiency and slow
convergence speed due to the sparse rewards in RL.To solve this problem, many
strategies have been proposed to give proper rewards when training RL, but
their rewards lack interpretability and cannot accurately estimate the
distribution of state-action pairs in real dialogs. In this paper, we propose a
multi-level reward modeling approach that factorizes a reward into a
three-level hierarchy: domain, act, and slot. Based on inverse adversarial
reinforcement learning, our designed reward model can provide more accurate and
explainable reward signals for state-action pairs.Extensive evaluations show
that our approach can be applied to a wide range of reinforcement
learning-based dialog systems and significantly improves both the performance
and the speed of convergence.
| [
{
"created": "Sat, 10 Apr 2021 12:20:23 GMT",
"version": "v1"
}
] | 2021-04-13 | [
[
"Hou",
"Zhengxu",
""
],
[
"Liu",
"Bang",
""
],
[
"Zhao",
"Ruihui",
""
],
[
"Ou",
"Zijing",
""
],
[
"Liu",
"Yafei",
""
],
[
"Chen",
"Xi",
""
],
[
"Zheng",
"Yefeng",
""
]
] | For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL.To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs. In this paper, we propose a multi-level reward modeling approach that factorizes a reward into a three-level hierarchy: domain, act, and slot. Based on inverse adversarial reinforcement learning, our designed reward model can provide more accurate and explainable reward signals for state-action pairs.Extensive evaluations show that our approach can be applied to a wide range of reinforcement learning-based dialog systems and significantly improves both the performance and the speed of convergence. |
1706.02494 | Rong Zhang | Rong Zhang and Lie-Liang Yang and Lajos Hanzo | Physical Layer Security of Generalised Pre-coded Spatial Modulation with
Antenna Scrambling | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We now advocate a novel physical layer security solution that is unique to
our previously proposed GPSM scheme with the aid of the proposed antenna
scrambling. The novelty and contribution of our paper lies in three aspects: 1/
principle: we introduce a `security key' generated at Alice that is unknown to
both Bob and Eve, where the design goal is that the publicly unknown security
key only imposes barrier for Eve. 2/ approach: we achieve it by conveying
useful information only through the activation of RA indices, which is in turn
concealed by the unknown security key in terms of the randomly scrambled
symbols used in place of the conventional modulated symbols in GPSM scheme. 3/
design: we consider both Circular Antenna Scrambling (CAS) and Gaussian Antenna
Scrambling (GAS) in detail and the resultant security capacity of both designs
are quantified and compared.
| [
{
"created": "Thu, 8 Jun 2017 09:48:08 GMT",
"version": "v1"
}
] | 2017-06-09 | [
[
"Zhang",
"Rong",
""
],
[
"Yang",
"Lie-Liang",
""
],
[
"Hanzo",
"Lajos",
""
]
] | We now advocate a novel physical layer security solution that is unique to our previously proposed GPSM scheme with the aid of the proposed antenna scrambling. The novelty and contribution of our paper lies in three aspects: 1/ principle: we introduce a `security key' generated at Alice that is unknown to both Bob and Eve, where the design goal is that the publicly unknown security key only imposes barrier for Eve. 2/ approach: we achieve it by conveying useful information only through the activation of RA indices, which is in turn concealed by the unknown security key in terms of the randomly scrambled symbols used in place of the conventional modulated symbols in GPSM scheme. 3/ design: we consider both Circular Antenna Scrambling (CAS) and Gaussian Antenna Scrambling (GAS) in detail and the resultant security capacity of both designs are quantified and compared. |
0705.0561 | Jingchao Chen | Jing-Chao Chen | Iterative Rounding for the Closest String Problem | This paper has been published in abstract Booklet of CiE09 | null | null | null | cs.DS cs.CC | http://creativecommons.org/licenses/by-nc-sa/3.0/ | The closest string problem is an NP-hard problem, whose task is to find a
string that minimizes maximum Hamming distance to a given set of strings. This
can be reduced to an integer program (IP). However, to date, there exists no
known polynomial-time algorithm for IP. In 2004, Meneses et al. introduced a
branch-and-bound (B & B) method for solving the IP problem. Their algorithm is
not always efficient and has the exponential time complexity. In the paper, we
attempt to solve efficiently the IP problem by a greedy iterative rounding
technique. The proposed algorithm is polynomial time and much faster than the
existing B & B IP for the CSP. If the number of strings is limited to 3, the
algorithm is provably at most 1 away from the optimum. The empirical results
show that in many cases we can find an exact solution. Even though we fail to
find an exact solution, the solution found is very close to exact solution.
| [
{
"created": "Fri, 4 May 2007 03:01:42 GMT",
"version": "v1"
},
{
"created": "Wed, 11 May 2011 00:18:55 GMT",
"version": "v2"
}
] | 2011-05-12 | [
[
"Chen",
"Jing-Chao",
""
]
] | The closest string problem is an NP-hard problem, whose task is to find a string that minimizes maximum Hamming distance to a given set of strings. This can be reduced to an integer program (IP). However, to date, there exists no known polynomial-time algorithm for IP. In 2004, Meneses et al. introduced a branch-and-bound (B & B) method for solving the IP problem. Their algorithm is not always efficient and has the exponential time complexity. In the paper, we attempt to solve efficiently the IP problem by a greedy iterative rounding technique. The proposed algorithm is polynomial time and much faster than the existing B & B IP for the CSP. If the number of strings is limited to 3, the algorithm is provably at most 1 away from the optimum. The empirical results show that in many cases we can find an exact solution. Even though we fail to find an exact solution, the solution found is very close to exact solution. |
2208.05810 | Minji Kim | Minji Kim, Seungkwan Lee, Jungseul Ok, Bohyung Han, Minsu Cho | Towards Sequence-Level Training for Visual Tracking | ECCV 2022 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the extensive adoption of machine learning on the task of visual
object tracking, recent learning-based approaches have largely overlooked the
fact that visual tracking is a sequence-level task in its nature; they rely
heavily on frame-level training, which inevitably induces inconsistency between
training and testing in terms of both data distributions and task objectives.
This work introduces a sequence-level training strategy for visual tracking
based on reinforcement learning and discusses how a sequence-level design of
data sampling, learning objectives, and data augmentation can improve the
accuracy and robustness of tracking algorithms. Our experiments on standard
benchmarks including LaSOT, TrackingNet, and GOT-10k demonstrate that four
representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP,
consistently improve by incorporating the proposed methods in training without
modifying architectures.
| [
{
"created": "Thu, 11 Aug 2022 13:15:36 GMT",
"version": "v1"
},
{
"created": "Tue, 20 Sep 2022 12:46:53 GMT",
"version": "v2"
},
{
"created": "Sun, 16 Oct 2022 16:05:12 GMT",
"version": "v3"
}
] | 2022-10-18 | [
[
"Kim",
"Minji",
""
],
[
"Lee",
"Seungkwan",
""
],
[
"Ok",
"Jungseul",
""
],
[
"Han",
"Bohyung",
""
],
[
"Cho",
"Minsu",
""
]
] | Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is a sequence-level task in its nature; they rely heavily on frame-level training, which inevitably induces inconsistency between training and testing in terms of both data distributions and task objectives. This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning and discusses how a sequence-level design of data sampling, learning objectives, and data augmentation can improve the accuracy and robustness of tracking algorithms. Our experiments on standard benchmarks including LaSOT, TrackingNet, and GOT-10k demonstrate that four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training without modifying architectures. |
1905.12261 | Che-Han Chang | Che-Han Chang, Chun-Hsien Yu, Szu-Ying Chen, Edward Y. Chang | KG-GAN: Knowledge-Guided Generative Adversarial Networks | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Can generative adversarial networks (GANs) generate roses of various colors
given only roses of red petals as input? The answer is negative, since GANs'
discriminator would reject all roses of unseen petal colors. In this study, we
propose knowledge-guided GAN (KG-GAN) to fuse domain knowledge with the GAN
framework. KG-GAN trains two generators; one learns from data whereas the other
learns from knowledge with a constraint function. Experimental results
demonstrate the effectiveness of KG-GAN in generating unseen flower categories
from seen categories given textual descriptions of the unseen ones.
| [
{
"created": "Wed, 29 May 2019 07:55:46 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Sep 2019 09:48:33 GMT",
"version": "v2"
}
] | 2019-09-24 | [
[
"Chang",
"Che-Han",
""
],
[
"Yu",
"Chun-Hsien",
""
],
[
"Chen",
"Szu-Ying",
""
],
[
"Chang",
"Edward Y.",
""
]
] | Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input? The answer is negative, since GANs' discriminator would reject all roses of unseen petal colors. In this study, we propose knowledge-guided GAN (KG-GAN) to fuse domain knowledge with the GAN framework. KG-GAN trains two generators; one learns from data whereas the other learns from knowledge with a constraint function. Experimental results demonstrate the effectiveness of KG-GAN in generating unseen flower categories from seen categories given textual descriptions of the unseen ones. |
2108.13015 | Pengguang Chen | Pengguang Chen, Yixin Chen, Shu Liu, Mingchang Yang, Jiaya Jia | Exploring and Improving Mobile Level Vision Transformers | 10 pages; 5 figures; preprint | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the vision transformer structure in the mobile level in this paper,
and find a dramatic performance drop. We analyze the reason behind this
phenomenon, and propose a novel irregular patch embedding module and adaptive
patch fusion module to improve the performance. We conjecture that the vision
transformer blocks (which consist of multi-head attention and feed-forward
network) are more suitable to handle high-level information than low-level
features. The irregular patch embedding module extracts patches that contain
rich high-level information with different receptive fields. The transformer
blocks can obtain the most useful information from these irregular patches.
Then the processed patches pass the adaptive patch merging module to get the
final features for the classifier. With our proposed improvements, the
traditional uniform vision transformer structure can achieve state-of-the-art
results in mobile level. We improve the DeiT baseline by more than 9\% under
the mobile-level settings and surpass other transformer architectures like Swin
and CoaT by a large margin.
| [
{
"created": "Mon, 30 Aug 2021 06:42:49 GMT",
"version": "v1"
}
] | 2021-08-31 | [
[
"Chen",
"Pengguang",
""
],
[
"Chen",
"Yixin",
""
],
[
"Liu",
"Shu",
""
],
[
"Yang",
"Mingchang",
""
],
[
"Jia",
"Jiaya",
""
]
] | We study the vision transformer structure in the mobile level in this paper, and find a dramatic performance drop. We analyze the reason behind this phenomenon, and propose a novel irregular patch embedding module and adaptive patch fusion module to improve the performance. We conjecture that the vision transformer blocks (which consist of multi-head attention and feed-forward network) are more suitable to handle high-level information than low-level features. The irregular patch embedding module extracts patches that contain rich high-level information with different receptive fields. The transformer blocks can obtain the most useful information from these irregular patches. Then the processed patches pass the adaptive patch merging module to get the final features for the classifier. With our proposed improvements, the traditional uniform vision transformer structure can achieve state-of-the-art results in mobile level. We improve the DeiT baseline by more than 9\% under the mobile-level settings and surpass other transformer architectures like Swin and CoaT by a large margin. |
2201.02850 | Rayson Laroca | Gabriel Salomon, Rayson Laroca, David Menotti | Image-based Automatic Dial Meter Reading in Unconstrained Scenarios | null | Measurement, vol. 204, p. 112025, 2022 | 10.1016/j.measurement.2022.112025 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The replacement of analog meters with smart meters is costly, laborious, and
far from complete in developing countries. The Energy Company of Parana (Copel)
(Brazil) performs more than 4 million meter readings (almost entirely of
non-smart devices) per month, and we estimate that 850 thousand of them are
from dial meters. Therefore, an image-based automatic reading system can reduce
human errors, create a proof of reading, and enable the customers to perform
the reading themselves through a mobile application. We propose novel
approaches for Automatic Dial Meter Reading (ADMR) and introduce a new dataset
for ADMR in unconstrained scenarios, called UFPR-ADMR-v2. Our best-performing
method combines YOLOv4 with a novel regression approach (AngReg), and explores
several postprocessing techniques. Compared to previous works, it decreased the
Mean Absolute Error (MAE) from 1,343 to 129 and achieved a meter recognition
rate (MRR) of 98.90% -- with an error tolerance of 1 Kilowatt-hour (kWh).
| [
{
"created": "Sat, 8 Jan 2022 16:03:46 GMT",
"version": "v1"
},
{
"created": "Sun, 23 Oct 2022 11:56:38 GMT",
"version": "v2"
}
] | 2022-10-25 | [
[
"Salomon",
"Gabriel",
""
],
[
"Laroca",
"Rayson",
""
],
[
"Menotti",
"David",
""
]
] | The replacement of analog meters with smart meters is costly, laborious, and far from complete in developing countries. The Energy Company of Parana (Copel) (Brazil) performs more than 4 million meter readings (almost entirely of non-smart devices) per month, and we estimate that 850 thousand of them are from dial meters. Therefore, an image-based automatic reading system can reduce human errors, create a proof of reading, and enable the customers to perform the reading themselves through a mobile application. We propose novel approaches for Automatic Dial Meter Reading (ADMR) and introduce a new dataset for ADMR in unconstrained scenarios, called UFPR-ADMR-v2. Our best-performing method combines YOLOv4 with a novel regression approach (AngReg), and explores several postprocessing techniques. Compared to previous works, it decreased the Mean Absolute Error (MAE) from 1,343 to 129 and achieved a meter recognition rate (MRR) of 98.90% -- with an error tolerance of 1 Kilowatt-hour (kWh). |
1906.10495 | Joshua Cook | Joshua Alan Cook | Approximating Unitary Preparations of Orthogonal Black Box States | A Class project Paper for CS395T Quantum Complexity Theory at UT
Austin in Spring 2019 under Scott Aaronson | null | null | null | cs.CC quant-ph | http://creativecommons.org/licenses/by/4.0/ | In this paper, I take a step toward answering the following question: for m
different small circuits that compute m orthogonal n qubit states, is there a
small circuit that will map m computational basis states to these m states
without any input leaving any auxiliary bits changed. While this may seem
simple, the constraint that auxiliary bits always be returned to 0 on any input
(even ones besides the m we care about) led me to use sophisticated techniques.
I give an approximation of such a unitary in the m = 2 case that has size
polynomial in the approximation error, and the number of qubits n.
| [
{
"created": "Sun, 23 Jun 2019 01:21:52 GMT",
"version": "v1"
}
] | 2019-06-26 | [
[
"Cook",
"Joshua Alan",
""
]
] | In this paper, I take a step toward answering the following question: for m different small circuits that compute m orthogonal n qubit states, is there a small circuit that will map m computational basis states to these m states without any input leaving any auxiliary bits changed. While this may seem simple, the constraint that auxiliary bits always be returned to 0 on any input (even ones besides the m we care about) led me to use sophisticated techniques. I give an approximation of such a unitary in the m = 2 case that has size polynomial in the approximation error, and the number of qubits n. |
cs/0407066 | Tentyukov Mikhail | M.Tentyukov, D.Fliegner, M.Frank, A.Onischenko, A.Retey,
H.M.Staudenmaier and J.A.M.Vermaseren | ParFORM: Parallel Version of the Symbolic Manipulation Program FORM | 5 pages, 4 Encapsulated postscript figures, LaTeX2e uses casc.cls
(included). Presented at CASC'04 http://wwwmayr.in.tum.de/CASC2004/ | null | null | TTP04-15 | cs.SC cs.DC hep-ph | null | After an introduction to the sequential version of FORM and the mechanisms
behind, we report on the status of our project of parallelization. We have now
a parallel version of FORM running on Cluster- and SMP-architectures. This
version can be used to run arbitrary FORM programs in parallel.
| [
{
"created": "Fri, 30 Jul 2004 10:06:16 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Tentyukov",
"M.",
""
],
[
"Fliegner",
"D.",
""
],
[
"Frank",
"M.",
""
],
[
"Onischenko",
"A.",
""
],
[
"Retey",
"A.",
""
],
[
"Staudenmaier",
"H. M.",
""
],
[
"Vermaseren",
"J. A. M.",
""
]
] | After an introduction to the sequential version of FORM and the mechanisms behind, we report on the status of our project of parallelization. We have now a parallel version of FORM running on Cluster- and SMP-architectures. This version can be used to run arbitrary FORM programs in parallel. |
2212.01545 | Ruihao Zheng | Ruihao Zheng and Zhenkun Wang | A Generalized Scalarization Method for Evolutionary Multi-Objective
Optimization | Correct some typos. (Accepted for presentation at Thirty-Seventh AAAI
Conference on Artificial Intelligence (AAAI-23)) | null | 10.1609/aaai.v37i10.26474 | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The decomposition-based multi-objective evolutionary algorithm (MOEA/D)
transforms a multi-objective optimization problem (MOP) into a set of
single-objective subproblems for collaborative optimization. Mismatches between
subproblems and solutions can lead to severe performance degradation of MOEA/D.
Most existing mismatch coping strategies only work when the $L_{\infty}$
scalarization is used. A mismatch coping strategy that can use any $L_{p}$
scalarization, even when facing MOPs with non-convex Pareto fronts, is of great
significance for MOEA/D. This paper uses the global replacement (GR) as the
backbone. We analyze how GR can no longer avoid mismatches when $L_{\infty}$ is
replaced by another $L_{p}$ with $p\in [1,\infty)$, and find that the
$L_p$-based ($1\leq p<\infty$) subproblems having inconsistently large
preference regions. When $p$ is set to a small value, some middle subproblems
have very small preference regions so that their direction vectors cannot pass
through their corresponding preference regions. Therefore, we propose a
generalized $L_p$ (G$L_p$) scalarization to ensure that the subproblem's
direction vector passes through its preference region. Our theoretical analysis
shows that GR can always avoid mismatches when using the G$L_p$ scalarization
for any $p\geq 1$. The experimental studies on various MOPs conform to the
theoretical analysis.
| [
{
"created": "Sat, 3 Dec 2022 05:55:04 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Nov 2023 00:46:59 GMT",
"version": "v2"
}
] | 2023-11-08 | [
[
"Zheng",
"Ruihao",
""
],
[
"Wang",
"Zhenkun",
""
]
] | The decomposition-based multi-objective evolutionary algorithm (MOEA/D) transforms a multi-objective optimization problem (MOP) into a set of single-objective subproblems for collaborative optimization. Mismatches between subproblems and solutions can lead to severe performance degradation of MOEA/D. Most existing mismatch coping strategies only work when the $L_{\infty}$ scalarization is used. A mismatch coping strategy that can use any $L_{p}$ scalarization, even when facing MOPs with non-convex Pareto fronts, is of great significance for MOEA/D. This paper uses the global replacement (GR) as the backbone. We analyze how GR can no longer avoid mismatches when $L_{\infty}$ is replaced by another $L_{p}$ with $p\in [1,\infty)$, and find that the $L_p$-based ($1\leq p<\infty$) subproblems having inconsistently large preference regions. When $p$ is set to a small value, some middle subproblems have very small preference regions so that their direction vectors cannot pass through their corresponding preference regions. Therefore, we propose a generalized $L_p$ (G$L_p$) scalarization to ensure that the subproblem's direction vector passes through its preference region. Our theoretical analysis shows that GR can always avoid mismatches when using the G$L_p$ scalarization for any $p\geq 1$. The experimental studies on various MOPs conform to the theoretical analysis. |
2108.09939 | Mashiat Mostafa | Mashiat Mostafa and Faheem Hussain | Transcending Old Boundaries: Digital Afterlife in the Age of COVID-19 | In proceedings of the 1st Virtual Conference on Implications of
Information and Digital Technologies for Development, 2021 | null | null | null | cs.CY | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The primary objective of our exploratory research is to contribute to the
ongoing conversation on Digital Afterlife from the lenses of Global South
during the COVID-19 period. Digital Afterlife is fast becoming a challenge for
our increasingly connected society. Moreover, the situation got worse with the
COVID-19 pandemic. The on-going research is to address the disparity in the
Global South, specifically in countries like Indonesia, India and The
Philippines compared to the Global North for Digital Afterlife services such as
policies and digital mourning services. By addressing the research question,
'What services and policy frameworks are available for Digital Afterlife in the
Global South during COVID-19?', we aim to find the multitude of ways people in
the Global South are managing their digital footprints. Our preliminary
findings show that some considerable research and death related digital
services and innovation have taken place during the pandemic. However,
overwhelming majority of these works are western-centric and mainly dealing
with post-mortem personal asset management. Cultural nuances, socio-economic
perspectives, religion, political climate, regional infrastructures are mostly
sidelined. We found significant disparity in Digital Afterlife product and
service designs, which got worse during the global pandemic. Our goal is to
collect further in-depth data within the three big ICT powerhouses of global
south (Indonesia, India and The Philippines), identify the challenges as well
as the innovations around Digital Afterlife.We envision proposing a set of
recommendations, based on our findings, for developing a more inclusive and
equitable digital space in this pandemic-stricken world.
| [
{
"created": "Mon, 23 Aug 2021 05:21:03 GMT",
"version": "v1"
}
] | 2021-08-24 | [
[
"Mostafa",
"Mashiat",
""
],
[
"Hussain",
"Faheem",
""
]
] | The primary objective of our exploratory research is to contribute to the ongoing conversation on Digital Afterlife from the lenses of Global South during the COVID-19 period. Digital Afterlife is fast becoming a challenge for our increasingly connected society. Moreover, the situation got worse with the COVID-19 pandemic. The on-going research is to address the disparity in the Global South, specifically in countries like Indonesia, India and The Philippines compared to the Global North for Digital Afterlife services such as policies and digital mourning services. By addressing the research question, 'What services and policy frameworks are available for Digital Afterlife in the Global South during COVID-19?', we aim to find the multitude of ways people in the Global South are managing their digital footprints. Our preliminary findings show that some considerable research and death related digital services and innovation have taken place during the pandemic. However, overwhelming majority of these works are western-centric and mainly dealing with post-mortem personal asset management. Cultural nuances, socio-economic perspectives, religion, political climate, regional infrastructures are mostly sidelined. We found significant disparity in Digital Afterlife product and service designs, which got worse during the global pandemic. Our goal is to collect further in-depth data within the three big ICT powerhouses of global south (Indonesia, India and The Philippines), identify the challenges as well as the innovations around Digital Afterlife.We envision proposing a set of recommendations, based on our findings, for developing a more inclusive and equitable digital space in this pandemic-stricken world. |
0803.2365 | Ganesh Narayan | V Sriram, Ganesh Narayan, K Gopinath | SAFIUS - A secure and accountable filesystem over untrusted storage | 11pt, 12 pages, 16 figures | Fourth International IEEE Security in Storage Workshop, 2007 -
SISW '07. Publication Date: 27-27 Sept. 2007 On page(s): 34-45 | 10.1109/SISW.2007.7 | null | cs.OS cs.CR cs.DC cs.NI cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe SAFIUS, a secure accountable file system that resides over an
untrusted storage. SAFIUS provides strong security guarantees like
confidentiality, integrity, prevention from rollback attacks, and
accountability. SAFIUS also enables read/write sharing of data and provides the
standard UNIX-like interface for applications. To achieve accountability with
good performance, it uses asynchronous signatures; to reduce the space required
for storing these signatures, a novel signature pruning mechanism is used.
SAFIUS has been implemented on a GNU/Linux based system modifying OpenGFS.
Preliminary performance studies show that SAFIUS has a tolerable overhead for
providing secure storage: while it has an overhead of about 50% of OpenGFS in
data intensive workloads (due to the overhead of performing
encryption/decryption in software), it is comparable (or better in some cases)
to OpenGFS in metadata intensive workloads.
| [
{
"created": "Sun, 16 Mar 2008 18:24:13 GMT",
"version": "v1"
}
] | 2016-11-18 | [
[
"Sriram",
"V",
""
],
[
"Narayan",
"Ganesh",
""
],
[
"Gopinath",
"K",
""
]
] | We describe SAFIUS, a secure accountable file system that resides over an untrusted storage. SAFIUS provides strong security guarantees like confidentiality, integrity, prevention from rollback attacks, and accountability. SAFIUS also enables read/write sharing of data and provides the standard UNIX-like interface for applications. To achieve accountability with good performance, it uses asynchronous signatures; to reduce the space required for storing these signatures, a novel signature pruning mechanism is used. SAFIUS has been implemented on a GNU/Linux based system modifying OpenGFS. Preliminary performance studies show that SAFIUS has a tolerable overhead for providing secure storage: while it has an overhead of about 50% of OpenGFS in data intensive workloads (due to the overhead of performing encryption/decryption in software), it is comparable (or better in some cases) to OpenGFS in metadata intensive workloads. |
2105.03389 | EPTCS | Patricia Johann (Appalachian State University), Enrico Ghiorzi
(Appalachian State University), Daniel Jeffries (Appalachian State
University) | GADTs, Functoriality, Parametricity: Pick Two | In Proceedings LSFA 2021, arXiv:2204.03415 | EPTCS 357, 2022, pp. 77-92 | 10.4204/EPTCS.357.6 | null | cs.LO cs.PL | http://creativecommons.org/licenses/by/4.0/ | GADTs can be represented either as their Church encodings a la Atkey, or as
fixpoints a la Johann and Polonsky. While a GADT represented as its Church
encoding need not support a map function satisfying the functor laws, the
fixpoint representation of a GADT must support such a map function even to be
well-defined. The two representations of a GADT thus need not be the same in
general. This observation forces a choice of representation of data types in
languages supporting GADTs. In this paper we show that choosing whether to
represent data types as their Church encodings or as fixpoints determines
whether or not a language supporting GADTs can have parametric models. This
choice thus has important consequences for how we can program with, and reason
about, these advanced data types.
| [
{
"created": "Fri, 7 May 2021 16:50:42 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Dec 2021 11:06:49 GMT",
"version": "v2"
},
{
"created": "Fri, 8 Apr 2022 07:18:08 GMT",
"version": "v3"
}
] | 2022-04-11 | [
[
"Johann",
"Patricia",
"",
"Appalachian State University"
],
[
"Ghiorzi",
"Enrico",
"",
"Appalachian State University"
],
[
"Jeffries",
"Daniel",
"",
"Appalachian State\n University"
]
] | GADTs can be represented either as their Church encodings a la Atkey, or as fixpoints a la Johann and Polonsky. While a GADT represented as its Church encoding need not support a map function satisfying the functor laws, the fixpoint representation of a GADT must support such a map function even to be well-defined. The two representations of a GADT thus need not be the same in general. This observation forces a choice of representation of data types in languages supporting GADTs. In this paper we show that choosing whether to represent data types as their Church encodings or as fixpoints determines whether or not a language supporting GADTs can have parametric models. This choice thus has important consequences for how we can program with, and reason about, these advanced data types. |
1208.0944 | Nader Ale Ebrahim | Nader Ale Ebrahim, Shamsuddin Ahmed, Zahari Taha | Establishing Virtual R&D Teams: Obliged Policy | 6th IMC (International Management Conference). Tehran, Iran 2008 | null | null | null | cs.OH | http://creativecommons.org/licenses/by/3.0/ | In a global and technology oriented world the requirements that products and
services have to fulfill are increasing and are getting more complicated.
Research and development (R&D) is becoming increasingly important in creating
the knowledge that makes research and business more competitive. Companies are
obliged to produce more rapidly, more effectively and more efficiently. In
order to meet these requirements and to secure the viability of business
processes, services and products R&D teams need to access and retrieve
information from as many sources as possible. From the other perspective
virtual teams are important mechanisms for organizations seeking to leverage
scarce resources across geographic and other boundaries moreover; virtual
collaboration has become vital for most organizations. This is particularly
true in the context of designing new product and service innovation. Such
collaboration often involves a network of partners located around the world.
However at the R&D project level, dealing with such distributed teams
challenges both managers and specialists. In new product development, it is
necessary to put together the growing different capabilities and services with
the goal, through cooperation between suppliers and customers, service
providers and scientific institutions to achieve innovations of high quality.
In this paper based on comprehensive literature review of recent articles, at
the first step provides an primary definition and characterization of virtual
R&D team; next, the potential value created by virtual R&D teams for new
product development is explored and lastly along with a guide line for future
study, it is argued that the establishing of virtual R&D teams should be given
consideration in the management of R&D projects.
| [
{
"created": "Sat, 4 Aug 2012 16:35:48 GMT",
"version": "v1"
}
] | 2012-08-07 | [
[
"Ebrahim",
"Nader Ale",
""
],
[
"Ahmed",
"Shamsuddin",
""
],
[
"Taha",
"Zahari",
""
]
] | In a global and technology oriented world the requirements that products and services have to fulfill are increasing and are getting more complicated. Research and development (R&D) is becoming increasingly important in creating the knowledge that makes research and business more competitive. Companies are obliged to produce more rapidly, more effectively and more efficiently. In order to meet these requirements and to secure the viability of business processes, services and products R&D teams need to access and retrieve information from as many sources as possible. From the other perspective virtual teams are important mechanisms for organizations seeking to leverage scarce resources across geographic and other boundaries moreover; virtual collaboration has become vital for most organizations. This is particularly true in the context of designing new product and service innovation. Such collaboration often involves a network of partners located around the world. However at the R&D project level, dealing with such distributed teams challenges both managers and specialists. In new product development, it is necessary to put together the growing different capabilities and services with the goal, through cooperation between suppliers and customers, service providers and scientific institutions to achieve innovations of high quality. In this paper based on comprehensive literature review of recent articles, at the first step provides an primary definition and characterization of virtual R&D team; next, the potential value created by virtual R&D teams for new product development is explored and lastly along with a guide line for future study, it is argued that the establishing of virtual R&D teams should be given consideration in the management of R&D projects. |
1802.00771 | Vinay Namboodiri | Shashank Sharma and Vinay P. Namboodiri | No Modes left behind: Capturing the data distribution effectively using
GANs | accepted to AAAI 2018 conference | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generative adversarial networks (GANs) while being very versatile in
realistic image synthesis, still are sensitive to the input distribution. Given
a set of data that has an imbalance in the distribution, the networks are
susceptible to missing modes and not capturing the data distribution. While
various methods have been tried to improve training of GANs, these have not
addressed the challenges of covering the full data distribution. Specifically,
a generator is not penalized for missing a mode. We show that these are
therefore still susceptible to not capturing the full data distribution.
In this paper, we propose a simple approach that combines an encoder based
objective with novel loss functions for generator and discriminator that
improves the solution in terms of capturing missing modes. We validate that the
proposed method results in substantial improvements through its detailed
analysis on toy and real datasets. The quantitative and qualitative results
demonstrate that the proposed method improves the solution for the problem of
missing modes and improves training of GANs.
| [
{
"created": "Fri, 2 Feb 2018 17:10:55 GMT",
"version": "v1"
}
] | 2018-02-05 | [
[
"Sharma",
"Shashank",
""
],
[
"Namboodiri",
"Vinay P.",
""
]
] | Generative adversarial networks (GANs) while being very versatile in realistic image synthesis, still are sensitive to the input distribution. Given a set of data that has an imbalance in the distribution, the networks are susceptible to missing modes and not capturing the data distribution. While various methods have been tried to improve training of GANs, these have not addressed the challenges of covering the full data distribution. Specifically, a generator is not penalized for missing a mode. We show that these are therefore still susceptible to not capturing the full data distribution. In this paper, we propose a simple approach that combines an encoder based objective with novel loss functions for generator and discriminator that improves the solution in terms of capturing missing modes. We validate that the proposed method results in substantial improvements through its detailed analysis on toy and real datasets. The quantitative and qualitative results demonstrate that the proposed method improves the solution for the problem of missing modes and improves training of GANs. |
2108.00320 | Stefan Konigorski | Alexander M. Zenner, Erwin B\"ottinger, Stefan Konigorski | StudyMe: A New Mobile App for User-Centric N-of-1 Trials | null | null | null | null | cs.HC cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | N-of-1 trials are multi-crossover self-experiments that allow individuals to
systematically evaluate the effect of interventions on their personal health
goals. Although several tools for N-of-1 trials exist, none support non-experts
in conducting their own user-centric trials. In this study we present StudyMe,
an open-source mobile application that is freely available from
https://play.google.com/store/apps/details?id=health.studyu.me and offers users
flexibility and guidance in configuring every component of their trials. We
also present research that informed the development of StudyMe. Through an
initial survey with 272 participants, we learned that individuals are
interested in a variety of personal health aspects and have unique ideas on how
to improve them. In an iterative, user-centered development process with
intermediate user tests we developed StudyMe that also features an educational
part to communicate N-of-1 trial concepts. A final empirical evaluation of
StudyMe showed that all participants were able to create their own trials
successfully using StudyMe and the app achieved a very good usability rating.
Our findings suggest that StudyMe provides a significant step towards enabling
individuals to apply a systematic science-oriented approach to personalize
health-related interventions and behavior modifications in their everyday
lives.
| [
{
"created": "Sat, 31 Jul 2021 20:43:36 GMT",
"version": "v1"
}
] | 2021-08-03 | [
[
"Zenner",
"Alexander M.",
""
],
[
"Böttinger",
"Erwin",
""
],
[
"Konigorski",
"Stefan",
""
]
] | N-of-1 trials are multi-crossover self-experiments that allow individuals to systematically evaluate the effect of interventions on their personal health goals. Although several tools for N-of-1 trials exist, none support non-experts in conducting their own user-centric trials. In this study we present StudyMe, an open-source mobile application that is freely available from https://play.google.com/store/apps/details?id=health.studyu.me and offers users flexibility and guidance in configuring every component of their trials. We also present research that informed the development of StudyMe. Through an initial survey with 272 participants, we learned that individuals are interested in a variety of personal health aspects and have unique ideas on how to improve them. In an iterative, user-centered development process with intermediate user tests we developed StudyMe that also features an educational part to communicate N-of-1 trial concepts. A final empirical evaluation of StudyMe showed that all participants were able to create their own trials successfully using StudyMe and the app achieved a very good usability rating. Our findings suggest that StudyMe provides a significant step towards enabling individuals to apply a systematic science-oriented approach to personalize health-related interventions and behavior modifications in their everyday lives. |
2205.02754 | Mulong Luo | Mulong Luo, G. Edward Suh | Accelerating Path Planning for Autonomous Driving with Hardware-Assisted
Memoization | null | null | null | null | cs.RO cs.AR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Path planning for autonomous driving with dynamic obstacles poses a challenge
because it needs to perform a higher-dimensional search (with time-dimension)
while still meeting real-time constraints. This paper proposes an
algorithm-hardware co-optimization approach to accelerate path planning with
high-dimensional search space. First, we reduce the time for a nearest neighbor
search and collision detection by mapping nodes and obstacles to a
lower-dimensional space and memoizing recent search results. Then, we propose a
hardware extension for efficient memoization. The experimental results on a
modern processor and a cycle-level simulator show that the hardware-assisted
memoization significantly reduces the execution time of path planning.
| [
{
"created": "Thu, 5 May 2022 16:31:14 GMT",
"version": "v1"
},
{
"created": "Fri, 27 May 2022 15:35:50 GMT",
"version": "v2"
}
] | 2022-05-30 | [
[
"Luo",
"Mulong",
""
],
[
"Suh",
"G. Edward",
""
]
] | Path planning for autonomous driving with dynamic obstacles poses a challenge because it needs to perform a higher-dimensional search (with time-dimension) while still meeting real-time constraints. This paper proposes an algorithm-hardware co-optimization approach to accelerate path planning with high-dimensional search space. First, we reduce the time for a nearest neighbor search and collision detection by mapping nodes and obstacles to a lower-dimensional space and memoizing recent search results. Then, we propose a hardware extension for efficient memoization. The experimental results on a modern processor and a cycle-level simulator show that the hardware-assisted memoization significantly reduces the execution time of path planning. |
2404.13798 | Jensen Hwa | Jensen Hwa, Qingyu Zhao, Aditya Lahiri, Adnan Masood, Babak Salimi,
Ehsan Adeli | Enforcing Conditional Independence for Fair Representation Learning and
Causal Image Generation | To appear at the 2024 IEEE CVPR Workshop on Fair, Data-Efficient, and
Trusted Computer Vision | null | null | null | cs.CV | http://creativecommons.org/licenses/by-sa/4.0/ | Conditional independence (CI) constraints are critical for defining and
evaluating fairness in machine learning, as well as for learning unconfounded
or causal representations. Traditional methods for ensuring fairness either
blindly learn invariant features with respect to a protected variable (e.g.,
race when classifying sex from face images) or enforce CI relative to the
protected attribute only on the model output (e.g., the sex label). Neither of
these methods are effective in enforcing CI in high-dimensional feature spaces.
In this paper, we focus on a nascent approach characterizing the CI constraint
in terms of two Jensen-Shannon divergence terms, and we extend it to
high-dimensional feature spaces using a novel dynamic sampling strategy. In
doing so, we introduce a new training paradigm that can be applied to any
encoder architecture. We are able to enforce conditional independence of the
diffusion autoencoder latent representation with respect to any protected
attribute under the equalized odds constraint and show that this approach
enables causal image generation with controllable latent spaces. Our
experimental results demonstrate that our approach can achieve high accuracy on
downstream tasks while upholding equality of odds.
| [
{
"created": "Sun, 21 Apr 2024 23:34:45 GMT",
"version": "v1"
}
] | 2024-04-23 | [
[
"Hwa",
"Jensen",
""
],
[
"Zhao",
"Qingyu",
""
],
[
"Lahiri",
"Aditya",
""
],
[
"Masood",
"Adnan",
""
],
[
"Salimi",
"Babak",
""
],
[
"Adeli",
"Ehsan",
""
]
] | Conditional independence (CI) constraints are critical for defining and evaluating fairness in machine learning, as well as for learning unconfounded or causal representations. Traditional methods for ensuring fairness either blindly learn invariant features with respect to a protected variable (e.g., race when classifying sex from face images) or enforce CI relative to the protected attribute only on the model output (e.g., the sex label). Neither of these methods are effective in enforcing CI in high-dimensional feature spaces. In this paper, we focus on a nascent approach characterizing the CI constraint in terms of two Jensen-Shannon divergence terms, and we extend it to high-dimensional feature spaces using a novel dynamic sampling strategy. In doing so, we introduce a new training paradigm that can be applied to any encoder architecture. We are able to enforce conditional independence of the diffusion autoencoder latent representation with respect to any protected attribute under the equalized odds constraint and show that this approach enables causal image generation with controllable latent spaces. Our experimental results demonstrate that our approach can achieve high accuracy on downstream tasks while upholding equality of odds. |
2405.12945 | Rishikesh Gajjala | Rishikesh Gajjala and Jayanth Ravi | Improved upper bounds for the Heilbronn's Problem for $k$-gons | To appear in the Canadian Conference on Computational Geometry (CCCG)
2024 | null | null | null | cs.DM cs.CG math.CO | http://creativecommons.org/licenses/by/4.0/ | The Heilbronn triangle problem asks for the placement of $n$ points in a unit
square that maximizes the smallest area of a triangle formed by any three of
those points. In $1972$, Schmidt considered a natural generalization of this
problem. He asked for the placement of $n$ points in a unit square that
maximizes the smallest area of the convex hull formed by any four of those
points. He showed a lower bound of $\Omega(n^{-3/2})$, which was improved to
$\Omega(n^{-3/2}\log{n})$ by Leffman.
A trivial upper bound of $3/n$ could be obtained, and Schmidt asked if this
could be improved asymptotically. However, despite several efforts, no
asymptotic improvement over the trivial upper bound was known for the last $50$
years, and the problem started to get the tag of being notoriously hard.
Szemer{\'e}di posed the question of whether one can, at least, improve the
constant in this trivial upper bound. In this work, we answer this question by
proving an upper bound of $2/n+o(1/n)$. We also extend our results to any
convex hulls formed by $k\geq 4$ points.
| [
{
"created": "Tue, 21 May 2024 17:17:25 GMT",
"version": "v1"
}
] | 2024-05-22 | [
[
"Gajjala",
"Rishikesh",
""
],
[
"Ravi",
"Jayanth",
""
]
] | The Heilbronn triangle problem asks for the placement of $n$ points in a unit square that maximizes the smallest area of a triangle formed by any three of those points. In $1972$, Schmidt considered a natural generalization of this problem. He asked for the placement of $n$ points in a unit square that maximizes the smallest area of the convex hull formed by any four of those points. He showed a lower bound of $\Omega(n^{-3/2})$, which was improved to $\Omega(n^{-3/2}\log{n})$ by Leffman. A trivial upper bound of $3/n$ could be obtained, and Schmidt asked if this could be improved asymptotically. However, despite several efforts, no asymptotic improvement over the trivial upper bound was known for the last $50$ years, and the problem started to get the tag of being notoriously hard. Szemer{\'e}di posed the question of whether one can, at least, improve the constant in this trivial upper bound. In this work, we answer this question by proving an upper bound of $2/n+o(1/n)$. We also extend our results to any convex hulls formed by $k\geq 4$ points. |
1904.02322 | Youshan Zhang | Youshan Zhang, Brian D. Davison | Modified Distribution Alignment for Domain Adaptation with Pre-trained
Inception ResNet | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks have been widely used in computer vision. There are
several well trained deep neural networks for the ImageNet classification
challenge, which has played a significant role in image recognition. However,
little work has explored pre-trained neural networks for image recognition in
domain adaption. In this paper, we are the first to extract better-represented
features from a pre-trained Inception ResNet model for domain adaptation. We
then present a modified distribution alignment method for classification using
the extracted features. We test our model using three benchmark datasets
(Office+Caltech-10, Office-31, and Office-Home). Extensive experiments
demonstrate significant improvements (4.8%, 5.5%, and 10%) in classification
accuracy over the state-of-the-art.
| [
{
"created": "Thu, 4 Apr 2019 03:00:24 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Apr 2019 15:04:36 GMT",
"version": "v2"
}
] | 2019-04-19 | [
[
"Zhang",
"Youshan",
""
],
[
"Davison",
"Brian D.",
""
]
] | Deep neural networks have been widely used in computer vision. There are several well trained deep neural networks for the ImageNet classification challenge, which has played a significant role in image recognition. However, little work has explored pre-trained neural networks for image recognition in domain adaption. In this paper, we are the first to extract better-represented features from a pre-trained Inception ResNet model for domain adaptation. We then present a modified distribution alignment method for classification using the extracted features. We test our model using three benchmark datasets (Office+Caltech-10, Office-31, and Office-Home). Extensive experiments demonstrate significant improvements (4.8%, 5.5%, and 10%) in classification accuracy over the state-of-the-art. |
1904.05394 | Marco Huber | Nina Schaaf, Marco F. Huber, and Johannes Maucher | Enhancing Decision Tree based Interpretation of Deep Neural Networks
through L1-Orthogonal Regularization | 8 pages, 18th IEEE International Conference on Machine Learning and
Applications (ICMLA) 2019 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One obstacle that so far prevents the introduction of machine learning models
primarily in critical areas is the lack of explainability. In this work, a
practicable approach of gaining explainability of deep artificial neural
networks (NN) using an interpretable surrogate model based on decision trees is
presented. Simply fitting a decision tree to a trained NN usually leads to
unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal
regularization during training, however, preserves the accuracy of the NN,
while it can be closely approximated by small decision trees. Tests with
different data sets confirm that L1-orthogonal regularization yields models of
lower complexity and at the same time higher fidelity compared to other
regularizers.
| [
{
"created": "Wed, 10 Apr 2019 19:11:47 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Oct 2019 19:57:24 GMT",
"version": "v2"
}
] | 2019-10-07 | [
[
"Schaaf",
"Nina",
""
],
[
"Huber",
"Marco F.",
""
],
[
"Maucher",
"Johannes",
""
]
] | One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an interpretable surrogate model based on decision trees is presented. Simply fitting a decision tree to a trained NN usually leads to unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal regularization during training, however, preserves the accuracy of the NN, while it can be closely approximated by small decision trees. Tests with different data sets confirm that L1-orthogonal regularization yields models of lower complexity and at the same time higher fidelity compared to other regularizers. |
1706.06810 | Jongpil Lee | Jongpil Lee, Juhan Nam | Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep
Convolutional Neural Networks for Music Classification | ICML Music Discovery Workshop 2017 | null | null | null | cs.SD cs.LG cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Music tag words that describe music audio by text have different levels of
abstraction. Taking this issue into account, we propose a music classification
approach that aggregates multi-level and multi-scale features using pre-trained
feature extractors. In particular, the feature extractors are trained in
sample-level deep convolutional neural networks using raw waveforms. We show
that this approach achieves state-of-the-art results on several music
classification datasets.
| [
{
"created": "Wed, 21 Jun 2017 09:57:24 GMT",
"version": "v1"
}
] | 2017-06-22 | [
[
"Lee",
"Jongpil",
""
],
[
"Nam",
"Juhan",
""
]
] | Music tag words that describe music audio by text have different levels of abstraction. Taking this issue into account, we propose a music classification approach that aggregates multi-level and multi-scale features using pre-trained feature extractors. In particular, the feature extractors are trained in sample-level deep convolutional neural networks using raw waveforms. We show that this approach achieves state-of-the-art results on several music classification datasets. |
1108.6123 | Aleksandar Nikolov | Jean Bolot, Nadia Fawaz, S. Muthukrishnan, Aleksandar Nikolov, Nina
Taft | Private Decayed Sum Estimation under Continual Observation | null | null | 10.1145/2448496.2448530 | null | cs.DS cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In monitoring applications, recent data is more important than distant data.
How does this affect privacy of data analysis? We study a general class of data
analyses - computing predicate sums - with privacy. Formally, we study the
problem of estimating predicate sums {\em privately}, for sliding windows (and
other well-known decay models of data, i.e. exponential and polynomial decay).
We extend the recently proposed continual privacy model of Dwork et al.
We present algorithms for decayed sum which are $\eps$-differentially
private, and are accurate. For window and exponential decay sums, our
algorithms are accurate up to additive $1/\eps$ and polylog terms in the range
of the computed function; for polynomial decay sums which are technically more
challenging because partial solutions do not compose easily, our algorithms
incur additional relative error. Further, we show lower bounds, tight within
polylog factors and tight with respect to the dependence on the probability of
error.
| [
{
"created": "Wed, 31 Aug 2011 03:56:50 GMT",
"version": "v1"
},
{
"created": "Sat, 3 Mar 2012 01:06:44 GMT",
"version": "v2"
}
] | 2013-08-05 | [
[
"Bolot",
"Jean",
""
],
[
"Fawaz",
"Nadia",
""
],
[
"Muthukrishnan",
"S.",
""
],
[
"Nikolov",
"Aleksandar",
""
],
[
"Taft",
"Nina",
""
]
] | In monitoring applications, recent data is more important than distant data. How does this affect privacy of data analysis? We study a general class of data analyses - computing predicate sums - with privacy. Formally, we study the problem of estimating predicate sums {\em privately}, for sliding windows (and other well-known decay models of data, i.e. exponential and polynomial decay). We extend the recently proposed continual privacy model of Dwork et al. We present algorithms for decayed sum which are $\eps$-differentially private, and are accurate. For window and exponential decay sums, our algorithms are accurate up to additive $1/\eps$ and polylog terms in the range of the computed function; for polynomial decay sums which are technically more challenging because partial solutions do not compose easily, our algorithms incur additional relative error. Further, we show lower bounds, tight within polylog factors and tight with respect to the dependence on the probability of error. |
2407.13490 | Alexandre Bonlarron | Florian R\'egin, Elisabetta De Maria and Alexandre Bonlarron | Combining Constraint Programming Reasoning with Large Language Model
Predictions | To appear at The 30th International Conference on Principles and
Practice of Constraint Programming (CP 2024) | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Constraint Programming (CP) and Machine Learning (ML) face challenges in text
generation due to CP's struggle with implementing "meaning'' and ML's
difficulty with structural constraints. This paper proposes a solution by
combining both approaches and embedding a Large Language Model (LLM) in CP. The
LLM handles word generation and meaning, while CP manages structural
constraints. This approach builds on GenCP, an improved version of On-the-fly
Constraint Programming Search (OTFS) using LLM-generated domains. Compared to
Beam Search (BS), a standard NLP method, this combined approach (GenCP with
LLM) is faster and produces better results, ensuring all constraints are
satisfied. This fusion of CP and ML presents new possibilities for enhancing
text generation under constraints.
| [
{
"created": "Thu, 18 Jul 2024 13:15:55 GMT",
"version": "v1"
}
] | 2024-07-19 | [
[
"Régin",
"Florian",
""
],
[
"De Maria",
"Elisabetta",
""
],
[
"Bonlarron",
"Alexandre",
""
]
] | Constraint Programming (CP) and Machine Learning (ML) face challenges in text generation due to CP's struggle with implementing "meaning'' and ML's difficulty with structural constraints. This paper proposes a solution by combining both approaches and embedding a Large Language Model (LLM) in CP. The LLM handles word generation and meaning, while CP manages structural constraints. This approach builds on GenCP, an improved version of On-the-fly Constraint Programming Search (OTFS) using LLM-generated domains. Compared to Beam Search (BS), a standard NLP method, this combined approach (GenCP with LLM) is faster and produces better results, ensuring all constraints are satisfied. This fusion of CP and ML presents new possibilities for enhancing text generation under constraints. |
1602.02698 | Yehia Elkhatib PhD | Yehia Elkhatib | Defining Cross-Cloud Systems | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent years have seen an increasing number of cross-cloud architectures,
i.e. systems that span across cloud provisioning boundaries. However, the cloud
computing world still lacks any standards in terms of programming interfaces,
which has a knock-on effect on the costs associated with interoperability and
severely limits the flexibility and portability of applications and virtual
infrastructures. This paper outlines the different types of cross-cloud
systems, and the associated design decisions.
| [
{
"created": "Mon, 8 Feb 2016 19:13:32 GMT",
"version": "v1"
}
] | 2016-02-09 | [
[
"Elkhatib",
"Yehia",
""
]
] | Recent years have seen an increasing number of cross-cloud architectures, i.e. systems that span across cloud provisioning boundaries. However, the cloud computing world still lacks any standards in terms of programming interfaces, which has a knock-on effect on the costs associated with interoperability and severely limits the flexibility and portability of applications and virtual infrastructures. This paper outlines the different types of cross-cloud systems, and the associated design decisions. |
2105.05517 | Nicky Williams | Nicky Williams (LSL) | Towards exhaustive branch coverage with PathCrawler | null | 2nd ACM/IEEE International Conference on Automation of Software
Test AST 2021, May 2021, Madrid, Spain | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Branch coverage of source code is a very widely used test criterion.
Moreover, branch coverage is a similar problem to line coverage, MC/DC and the
coverage of assertion violations, certain runtime errors and various other
types of test objective. Indeed, establishing that a large number of test
objectives are unreachable, or conversely, providing the test inputs which
reach them, is at the heart of many verification tasks. However, automatic test
generation for exhaustive branch coverage remains an elusive goal: many modern
tools obtain high coverage scores without being able to provide an explanation
for why some branches are not covered, such as a demonstration that they are
unreachable. Concolic test generation offers the promise of exhaustive coverage
but covers paths more efficiently than branches. In this paper, I explain why,
and propose different strategies to improve its performance on exhaustive
branch coverage. A comparison of these strategies on examples of real code
shows promising results.
| [
{
"created": "Wed, 12 May 2021 08:55:13 GMT",
"version": "v1"
}
] | 2021-05-13 | [
[
"Williams",
"Nicky",
"",
"LSL"
]
] | Branch coverage of source code is a very widely used test criterion. Moreover, branch coverage is a similar problem to line coverage, MC/DC and the coverage of assertion violations, certain runtime errors and various other types of test objective. Indeed, establishing that a large number of test objectives are unreachable, or conversely, providing the test inputs which reach them, is at the heart of many verification tasks. However, automatic test generation for exhaustive branch coverage remains an elusive goal: many modern tools obtain high coverage scores without being able to provide an explanation for why some branches are not covered, such as a demonstration that they are unreachable. Concolic test generation offers the promise of exhaustive coverage but covers paths more efficiently than branches. In this paper, I explain why, and propose different strategies to improve its performance on exhaustive branch coverage. A comparison of these strategies on examples of real code shows promising results. |
2209.09729 | Andr\'as Kov\'acs | Andr\'as Kov\'acs | Staged Compilation with Two-Level Type Theory | null | null | 10.1145/3547641 | null | cs.PL cs.LO | http://creativecommons.org/licenses/by/4.0/ | The aim of staged compilation is to enable metaprogramming in a way such that
we have guarantees about the well-formedness of code output, and we can also
mix together object-level and meta-level code in a concise and convenient
manner. In this work, we observe that two-level type theory (2LTT), a system
originally devised for the purpose of developing synthetic homotopy theory,
also serves as a system for staged compilation with dependent types. 2LTT has
numerous good properties for this use case: it has a concise specification,
well-behaved model theory, and it supports a wide range of language features
both at the object and the meta level. First, we give an overview of 2LTT's
features and applications in staging. Then, we present a staging algorithm and
prove its correctness. Our algorithm is "staging-by-evaluation", analogously to
the technique of normalization-by-evaluation, in that staging is given by the
evaluation of 2LTT syntax in a semantic domain. The staging algorithm together
with its correctness constitutes a proof of strong conservativity of 2LLT over
the object theory. To our knowledge, this is the first description of staged
compilation which supports full dependent types and unrestricted staging for
types.
| [
{
"created": "Tue, 20 Sep 2022 14:00:15 GMT",
"version": "v1"
}
] | 2022-09-21 | [
[
"Kovács",
"András",
""
]
] | The aim of staged compilation is to enable metaprogramming in a way such that we have guarantees about the well-formedness of code output, and we can also mix together object-level and meta-level code in a concise and convenient manner. In this work, we observe that two-level type theory (2LTT), a system originally devised for the purpose of developing synthetic homotopy theory, also serves as a system for staged compilation with dependent types. 2LTT has numerous good properties for this use case: it has a concise specification, well-behaved model theory, and it supports a wide range of language features both at the object and the meta level. First, we give an overview of 2LTT's features and applications in staging. Then, we present a staging algorithm and prove its correctness. Our algorithm is "staging-by-evaluation", analogously to the technique of normalization-by-evaluation, in that staging is given by the evaluation of 2LTT syntax in a semantic domain. The staging algorithm together with its correctness constitutes a proof of strong conservativity of 2LLT over the object theory. To our knowledge, this is the first description of staged compilation which supports full dependent types and unrestricted staging for types. |
2311.01279 | Samie Mostafavi | Samie Mostafavi, Vishnu Narayanan Moothedath, Stefan R\"onngren,
Neelabhro Roy, Gourav Prateek Sharma, Sangwon Seo, Manuel Olgu\'in Mu\~noz,
James Gross | ExPECA: An Experimental Platform for Trustworthy Edge Computing
Applications | null | null | 10.1145/3583740.3626819 | null | cs.NI eess.SP | http://creativecommons.org/licenses/by/4.0/ | This paper presents ExPECA, an edge computing and wireless communication
research testbed designed to tackle two pressing challenges: comprehensive
end-to-end experimentation and high levels of experimental reproducibility.
Leveraging OpenStack-based Chameleon Infrastructure (CHI) framework for its
proven flexibility and ease of operation, ExPECA is located in a unique,
isolated underground facility, providing a highly controlled setting for
wireless experiments. The testbed is engineered to facilitate integrated
studies of both communication and computation, offering a diverse array of
Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and
wired links, as well as containerized computational environments. We exemplify
the experimental possibilities of the testbed using OpenRTiST, a
latency-sensitive, bandwidth-intensive application, and analyze its
performance. Lastly, we highlight an array of research domains and experimental
setups that stand to gain from ExPECA's features, including closed-loop
applications and time-sensitive networking.
| [
{
"created": "Thu, 2 Nov 2023 14:50:01 GMT",
"version": "v1"
}
] | 2023-11-03 | [
[
"Mostafavi",
"Samie",
""
],
[
"Moothedath",
"Vishnu Narayanan",
""
],
[
"Rönngren",
"Stefan",
""
],
[
"Roy",
"Neelabhro",
""
],
[
"Sharma",
"Gourav Prateek",
""
],
[
"Seo",
"Sangwon",
""
],
[
"Muñoz",
"Manuel Olguín",
""
],
[
"Gross",
"James",
""
]
] | This paper presents ExPECA, an edge computing and wireless communication research testbed designed to tackle two pressing challenges: comprehensive end-to-end experimentation and high levels of experimental reproducibility. Leveraging OpenStack-based Chameleon Infrastructure (CHI) framework for its proven flexibility and ease of operation, ExPECA is located in a unique, isolated underground facility, providing a highly controlled setting for wireless experiments. The testbed is engineered to facilitate integrated studies of both communication and computation, offering a diverse array of Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and wired links, as well as containerized computational environments. We exemplify the experimental possibilities of the testbed using OpenRTiST, a latency-sensitive, bandwidth-intensive application, and analyze its performance. Lastly, we highlight an array of research domains and experimental setups that stand to gain from ExPECA's features, including closed-loop applications and time-sensitive networking. |
2103.11520 | Gabriel Bertocco | Gabriel Bertocco and Fernanda Andal\'o and Anderson Rocha | Unsupervised and self-adaptative techniques for cross-domain person
re-identification | Published on IEEE Transactions on Information Forensics and Security | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Person Re-Identification (ReID) across non-overlapping cameras is a
challenging task and, for this reason, most works in the prior art rely on
supervised feature learning from a labeled dataset to match the same person in
different views. However, it demands the time-consuming task of labeling the
acquired data, prohibiting its fast deployment, specially in forensic
scenarios. Unsupervised Domain Adaptation (UDA) emerges as a promising
alternative, as it performs feature-learning adaptation from a model trained on
a source to a target domain without identity-label annotation. However, most
UDA-based algorithms rely upon a complex loss function with several
hyper-parameters, which hinders the generalization to different scenarios.
Moreover, as UDA depends on the translation between domains, it is important to
select the most reliable data from the unseen domain, thus avoiding error
propagation caused by noisy examples on the target data -- an often overlooked
problem. In this sense, we propose a novel UDA-based ReID method that optimizes
a simple loss function with only one hyper-parameter and that takes advantage
of triplets of samples created by a new offline strategy based on the diversity
of cameras within a cluster. This new strategy adapts the model and also
regularizes it, avoiding overfitting on the target domain. We also introduce a
new self-ensembling strategy, in which weights from different iterations are
aggregated to create a final model combining knowledge from distinct moments of
the adaptation. For evaluation, we consider three well-known deep learning
architectures and combine them for final decision-making. The proposed method
does not use person re-ranking nor any label on the target domain, and
outperforms the state of the art, with a much simpler setup, on the Market to
Duke, the challenging Market1501 to MSMT17, and Duke to MSMT17 adaptation
scenarios.
| [
{
"created": "Sun, 21 Mar 2021 23:58:39 GMT",
"version": "v1"
},
{
"created": "Fri, 26 Mar 2021 18:22:33 GMT",
"version": "v2"
},
{
"created": "Mon, 7 Feb 2022 13:29:38 GMT",
"version": "v3"
}
] | 2022-02-08 | [
[
"Bertocco",
"Gabriel",
""
],
[
"Andaló",
"Fernanda",
""
],
[
"Rocha",
"Anderson",
""
]
] | Person Re-Identification (ReID) across non-overlapping cameras is a challenging task and, for this reason, most works in the prior art rely on supervised feature learning from a labeled dataset to match the same person in different views. However, it demands the time-consuming task of labeling the acquired data, prohibiting its fast deployment, specially in forensic scenarios. Unsupervised Domain Adaptation (UDA) emerges as a promising alternative, as it performs feature-learning adaptation from a model trained on a source to a target domain without identity-label annotation. However, most UDA-based algorithms rely upon a complex loss function with several hyper-parameters, which hinders the generalization to different scenarios. Moreover, as UDA depends on the translation between domains, it is important to select the most reliable data from the unseen domain, thus avoiding error propagation caused by noisy examples on the target data -- an often overlooked problem. In this sense, we propose a novel UDA-based ReID method that optimizes a simple loss function with only one hyper-parameter and that takes advantage of triplets of samples created by a new offline strategy based on the diversity of cameras within a cluster. This new strategy adapts the model and also regularizes it, avoiding overfitting on the target domain. We also introduce a new self-ensembling strategy, in which weights from different iterations are aggregated to create a final model combining knowledge from distinct moments of the adaptation. For evaluation, we consider three well-known deep learning architectures and combine them for final decision-making. The proposed method does not use person re-ranking nor any label on the target domain, and outperforms the state of the art, with a much simpler setup, on the Market to Duke, the challenging Market1501 to MSMT17, and Duke to MSMT17 adaptation scenarios. |
1107.5743 | Siddhartha Jonnalagadda | Siddhartha Jonnalagadda, Philip Topham | NEMO: Extraction and normalization of organization names from PubMed
affiliation strings | null | Siddhartha Jonnalagadda, Philip Topham. NEMO: Extraction and
normalization of organization names from PubMed affiliation strings. Journal
of Biomedical Discovery and Collaboration, 2010 Oct 4;5:50-75 | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose NEMO, a system for extracting organization names in the
affiliation and normalizing them to a canonical organization name. Our parsing
process involves multi-layered rule matching with multiple dictionaries. The
system achieves more than 98% f-score in extracting organization names. Our
process of normalization that involves clustering based on local sequence
alignment metrics and local learning based on finding connected components. A
high precision was also observed in normalization. NEMO is the missing link in
associating each biomedical paper and its authors to an organization name in
its canonical form and the Geopolitical location of the organization. This
research could potentially help in analyzing large social networks of
organizations for landscaping a particular topic, improving performance of
author disambiguation, adding weak links in the co-author network of authors,
augmenting NLM's MARS system for correcting errors in OCR output of affiliation
field, and automatically indexing the PubMed citations with the normalized
organization name and country. Our system is available as a graphical user
interface available for download along with this paper.
| [
{
"created": "Thu, 28 Jul 2011 15:37:56 GMT",
"version": "v1"
}
] | 2011-07-29 | [
[
"Jonnalagadda",
"Siddhartha",
""
],
[
"Topham",
"Philip",
""
]
] | We propose NEMO, a system for extracting organization names in the affiliation and normalizing them to a canonical organization name. Our parsing process involves multi-layered rule matching with multiple dictionaries. The system achieves more than 98% f-score in extracting organization names. Our process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. A high precision was also observed in normalization. NEMO is the missing link in associating each biomedical paper and its authors to an organization name in its canonical form and the Geopolitical location of the organization. This research could potentially help in analyzing large social networks of organizations for landscaping a particular topic, improving performance of author disambiguation, adding weak links in the co-author network of authors, augmenting NLM's MARS system for correcting errors in OCR output of affiliation field, and automatically indexing the PubMed citations with the normalized organization name and country. Our system is available as a graphical user interface available for download along with this paper. |
1807.01079 | Anna Latour | Anna L.D. Latour, Behrouz Babaki, Siegfried Nijssen | Stochastic Constraint Optimization using Propagation on Ordered Binary
Decision Diagrams | Eighth International Workshop on Statistical Relational AI, in
conjunction with the 2018 International Joint Conference on Artificial
Intelligence (IJCAI 2018) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A number of problems in relational Artificial Intelligence can be viewed as
Stochastic Constraint Optimization Problems (SCOPs). These are constraint
optimization problems that involve objectives or constraints with a stochastic
component. Building on the recently proposed language SC-ProbLog for modeling
SCOPs, we propose a new method for solving these problems. Earlier methods used
Probabilistic Logic Programming (PLP) techniques to create Ordered Binary
Decision Diagrams (OBDDs), which were decomposed into smaller constraints in
order to exploit existing constraint programming (CP) solvers. We argue that
this approach has as drawback that a decomposed representation of an OBDD does
not guarantee domain consistency during search, and hence limits the efficiency
of the solver. For the specific case of monotonic distributions, we suggest an
alternative method for using CP in SCOP, based on the development of a new
propagator; we show that this propagator is linear in the size of the OBDD, and
has the potential to be more efficient than the decomposition method, as it
maintains domain consistency.
| [
{
"created": "Tue, 3 Jul 2018 10:58:38 GMT",
"version": "v1"
}
] | 2018-07-04 | [
[
"Latour",
"Anna L. D.",
""
],
[
"Babaki",
"Behrouz",
""
],
[
"Nijssen",
"Siegfried",
""
]
] | A number of problems in relational Artificial Intelligence can be viewed as Stochastic Constraint Optimization Problems (SCOPs). These are constraint optimization problems that involve objectives or constraints with a stochastic component. Building on the recently proposed language SC-ProbLog for modeling SCOPs, we propose a new method for solving these problems. Earlier methods used Probabilistic Logic Programming (PLP) techniques to create Ordered Binary Decision Diagrams (OBDDs), which were decomposed into smaller constraints in order to exploit existing constraint programming (CP) solvers. We argue that this approach has as drawback that a decomposed representation of an OBDD does not guarantee domain consistency during search, and hence limits the efficiency of the solver. For the specific case of monotonic distributions, we suggest an alternative method for using CP in SCOP, based on the development of a new propagator; we show that this propagator is linear in the size of the OBDD, and has the potential to be more efficient than the decomposition method, as it maintains domain consistency. |
1711.00913 | Mohit Dubey | Mohit Dubey, Garrett Kenyon, Nils Carlson, Austin Thresher | Does Phase Matter For Monaural Source Separation? | 4 pages, 2 figures, NIPS format | null | null | null | cs.SD cs.NE eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The "cocktail party" problem of fully separating multiple sources from a
single channel audio waveform remains unsolved. Current biological
understanding of neural encoding suggests that phase information is preserved
and utilized at every stage of the auditory pathway. However, current
computational approaches primarily discard phase information in order to mask
amplitude spectrograms of sound. In this paper, we seek to address whether
preserving phase information in spectral representations of sound provides
better results in monaural separation of vocals from a musical track by using a
neurally plausible sparse generative model. Our results demonstrate that
preserving phase information reduces artifacts in the separated tracks, as
quantified by the signal to artifact ratio (GSAR). Furthermore, our proposed
method achieves state-of-the-art performance for source separation, as
quantified by a mean signal to interference ratio (GSIR) of 19.46.
| [
{
"created": "Thu, 2 Nov 2017 20:10:00 GMT",
"version": "v1"
}
] | 2017-11-06 | [
[
"Dubey",
"Mohit",
""
],
[
"Kenyon",
"Garrett",
""
],
[
"Carlson",
"Nils",
""
],
[
"Thresher",
"Austin",
""
]
] | The "cocktail party" problem of fully separating multiple sources from a single channel audio waveform remains unsolved. Current biological understanding of neural encoding suggests that phase information is preserved and utilized at every stage of the auditory pathway. However, current computational approaches primarily discard phase information in order to mask amplitude spectrograms of sound. In this paper, we seek to address whether preserving phase information in spectral representations of sound provides better results in monaural separation of vocals from a musical track by using a neurally plausible sparse generative model. Our results demonstrate that preserving phase information reduces artifacts in the separated tracks, as quantified by the signal to artifact ratio (GSAR). Furthermore, our proposed method achieves state-of-the-art performance for source separation, as quantified by a mean signal to interference ratio (GSIR) of 19.46. |
2401.02180 | Ivo Sbalzarini | Johannes Pahlke, Ivo F. Sbalzarini | Proven Distributed Memory Parallelization of Particle Methods | 40 pages, 4 figures | null | null | null | cs.DC cs.DS cs.SE | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We provide a mathematically proven parallelization scheme for particle
methods on distributed-memory computer systems. Particle methods are a
versatile and widely used class of algorithms for computer simulations and
numerical predictions in various applications, ranging from continuum fluid
dynamics and granular flows, using methods such as Smoothed Particle
Hydrodynamics (SPH) and Discrete Element Methods (DEM) to Molecular Dynamics
(MD) simulations in molecular modeling. Particle methods naturally lend
themselves to implementation on parallel-computing hardware. So far, however, a
mathematical proof of correctness and equivalence to sequential implementations
was only available for shared-memory parallelism. Here, we leverage a formal
definition of the algorithmic class of particle methods to provide a proven
parallelization scheme for distributed-memory computers. We prove that these
parallelized particle methods on distributed memory computers are formally
equivalent to their sequential counterpart for a well-defined class of particle
methods. Notably, the here analyzed parallelization scheme is well-known and
commonly used. Our analysis is, therefore, of immediate practical relevance to
existing and new parallel software implementations of particle methods and
places them on solid theoretical grounds.
| [
{
"created": "Thu, 4 Jan 2024 10:22:26 GMT",
"version": "v1"
}
] | 2024-01-05 | [
[
"Pahlke",
"Johannes",
""
],
[
"Sbalzarini",
"Ivo F.",
""
]
] | We provide a mathematically proven parallelization scheme for particle methods on distributed-memory computer systems. Particle methods are a versatile and widely used class of algorithms for computer simulations and numerical predictions in various applications, ranging from continuum fluid dynamics and granular flows, using methods such as Smoothed Particle Hydrodynamics (SPH) and Discrete Element Methods (DEM) to Molecular Dynamics (MD) simulations in molecular modeling. Particle methods naturally lend themselves to implementation on parallel-computing hardware. So far, however, a mathematical proof of correctness and equivalence to sequential implementations was only available for shared-memory parallelism. Here, we leverage a formal definition of the algorithmic class of particle methods to provide a proven parallelization scheme for distributed-memory computers. We prove that these parallelized particle methods on distributed memory computers are formally equivalent to their sequential counterpart for a well-defined class of particle methods. Notably, the here analyzed parallelization scheme is well-known and commonly used. Our analysis is, therefore, of immediate practical relevance to existing and new parallel software implementations of particle methods and places them on solid theoretical grounds. |
1008.1043 | Sergiy Vorobyov A. | Zengmao Chen, Cheng-Xiang Wang, Xuemin Hong, John Thompson, Sergiy A.
Vorobyov, Xiaohu Ge, Hailin Xiao, and Feng Zhao | Aggregate Interference Modeling in Cognitive Radio Networks with Power
and Contention Control | 24 pages, 8 figures, submitted to IEEE Trans. Communications in July
2010 | Z. Chen, C.-X. Wang, S.A. Vorobyov, and et al, "Aggregate
interference modeling in cognitive radio networks with power and contention
control," IEEE Trans. Communications, vol. 60, no. 2, pp. 456-468, Feb. 2012 | 10.1109/TCOMM.2011.012012.100426 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present an interference model for cognitive radio (CR)
networks employing power control, contention control or hybrid power/contention
control schemes. For the first case, a power control scheme is proposed to
govern the transmission power of a CR node. For the second one, a contention
control scheme at the media access control (MAC) layer, based on carrier sense
multiple access with collision avoidance (CSMA/CA), is proposed to coordinate
the operation of CR nodes with transmission requests. The probability density
functions of the interference received at a primary receiver from a CR network
are first derived numerically for these two cases. For the hybrid case, where
power and contention controls are jointly adopted by a CR node to govern its
transmission, the interference is analyzed and compared with that of the first
two schemes by simulations. Then, the interference distributions under the
first two control schemes are fitted by log-normal distributions with greatly
reduced complexity. Moreover, the effect of a hidden primary receiver on the
interference experienced at the receiver is investigated. It is demonstrated
that both power and contention controls are effective approaches to alleviate
the interference caused by CR networks. Some in-depth analysis of the impact of
key parameters on the interference of CR networks is given via numerical
studies as well.
| [
{
"created": "Thu, 5 Aug 2010 19:20:21 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Chen",
"Zengmao",
""
],
[
"Wang",
"Cheng-Xiang",
""
],
[
"Hong",
"Xuemin",
""
],
[
"Thompson",
"John",
""
],
[
"Vorobyov",
"Sergiy A.",
""
],
[
"Ge",
"Xiaohu",
""
],
[
"Xiao",
"Hailin",
""
],
[
"Zhao",
"Feng",
""
]
] | In this paper, we present an interference model for cognitive radio (CR) networks employing power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference distributions under the first two control schemes are fitted by log-normal distributions with greatly reduced complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given via numerical studies as well. |
2210.01987 | Artem Vysogorets | Dhrupad Bhardwaj, Julia Kempe, Artem Vysogorets, Angela M. Teng, and
Evaristus C. Ezekwem | ImpressLearn: Continual Learning via Combined Task Impressions | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | This work proposes a new method to sequentially train deep neural networks on
multiple tasks without suffering catastrophic forgetting, while endowing it
with the capability to quickly adapt to unseen tasks. Starting from existing
work on network masking (Wortsman et al., 2020), we show that simply learning a
linear combination of a small number of task-specific supermasks (impressions)
on a randomly initialized backbone network is sufficient to both retain
accuracy on previously learned tasks, as well as achieve high accuracy on
unseen tasks. In contrast to previous methods, we do not require to generate
dedicated masks or contexts for each new task, instead leveraging transfer
learning to keep per-task parameter overhead small. Our work illustrates the
power of linearly combining individual impressions, each of which fares poorly
in isolation, to achieve performance comparable to a dedicated mask. Moreover,
even repeated impressions from the same task (homogeneous masks), when
combined, can approach the performance of heterogeneous combinations if
sufficiently many impressions are used. Our approach scales more efficiently
than existing methods, often requiring orders of magnitude fewer parameters and
can function without modification even when task identity is missing. In
addition, in the setting where task labels are not given at inference, our
algorithm gives an often favorable alternative to the one-shot procedure used
by Wortsman et al., 2020. We evaluate our method on a number of well-known
image classification datasets and network architectures.
| [
{
"created": "Wed, 5 Oct 2022 02:28:25 GMT",
"version": "v1"
},
{
"created": "Tue, 31 Jan 2023 19:52:37 GMT",
"version": "v2"
}
] | 2023-02-02 | [
[
"Bhardwaj",
"Dhrupad",
""
],
[
"Kempe",
"Julia",
""
],
[
"Vysogorets",
"Artem",
""
],
[
"Teng",
"Angela M.",
""
],
[
"Ezekwem",
"Evaristus C.",
""
]
] | This work proposes a new method to sequentially train deep neural networks on multiple tasks without suffering catastrophic forgetting, while endowing it with the capability to quickly adapt to unseen tasks. Starting from existing work on network masking (Wortsman et al., 2020), we show that simply learning a linear combination of a small number of task-specific supermasks (impressions) on a randomly initialized backbone network is sufficient to both retain accuracy on previously learned tasks, as well as achieve high accuracy on unseen tasks. In contrast to previous methods, we do not require to generate dedicated masks or contexts for each new task, instead leveraging transfer learning to keep per-task parameter overhead small. Our work illustrates the power of linearly combining individual impressions, each of which fares poorly in isolation, to achieve performance comparable to a dedicated mask. Moreover, even repeated impressions from the same task (homogeneous masks), when combined, can approach the performance of heterogeneous combinations if sufficiently many impressions are used. Our approach scales more efficiently than existing methods, often requiring orders of magnitude fewer parameters and can function without modification even when task identity is missing. In addition, in the setting where task labels are not given at inference, our algorithm gives an often favorable alternative to the one-shot procedure used by Wortsman et al., 2020. We evaluate our method on a number of well-known image classification datasets and network architectures. |
1811.11660 | Michiel de Bondt | Michiel de Bondt | A short and elegant proof of a theorem of J.-E. Pin | 11 pages, major update with new proof | null | null | null | cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We give a short proof of a theorem of J.-E. Pin (theorem 1.1 below), which
can be found in his thesis. The part of the proof which is my own (not Pin's)
is a complete replacement of the same part in an earlier version of this paper.
| [
{
"created": "Wed, 28 Nov 2018 16:36:15 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Sep 2022 11:52:28 GMT",
"version": "v2"
},
{
"created": "Thu, 15 Sep 2022 11:44:46 GMT",
"version": "v3"
}
] | 2022-09-16 | [
[
"de Bondt",
"Michiel",
""
]
] | We give a short proof of a theorem of J.-E. Pin (theorem 1.1 below), which can be found in his thesis. The part of the proof which is my own (not Pin's) is a complete replacement of the same part in an earlier version of this paper. |
2407.05246 | Yuxuan Yan | Yuxuan Yan, Na Lu, Ruofan Yan | Deep Online Probability Aggregation Clustering | 19 pages,2 figures, conference | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Combining machine clustering with deep models has shown remarkable
superiority in deep clustering. It modifies the data processing pipeline into
two alternating phases: feature clustering and model training. However, such
alternating schedule may lead to instability and computational burden issues.
We propose a centerless clustering algorithm called Probability Aggregation
Clustering (PAC) to proactively adapt deep learning technologies, enabling easy
deployment in online deep clustering. PAC circumvents the cluster center and
aligns the probability space and distribution space by formulating clustering
as an optimization problem with a novel objective function. Based on the
computation mechanism of the PAC, we propose a general online probability
aggregation module to perform stable and flexible feature clustering over
mini-batch data and further construct a deep visual clustering framework deep
PAC (DPAC). Extensive experiments demonstrate that PAC has superior clustering
robustness and performance and DPAC remarkably outperforms the state-of-the-art
deep clustering methods.
| [
{
"created": "Sun, 7 Jul 2024 03:31:00 GMT",
"version": "v1"
},
{
"created": "Sat, 13 Jul 2024 06:58:10 GMT",
"version": "v2"
}
] | 2024-07-16 | [
[
"Yan",
"Yuxuan",
""
],
[
"Lu",
"Na",
""
],
[
"Yan",
"Ruofan",
""
]
] | Combining machine clustering with deep models has shown remarkable superiority in deep clustering. It modifies the data processing pipeline into two alternating phases: feature clustering and model training. However, such alternating schedule may lead to instability and computational burden issues. We propose a centerless clustering algorithm called Probability Aggregation Clustering (PAC) to proactively adapt deep learning technologies, enabling easy deployment in online deep clustering. PAC circumvents the cluster center and aligns the probability space and distribution space by formulating clustering as an optimization problem with a novel objective function. Based on the computation mechanism of the PAC, we propose a general online probability aggregation module to perform stable and flexible feature clustering over mini-batch data and further construct a deep visual clustering framework deep PAC (DPAC). Extensive experiments demonstrate that PAC has superior clustering robustness and performance and DPAC remarkably outperforms the state-of-the-art deep clustering methods. |
1811.12240 | Geoffrey Goodell | Geoff Goodell and Tomaso Aste | Can Cryptocurrencies Preserve Privacy and Comply with Regulations? | 20 pages, 10 figures, 3 tables | null | 10.3389/fbloc.2019.00004 | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cryptocurrencies offer an alternative to traditional methods of electronic
value exchange, promising anonymous, cash-like electronic transfers, but in
practice they fall short for several key reasons. We consider the false choice
between total surveillance, as represented by banking as currently implemented
by institutions, and impenetrable lawlessness, as represented by
privacy-enhancing cryptocurrencies as currently deployed. We identify a range
of alternatives between those two extremes, and we consider two potential
compromise approaches that offer both the auditability required for regulators
and the anonymity required for users.
| [
{
"created": "Thu, 29 Nov 2018 15:21:07 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Mar 2019 16:34:47 GMT",
"version": "v2"
},
{
"created": "Tue, 7 May 2019 13:56:05 GMT",
"version": "v3"
}
] | 2019-06-05 | [
[
"Goodell",
"Geoff",
""
],
[
"Aste",
"Tomaso",
""
]
] | Cryptocurrencies offer an alternative to traditional methods of electronic value exchange, promising anonymous, cash-like electronic transfers, but in practice they fall short for several key reasons. We consider the false choice between total surveillance, as represented by banking as currently implemented by institutions, and impenetrable lawlessness, as represented by privacy-enhancing cryptocurrencies as currently deployed. We identify a range of alternatives between those two extremes, and we consider two potential compromise approaches that offer both the auditability required for regulators and the anonymity required for users. |
1711.02026 | Arman Shojaeifard | Arman Shojaeifard, Kai-Kit Wong, Wei Yu, Gan Zheng, Jie Tang | Full-Duplex Cloud Radio Access Network: Stochastic Design and Analysis | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Full-duplex (FD) has emerged as a disruptive communications paradigm for
enhancing the achievable spectral efficiency (SE), thanks to the recent major
breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex
(HD) SE gain, in cellular networks, is however largely limited by the
mutual-interference (MI) between the downlink (DL) and the uplink (UL). A
potential remedy for tackling the MI bottleneck is through cooperative
communications. This paper provides a stochastic design and analysis of FD
enabled cloud radio access network (C-RAN) under the Poisson point process
(PPP)-based abstraction model of multi-antenna radio units (RUs) and user
equipments (UEs). We consider different disjoint and user-centric approaches
towards the formation of finite clusters in the C-RAN. Contrary to most
existing studies, we explicitly take into consideration non-isotropic fading
channel conditions and finite-capacity fronthaul links. Accordingly,
upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics
of all intended and interfering signals, are derived. The performance of the FD
C-RAN is investigated through the proposed theoretical framework and
Monte-Carlo (MC) simulations. The results indicate that significant FD versus
HD C-RAN SE gains can be achieved, particularly in the presence of
sufficient-capacity fronthaul links and advanced interference cancellation
capabilities.
| [
{
"created": "Mon, 6 Nov 2017 17:32:13 GMT",
"version": "v1"
}
] | 2017-11-07 | [
[
"Shojaeifard",
"Arman",
""
],
[
"Wong",
"Kai-Kit",
""
],
[
"Yu",
"Wei",
""
],
[
"Zheng",
"Gan",
""
],
[
"Tang",
"Jie",
""
]
] | Full-duplex (FD) has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in cellular networks, is however largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process (PPP)-based abstraction model of multi-antenna radio units (RUs) and user equipments (UEs). We consider different disjoint and user-centric approaches towards the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo (MC) simulations. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient-capacity fronthaul links and advanced interference cancellation capabilities. |
2209.04053 | Thomas Steinke | Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke | Algorithms with More Granular Differential Privacy Guarantees | null | null | null | null | cs.CR cs.DS cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differential privacy is often applied with a privacy parameter that is larger
than the theory suggests is ideal; various informal justifications for
tolerating large privacy parameters have been proposed. In this work, we
consider partial differential privacy (DP), which allows quantifying the
privacy guarantee on a per-attribute basis. In this framework, we study several
basic data analysis and learning tasks, and design algorithms whose
per-attribute privacy parameter is smaller that the best possible privacy
parameter for the entire record of a person (i.e., all the attributes).
| [
{
"created": "Thu, 8 Sep 2022 22:43:50 GMT",
"version": "v1"
}
] | 2022-09-12 | [
[
"Ghazi",
"Badih",
""
],
[
"Kumar",
"Ravi",
""
],
[
"Manurangsi",
"Pasin",
""
],
[
"Steinke",
"Thomas",
""
]
] | Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed. In this work, we consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. In this framework, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person (i.e., all the attributes). |
2105.07856 | Edward Simmons | Edward Simmons | Correlations Between Learning Environments and Dropout Intention | null | null | 10.13140/RG.2.2.28550.50245 | null | cs.CY math.ST stat.TH | http://creativecommons.org/licenses/by/4.0/ | This research is comparing learning environments to students dropout
intentions. While using statistics I looked at data and the correlations
between two articles to see how the two studies looked side to side. Learning
environments and dropout intentions can both have vary effects on students.
They can both determine if a student does well, or bad in school especially
math.
| [
{
"created": "Fri, 7 May 2021 10:08:47 GMT",
"version": "v1"
}
] | 2021-06-01 | [
[
"Simmons",
"Edward",
""
]
] | This research is comparing learning environments to students dropout intentions. While using statistics I looked at data and the correlations between two articles to see how the two studies looked side to side. Learning environments and dropout intentions can both have vary effects on students. They can both determine if a student does well, or bad in school especially math. |
2107.12679 | Mingbo Zhao | Wenlong Cheng and Mingbo Zhao and Zhiling Ye and Shuhang Gu | MFAGAN: A Compression Framework for Memory-Efficient On-Device
Super-Resolution GAN | null | null | null | null | cs.AR cs.LG | http://creativecommons.org/publicdomain/zero/1.0/ | Generative adversarial networks (GANs) have promoted remarkable advances in
single-image super-resolution (SR) by recovering photo-realistic images.
However, high memory consumption of GAN-based SR (usually generators) causes
performance degradation and more energy consumption, hindering the deployment
of GAN-based SR into resource-constricted mobile devices. In this paper, we
propose a novel compression framework \textbf{M}ulti-scale \textbf{F}eature
\textbf{A}ggregation Net based \textbf{GAN} (MFAGAN) for reducing the memory
access cost of the generator. First, to overcome the memory explosion of dense
connections, we utilize a memory-efficient multi-scale feature aggregation net
as the generator. Second, for faster and more stable training, our method
introduces the PatchGAN discriminator. Third, to balance the student
discriminator and the compressed generator, we distill both the generator and
the discriminator. Finally, we perform a hardware-aware neural architecture
search (NAS) to find a specialized SubGenerator for the target mobile phone.
Benefiting from these improvements, the proposed MFAGAN achieves up to
\textbf{8.3}$\times$ memory saving and \textbf{42.9}$\times$ computation
reduction, with only minor visual quality degradation, compared with ESRGAN.
Empirical studies also show $\sim$\textbf{70} milliseconds latency on Qualcomm
Snapdragon 865 chipset.
| [
{
"created": "Tue, 27 Jul 2021 09:04:30 GMT",
"version": "v1"
}
] | 2021-07-28 | [
[
"Cheng",
"Wenlong",
""
],
[
"Zhao",
"Mingbo",
""
],
[
"Ye",
"Zhiling",
""
],
[
"Gu",
"Shuhang",
""
]
] | Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory consumption of GAN-based SR (usually generators) causes performance degradation and more energy consumption, hindering the deployment of GAN-based SR into resource-constricted mobile devices. In this paper, we propose a novel compression framework \textbf{M}ulti-scale \textbf{F}eature \textbf{A}ggregation Net based \textbf{GAN} (MFAGAN) for reducing the memory access cost of the generator. First, to overcome the memory explosion of dense connections, we utilize a memory-efficient multi-scale feature aggregation net as the generator. Second, for faster and more stable training, our method introduces the PatchGAN discriminator. Third, to balance the student discriminator and the compressed generator, we distill both the generator and the discriminator. Finally, we perform a hardware-aware neural architecture search (NAS) to find a specialized SubGenerator for the target mobile phone. Benefiting from these improvements, the proposed MFAGAN achieves up to \textbf{8.3}$\times$ memory saving and \textbf{42.9}$\times$ computation reduction, with only minor visual quality degradation, compared with ESRGAN. Empirical studies also show $\sim$\textbf{70} milliseconds latency on Qualcomm Snapdragon 865 chipset. |
2102.07886 | Johannes Sedlmeir | Johannes Sedlmeir and Hans Ulrich Buhl and Gilbert Fridgen and Robert
Keller | Recent Developments in Blockchain Technology and their Impact on Energy
Consumption | This is a translated version of a German article published in
Informatik Spektrum | null | 10.1007/s00287-020-01321-z | null | cs.CR cs.DC | http://creativecommons.org/licenses/by/4.0/ | The enormous power consumption of Bitcoin has led to undifferentiated
discussions in science and practice about the sustainability of blockchain and
distributed ledger technology in general. However, blockchain technology is far
from homogeneous - not only with regard to its applications, which now go far
beyond cryptocurrencies and have reached businesses and the public sector, but
also with regard to its technical characteristics and, in particular, its power
consumption. This paper summarizes the status quo of the power consumption of
various implementations of blockchain technology, with special emphasis on the
recent 'Bitcoin Halving' and so-called 'zk-rollups'. We argue that although
Bitcoin and other proof-of-work blockchains do indeed consume a lot of power,
alternative blockchain solutions with significantly lower power consumption are
already available today, and new promising concepts are being tested that could
further reduce in particular the power consumption of large blockchain networks
in the near future. From this we conclude that although the criticism of
Bitcoin's power consumption is legitimate, it should not be used to derive an
energy problem of blockchain technology in general. In many cases in which
processes can be digitised or improved with the help of more energy-efficient
blockchain variants, one can even expect net energy savings.
| [
{
"created": "Mon, 15 Feb 2021 22:55:30 GMT",
"version": "v1"
}
] | 2021-02-17 | [
[
"Sedlmeir",
"Johannes",
""
],
[
"Buhl",
"Hans Ulrich",
""
],
[
"Fridgen",
"Gilbert",
""
],
[
"Keller",
"Robert",
""
]
] | The enormous power consumption of Bitcoin has led to undifferentiated discussions in science and practice about the sustainability of blockchain and distributed ledger technology in general. However, blockchain technology is far from homogeneous - not only with regard to its applications, which now go far beyond cryptocurrencies and have reached businesses and the public sector, but also with regard to its technical characteristics and, in particular, its power consumption. This paper summarizes the status quo of the power consumption of various implementations of blockchain technology, with special emphasis on the recent 'Bitcoin Halving' and so-called 'zk-rollups'. We argue that although Bitcoin and other proof-of-work blockchains do indeed consume a lot of power, alternative blockchain solutions with significantly lower power consumption are already available today, and new promising concepts are being tested that could further reduce in particular the power consumption of large blockchain networks in the near future. From this we conclude that although the criticism of Bitcoin's power consumption is legitimate, it should not be used to derive an energy problem of blockchain technology in general. In many cases in which processes can be digitised or improved with the help of more energy-efficient blockchain variants, one can even expect net energy savings. |
1603.09012 | Laleh Jalali | Laleh Jalali and Ramesh Jain | A framework for event co-occurrence detection in event streams | null | null | null | null | cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper shows that characterizing co-occurrence between events is an
important but non-trivial and neglected aspect of discovering potential causal
relationships in multimedia event streams. First an introduction to the notion
of event co-occurrence and its relation to co-occurrence pattern detection is
given. Then a finite state automaton extended with a time model and event
parameterization is introduced to convert high level co-occurrence pattern
definition to its corresponding pattern matching automaton. Finally a
processing algorithm is applied to count the occurrence frequency of a
collection of patterns with only one pass through input event streams. The
method proposed in this paper can be used for detecting co-occurrences between
both events of one event stream (Auto co-occurrence), and events from multiple
event streams (Cross co-occurrence). Some fundamental results concerning the
characterization of event co-occurrence are presented in form of a visual co-
occurrence matrix. Reusable causality rules can be extracted easily from
co-occurrence matrix and fed into various analysis tools, such as
recommendation systems and complex event processing systems for further
analysis.
| [
{
"created": "Wed, 30 Mar 2016 01:16:37 GMT",
"version": "v1"
}
] | 2016-03-31 | [
[
"Jalali",
"Laleh",
""
],
[
"Jain",
"Ramesh",
""
]
] | This paper shows that characterizing co-occurrence between events is an important but non-trivial and neglected aspect of discovering potential causal relationships in multimedia event streams. First an introduction to the notion of event co-occurrence and its relation to co-occurrence pattern detection is given. Then a finite state automaton extended with a time model and event parameterization is introduced to convert high level co-occurrence pattern definition to its corresponding pattern matching automaton. Finally a processing algorithm is applied to count the occurrence frequency of a collection of patterns with only one pass through input event streams. The method proposed in this paper can be used for detecting co-occurrences between both events of one event stream (Auto co-occurrence), and events from multiple event streams (Cross co-occurrence). Some fundamental results concerning the characterization of event co-occurrence are presented in form of a visual co- occurrence matrix. Reusable causality rules can be extracted easily from co-occurrence matrix and fed into various analysis tools, such as recommendation systems and complex event processing systems for further analysis. |
2408.05476 | Jonas Oppenlaender | Jonas Oppenlaender, Hannah Johnston, Johanna Silvennoinen, Helena
Barranha | Artworks Reimagined: Exploring Human-AI Co-Creation through Body
Prompting | 16 pages, 5 figures, 2 tables | null | null | null | cs.HC cs.AI cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Image generation using generative artificial intelligence is a popular
activity. However, it is almost exclusively performed in the privacy of an
individual's home via typing on a keyboard. In this article, we explore body
prompting as input for image generation. Body prompting extends interaction
with generative AI beyond textual inputs to reconnect the creative act of image
generation with the physical act of creating artworks. We implement this
concept in an interactive art installation, Artworks Reimagined, designed to
transform artworks via body prompting. We deployed the installation at an event
with hundreds of visitors in a public and private setting. Our results from a
sample of visitors (N=79) show that body prompting was well-received and
provides an engaging and fun experience. We identify three distinct patterns of
embodied interaction with the generative AI and present insights into
participants' experience of body prompting and AI co-creation. We provide
valuable recommendations for practitioners seeking to design interactive
generative AI experiences in museums, galleries, and other public cultural
spaces.
| [
{
"created": "Sat, 10 Aug 2024 08:05:59 GMT",
"version": "v1"
}
] | 2024-08-13 | [
[
"Oppenlaender",
"Jonas",
""
],
[
"Johnston",
"Hannah",
""
],
[
"Silvennoinen",
"Johanna",
""
],
[
"Barranha",
"Helena",
""
]
] | Image generation using generative artificial intelligence is a popular activity. However, it is almost exclusively performed in the privacy of an individual's home via typing on a keyboard. In this article, we explore body prompting as input for image generation. Body prompting extends interaction with generative AI beyond textual inputs to reconnect the creative act of image generation with the physical act of creating artworks. We implement this concept in an interactive art installation, Artworks Reimagined, designed to transform artworks via body prompting. We deployed the installation at an event with hundreds of visitors in a public and private setting. Our results from a sample of visitors (N=79) show that body prompting was well-received and provides an engaging and fun experience. We identify three distinct patterns of embodied interaction with the generative AI and present insights into participants' experience of body prompting and AI co-creation. We provide valuable recommendations for practitioners seeking to design interactive generative AI experiences in museums, galleries, and other public cultural spaces. |
2201.12590 | Christopher Bl\"ocker | Christopher Bl\"ocker, Juan Carlos Nieves, Martin Rosvall | Map Equation Centrality: Community-aware Centrality based on the Map
Equation | null | Appl Netw Sci 7, 56 (2022) | 10.1007/s41109-022-00477-9 | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To measure node importance, network scientists employ centrality scores that
typically take a microscopic or macroscopic perspective, relying on node
features or global network structure. However, traditional centrality measures
such as degree centrality, betweenness centrality, or PageRank neglect the
community structure found in real-world networks. To study node importance
based on network flows from a mesoscopic perspective, we analytically derive a
community-aware information-theoretic centrality score based on network flow
and the coding principles behind the map equation: map equation centrality. Map
equation centrality measures how much further we can compress the network's
modular description by not coding for random walker transitions to the
respective node, using an adapted coding scheme and determining node importance
from a network flow-based point of view. The information-theoretic centrality
measure can be determined from a node's local network context alone because
changes to the coding scheme only affect other nodes in the same module. Map
equation centrality is agnostic to the chosen network flow model and allows
researchers to select the model that best reflects the dynamics of the process
under study. Applied to synthetic networks, we highlight how our approach
enables a more fine-grained differentiation between nodes than node-local or
network-global measures. Predicting influential nodes for two different
dynamical processes on real-world networks with traditional and other
community-aware centrality measures, we find that activating nodes based on map
equation centrality scores tends to create the largest cascades in a linear
threshold model.
| [
{
"created": "Sat, 29 Jan 2022 13:47:27 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Aug 2022 07:26:00 GMT",
"version": "v2"
}
] | 2022-08-18 | [
[
"Blöcker",
"Christopher",
""
],
[
"Nieves",
"Juan Carlos",
""
],
[
"Rosvall",
"Martin",
""
]
] | To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To study node importance based on network flows from a mesoscopic perspective, we analytically derive a community-aware information-theoretic centrality score based on network flow and the coding principles behind the map equation: map equation centrality. Map equation centrality measures how much further we can compress the network's modular description by not coding for random walker transitions to the respective node, using an adapted coding scheme and determining node importance from a network flow-based point of view. The information-theoretic centrality measure can be determined from a node's local network context alone because changes to the coding scheme only affect other nodes in the same module. Map equation centrality is agnostic to the chosen network flow model and allows researchers to select the model that best reflects the dynamics of the process under study. Applied to synthetic networks, we highlight how our approach enables a more fine-grained differentiation between nodes than node-local or network-global measures. Predicting influential nodes for two different dynamical processes on real-world networks with traditional and other community-aware centrality measures, we find that activating nodes based on map equation centrality scores tends to create the largest cascades in a linear threshold model. |
1603.05739 | Elliot Schumacher | Elliot Schumacher, Maxine Eskenazi | A Readability Analysis of Campaign Speeches from the 2016 US
Presidential Campaign | null | null | null | CMU-LTI-16-001 | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Readability is defined as the reading level of the speech from grade 1 to
grade 12. It results from the use of the REAP readability analysis (vocabulary
- Collins-Thompson and Callan, 2004; syntax - Heilman et al ,2006, 2007), which
use the lexical contents and grammatical structure of the sentences in a
document to predict the reading level. After analysis, results were grouped
into the average readability of each candidate, the evolution of the
candidate's speeches' readability over time and the standard deviation, or how
much each candidate varied their speech from one venue to another. For
comparison, one speech from four past presidents and the Gettysburg Address
were also analyzed.
| [
{
"created": "Fri, 18 Mar 2016 00:55:52 GMT",
"version": "v1"
}
] | 2016-03-21 | [
[
"Schumacher",
"Elliot",
""
],
[
"Eskenazi",
"Maxine",
""
]
] | Readability is defined as the reading level of the speech from grade 1 to grade 12. It results from the use of the REAP readability analysis (vocabulary - Collins-Thompson and Callan, 2004; syntax - Heilman et al ,2006, 2007), which use the lexical contents and grammatical structure of the sentences in a document to predict the reading level. After analysis, results were grouped into the average readability of each candidate, the evolution of the candidate's speeches' readability over time and the standard deviation, or how much each candidate varied their speech from one venue to another. For comparison, one speech from four past presidents and the Gettysburg Address were also analyzed. |
2305.15055 | Mayank Singh | Mayank Kumar Singh, Naoya Takahashi, Onoe Naoyuki | Iteratively Improving Speech Recognition and Voice Conversion | null | null | null | null | cs.SD cs.AI eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many existing works on voice conversion (VC) tasks use automatic speech
recognition (ASR) models for ensuring linguistic consistency between source and
converted samples. However, for the low-data resource domains, training a
high-quality ASR remains to be a challenging task. In this work, we propose a
novel iterative way of improving both the ASR and VC models. We first train an
ASR model which is used to ensure content preservation while training a VC
model. In the next iteration, the VC model is used as a data augmentation
method to further fine-tune the ASR model and generalize it to diverse
speakers. By iteratively leveraging the improved ASR model to train VC model
and vice-versa, we experimentally show improvement in both the models. Our
proposed framework outperforms the ASR and one-shot VC baseline models on
English singing and Hindi speech domains in subjective and objective
evaluations in low-data resource settings.
| [
{
"created": "Wed, 24 May 2023 11:45:42 GMT",
"version": "v1"
}
] | 2023-05-25 | [
[
"Singh",
"Mayank Kumar",
""
],
[
"Takahashi",
"Naoya",
""
],
[
"Naoyuki",
"Onoe",
""
]
] | Many existing works on voice conversion (VC) tasks use automatic speech recognition (ASR) models for ensuring linguistic consistency between source and converted samples. However, for the low-data resource domains, training a high-quality ASR remains to be a challenging task. In this work, we propose a novel iterative way of improving both the ASR and VC models. We first train an ASR model which is used to ensure content preservation while training a VC model. In the next iteration, the VC model is used as a data augmentation method to further fine-tune the ASR model and generalize it to diverse speakers. By iteratively leveraging the improved ASR model to train VC model and vice-versa, we experimentally show improvement in both the models. Our proposed framework outperforms the ASR and one-shot VC baseline models on English singing and Hindi speech domains in subjective and objective evaluations in low-data resource settings. |
2403.14292 | Saad Noufel | Saad Noufel, Nadir Maaroufi, Mehdi Najib, Mohamed Bakhouya | HySim: An Efficient Hybrid Similarity Measure for Patch Matching in
Image Inpainting | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Inpainting, for filling missing image regions, is a crucial task in various
applications, such as medical imaging and remote sensing. Trending data-driven
approaches efficiency, for image inpainting, often requires extensive data
preprocessing. In this sense, there is still a need for model-driven approaches
in case of application constrained with data availability and quality,
especially for those related for time series forecasting using image inpainting
techniques. This paper proposes an improved modeldriven approach relying on
patch-based techniques. Our approach deviates from the standard Sum of Squared
Differences (SSD) similarity measure by introducing a Hybrid Similarity
(HySim), which combines both strengths of Chebychev and Minkowski distances.
This hybridization enhances patch selection, leading to high-quality inpainting
results with reduced mismatch errors. Experimental results proved the
effectiveness of our approach against other model-driven techniques, such as
diffusion or patch-based approaches, showcasing its effectiveness in achieving
visually pleasing restorations.
| [
{
"created": "Thu, 21 Mar 2024 10:59:44 GMT",
"version": "v1"
}
] | 2024-03-22 | [
[
"Noufel",
"Saad",
""
],
[
"Maaroufi",
"Nadir",
""
],
[
"Najib",
"Mehdi",
""
],
[
"Bakhouya",
"Mohamed",
""
]
] | Inpainting, for filling missing image regions, is a crucial task in various applications, such as medical imaging and remote sensing. Trending data-driven approaches efficiency, for image inpainting, often requires extensive data preprocessing. In this sense, there is still a need for model-driven approaches in case of application constrained with data availability and quality, especially for those related for time series forecasting using image inpainting techniques. This paper proposes an improved modeldriven approach relying on patch-based techniques. Our approach deviates from the standard Sum of Squared Differences (SSD) similarity measure by introducing a Hybrid Similarity (HySim), which combines both strengths of Chebychev and Minkowski distances. This hybridization enhances patch selection, leading to high-quality inpainting results with reduced mismatch errors. Experimental results proved the effectiveness of our approach against other model-driven techniques, such as diffusion or patch-based approaches, showcasing its effectiveness in achieving visually pleasing restorations. |
1502.01877 | Helio M. de Oliveira | H.M. de Oliveira and T.H. Falk | On Wavelet Decomposition over Finite Fields | 4 pages, 1 figure. conference: XIX Simposio Brasileiro de
Telecomunicacoes, 2001, Fortaleza, CE, Brazil | null | null | null | cs.IT math.IT math.NT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces some foundations of wavelets over Galois fields.
Standard orthogonal finite-field wavelets (FF-Wavelets) including FF-Haar and
FF-Daubechies are derived. Non-orthogonal FF-wavelets such as B-spline over
GF(p) are also considered. A few examples of multiresolution analysis over
Finite fields are presented showing how to perform Laplacian pyramid filtering
of finite block lengths sequences. An application of FF-wavelets to design
spread-spectrum sequences is presented.
| [
{
"created": "Fri, 6 Feb 2015 13:09:24 GMT",
"version": "v1"
}
] | 2020-06-01 | [
[
"de Oliveira",
"H. M.",
""
],
[
"Falk",
"T. H.",
""
]
] | This paper introduces some foundations of wavelets over Galois fields. Standard orthogonal finite-field wavelets (FF-Wavelets) including FF-Haar and FF-Daubechies are derived. Non-orthogonal FF-wavelets such as B-spline over GF(p) are also considered. A few examples of multiresolution analysis over Finite fields are presented showing how to perform Laplacian pyramid filtering of finite block lengths sequences. An application of FF-wavelets to design spread-spectrum sequences is presented. |
2304.11354 | YuanFu Yang | Yuan-Fu Yang, Iuan-Kai Fang, Min Sun, Su-Chu Hsu | Medium. Permeation: SARS-COV-2 Painting Creation by Generative Model | Keywords: SARS-CoV-2; Generative Art; Graph Neural Network. arXiv
admin note: text overlap with arXiv:1706.07068 by other authors | null | null | null | cs.CV cs.GR | http://creativecommons.org/licenses/by/4.0/ | Airborne particles are the medium for SARS-CoV-2 to invade the human body.
Light also reflects through suspended particles in the air, allowing people to
see a colorful world. Impressionism is the most prominent art school that
explores the spectrum of color created through color reflection of light. We
find similarities of color structure and color stacking in the Impressionist
paintings and the illustrations of the novel coronavirus by artists around the
world. With computerized data analysis through the main tones, the way of color
layout, and the way of color stacking in the paintings of the Impressionists,
we train computers to draw the novel coronavirus in an Impressionist style
using a Generative Adversarial Network to create our artwork "Medium.
Permeation". This artwork is composed of 196 randomly generated viral pictures
arranged in a 14 by 14 matrix to form a large-scale painting. In addition, we
have developed an extended work: Gradual Change, which is presented as video
art. We use Graph Neural Network to present 196 paintings of the new
coronavirus to the audience one by one in a gradual manner. In front of LED TV
screen, audience will find 196 virus paintings whose colors will change
continuously. This large video painting symbolizes that worldwide 196 countries
have been invaded by the epidemic, and every nation continuously pops up mutant
viruses. The speed of vaccine development cannot keep up with the speed of
virus mutation. This is also the first generative art in the world based on the
common features and a metaphorical symbiosis between Impressionist art and the
novel coronavirus. This work warns us of the unprecedented challenges posed by
the SARS-CoV-2, implying that the world should not ignore the invisible enemy
who uses air as a medium.
| [
{
"created": "Sat, 22 Apr 2023 09:27:47 GMT",
"version": "v1"
}
] | 2023-04-25 | [
[
"Yang",
"Yuan-Fu",
""
],
[
"Fang",
"Iuan-Kai",
""
],
[
"Sun",
"Min",
""
],
[
"Hsu",
"Su-Chu",
""
]
] | Airborne particles are the medium for SARS-CoV-2 to invade the human body. Light also reflects through suspended particles in the air, allowing people to see a colorful world. Impressionism is the most prominent art school that explores the spectrum of color created through color reflection of light. We find similarities of color structure and color stacking in the Impressionist paintings and the illustrations of the novel coronavirus by artists around the world. With computerized data analysis through the main tones, the way of color layout, and the way of color stacking in the paintings of the Impressionists, we train computers to draw the novel coronavirus in an Impressionist style using a Generative Adversarial Network to create our artwork "Medium. Permeation". This artwork is composed of 196 randomly generated viral pictures arranged in a 14 by 14 matrix to form a large-scale painting. In addition, we have developed an extended work: Gradual Change, which is presented as video art. We use Graph Neural Network to present 196 paintings of the new coronavirus to the audience one by one in a gradual manner. In front of LED TV screen, audience will find 196 virus paintings whose colors will change continuously. This large video painting symbolizes that worldwide 196 countries have been invaded by the epidemic, and every nation continuously pops up mutant viruses. The speed of vaccine development cannot keep up with the speed of virus mutation. This is also the first generative art in the world based on the common features and a metaphorical symbiosis between Impressionist art and the novel coronavirus. This work warns us of the unprecedented challenges posed by the SARS-CoV-2, implying that the world should not ignore the invisible enemy who uses air as a medium. |
1806.04584 | Chujie Wang | Chujie Wang, Zhifeng Zhao, Qi Sun, Honggang Zhang | Deep Learning-based Intelligent Dual Connectivity for Mobility
Management in Dense Network | 5 pages, 9 figures, conference | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Ultra-dense network deployment has been proposed as a key technique for
achieving capacity goals in the fifth-generation (5G) mobile communication
system. However, the deployment of smaller cells inevitably leads to more
frequent handovers, thus making mobility management more challenging and
reducing the capacity gains offered by the dense network deployment. In order
to fully reap the gains for mobile users in such a network environment, we
propose an intelligent dual connectivity mechanism for mobility management
through deep learning-based mobility prediction. We first use LSTM (Long Short
Term Memory) algorithm, one of deep learning algorithms, to learn every user
equipment's (UE's) mobility pattern from its historical trajectories and
predict its movement trends in the future. Based on the corresponding
prediction results, the network will judge whether a handover is required for
the UE. For the handover case, a dual connection will be established for the
related UE. Thus, the UE can get the radio signal from two base stations in the
handover process. Simulation results verify that the proposed intelligent dual
connectivity mechanism can significantly improve the quality of service of
mobile users in the handover process while guaranteeing the network energy
efficiency.
| [
{
"created": "Wed, 30 May 2018 07:59:12 GMT",
"version": "v1"
}
] | 2018-06-13 | [
[
"Wang",
"Chujie",
""
],
[
"Zhao",
"Zhifeng",
""
],
[
"Sun",
"Qi",
""
],
[
"Zhang",
"Honggang",
""
]
] | Ultra-dense network deployment has been proposed as a key technique for achieving capacity goals in the fifth-generation (5G) mobile communication system. However, the deployment of smaller cells inevitably leads to more frequent handovers, thus making mobility management more challenging and reducing the capacity gains offered by the dense network deployment. In order to fully reap the gains for mobile users in such a network environment, we propose an intelligent dual connectivity mechanism for mobility management through deep learning-based mobility prediction. We first use LSTM (Long Short Term Memory) algorithm, one of deep learning algorithms, to learn every user equipment's (UE's) mobility pattern from its historical trajectories and predict its movement trends in the future. Based on the corresponding prediction results, the network will judge whether a handover is required for the UE. For the handover case, a dual connection will be established for the related UE. Thus, the UE can get the radio signal from two base stations in the handover process. Simulation results verify that the proposed intelligent dual connectivity mechanism can significantly improve the quality of service of mobile users in the handover process while guaranteeing the network energy efficiency. |
2209.01386 | Chao Zhang | Chao Zhang, Zijian Tang, Taoming Guo, Jiaxin Lei, Jiaxin Xiao, Anhe
Wang, Shuo Bai, Milin Zhang | SaleNet: A low-power end-to-end CNN accelerator for sustained attention
level evaluation using EEG | 5 pages, 4 figures, to be published in IEEE International Symposium
on Circuits and Systems (ISCAS) 2022 | null | null | null | cs.AR cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes SaleNet - an end-to-end convolutional neural network
(CNN) for sustained attention level evaluation using prefrontal
electroencephalogram (EEG). A bias-driven pruning method is proposed together
with group convolution, global average pooling (GAP), near-zero pruning, weight
clustering and quantization for the model compression, achieving a total
compression ratio of 183.11x. The compressed SaleNet obtains a state-of-the-art
subject-independent sustained attention level classification accuracy of 84.2%
on the recorded 6-subject EEG database in this work. The SaleNet is implemented
on a Artix-7 FPGA with a competitive power consumption of 0.11 W and an
energy-efficiency of 8.19 GOps/W.
| [
{
"created": "Sat, 3 Sep 2022 09:49:37 GMT",
"version": "v1"
}
] | 2022-09-07 | [
[
"Zhang",
"Chao",
""
],
[
"Tang",
"Zijian",
""
],
[
"Guo",
"Taoming",
""
],
[
"Lei",
"Jiaxin",
""
],
[
"Xiao",
"Jiaxin",
""
],
[
"Wang",
"Anhe",
""
],
[
"Bai",
"Shuo",
""
],
[
"Zhang",
"Milin",
""
]
] | This paper proposes SaleNet - an end-to-end convolutional neural network (CNN) for sustained attention level evaluation using prefrontal electroencephalogram (EEG). A bias-driven pruning method is proposed together with group convolution, global average pooling (GAP), near-zero pruning, weight clustering and quantization for the model compression, achieving a total compression ratio of 183.11x. The compressed SaleNet obtains a state-of-the-art subject-independent sustained attention level classification accuracy of 84.2% on the recorded 6-subject EEG database in this work. The SaleNet is implemented on a Artix-7 FPGA with a competitive power consumption of 0.11 W and an energy-efficiency of 8.19 GOps/W. |
1906.00189 | Tongliang Liu | Xiaobo Xia and Tongliang Liu and Nannan Wang and Bo Han and Chen Gong
and Gang Niu and Masashi Sugiyama | Are Anchor Points Really Indispensable in Label-Noise Learning? | Accepted by NeurIPS 2019 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In label-noise learning, \textit{noise transition matrix}, denoting the
probabilities that clean labels flip into noisy labels, plays a central role in
building \textit{statistically consistent classifiers}. Existing theories have
shown that the transition matrix can be learned by exploiting \textit{anchor
points} (i.e., data points that belong to a specific class almost surely).
However, when there are no anchor points, the transition matrix will be poorly
learned, and those current consistent classifiers will significantly
degenerate. In this paper, without employing anchor points, we propose a
\textit{transition-revision} ($T$-Revision) method to effectively learn
transition matrices, leading to better classifiers. Specifically, to learn a
transition matrix, we first initialize it by exploiting data points that are
similar to anchor points, having high \textit{noisy class posterior
probabilities}. Then, we modify the initialized matrix by adding a
\textit{slack variable}, which can be learned and validated together with the
classifier by using noisy data. Empirical results on benchmark-simulated and
real-world label-noise datasets demonstrate that without using exact anchor
points, the proposed method is superior to the state-of-the-art label-noise
learning methods.
| [
{
"created": "Sat, 1 Jun 2019 09:14:54 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Dec 2019 02:23:29 GMT",
"version": "v2"
}
] | 2019-12-18 | [
[
"Xia",
"Xiaobo",
""
],
[
"Liu",
"Tongliang",
""
],
[
"Wang",
"Nannan",
""
],
[
"Han",
"Bo",
""
],
[
"Gong",
"Chen",
""
],
[
"Niu",
"Gang",
""
],
[
"Sugiyama",
"Masashi",
""
]
] | In label-noise learning, \textit{noise transition matrix}, denoting the probabilities that clean labels flip into noisy labels, plays a central role in building \textit{statistically consistent classifiers}. Existing theories have shown that the transition matrix can be learned by exploiting \textit{anchor points} (i.e., data points that belong to a specific class almost surely). However, when there are no anchor points, the transition matrix will be poorly learned, and those current consistent classifiers will significantly degenerate. In this paper, without employing anchor points, we propose a \textit{transition-revision} ($T$-Revision) method to effectively learn transition matrices, leading to better classifiers. Specifically, to learn a transition matrix, we first initialize it by exploiting data points that are similar to anchor points, having high \textit{noisy class posterior probabilities}. Then, we modify the initialized matrix by adding a \textit{slack variable}, which can be learned and validated together with the classifier by using noisy data. Empirical results on benchmark-simulated and real-world label-noise datasets demonstrate that without using exact anchor points, the proposed method is superior to the state-of-the-art label-noise learning methods. |
2010.13816 | Maarten Sap | Xinyao Ma, Maarten Sap, Hannah Rashkin, Yejin Choi | PowerTransformer: Unsupervised Controllable Revision for Biased Language
Correction | EMNLP 2020 | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unconscious biases continue to be prevalent in modern text and media, calling
for algorithms that can assist writers with bias correction. For example, a
female character in a story is often portrayed as passive and powerless ("She
daydreams about being a doctor") while a man is portrayed as more proactive and
powerful ("He pursues his dream of being a doctor").
We formulate *Controllable Debiasing*, a new revision task that aims to
rewrite a given text to correct the implicit and potentially undesirable bias
in character portrayals. We then introduce PowerTransformer as an approach that
debiases text through the lens of connotation frames (Sap et al., 2017), which
encode pragmatic knowledge of implied power dynamics with respect to verb
predicates. One key challenge of our task is the lack of parallel corpora. To
address this challenge, we adopt an unsupervised approach using auxiliary
supervision with related tasks such as paraphrasing and self-supervision based
on a reconstruction loss, building on pretrained language models.
Through comprehensive experiments based on automatic and human evaluations,
we demonstrate that our approach outperforms ablations and existing methods
from related tasks. Furthermore, we demonstrate the use of PowerTransformer as
a step toward mitigating the well-documented gender bias in character portrayal
in movie scripts.
| [
{
"created": "Mon, 26 Oct 2020 18:05:48 GMT",
"version": "v1"
}
] | 2020-10-28 | [
[
"Ma",
"Xinyao",
""
],
[
"Sap",
"Maarten",
""
],
[
"Rashkin",
"Hannah",
""
],
[
"Choi",
"Yejin",
""
]
] | Unconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a story is often portrayed as passive and powerless ("She daydreams about being a doctor") while a man is portrayed as more proactive and powerful ("He pursues his dream of being a doctor"). We formulate *Controllable Debiasing*, a new revision task that aims to rewrite a given text to correct the implicit and potentially undesirable bias in character portrayals. We then introduce PowerTransformer as an approach that debiases text through the lens of connotation frames (Sap et al., 2017), which encode pragmatic knowledge of implied power dynamics with respect to verb predicates. One key challenge of our task is the lack of parallel corpora. To address this challenge, we adopt an unsupervised approach using auxiliary supervision with related tasks such as paraphrasing and self-supervision based on a reconstruction loss, building on pretrained language models. Through comprehensive experiments based on automatic and human evaluations, we demonstrate that our approach outperforms ablations and existing methods from related tasks. Furthermore, we demonstrate the use of PowerTransformer as a step toward mitigating the well-documented gender bias in character portrayal in movie scripts. |
1703.05446 | Ke Gong | Ke Gong, Xiaodan Liang, Dongyu Zhang, Xiaohui Shen, Liang Lin | Look into Person: Self-supervised Structure-sensitive Learning and A New
Benchmark for Human Parsing | Accepted to appear in CVPR 2017 | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human parsing has recently attracted a lot of research interests due to its
huge application potentials. However existing datasets have limited number of
images and annotations, and lack the variety of human appearances and the
coverage of challenging cases in unconstrained environment. In this paper, we
introduce a new benchmark "Look into Person (LIP)" that makes a significant
advance in terms of scalability, diversity and difficulty, a contribution that
we feel is crucial for future developments in human-centric analysis. This
comprehensive dataset contains over 50,000 elaborately annotated images with 19
semantic part labels, which are captured from a wider range of viewpoints,
occlusions and background complexity. Given these rich annotations we perform
detailed analyses of the leading human parsing approaches, gaining insights
into the success and failures of these methods. Furthermore, in contrast to the
existing efforts on improving the feature discriminative capability, we solve
human parsing by exploring a novel self-supervised structure-sensitive learning
approach, which imposes human pose structures into parsing results without
resorting to extra supervision (i.e., no need for specifically labeling human
joints in model training). Our self-supervised learning framework can be
injected into any advanced neural networks to help incorporate rich high-level
knowledge regarding human joints from a global perspective and improve the
parsing results. Extensive evaluations on our LIP and the public
PASCAL-Person-Part dataset demonstrate the superiority of our method.
| [
{
"created": "Thu, 16 Mar 2017 01:14:36 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Jul 2017 01:41:39 GMT",
"version": "v2"
}
] | 2017-07-31 | [
[
"Gong",
"Ke",
""
],
[
"Liang",
"Xiaodan",
""
],
[
"Zhang",
"Dongyu",
""
],
[
"Shen",
"Xiaohui",
""
],
[
"Lin",
"Liang",
""
]
] | Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances and the coverage of challenging cases in unconstrained environment. In this paper, we introduce a new benchmark "Look into Person (LIP)" that makes a significant advance in terms of scalability, diversity and difficulty, a contribution that we feel is crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels, which are captured from a wider range of viewpoints, occlusions and background complexity. Given these rich annotations we perform detailed analyses of the leading human parsing approaches, gaining insights into the success and failures of these methods. Furthermore, in contrast to the existing efforts on improving the feature discriminative capability, we solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision (i.e., no need for specifically labeling human joints in model training). Our self-supervised learning framework can be injected into any advanced neural networks to help incorporate rich high-level knowledge regarding human joints from a global perspective and improve the parsing results. Extensive evaluations on our LIP and the public PASCAL-Person-Part dataset demonstrate the superiority of our method. |
1110.2849 | Karthick Jayaraman | Karthick Jayaraman, Vijay Ganesh, Mahesh Tripunitara, Martin C Rinard,
Steve J. Chapin | ARBAC Policy for a Large Multi-National Bank | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Administrative role-based access control (ARBAC) is the first comprehensive
administrative model proposed for role-based access control (RBAC). ARBAC has
several features for designing highly expressive policies, but current work has
not highlighted the utility of these expressive policies. In this report, we
present a case study of designing an ARBAC policy for a bank comprising 18
branches. Using this case study we provide an assessment about the features of
ARBAC that are likely to be used in realistic policies.
| [
{
"created": "Thu, 13 Oct 2011 07:13:11 GMT",
"version": "v1"
}
] | 2011-10-14 | [
[
"Jayaraman",
"Karthick",
""
],
[
"Ganesh",
"Vijay",
""
],
[
"Tripunitara",
"Mahesh",
""
],
[
"Rinard",
"Martin C",
""
],
[
"Chapin",
"Steve J.",
""
]
] | Administrative role-based access control (ARBAC) is the first comprehensive administrative model proposed for role-based access control (RBAC). ARBAC has several features for designing highly expressive policies, but current work has not highlighted the utility of these expressive policies. In this report, we present a case study of designing an ARBAC policy for a bank comprising 18 branches. Using this case study we provide an assessment about the features of ARBAC that are likely to be used in realistic policies. |
2301.03094 | Jonas Witt | Jonas Witt, Stef Rasing, Sebastijan Duman\v{c}i\'c, Tias Guns and
Claus-Christian Carbon | A Divide-Align-Conquer Strategy for Program Synthesis | 11 pages, 9 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major bottleneck in search-based program synthesis is the exponentially
growing search space which makes learning large programs intractable. Humans
mitigate this problem by leveraging the compositional nature of the real world:
In structured domains, a logical specification can often be decomposed into
smaller, complementary solution programs. We show that compositional
segmentation can be applied in the programming by examples setting to divide
the search for large programs across multiple smaller program synthesis
problems. For each example, we search for a decomposition into smaller units
which maximizes the reconstruction accuracy in the output under a latent task
program. A structural alignment of the constituent parts in the input and
output leads to pairwise correspondences used to guide the program synthesis
search. In order to align the input/output structures, we make use of the
Structure-Mapping Theory (SMT), a formal model of human analogical reasoning
which originated in the cognitive sciences. We show that decomposition-driven
program synthesis with structural alignment outperforms Inductive Logic
Programming (ILP) baselines on string transformation tasks even with minimal
knowledge priors. Unlike existing methods, the predictive accuracy of our agent
monotonically increases for additional examples and achieves an average time
complexity of $\mathcal{O}(m)$ in the number $m$ of partial programs for highly
structured domains such as strings. We extend this method to the complex
setting of visual reasoning in the Abstraction and Reasoning Corpus (ARC) for
which ILP methods were previously infeasible.
| [
{
"created": "Sun, 8 Jan 2023 19:10:55 GMT",
"version": "v1"
}
] | 2023-01-10 | [
[
"Witt",
"Jonas",
""
],
[
"Rasing",
"Stef",
""
],
[
"Dumančić",
"Sebastijan",
""
],
[
"Guns",
"Tias",
""
],
[
"Carbon",
"Claus-Christian",
""
]
] | A major bottleneck in search-based program synthesis is the exponentially growing search space which makes learning large programs intractable. Humans mitigate this problem by leveraging the compositional nature of the real world: In structured domains, a logical specification can often be decomposed into smaller, complementary solution programs. We show that compositional segmentation can be applied in the programming by examples setting to divide the search for large programs across multiple smaller program synthesis problems. For each example, we search for a decomposition into smaller units which maximizes the reconstruction accuracy in the output under a latent task program. A structural alignment of the constituent parts in the input and output leads to pairwise correspondences used to guide the program synthesis search. In order to align the input/output structures, we make use of the Structure-Mapping Theory (SMT), a formal model of human analogical reasoning which originated in the cognitive sciences. We show that decomposition-driven program synthesis with structural alignment outperforms Inductive Logic Programming (ILP) baselines on string transformation tasks even with minimal knowledge priors. Unlike existing methods, the predictive accuracy of our agent monotonically increases for additional examples and achieves an average time complexity of $\mathcal{O}(m)$ in the number $m$ of partial programs for highly structured domains such as strings. We extend this method to the complex setting of visual reasoning in the Abstraction and Reasoning Corpus (ARC) for which ILP methods were previously infeasible. |
cs/0212044 | Sandor P. Fekete | Sandor P. Fekete, Henk Meijer, Andre Rohe, and Walter Tietze | Solving a "Hard" Problem to Approximate an "Easy" One: Heuristics for
Maximum Matchings and Maximum Traveling Salesman Problems | 20 pages, 14 figures, Latex, to appear in Journal of Experimental
Algorithms, 2002 | Journal of Experimental Algorithms, 7 (2002), article 11. | null | null | cs.DS | null | We consider geometric instances of the Maximum Weighted Matching Problem
(MWMP) and the Maximum Traveling Salesman Problem (MTSP) with up to 3,000,000
vertices. Making use of a geometric duality relationship between MWMP, MTSP,
and the Fermat-Weber-Problem (FWP), we develop a heuristic approach that yields
in near-linear time solutions as well as upper bounds. Using various
computational tools, we get solutions within considerably less than 1% of the
optimum.
An interesting feature of our approach is that, even though an FWP is hard to
compute in theory and Edmonds' algorithm for maximum weighted matching yields a
polynomial solution for the MWMP, the practical behavior is just the opposite,
and we can solve the FWP with high accuracy in order to find a good heuristic
solution for the MWMP.
| [
{
"created": "Mon, 16 Dec 2002 09:39:16 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Fekete",
"Sandor P.",
""
],
[
"Meijer",
"Henk",
""
],
[
"Rohe",
"Andre",
""
],
[
"Tietze",
"Walter",
""
]
] | We consider geometric instances of the Maximum Weighted Matching Problem (MWMP) and the Maximum Traveling Salesman Problem (MTSP) with up to 3,000,000 vertices. Making use of a geometric duality relationship between MWMP, MTSP, and the Fermat-Weber-Problem (FWP), we develop a heuristic approach that yields in near-linear time solutions as well as upper bounds. Using various computational tools, we get solutions within considerably less than 1% of the optimum. An interesting feature of our approach is that, even though an FWP is hard to compute in theory and Edmonds' algorithm for maximum weighted matching yields a polynomial solution for the MWMP, the practical behavior is just the opposite, and we can solve the FWP with high accuracy in order to find a good heuristic solution for the MWMP. |
1508.07504 | Joseph Cheriyan | Joe Cheriyan, Zhihan Gao | Approximating (Unweighted) Tree Augmentation via Lift-and-Project, Part
I: Stemless TAP | 24 pages, 11 figures | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Part I, we study a special case of the unweighted Tree Augmentation
Problem (TAP) via the Lasserre (Sum of Squares) system. In the special case, we
forbid so-called stems; these are a particular type of subtree configuration.
For stemless TAP, we prove that the integrality ratio of an SDP relaxation (the
Lasserre tightening of an LP relaxation) is $\leq \frac{3}{2}+\epsilon$, where
$\epsilon>0$ can be any small constant. We obtain this result by designing a
polynomial-time algorithm for stemless TAP that achieves an approximation
guarantee of ($\frac32+\epsilon$) relative to the SDP relaxation. The algorithm
is combinatorial and does not solve the SDP relaxation, but our analysis relies
on the SDP relaxation.
We generalize the combinatorial analysis of integral solutions from the
previous literature to fractional solutions by identifying some properties of
fractional solutions of the Lasserre system via the decomposition result of
Karlin, Mathieu and Nguyen (IPCO 2011).
Also, we present an example of stemless TAP such that the approximation
guarantee of $\frac32$ is tight for the algorithm.
In Part II of this paper, we extend the methods of Part I to prove the same
results relative to the same SDP relaxation for TAP.
| [
{
"created": "Sat, 29 Aug 2015 20:48:09 GMT",
"version": "v1"
}
] | 2015-09-01 | [
[
"Cheriyan",
"Joe",
""
],
[
"Gao",
"Zhihan",
""
]
] | In Part I, we study a special case of the unweighted Tree Augmentation Problem (TAP) via the Lasserre (Sum of Squares) system. In the special case, we forbid so-called stems; these are a particular type of subtree configuration. For stemless TAP, we prove that the integrality ratio of an SDP relaxation (the Lasserre tightening of an LP relaxation) is $\leq \frac{3}{2}+\epsilon$, where $\epsilon>0$ can be any small constant. We obtain this result by designing a polynomial-time algorithm for stemless TAP that achieves an approximation guarantee of ($\frac32+\epsilon$) relative to the SDP relaxation. The algorithm is combinatorial and does not solve the SDP relaxation, but our analysis relies on the SDP relaxation. We generalize the combinatorial analysis of integral solutions from the previous literature to fractional solutions by identifying some properties of fractional solutions of the Lasserre system via the decomposition result of Karlin, Mathieu and Nguyen (IPCO 2011). Also, we present an example of stemless TAP such that the approximation guarantee of $\frac32$ is tight for the algorithm. In Part II of this paper, we extend the methods of Part I to prove the same results relative to the same SDP relaxation for TAP. |
1908.00485 | Zhun Zhong | Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li and Yi Yang | Learning to Adapt Invariance in Memory for Person Re-identification | Extension of conference version: arXiv:1904.01990 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work considers the problem of unsupervised domain adaptation in person
re-identification (re-ID), which aims to transfer knowledge from the source
domain to the target domain. Existing methods are primary to reduce the
inter-domain shift between the domains, which however usually overlook the
relations among target samples. This paper investigates into the intra-domain
variations of the target domain and proposes a novel adaptation framework
w.r.t. three types of underlying invariance, i.e., Exemplar-Invariance,
Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar
memory is introduced to store features of samples, which can effectively and
efficiently enforce the invariance constraints over the global dataset. We
further present the Graph-based Positive Prediction (GPP) method to explore
reliable neighbors for the target domain, which is built upon the memory and is
trained on the source samples. Experiments demonstrate that 1) the three
invariance properties are indispensable for effective domain adaptation, 2) the
memory plays a key role in implementing invariance learning and improves the
performance with limited extra computation cost, 3) GPP could facilitate the
invariance learning and thus significantly improves the results, and 4) our
approach produces new state-of-the-art adaptation accuracy on three re-ID
large-scale benchmarks.
| [
{
"created": "Thu, 1 Aug 2019 16:20:16 GMT",
"version": "v1"
}
] | 2019-08-02 | [
[
"Zhong",
"Zhun",
""
],
[
"Zheng",
"Liang",
""
],
[
"Luo",
"Zhiming",
""
],
[
"Li",
"Shaozi",
""
],
[
"Yang",
"Yi",
""
]
] | This work considers the problem of unsupervised domain adaptation in person re-identification (re-ID), which aims to transfer knowledge from the source domain to the target domain. Existing methods are primary to reduce the inter-domain shift between the domains, which however usually overlook the relations among target samples. This paper investigates into the intra-domain variations of the target domain and proposes a novel adaptation framework w.r.t. three types of underlying invariance, i.e., Exemplar-Invariance, Camera-Invariance, and Neighborhood-Invariance. Specifically, an exemplar memory is introduced to store features of samples, which can effectively and efficiently enforce the invariance constraints over the global dataset. We further present the Graph-based Positive Prediction (GPP) method to explore reliable neighbors for the target domain, which is built upon the memory and is trained on the source samples. Experiments demonstrate that 1) the three invariance properties are indispensable for effective domain adaptation, 2) the memory plays a key role in implementing invariance learning and improves the performance with limited extra computation cost, 3) GPP could facilitate the invariance learning and thus significantly improves the results, and 4) our approach produces new state-of-the-art adaptation accuracy on three re-ID large-scale benchmarks. |
2408.01962 | Robert Wolfe | Robert Wolfe, Tanushree Mitra | The Implications of Open Generative Models in Human-Centered Data
Science Work: A Case Study with Fact-Checking Organizations | Accepted at Artificial Intelligence, Ethics, and Society 2024 | null | null | null | cs.HC cs.AI cs.CL cs.CY cs.ET | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Calls to use open generative language models in academic research have
highlighted the need for reproducibility and transparency in scientific
research. However, the impact of generative AI extends well beyond academia, as
corporations and public interest organizations have begun integrating these
models into their data science pipelines. We expand this lens to include the
impact of open models on organizations, focusing specifically on fact-checking
organizations, which use AI to observe and analyze large volumes of circulating
misinformation, yet must also ensure the reproducibility and impartiality of
their work. We wanted to understand where fact-checking organizations use open
models in their data science pipelines; what motivates their use of open models
or proprietary models; and how their use of open or proprietary models can
inform research on the societal impact of generative AI. To answer these
questions, we conducted an interview study with N=24 professionals at 20
fact-checking organizations on six continents. Based on these interviews, we
offer a five-component conceptual model of where fact-checking organizations
employ generative AI to support or automate parts of their data science
pipeline, including Data Ingestion, Data Analysis, Data Retrieval, Data
Delivery, and Data Sharing. We then provide taxonomies of fact-checking
organizations' motivations for using open models and the limitations that
prevent them for further adopting open models, finding that they prefer open
models for Organizational Autonomy, Data Privacy and Ownership, Application
Specificity, and Capability Transparency. However, they nonetheless use
proprietary models due to perceived advantages in Performance, Usability, and
Safety, as well as Opportunity Costs related to participation in emerging
generative AI ecosystems. Our work provides novel perspective on open models in
data-driven organizations.
| [
{
"created": "Sun, 4 Aug 2024 08:41:48 GMT",
"version": "v1"
}
] | 2024-08-06 | [
[
"Wolfe",
"Robert",
""
],
[
"Mitra",
"Tanushree",
""
]
] | Calls to use open generative language models in academic research have highlighted the need for reproducibility and transparency in scientific research. However, the impact of generative AI extends well beyond academia, as corporations and public interest organizations have begun integrating these models into their data science pipelines. We expand this lens to include the impact of open models on organizations, focusing specifically on fact-checking organizations, which use AI to observe and analyze large volumes of circulating misinformation, yet must also ensure the reproducibility and impartiality of their work. We wanted to understand where fact-checking organizations use open models in their data science pipelines; what motivates their use of open models or proprietary models; and how their use of open or proprietary models can inform research on the societal impact of generative AI. To answer these questions, we conducted an interview study with N=24 professionals at 20 fact-checking organizations on six continents. Based on these interviews, we offer a five-component conceptual model of where fact-checking organizations employ generative AI to support or automate parts of their data science pipeline, including Data Ingestion, Data Analysis, Data Retrieval, Data Delivery, and Data Sharing. We then provide taxonomies of fact-checking organizations' motivations for using open models and the limitations that prevent them for further adopting open models, finding that they prefer open models for Organizational Autonomy, Data Privacy and Ownership, Application Specificity, and Capability Transparency. However, they nonetheless use proprietary models due to perceived advantages in Performance, Usability, and Safety, as well as Opportunity Costs related to participation in emerging generative AI ecosystems. Our work provides novel perspective on open models in data-driven organizations. |
2311.03725 | Arti Kumbhar | Arti Kumbhar, Amruta Chougule, Priya Lokhande, Saloni Navaghane, Aditi
Burud, Saee Nimbalkar | DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries | Research Paper for Defect Detection for Manufacturing Industries
Using Deep Learning Techniques: 5 pages, 8 figures | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks
(RNNs), and Generative Adversarial Networks (GANs), our system introduces an
innovative approach to defect detection in manufacturing. This technology
excels in precisely identifying faults by extracting intricate details from
product photographs, utilizing RNNs to detect evolving errors and generating
synthetic defect data to bolster the model's robustness and adaptability across
various defect scenarios. The project leverages a deep learning framework to
automate real-time flaw detection in the manufacturing process. It harnesses
extensive datasets of annotated images to discern complex defect patterns. This
integrated system seamlessly fits into production workflows, thereby boosting
efficiency and elevating product quality. As a result, it reduces waste and
operational costs, ultimately enhancing market competitiveness.
| [
{
"created": "Tue, 7 Nov 2023 04:59:43 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Nov 2023 07:45:58 GMT",
"version": "v2"
}
] | 2023-11-09 | [
[
"Kumbhar",
"Arti",
""
],
[
"Chougule",
"Amruta",
""
],
[
"Lokhande",
"Priya",
""
],
[
"Navaghane",
"Saloni",
""
],
[
"Burud",
"Aditi",
""
],
[
"Nimbalkar",
"Saee",
""
]
] | Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This technology excels in precisely identifying faults by extracting intricate details from product photographs, utilizing RNNs to detect evolving errors and generating synthetic defect data to bolster the model's robustness and adaptability across various defect scenarios. The project leverages a deep learning framework to automate real-time flaw detection in the manufacturing process. It harnesses extensive datasets of annotated images to discern complex defect patterns. This integrated system seamlessly fits into production workflows, thereby boosting efficiency and elevating product quality. As a result, it reduces waste and operational costs, ultimately enhancing market competitiveness. |
2103.11297 | Ryan Rossi | Camille Harris, Ryan A. Rossi, Sana Malik, Jane Hoffswell, Fan Du, Tak
Yeon Lee, Eunyee Koh, Handong Zhao | Insight-centric Visualization Recommendation | null | null | null | null | cs.HC cs.AI cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visualization recommendation systems simplify exploratory data analysis (EDA)
and make understanding data more accessible to users of all skill levels by
automatically generating visualizations for users to explore. However, most
existing visualization recommendation systems focus on ranking all
visualizations into a single list or set of groups based on particular
attributes or encodings. This global ranking makes it difficult and
time-consuming for users to find the most interesting or relevant insights. To
address these limitations, we introduce a novel class of visualization
recommendation systems that automatically rank and recommend both groups of
related insights as well as the most important insights within each group. Our
proposed approach combines results from many different learning-based methods
to discover insights automatically. A key advantage is that this approach
generalizes to a wide variety of attribute types such as categorical,
numerical, and temporal, as well as complex non-trivial combinations of these
different attribute types. To evaluate the effectiveness of our approach, we
implemented a new insight-centric visualization recommendation system,
SpotLight, which generates and ranks annotated visualizations to explain each
insight. We conducted a user study with 12 participants and two datasets which
showed that users are able to quickly understand and find relevant insights in
unfamiliar data.
| [
{
"created": "Sun, 21 Mar 2021 03:30:22 GMT",
"version": "v1"
}
] | 2021-03-23 | [
[
"Harris",
"Camille",
""
],
[
"Rossi",
"Ryan A.",
""
],
[
"Malik",
"Sana",
""
],
[
"Hoffswell",
"Jane",
""
],
[
"Du",
"Fan",
""
],
[
"Lee",
"Tak Yeon",
""
],
[
"Koh",
"Eunyee",
""
],
[
"Zhao",
"Handong",
""
]
] | Visualization recommendation systems simplify exploratory data analysis (EDA) and make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all visualizations into a single list or set of groups based on particular attributes or encodings. This global ranking makes it difficult and time-consuming for users to find the most interesting or relevant insights. To address these limitations, we introduce a novel class of visualization recommendation systems that automatically rank and recommend both groups of related insights as well as the most important insights within each group. Our proposed approach combines results from many different learning-based methods to discover insights automatically. A key advantage is that this approach generalizes to a wide variety of attribute types such as categorical, numerical, and temporal, as well as complex non-trivial combinations of these different attribute types. To evaluate the effectiveness of our approach, we implemented a new insight-centric visualization recommendation system, SpotLight, which generates and ranks annotated visualizations to explain each insight. We conducted a user study with 12 participants and two datasets which showed that users are able to quickly understand and find relevant insights in unfamiliar data. |
2310.15624 | Yan Lu | Yan Lu, Xinzhu Ma, Lei Yang, Tianzhu Zhang, Yating Liu, Qi Chu, Tong
He, Yonghui Li, Wanli Ouyang | GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D
Object Detection | 18 pages, 9 figures | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Geometry plays a significant role in monocular 3D object detection. It can be
used to estimate object depth by using the perspective projection between
object's physical size and 2D projection in the image plane, which can
introduce mathematical priors into deep models. However, this projection
process also introduces error amplification, where the error of the estimated
height is amplified and reflected into the projected depth. It leads to
unreliable depth inferences and also impairs training stability. To tackle this
problem, we propose a novel Geometry Uncertainty Propagation Network (GUPNet++)
by modeling geometry projection in a probabilistic manner. This ensures depth
predictions are well-bounded and associated with a reasonable uncertainty. The
significance of introducing such geometric uncertainty is two-fold: (1). It
models the uncertainty propagation relationship of the geometry projection
during training, improving the stability and efficiency of the end-to-end model
learning. (2). It can be derived to a highly reliable confidence to indicate
the quality of the 3D detection result, enabling more reliable detection
inference. Experiments show that the proposed approach not only obtains
(state-of-the-art) SOTA performance in image-based monocular 3D detection but
also demonstrates superiority in efficacy with a simplified framework.
| [
{
"created": "Tue, 24 Oct 2023 08:45:15 GMT",
"version": "v1"
}
] | 2023-10-25 | [
[
"Lu",
"Yan",
""
],
[
"Ma",
"Xinzhu",
""
],
[
"Yang",
"Lei",
""
],
[
"Zhang",
"Tianzhu",
""
],
[
"Liu",
"Yating",
""
],
[
"Chu",
"Qi",
""
],
[
"He",
"Tong",
""
],
[
"Li",
"Yonghui",
""
],
[
"Ouyang",
"Wanli",
""
]
] | Geometry plays a significant role in monocular 3D object detection. It can be used to estimate object depth by using the perspective projection between object's physical size and 2D projection in the image plane, which can introduce mathematical priors into deep models. However, this projection process also introduces error amplification, where the error of the estimated height is amplified and reflected into the projected depth. It leads to unreliable depth inferences and also impairs training stability. To tackle this problem, we propose a novel Geometry Uncertainty Propagation Network (GUPNet++) by modeling geometry projection in a probabilistic manner. This ensures depth predictions are well-bounded and associated with a reasonable uncertainty. The significance of introducing such geometric uncertainty is two-fold: (1). It models the uncertainty propagation relationship of the geometry projection during training, improving the stability and efficiency of the end-to-end model learning. (2). It can be derived to a highly reliable confidence to indicate the quality of the 3D detection result, enabling more reliable detection inference. Experiments show that the proposed approach not only obtains (state-of-the-art) SOTA performance in image-based monocular 3D detection but also demonstrates superiority in efficacy with a simplified framework. |
2105.10016 | Xinyu Liu | Xinyu Liu, Qi Zhou, Joy Arulraj, and Alessandro Orso | Testing DBMS Performance with Mutations | null | null | null | null | cs.DB cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Because database systems are the critical component of modern data-intensive
applications, it is important to ensure that they operate correctly. To this
end, developers extensively test these systems to eliminate bugs that
negatively affect functionality. In addition to functional bugs, however, there
is another important class of bugs: performance bugs. These bugs negatively
affect the response time of a database system and can therefore affect the
overall performance of the system. Despite their impact on end-user experience,
performance bugs have received considerably less attention than functional
bugs.
In this paper, we present AMOEBA, a system for automatically detecting
performance bugs in database systems. The core idea behind AMOEBA is to
construct query pairs that are semantically equivalent to each other and then
compare their response time on the same database system. If the queries exhibit
a significant difference in their runtime performance, then the root cause is
likely a performance bug in the system. We propose a novel set of structure and
predicate mutation rules for constructing query pairs that are likely to
uncover performance bugs. We introduce feedback mechanisms for improving the
efficacy and computational efficiency of the tool. We evaluate AMOEBA on two
widely-used DBMSs, namely PostgreSQL and CockroachDB. AMOEBA has discovered 20
previously-unknown performance bugs, among which developers have already
confirmed 14 and fixed 4.
| [
{
"created": "Thu, 20 May 2021 20:18:43 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Sep 2021 01:49:11 GMT",
"version": "v2"
}
] | 2021-09-03 | [
[
"Liu",
"Xinyu",
""
],
[
"Zhou",
"Qi",
""
],
[
"Arulraj",
"Joy",
""
],
[
"Orso",
"Alessandro",
""
]
] | Because database systems are the critical component of modern data-intensive applications, it is important to ensure that they operate correctly. To this end, developers extensively test these systems to eliminate bugs that negatively affect functionality. In addition to functional bugs, however, there is another important class of bugs: performance bugs. These bugs negatively affect the response time of a database system and can therefore affect the overall performance of the system. Despite their impact on end-user experience, performance bugs have received considerably less attention than functional bugs. In this paper, we present AMOEBA, a system for automatically detecting performance bugs in database systems. The core idea behind AMOEBA is to construct query pairs that are semantically equivalent to each other and then compare their response time on the same database system. If the queries exhibit a significant difference in their runtime performance, then the root cause is likely a performance bug in the system. We propose a novel set of structure and predicate mutation rules for constructing query pairs that are likely to uncover performance bugs. We introduce feedback mechanisms for improving the efficacy and computational efficiency of the tool. We evaluate AMOEBA on two widely-used DBMSs, namely PostgreSQL and CockroachDB. AMOEBA has discovered 20 previously-unknown performance bugs, among which developers have already confirmed 14 and fixed 4. |
2005.10801 | Farhad Pakdaman | Farhad Pakdaman, Mohammad Ali Adelimanesh, Moncef Gabbouj, Mahmoud
Reza Hashemi | Complexity Analysis Of Next-Generation VVC Encoding and Decoding | IEEE ICIP 2020 | Proceedings of International Conference on Image Processing
(ICIP), (2020) 3134-3138 | 10.1109/ICIP40778.2020.9190983 | null | cs.MM cs.CC eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While the next generation video compression standard, Versatile Video Coding
(VVC), provides a superior compression efficiency, its computational complexity
dramatically increases. This paper thoroughly analyzes this complexity for both
encoder and decoder of VVC Test Model 6, by quantifying the complexity
break-down for each coding tool and measuring the complexity and memory
requirements for VVC encoding/decoding. These extensive analyses are performed
for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD),
Random-Access (RA), and All-Intra (AI) conditions (a total of 320
encoding/decoding). Results indicate that the VVC encoder and decoder are 5x
and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI,
respectively. Detailed analysis of coding tools reveals that in LD on average,
motion estimation tools with 53%, transformation and quantization with 22%, and
entropy coding with 7% dominate the encoding complexity. In decoding, loop
filters with 30%, motion compensation with 20%, and entropy decoding with 16%,
are the most complex modules. Moreover, the required memory bandwidth for VVC
encoding/decoding are measured through memory profiling, which are 30x and 3x
of HEVC. The reported results and insights are a guide for future research and
implementations of energy-efficient VVC encoder/decoder.
| [
{
"created": "Thu, 21 May 2020 17:30:42 GMT",
"version": "v1"
}
] | 2020-10-08 | [
[
"Pakdaman",
"Farhad",
""
],
[
"Adelimanesh",
"Mohammad Ali",
""
],
[
"Gabbouj",
"Moncef",
""
],
[
"Hashemi",
"Mahmoud Reza",
""
]
] | While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases. This paper thoroughly analyzes this complexity for both encoder and decoder of VVC Test Model 6, by quantifying the complexity break-down for each coding tool and measuring the complexity and memory requirements for VVC encoding/decoding. These extensive analyses are performed for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD), Random-Access (RA), and All-Intra (AI) conditions (a total of 320 encoding/decoding). Results indicate that the VVC encoder and decoder are 5x and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI, respectively. Detailed analysis of coding tools reveals that in LD on average, motion estimation tools with 53%, transformation and quantization with 22%, and entropy coding with 7% dominate the encoding complexity. In decoding, loop filters with 30%, motion compensation with 20%, and entropy decoding with 16%, are the most complex modules. Moreover, the required memory bandwidth for VVC encoding/decoding are measured through memory profiling, which are 30x and 3x of HEVC. The reported results and insights are a guide for future research and implementations of energy-efficient VVC encoder/decoder. |
2206.10025 | Petra Wolf | Jonas Lingg, Mateus de Oliveira Oliveira, Petra Wolf | Learning from Positive and Negative Examples: New Proof for Binary
Alphabets | null | null | null | null | cs.FL cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the most fundamental problems in computational learning theory is the
the problem of learning a finite automaton $A$ consistent with a finite set $P$
of positive examples and with a finite set $N$ of negative examples. By
consistency, we mean that $A$ accepts all strings in $P$ and rejects all
strings in $N$. It is well known that this problem is NP-complete. In the
literature, it is stated that this NP-hardness holds even in the case of a
binary alphabet. As a standard reference for this theorem, the work of Gold
from 1978 is either cited or adapted. But as a crucial detail, the work of Gold
actually considered Mealy machines and not deterministic finite state automata
(DFAs) as they are considered nowadays. As Mealy automata are equipped with an
output function, they can be more compact than DFAs which accept the same
language. We show that the adaptions of Gold's construction for Mealy machines
stated in the literature have some issues and give a new construction for DFAs
with a binary alphabet ourselves.
| [
{
"created": "Mon, 20 Jun 2022 22:20:48 GMT",
"version": "v1"
}
] | 2022-06-22 | [
[
"Lingg",
"Jonas",
""
],
[
"Oliveira",
"Mateus de Oliveira",
""
],
[
"Wolf",
"Petra",
""
]
] | One of the most fundamental problems in computational learning theory is the the problem of learning a finite automaton $A$ consistent with a finite set $P$ of positive examples and with a finite set $N$ of negative examples. By consistency, we mean that $A$ accepts all strings in $P$ and rejects all strings in $N$. It is well known that this problem is NP-complete. In the literature, it is stated that this NP-hardness holds even in the case of a binary alphabet. As a standard reference for this theorem, the work of Gold from 1978 is either cited or adapted. But as a crucial detail, the work of Gold actually considered Mealy machines and not deterministic finite state automata (DFAs) as they are considered nowadays. As Mealy automata are equipped with an output function, they can be more compact than DFAs which accept the same language. We show that the adaptions of Gold's construction for Mealy machines stated in the literature have some issues and give a new construction for DFAs with a binary alphabet ourselves. |
2102.12684 | Wil Thomason | Claire Liang (1), Wil Thomason (2), E. Andy Ricci (1), and Soham
Sankaran (1, 3) ((1) Cornell University Department of Computer Science, (2)
Rice University Department of Computer Science, (3) Pashi Corp.) | Ensuring Progress for Multiple Mobile Robots via Space Partitioning,
Motion Rules, and Adaptively Centralized Conflict Resolution | 9 pages, 4 figures. Submitted to IROS 2021 | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In environments where multiple robots must coordinate in a shared space,
decentralized approaches allow for decoupled planning at the cost of global
guarantees, while centralized approaches make the opposite trade-off. These
solutions make a range of assumptions - commonly, that all the robots share the
same planning strategies. In this work, we present a framework that ensures
progress for all robots without assumptions on any robot's planning strategy by
(1) generating a partition of the environment into "flow", "open", and
"passage" regions and (2) imposing a set of rules for robot motion in these
regions. These rules for robot motion prevent deadlock through an adaptively
centralized protocol for resolving spatial conflicts between robots. Our
proposed framework ensures progress for all robots without a grid-like
discretization of the environment or strong requirements on robot
communication, coordination, or cooperation. Each robot can freely choose how
to plan and coordinate for itself, without being vulnerable to other robots or
groups of robots blocking them from their goals, as long as they follow the
rules when necessary. We describe our space partition and motion rules, prove
that the motion rules suffice to guarantee progress in partitioned
environments, and demonstrate several cases in simulated polygonal
environments. This work strikes a balance between each robot's planning
independence and a guarantee that each robot can always reach any goal in
finite time.
| [
{
"created": "Thu, 25 Feb 2021 04:51:09 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Mar 2022 18:45:50 GMT",
"version": "v2"
}
] | 2022-03-08 | [
[
"Liang",
"Claire",
""
],
[
"Thomason",
"Wil",
""
],
[
"Ricci",
"E. Andy",
""
],
[
"Sankaran",
"Soham",
""
]
] | In environments where multiple robots must coordinate in a shared space, decentralized approaches allow for decoupled planning at the cost of global guarantees, while centralized approaches make the opposite trade-off. These solutions make a range of assumptions - commonly, that all the robots share the same planning strategies. In this work, we present a framework that ensures progress for all robots without assumptions on any robot's planning strategy by (1) generating a partition of the environment into "flow", "open", and "passage" regions and (2) imposing a set of rules for robot motion in these regions. These rules for robot motion prevent deadlock through an adaptively centralized protocol for resolving spatial conflicts between robots. Our proposed framework ensures progress for all robots without a grid-like discretization of the environment or strong requirements on robot communication, coordination, or cooperation. Each robot can freely choose how to plan and coordinate for itself, without being vulnerable to other robots or groups of robots blocking them from their goals, as long as they follow the rules when necessary. We describe our space partition and motion rules, prove that the motion rules suffice to guarantee progress in partitioned environments, and demonstrate several cases in simulated polygonal environments. This work strikes a balance between each robot's planning independence and a guarantee that each robot can always reach any goal in finite time. |
1802.05666 | Jonathan Uesato | Jonathan Uesato, Brendan O'Donoghue, Aaron van den Oord, Pushmeet
Kohli | Adversarial Risk and the Dangers of Evaluating Against Weak Attacks | null | null | null | null | cs.LG cs.CR stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates recently proposed approaches for defending against
adversarial examples and evaluating adversarial robustness. We motivate
'adversarial risk' as an objective for achieving models robust to worst-case
inputs. We then frame commonly used attacks and evaluation metrics as defining
a tractable surrogate objective to the true adversarial risk. This suggests
that models may optimize this surrogate rather than the true adversarial risk.
We formalize this notion as 'obscurity to an adversary,' and develop tools and
heuristics for identifying obscured models and designing transparent models. We
demonstrate that this is a significant problem in practice by repurposing
gradient-free optimization techniques into adversarial attacks, which we use to
decrease the accuracy of several recently proposed defenses to near zero. Our
hope is that our formulations and results will help researchers to develop more
powerful defenses.
| [
{
"created": "Thu, 15 Feb 2018 17:13:18 GMT",
"version": "v1"
},
{
"created": "Tue, 12 Jun 2018 14:20:27 GMT",
"version": "v2"
}
] | 2018-06-13 | [
[
"Uesato",
"Jonathan",
""
],
[
"O'Donoghue",
"Brendan",
""
],
[
"Oord",
"Aaron van den",
""
],
[
"Kohli",
"Pushmeet",
""
]
] | This paper investigates recently proposed approaches for defending against adversarial examples and evaluating adversarial robustness. We motivate 'adversarial risk' as an objective for achieving models robust to worst-case inputs. We then frame commonly used attacks and evaluation metrics as defining a tractable surrogate objective to the true adversarial risk. This suggests that models may optimize this surrogate rather than the true adversarial risk. We formalize this notion as 'obscurity to an adversary,' and develop tools and heuristics for identifying obscured models and designing transparent models. We demonstrate that this is a significant problem in practice by repurposing gradient-free optimization techniques into adversarial attacks, which we use to decrease the accuracy of several recently proposed defenses to near zero. Our hope is that our formulations and results will help researchers to develop more powerful defenses. |
2210.15804 | Gayathri Manikutty | Sreejith Sasidharan, Pranav Prabha, Devasena Pasupuleti, Anand M Das,
Chaitanya Kapoor, Gayathri Manikutty, Praveen Pankajakshan, Bhavani Rao | Handwashing Action Detection System for an Autonomous Social Robot | null | null | 10.1109/TENCON55691.2022.9977684 | null | cs.RO cs.AI | http://creativecommons.org/licenses/by/4.0/ | Young children are at an increased risk of contracting contagious diseases
such as COVID-19 due to improper hand hygiene. An autonomous social agent that
observes children while handwashing and encourages good hand washing practices
could provide an opportunity for handwashing behavior to become a habit. In
this article, we present a human action recognition system, which is part of
the vision system of a social robot platform, to assist children in developing
a correct handwashing technique. A modified convolution neural network (CNN)
architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with
a VGG-16 architecture as the backbone is trained and validated on an augmented
dataset. The modified architecture generalizes well with an accuracy of 90% for
the WHO-prescribed handwashing steps even in an unseen environment. Our
findings indicate that the approach can recognize even subtle hand movements in
the video and can be used for gesture detection and classification in social
robotics.
| [
{
"created": "Thu, 27 Oct 2022 23:46:56 GMT",
"version": "v1"
}
] | 2023-06-21 | [
[
"Sasidharan",
"Sreejith",
""
],
[
"Prabha",
"Pranav",
""
],
[
"Pasupuleti",
"Devasena",
""
],
[
"Das",
"Anand M",
""
],
[
"Kapoor",
"Chaitanya",
""
],
[
"Manikutty",
"Gayathri",
""
],
[
"Pankajakshan",
"Praveen",
""
],
[
"Rao",
"Bhavani",
""
]
] | Young children are at an increased risk of contracting contagious diseases such as COVID-19 due to improper hand hygiene. An autonomous social agent that observes children while handwashing and encourages good hand washing practices could provide an opportunity for handwashing behavior to become a habit. In this article, we present a human action recognition system, which is part of the vision system of a social robot platform, to assist children in developing a correct handwashing technique. A modified convolution neural network (CNN) architecture with Channel Spatial Attention Bilinear Pooling (CSAB) frame, with a VGG-16 architecture as the backbone is trained and validated on an augmented dataset. The modified architecture generalizes well with an accuracy of 90% for the WHO-prescribed handwashing steps even in an unseen environment. Our findings indicate that the approach can recognize even subtle hand movements in the video and can be used for gesture detection and classification in social robotics. |
2312.05594 | Dong In Kim | Nguyen Van Huynh, Jiacheng Wang, Hongyang Du, Dinh Thai Hoang, Dusit
Niyato, Diep N. Nguyen, Dong In Kim, and Khaled B. Letaief | Generative AI for Physical Layer Communications: A Survey | null | null | null | null | cs.NI cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recent evolution of generative artificial intelligence (GAI) leads to the
emergence of groundbreaking applications such as ChatGPT, which not only
enhances the efficiency of digital content production, such as text, audio,
video, or even network traffic data, but also enriches its diversity. Beyond
digital content creation, GAI's capability in analyzing complex data
distributions offers great potential for wireless communications, particularly
amidst a rapid expansion of new physical layer communication technologies. For
example, the diffusion model can learn input signal distributions and use them
to improve the channel estimation accuracy, while the variational autoencoder
can model channel distribution and infer latent variables for blind channel
equalization. Therefore, this paper presents a comprehensive investigation of
GAI's applications for communications at the physical layer, ranging from
traditional issues, including signal classification, channel estimation, and
equalization, to emerging topics, such as intelligent reflecting surfaces and
joint source channel coding. We also compare GAI-enabled physical layer
communications with those supported by traditional AI, highlighting GAI's
inherent capabilities and unique contributions in these areas. Finally, the
paper discusses open issues and proposes several future research directions,
laying a foundation for further exploration and advancement of GAI in physical
layer communications.
| [
{
"created": "Sat, 9 Dec 2023 15:20:56 GMT",
"version": "v1"
}
] | 2023-12-12 | [
[
"Van Huynh",
"Nguyen",
""
],
[
"Wang",
"Jiacheng",
""
],
[
"Du",
"Hongyang",
""
],
[
"Hoang",
"Dinh Thai",
""
],
[
"Niyato",
"Dusit",
""
],
[
"Nguyen",
"Diep N.",
""
],
[
"Kim",
"Dong In",
""
],
[
"Letaief",
"Khaled B.",
""
]
] | The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity. Beyond digital content creation, GAI's capability in analyzing complex data distributions offers great potential for wireless communications, particularly amidst a rapid expansion of new physical layer communication technologies. For example, the diffusion model can learn input signal distributions and use them to improve the channel estimation accuracy, while the variational autoencoder can model channel distribution and infer latent variables for blind channel equalization. Therefore, this paper presents a comprehensive investigation of GAI's applications for communications at the physical layer, ranging from traditional issues, including signal classification, channel estimation, and equalization, to emerging topics, such as intelligent reflecting surfaces and joint source channel coding. We also compare GAI-enabled physical layer communications with those supported by traditional AI, highlighting GAI's inherent capabilities and unique contributions in these areas. Finally, the paper discusses open issues and proposes several future research directions, laying a foundation for further exploration and advancement of GAI in physical layer communications. |
2307.00771 | Ning Lin | Ning Lin, Shaocong Wang, Yi Li, Bo Wang, Shuhui Shi, Yangu He, Woyu
Zhang, Yifei Yu, Yue Zhang, Xiaojuan Qi, Xiaoming Chen, Hao Jiang, Xumeng
Zhang, Peng Lin, Xiaoxin Xu, Qi Liu, Zhongrui Wang, Dashan Shang and Ming Liu | Resistive memory-based zero-shot liquid state machine for multimodal
event data learning | null | null | null | null | cs.ET | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The human brain is a complex spiking neural network (SNN) that learns
multimodal signals in a zero-shot manner by generalizing existing knowledge.
Remarkably, the brain achieves this with minimal power consumption, using
event-based signals that propagate within its structure. However, mimicking the
human brain in neuromorphic hardware presents both hardware and software
challenges. Hardware limitations, such as the slowdown of Moore's law and the
von Neumann bottleneck, hinder the efficiency of digital computers. On the
software side, SNNs are known for their difficult training, especially when
learning multimodal signals. To overcome these challenges, we propose a
hardware-software co-design that combines a fixed and random liquid state
machine (LSM) SNN encoder with trainable artificial neural network (ANN)
projections. The LSM is physically implemented using analogue resistive memory,
leveraging the inherent stochasticity of resistive switching to generate random
weights. This highly efficient and nanoscale in-memory computing approach
effectively addresses the von Neumann bottleneck and the slowdown of Moore's
law. The ANN projections are implemented digitally, allowing for easy
optimization using contrastive loss, which helps to overcome the difficulties
associated with SNN training. We experimentally implement this co-design on a
40nm 256Kb in-memory computing macro. We first demonstrate LSM-based event
encoding through supervised classification and linear probing on the N-MNIST
and N-TIDIGITS datasets.
| [
{
"created": "Mon, 3 Jul 2023 06:21:05 GMT",
"version": "v1"
}
] | 2023-07-04 | [
[
"Lin",
"Ning",
""
],
[
"Wang",
"Shaocong",
""
],
[
"Li",
"Yi",
""
],
[
"Wang",
"Bo",
""
],
[
"Shi",
"Shuhui",
""
],
[
"He",
"Yangu",
""
],
[
"Zhang",
"Woyu",
""
],
[
"Yu",
"Yifei",
""
],
[
"Zhang",
"Yue",
""
],
[
"Qi",
"Xiaojuan",
""
],
[
"Chen",
"Xiaoming",
""
],
[
"Jiang",
"Hao",
""
],
[
"Zhang",
"Xumeng",
""
],
[
"Lin",
"Peng",
""
],
[
"Xu",
"Xiaoxin",
""
],
[
"Liu",
"Qi",
""
],
[
"Wang",
"Zhongrui",
""
],
[
"Shang",
"Dashan",
""
],
[
"Liu",
"Ming",
""
]
] | The human brain is a complex spiking neural network (SNN) that learns multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, the brain achieves this with minimal power consumption, using event-based signals that propagate within its structure. However, mimicking the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and the von Neumann bottleneck, hinder the efficiency of digital computers. On the software side, SNNs are known for their difficult training, especially when learning multimodal signals. To overcome these challenges, we propose a hardware-software co-design that combines a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. The LSM is physically implemented using analogue resistive memory, leveraging the inherent stochasticity of resistive switching to generate random weights. This highly efficient and nanoscale in-memory computing approach effectively addresses the von Neumann bottleneck and the slowdown of Moore's law. The ANN projections are implemented digitally, allowing for easy optimization using contrastive loss, which helps to overcome the difficulties associated with SNN training. We experimentally implement this co-design on a 40nm 256Kb in-memory computing macro. We first demonstrate LSM-based event encoding through supervised classification and linear probing on the N-MNIST and N-TIDIGITS datasets. |
0812.1394 | Sahbi Sidhom | Sahbi Sidhom (LORIA, Sii) | Conceptual approach through an annotation process for the representation
and the information contents enhancement in economic intelligence (EI) | null | Journal of Global Management Research (2008) 15 pages | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the era of the information society, the impact of the information systems
on the economy of material and immaterial is certainly perceptible. With
regards to the information resources of an organization, the annotation
involved to enrich informational content, to track the intellectual activities
on a document and to set the added value on information for the benefit of
solving a decision-making problem in the context of economic intelligence. Our
contribution is distinguished by the representation of an annotation process
and its inherent concepts to lead the decisionmaker to an anticipated decision:
the provision of relevant and annotated information. Such information in the
system is made easy by taking into account the diversity of resources and those
that are well annotated so formally and informally by the EI actors. A capital
research framework consist of integrating in the decision-making process the
annotator activity, the software agent (or the reasoning mechanisms) and the
information resources enhancement.
| [
{
"created": "Sun, 7 Dec 2008 20:07:37 GMT",
"version": "v1"
}
] | 2008-12-10 | [
[
"Sidhom",
"Sahbi",
"",
"LORIA, Sii"
]
] | In the era of the information society, the impact of the information systems on the economy of material and immaterial is certainly perceptible. With regards to the information resources of an organization, the annotation involved to enrich informational content, to track the intellectual activities on a document and to set the added value on information for the benefit of solving a decision-making problem in the context of economic intelligence. Our contribution is distinguished by the representation of an annotation process and its inherent concepts to lead the decisionmaker to an anticipated decision: the provision of relevant and annotated information. Such information in the system is made easy by taking into account the diversity of resources and those that are well annotated so formally and informally by the EI actors. A capital research framework consist of integrating in the decision-making process the annotator activity, the software agent (or the reasoning mechanisms) and the information resources enhancement. |
1703.10772 | Irshad Bhat | Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava and Dipti
Misra Sharma | Joining Hands: Exploiting Monolingual Treebanks for Parsing of
Code-mixing Data | 5 pages, EACL 2017 short paper | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose efficient and less resource-intensive strategies
for parsing of code-mixed data. These strategies are not constrained by
in-domain annotations, rather they leverage pre-existing monolingual annotated
resources for training. We show that these methods can produce significantly
better results as compared to an informed baseline. Besides, we also present a
data set of 450 Hindi and English code-mixed tweets of Hindi multilingual
speakers for evaluation. The data set is manually annotated with Universal
Dependencies.
| [
{
"created": "Fri, 31 Mar 2017 07:10:30 GMT",
"version": "v1"
}
] | 2017-04-03 | [
[
"Bhat",
"Irshad Ahmad",
""
],
[
"Bhat",
"Riyaz Ahmad",
""
],
[
"Shrivastava",
"Manish",
""
],
[
"Sharma",
"Dipti Misra",
""
]
] | In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Besides, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation. The data set is manually annotated with Universal Dependencies. |
2404.15194 | Michal Nazarczuk | Michal Nazarczuk, Jan Kristof Behrens, Karla Stepanova, Matej
Hoffmann, Krystian Mikolajczyk | Closed Loop Interactive Embodied Reasoning for Robot Manipulation | null | null | null | null | cs.RO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Embodied reasoning systems integrate robotic hardware and cognitive processes
to perform complex tasks typically in response to a natural language query
about a specific physical environment. This usually involves changing the
belief about the scene or physically interacting and changing the scene (e.g.
'Sort the objects from lightest to heaviest'). In order to facilitate the
development of such systems we introduce a new simulating environment that
makes use of MuJoCo physics engine and high-quality renderer Blender to provide
realistic visual observations that are also accurate to the physical state of
the scene. Together with the simulator we propose a new benchmark composed of
10 classes of multi-step reasoning scenarios that require simultaneous visual
and physical measurements. Finally, we develop a new modular Closed Loop
Interactive Reasoning (CLIER) approach that takes into account the measurements
of non-visual object properties, changes in the scene caused by external
disturbances as well as uncertain outcomes of robotic actions. We extensively
evaluate our reasoning approach in simulation and in the real world
manipulation tasks with a success rate above 76% and 64%, respectively.
| [
{
"created": "Tue, 23 Apr 2024 16:33:28 GMT",
"version": "v1"
}
] | 2024-04-24 | [
[
"Nazarczuk",
"Michal",
""
],
[
"Behrens",
"Jan Kristof",
""
],
[
"Stepanova",
"Karla",
""
],
[
"Hoffmann",
"Matej",
""
],
[
"Mikolajczyk",
"Krystian",
""
]
] | Embodied reasoning systems integrate robotic hardware and cognitive processes to perform complex tasks typically in response to a natural language query about a specific physical environment. This usually involves changing the belief about the scene or physically interacting and changing the scene (e.g. 'Sort the objects from lightest to heaviest'). In order to facilitate the development of such systems we introduce a new simulating environment that makes use of MuJoCo physics engine and high-quality renderer Blender to provide realistic visual observations that are also accurate to the physical state of the scene. Together with the simulator we propose a new benchmark composed of 10 classes of multi-step reasoning scenarios that require simultaneous visual and physical measurements. Finally, we develop a new modular Closed Loop Interactive Reasoning (CLIER) approach that takes into account the measurements of non-visual object properties, changes in the scene caused by external disturbances as well as uncertain outcomes of robotic actions. We extensively evaluate our reasoning approach in simulation and in the real world manipulation tasks with a success rate above 76% and 64%, respectively. |
2303.06644 | Tian Wen | Yan Lei, Tiantian Wen, Huan Xie, Lingfeng Fu, Chunyan Liu, Lei Xu,
Hongxia Sun | Mitigating the Effect of Class Imbalance in Fault Localization Using
Context-aware Generative Adversarial Network | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fault localization (FL) analyzes the execution information of a test suite to
pinpoint the root cause of a failure. The class imbalance of a test suite,
i.e., the imbalanced class proportion between passing test cases (i.e.,
majority class) and failing ones (i.e., minority class), adversely affects FL
effectiveness. To mitigate the effect of class imbalance in FL, we propose
CGAN4FL: a data augmentation approach using Context-aware Generative
Adversarial Network for Fault Localization. Specifically, CGAN4FL uses program
dependencies to construct a failure-inducing context showing how a failure is
caused. Then, CGAN4FL leverages a generative adversarial network to analyze the
failure-inducing context and synthesize the minority class of test cases (i.e.,
failing test cases). Finally, CGAN4FL augments the synthesized data into
original test cases to acquire a class-balanced dataset for FL. Our experiments
show that CGAN4FL significantly improves FL effectiveness, e.g., promoting
MLP-FL by 200.00%, 25.49%, and 17.81% under the Top-1, Top-5, and Top-10
respectively.
| [
{
"created": "Sun, 12 Mar 2023 12:26:52 GMT",
"version": "v1"
}
] | 2023-03-14 | [
[
"Lei",
"Yan",
""
],
[
"Wen",
"Tiantian",
""
],
[
"Xie",
"Huan",
""
],
[
"Fu",
"Lingfeng",
""
],
[
"Liu",
"Chunyan",
""
],
[
"Xu",
"Lei",
""
],
[
"Sun",
"Hongxia",
""
]
] | Fault localization (FL) analyzes the execution information of a test suite to pinpoint the root cause of a failure. The class imbalance of a test suite, i.e., the imbalanced class proportion between passing test cases (i.e., majority class) and failing ones (i.e., minority class), adversely affects FL effectiveness. To mitigate the effect of class imbalance in FL, we propose CGAN4FL: a data augmentation approach using Context-aware Generative Adversarial Network for Fault Localization. Specifically, CGAN4FL uses program dependencies to construct a failure-inducing context showing how a failure is caused. Then, CGAN4FL leverages a generative adversarial network to analyze the failure-inducing context and synthesize the minority class of test cases (i.e., failing test cases). Finally, CGAN4FL augments the synthesized data into original test cases to acquire a class-balanced dataset for FL. Our experiments show that CGAN4FL significantly improves FL effectiveness, e.g., promoting MLP-FL by 200.00%, 25.49%, and 17.81% under the Top-1, Top-5, and Top-10 respectively. |
1105.1824 | Haris Aziz | Haris Aziz and Paul Harrenstein and Evangelia Pyrga | Individual-based stability in hedonic games depending on the best or
worst players | 16 pages | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider coalition formation games in which each player has preferences
over the other players and his preferences over coalitions are based on the
best player ($\mathcal{B}$-/B-hedonic games) or the worst player
($\mathcal{W}$/W-hedonic games) in the coalition. We show that for
$\mathcal{B}$-hedonic games, an individually stable partition is guaranteed to
exist and can be computed efficiently. Similarly, there exists a
polynomial-time algorithm which returns a Nash stable partition (if one exists)
for $\mathcal{B}$-hedonic games with strict preferences. Both $\mathcal{W}$-
and W-hedonic games are equivalent if individual rationality is assumed. It is
also shown that for B- or $\mathcal{W}$-hedonic games, checking whether a Nash
stable partition or an individually stable partition exists is NP-complete even
in some cases for strict preferences. We identify a key source of
intractability in compact coalition formation games in which preferences over
players are extended to preferences over coalitions.
| [
{
"created": "Mon, 9 May 2011 23:51:47 GMT",
"version": "v1"
},
{
"created": "Sat, 3 Dec 2011 08:20:21 GMT",
"version": "v2"
}
] | 2011-12-06 | [
[
"Aziz",
"Haris",
""
],
[
"Harrenstein",
"Paul",
""
],
[
"Pyrga",
"Evangelia",
""
]
] | We consider coalition formation games in which each player has preferences over the other players and his preferences over coalitions are based on the best player ($\mathcal{B}$-/B-hedonic games) or the worst player ($\mathcal{W}$/W-hedonic games) in the coalition. We show that for $\mathcal{B}$-hedonic games, an individually stable partition is guaranteed to exist and can be computed efficiently. Similarly, there exists a polynomial-time algorithm which returns a Nash stable partition (if one exists) for $\mathcal{B}$-hedonic games with strict preferences. Both $\mathcal{W}$- and W-hedonic games are equivalent if individual rationality is assumed. It is also shown that for B- or $\mathcal{W}$-hedonic games, checking whether a Nash stable partition or an individually stable partition exists is NP-complete even in some cases for strict preferences. We identify a key source of intractability in compact coalition formation games in which preferences over players are extended to preferences over coalitions. |
cs/0408065 | Somdeb Lahiri | Somdeb Lahiri | The Core of Directed Network Problems with Quotas | 6 pages, 0 figures, source file: MS Word; definitions of the feasible
allocations have been strengthened; examples provided; network obtained by
the procedure can be decentralized | null | null | null | cs.GT | null | This paper proves the existence of non-empty cores for directed network
problems with quotas and for those combinatorial allocation problems which
permit only exclusive allocations.
| [
{
"created": "Sat, 28 Aug 2004 10:12:17 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Sep 2004 11:18:12 GMT",
"version": "v2"
},
{
"created": "Mon, 6 Sep 2004 11:05:37 GMT",
"version": "v3"
},
{
"created": "Tue, 7 Sep 2004 09:37:08 GMT",
"version": "v4"
},
{
"created": "Wed, 8 Sep 2004 12:15:49 GMT",
"version": "v5"
},
{
"created": "Sat, 11 Sep 2004 10:06:31 GMT",
"version": "v6"
}
] | 2007-05-23 | [
[
"Lahiri",
"Somdeb",
""
]
] | This paper proves the existence of non-empty cores for directed network problems with quotas and for those combinatorial allocation problems which permit only exclusive allocations. |
1409.5715 | Stefan Schulte | Stefan Schulte, Christian Janiesch, Srikumar Venugopal, Ingo Weber,
Philipp Hoenisch | Elastic Business Process Management: State of the Art and Open
Challenges for BPM in the Cloud | Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and
P. Hoenisch (2015). Elastic Business Process Management: State of the Art and
Open Challenges for BPM in the Cloud. Future Generation Computer Systems,
Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.005 | Future Generation Computer Systems, Volume 46, 36-50 (2015) | 10.1016/j.future.2014.09.005 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the advent of cloud computing, organizations are nowadays able to react
rapidly to changing demands for computational resources. Not only individual
applications can be hosted on virtual cloud infrastructures, but also complete
business processes. This allows the realization of so-called elastic processes,
i.e., processes which are carried out using elastic cloud resources. Despite
the manifold benefits of elastic processes, there is still a lack of solutions
supporting them.
In this paper, we identify the state of the art of elastic Business Process
Management with a focus on infrastructural challenges. We conceptualize an
architecture for an elastic Business Process Management System and discuss
existing work on scheduling, resource allocation, monitoring, decentralized
coordination, and state management for elastic processes. Furthermore, we
present two representative elastic Business Process Management Systems which
are intended to counter these challenges. Based on our findings, we identify
open issues and outline possible research directions for the realization of
elastic processes and elastic Business Process Management.
| [
{
"created": "Fri, 19 Sep 2014 16:36:49 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Sep 2014 10:56:55 GMT",
"version": "v2"
}
] | 2017-08-21 | [
[
"Schulte",
"Stefan",
""
],
[
"Janiesch",
"Christian",
""
],
[
"Venugopal",
"Srikumar",
""
],
[
"Weber",
"Ingo",
""
],
[
"Hoenisch",
"Philipp",
""
]
] | With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.