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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2311.14883
|
Sheikh Rabiul Islam
|
Alana Cedeno, Rachel Liang, and Sheikh Rabiul Islam
|
Predicting Potential School Shooters from Social Media Posts
| null |
IEEE Big Data 2023
| null | null |
cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
The rate of terror attacks has surged over the past decade, resulting in the
tragic and senseless loss or alteration of numerous lives. Offenders behind
mass shootings, bombings, or other domestic terrorism incidents have
historically exhibited warning signs on social media before carrying out actual
incidents. However, due to inadequate and comprehensive police procedures,
authorities and social media platforms are often unable to detect these early
indicators of intent. To tackle this issue, we aim to create a multimodal model
capable of predicting sentiments simultaneously from both images (i.e., social
media photos) and text (i.e., social media posts), generating a unified
prediction. The proposed method involves segregating the image and text
components of an online post and utilizing a captioning model to generate
sentences summarizing the image's contents. Subsequently, a sentiment analyzer
evaluates this caption, or description, along with the original post's text to
determine whether the post is positive (i.e., concerning) or negative (i.e.,
benign). This undertaking represents a significant step toward implementing the
developed system in real-world scenarios.
|
[
{
"created": "Sat, 25 Nov 2023 00:30:23 GMT",
"version": "v1"
}
] |
2023-11-28
|
[
[
"Cedeno",
"Alana",
""
],
[
"Liang",
"Rachel",
""
],
[
"Islam",
"Sheikh Rabiul",
""
]
] |
The rate of terror attacks has surged over the past decade, resulting in the tragic and senseless loss or alteration of numerous lives. Offenders behind mass shootings, bombings, or other domestic terrorism incidents have historically exhibited warning signs on social media before carrying out actual incidents. However, due to inadequate and comprehensive police procedures, authorities and social media platforms are often unable to detect these early indicators of intent. To tackle this issue, we aim to create a multimodal model capable of predicting sentiments simultaneously from both images (i.e., social media photos) and text (i.e., social media posts), generating a unified prediction. The proposed method involves segregating the image and text components of an online post and utilizing a captioning model to generate sentences summarizing the image's contents. Subsequently, a sentiment analyzer evaluates this caption, or description, along with the original post's text to determine whether the post is positive (i.e., concerning) or negative (i.e., benign). This undertaking represents a significant step toward implementing the developed system in real-world scenarios.
|
2105.08872
|
Jiansheng Fang
|
Jiansheng Fang, Huazhu Fu, Dan Zeng, Xiao Yan, Yuguang Yan, and Jiang
Liu
|
Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval
|
11 pages,8 figures, JBHI Journal
| null | null | null |
cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When encountering a dubious diagnostic case, medical instance retrieval can
help radiologists make evidence-based diagnoses by finding images containing
instances similar to a query case from a large image database. The similarity
between the query case and retrieved similar cases is determined by visual
features extracted from pathologically abnormal regions. However, the
manifestation of these regions often lacks specificity, i.e., different
diseases can have the same manifestation, and different manifestations may
occur at different stages of the same disease. To combat the manifestation
ambiguity in medical instance retrieval, we propose a novel deep framework
called Y-Net, encoding images into compact hash-codes generated from
convolutional features by feature aggregation. Y-Net can learn highly
discriminative convolutional features by unifying the pixel-wise segmentation
loss and classification loss. The segmentation loss allows exploring subtle
spatial differences for good spatial-discriminability while the classification
loss utilizes class-aware semantic information for good semantic-separability.
As a result, Y-Net can enhance the visual features in pathologically abnormal
regions and suppress the disturbing of the background during model training,
which could effectively embed discriminative features into the hash-codes in
the retrieval stage. Extensive experiments on two medical image datasets
demonstrate that Y-Net can alleviate the ambiguity of pathologically abnormal
regions and its retrieval performance outperforms the state-of-the-art method
by an average of 9.27\% on the returned list of 10.
|
[
{
"created": "Wed, 19 May 2021 01:13:05 GMT",
"version": "v1"
}
] |
2021-05-20
|
[
[
"Fang",
"Jiansheng",
""
],
[
"Fu",
"Huazhu",
""
],
[
"Zeng",
"Dan",
""
],
[
"Yan",
"Xiao",
""
],
[
"Yan",
"Yuguang",
""
],
[
"Liu",
"Jiang",
""
]
] |
When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database. The similarity between the query case and retrieved similar cases is determined by visual features extracted from pathologically abnormal regions. However, the manifestation of these regions often lacks specificity, i.e., different diseases can have the same manifestation, and different manifestations may occur at different stages of the same disease. To combat the manifestation ambiguity in medical instance retrieval, we propose a novel deep framework called Y-Net, encoding images into compact hash-codes generated from convolutional features by feature aggregation. Y-Net can learn highly discriminative convolutional features by unifying the pixel-wise segmentation loss and classification loss. The segmentation loss allows exploring subtle spatial differences for good spatial-discriminability while the classification loss utilizes class-aware semantic information for good semantic-separability. As a result, Y-Net can enhance the visual features in pathologically abnormal regions and suppress the disturbing of the background during model training, which could effectively embed discriminative features into the hash-codes in the retrieval stage. Extensive experiments on two medical image datasets demonstrate that Y-Net can alleviate the ambiguity of pathologically abnormal regions and its retrieval performance outperforms the state-of-the-art method by an average of 9.27\% on the returned list of 10.
|
2310.06486
|
Guoyuan An
|
Guoyuan An, Juhyung Seon, Inkyu An, Yuchi Huo, Sung-Eui Yoon
|
Topological RANSAC for instance verification and retrieval without
fine-tuning
| null | null | null | null |
cs.AI cs.CV cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents an innovative approach to enhancing explainable image
retrieval, particularly in situations where a fine-tuning set is unavailable.
The widely-used SPatial verification (SP) method, despite its efficacy, relies
on a spatial model and the hypothesis-testing strategy for instance
recognition, leading to inherent limitations, including the assumption of
planar structures and neglect of topological relations among features. To
address these shortcomings, we introduce a pioneering technique that replaces
the spatial model with a topological one within the RANSAC process. We propose
bio-inspired saccade and fovea functions to verify the topological consistency
among features, effectively circumventing the issues associated with SP's
spatial model. Our experimental results demonstrate that our method
significantly outperforms SP, achieving state-of-the-art performance in
non-fine-tuning retrieval. Furthermore, our approach can enhance performance
when used in conjunction with fine-tuned features. Importantly, our method
retains high explainability and is lightweight, offering a practical and
adaptable solution for a variety of real-world applications.
|
[
{
"created": "Tue, 10 Oct 2023 09:53:59 GMT",
"version": "v1"
}
] |
2023-10-11
|
[
[
"An",
"Guoyuan",
""
],
[
"Seon",
"Juhyung",
""
],
[
"An",
"Inkyu",
""
],
[
"Huo",
"Yuchi",
""
],
[
"Yoon",
"Sung-Eui",
""
]
] |
This paper presents an innovative approach to enhancing explainable image retrieval, particularly in situations where a fine-tuning set is unavailable. The widely-used SPatial verification (SP) method, despite its efficacy, relies on a spatial model and the hypothesis-testing strategy for instance recognition, leading to inherent limitations, including the assumption of planar structures and neglect of topological relations among features. To address these shortcomings, we introduce a pioneering technique that replaces the spatial model with a topological one within the RANSAC process. We propose bio-inspired saccade and fovea functions to verify the topological consistency among features, effectively circumventing the issues associated with SP's spatial model. Our experimental results demonstrate that our method significantly outperforms SP, achieving state-of-the-art performance in non-fine-tuning retrieval. Furthermore, our approach can enhance performance when used in conjunction with fine-tuned features. Importantly, our method retains high explainability and is lightweight, offering a practical and adaptable solution for a variety of real-world applications.
|
2306.12881
|
Adrian Holzbock
|
Adrian Holzbock, Achyut Hegde, Klaus Dietmayer, and Vasileios
Belagiannis
|
Data-Free Backbone Fine-Tuning for Pruned Neural Networks
|
Accpeted for presentation at the 31st European Signal Processing
Conference (EUSIPCO) 2023, September 4-8, 2023, Helsinki, Finland
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Model compression techniques reduce the computational load and memory
consumption of deep neural networks. After the compression operation, e.g.
parameter pruning, the model is normally fine-tuned on the original training
dataset to recover from the performance drop caused by compression. However,
the training data is not always available due to privacy issues or other
factors. In this work, we present a data-free fine-tuning approach for pruning
the backbone of deep neural networks. In particular, the pruned network
backbone is trained with synthetically generated images, and our proposed
intermediate supervision to mimic the unpruned backbone's output feature map.
Afterwards, the pruned backbone can be combined with the original network head
to make predictions. We generate synthetic images by back-propagating gradients
to noise images while relying on L1-pruning for the backbone pruning. In our
experiments, we show that our approach is task-independent due to pruning only
the backbone. By evaluating our approach on 2D human pose estimation, object
detection, and image classification, we demonstrate promising performance
compared to the unpruned model. Our code is available at
https://github.com/holzbock/dfbf.
|
[
{
"created": "Thu, 22 Jun 2023 13:44:40 GMT",
"version": "v1"
}
] |
2023-06-23
|
[
[
"Holzbock",
"Adrian",
""
],
[
"Hegde",
"Achyut",
""
],
[
"Dietmayer",
"Klaus",
""
],
[
"Belagiannis",
"Vasileios",
""
]
] |
Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is normally fine-tuned on the original training dataset to recover from the performance drop caused by compression. However, the training data is not always available due to privacy issues or other factors. In this work, we present a data-free fine-tuning approach for pruning the backbone of deep neural networks. In particular, the pruned network backbone is trained with synthetically generated images, and our proposed intermediate supervision to mimic the unpruned backbone's output feature map. Afterwards, the pruned backbone can be combined with the original network head to make predictions. We generate synthetic images by back-propagating gradients to noise images while relying on L1-pruning for the backbone pruning. In our experiments, we show that our approach is task-independent due to pruning only the backbone. By evaluating our approach on 2D human pose estimation, object detection, and image classification, we demonstrate promising performance compared to the unpruned model. Our code is available at https://github.com/holzbock/dfbf.
|
2102.10607
|
Sivaramakrishnan Rajaraman
|
Sivaramakrishnan Rajaraman, Les Folio, Jane Dimperio, Philip Alderson
and Sameer Antani
|
Improved Semantic Segmentation of Tuberculosis-consistent findings in
Chest X-rays Using Augmented Training of Modality-specific U-Net Models with
Weak Localizations
|
31 pages, 19 figures, journal publication
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep learning (DL) has drawn tremendous attention in object localization and
recognition for both natural and medical images. U-Net segmentation models have
demonstrated superior performance compared to conventional handcrafted
feature-based methods. Medical image modality-specific DL models are better at
transferring domain knowledge to a relevant target task than those that are
pretrained on stock photography images. This helps improve model adaptation,
generalization, and class-specific region of interest (ROI) localization. In
this study, we train chest X-ray (CXR) modality-specific U-Nets and other
state-of-the-art U-Net models for semantic segmentation of tuberculosis
(TB)-consistent findings. Automated segmentation of such manifestations could
help radiologists reduce errors and supplement decision-making while improving
patient care and productivity. Our approach uses the publicly available TBX11K
CXR dataset with weak TB annotations, typically provided as bounding boxes, to
train a set of U-Net models. Next, we improve the results by augmenting the
training data with weak localizations, post-processed into an ROI mask, from a
DL classifier that is trained to classify CXRs as showing normal lungs or
suspected TB manifestations. Test data are individually derived from the TBX11K
CXR training distribution and other cross-institutional collections including
the Shenzhen TB and Montgomery TB CXR datasets. We observe that our augmented
training strategy helped the CXR modality-specific U-Net models achieve
superior performance with test data derived from the TBX11K CXR training
distribution as well as from cross-institutional collections (p < 0.05).
|
[
{
"created": "Sun, 21 Feb 2021 14:03:49 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Mar 2021 18:10:45 GMT",
"version": "v2"
},
{
"created": "Fri, 26 Mar 2021 17:14:27 GMT",
"version": "v3"
}
] |
2021-03-29
|
[
[
"Rajaraman",
"Sivaramakrishnan",
""
],
[
"Folio",
"Les",
""
],
[
"Dimperio",
"Jane",
""
],
[
"Alderson",
"Philip",
""
],
[
"Antani",
"Sameer",
""
]
] |
Deep learning (DL) has drawn tremendous attention in object localization and recognition for both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional handcrafted feature-based methods. Medical image modality-specific DL models are better at transferring domain knowledge to a relevant target task than those that are pretrained on stock photography images. This helps improve model adaptation, generalization, and class-specific region of interest (ROI) localization. In this study, we train chest X-ray (CXR) modality-specific U-Nets and other state-of-the-art U-Net models for semantic segmentation of tuberculosis (TB)-consistent findings. Automated segmentation of such manifestations could help radiologists reduce errors and supplement decision-making while improving patient care and productivity. Our approach uses the publicly available TBX11K CXR dataset with weak TB annotations, typically provided as bounding boxes, to train a set of U-Net models. Next, we improve the results by augmenting the training data with weak localizations, post-processed into an ROI mask, from a DL classifier that is trained to classify CXRs as showing normal lungs or suspected TB manifestations. Test data are individually derived from the TBX11K CXR training distribution and other cross-institutional collections including the Shenzhen TB and Montgomery TB CXR datasets. We observe that our augmented training strategy helped the CXR modality-specific U-Net models achieve superior performance with test data derived from the TBX11K CXR training distribution as well as from cross-institutional collections (p < 0.05).
|
1905.03767
|
Ashkan Khakzar
|
Ashkan Khakzar, Shadi Albarqouni, Nassir Navab
|
Learning Interpretable Features via Adversarially Robust Optimization
|
MICCAI 2019 (Medical Image Computing and Computer Assisted
Interventions)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neural networks are proven to be remarkably successful for classification and
diagnosis in medical applications. However, the ambiguity in the
decision-making process and the interpretability of the learned features is a
matter of concern. In this work, we propose a method for improving the feature
interpretability of neural network classifiers. Initially, we propose a
baseline convolutional neural network with state of the art performance in
terms of accuracy and weakly supervised localization. Subsequently, the loss is
modified to integrate robustness to adversarial examples into the training
process. In this work, feature interpretability is quantified via evaluating
the weakly supervised localization using the ground truth bounding boxes.
Interpretability is also visually assessed using class activation maps and
saliency maps. The method is applied to NIH ChestX-ray14, the largest publicly
available chest x-rays dataset. We demonstrate that the adversarially robust
optimization paradigm improves feature interpretability both quantitatively and
visually.
|
[
{
"created": "Thu, 9 May 2019 17:50:25 GMT",
"version": "v1"
},
{
"created": "Mon, 19 Aug 2019 09:18:32 GMT",
"version": "v2"
}
] |
2019-08-20
|
[
[
"Khakzar",
"Ashkan",
""
],
[
"Albarqouni",
"Shadi",
""
],
[
"Navab",
"Nassir",
""
]
] |
Neural networks are proven to be remarkably successful for classification and diagnosis in medical applications. However, the ambiguity in the decision-making process and the interpretability of the learned features is a matter of concern. In this work, we propose a method for improving the feature interpretability of neural network classifiers. Initially, we propose a baseline convolutional neural network with state of the art performance in terms of accuracy and weakly supervised localization. Subsequently, the loss is modified to integrate robustness to adversarial examples into the training process. In this work, feature interpretability is quantified via evaluating the weakly supervised localization using the ground truth bounding boxes. Interpretability is also visually assessed using class activation maps and saliency maps. The method is applied to NIH ChestX-ray14, the largest publicly available chest x-rays dataset. We demonstrate that the adversarially robust optimization paradigm improves feature interpretability both quantitatively and visually.
|
1902.03519
|
Ali Vakilian
|
Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali
Vakilian, Tal Wagner
|
Scalable Fair Clustering
|
ICML 2019
| null | null | null |
cs.DS cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the fair variant of the classic $k$-median problem introduced by
Chierichetti et al. [2017]. In the standard $k$-median problem, given an input
pointset $P$, the goal is to find $k$ centers $C$ and assign each input point
to one of the centers in $C$ such that the average distance of points to their
cluster center is minimized.
In the fair variant of $k$-median, the points are colored, and the goal is to
minimize the same average distance objective while ensuring that all clusters
have an "approximately equal" number of points of each color.
Chierichetti et al. proposed a two-phase algorithm for fair $k$-clustering.
In the first step, the pointset is partitioned into subsets called fairlets
that satisfy the fairness requirement and approximately preserve the $k$-median
objective. In the second step, fairlets are merged into $k$ clusters by one of
the existing $k$-median algorithms. The running time of this algorithm is
dominated by the first step, which takes super-quadratic time.
In this paper, we present a practical approximate fairlet decomposition
algorithm that runs in nearly linear time. Our algorithm additionally allows
for finer control over the balance of resulting clusters than the original
work. We complement our theoretical bounds with empirical evaluation.
|
[
{
"created": "Sun, 10 Feb 2019 00:04:34 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Jun 2019 18:19:34 GMT",
"version": "v2"
}
] |
2019-06-12
|
[
[
"Backurs",
"Arturs",
""
],
[
"Indyk",
"Piotr",
""
],
[
"Onak",
"Krzysztof",
""
],
[
"Schieber",
"Baruch",
""
],
[
"Vakilian",
"Ali",
""
],
[
"Wagner",
"Tal",
""
]
] |
We study the fair variant of the classic $k$-median problem introduced by Chierichetti et al. [2017]. In the standard $k$-median problem, given an input pointset $P$, the goal is to find $k$ centers $C$ and assign each input point to one of the centers in $C$ such that the average distance of points to their cluster center is minimized. In the fair variant of $k$-median, the points are colored, and the goal is to minimize the same average distance objective while ensuring that all clusters have an "approximately equal" number of points of each color. Chierichetti et al. proposed a two-phase algorithm for fair $k$-clustering. In the first step, the pointset is partitioned into subsets called fairlets that satisfy the fairness requirement and approximately preserve the $k$-median objective. In the second step, fairlets are merged into $k$ clusters by one of the existing $k$-median algorithms. The running time of this algorithm is dominated by the first step, which takes super-quadratic time. In this paper, we present a practical approximate fairlet decomposition algorithm that runs in nearly linear time. Our algorithm additionally allows for finer control over the balance of resulting clusters than the original work. We complement our theoretical bounds with empirical evaluation.
|
1910.13620
|
Xiang Huang
|
Xiang Huang, Jack H. Lutz, and Andrei N. Migunov
|
Algorithmic Randomness in Continuous-Time Markov Chains
| null | null | null | null |
cs.IT cs.LO math.IT math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we develop the elements of the theory of algorithmic
randomness in continuous-time Markov chains (CTMCs). Our main contribution is a
rigorous, useful notion of what it means for an $\textit{ individual trajectory
}$ of a CTMC to be ${ \textit random }$. CTMCs have discrete state spaces and
operate in continuous time. This, together with the fact that trajectories may
or may not halt, presents challenges not encountered in more conventional
developments of algorithmic randomness.
Although we formulate algorithmic randomness in the general context of CTMCs,
we are primarily interested in the $\textit{ computational }$ power of
stochastic chemical reaction networks, which are special cases of CTMCs. This
leads us to embrace situations in which the long-term behavior of a network
depends essentially on its initial state and hence to eschew assumptions that
are frequently made in Markov chain theory to avoid such dependencies.
After defining the randomness of trajectories in terms of a new kind of
martingale (algorithmic betting strategy), we prove equivalent
characterizations in terms of constructive measure theory and Kolmogorov
complexity. As a preliminary application, we prove that, in any stochastic
chemical reaction network, $\textit{ every }$ random trajectory with bounded
molecular counts has the $\textit{ non-Zeno property }$ that infinitely many
reactions do not occur in any finite interval of time.
|
[
{
"created": "Wed, 30 Oct 2019 01:48:38 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Dec 2021 05:03:22 GMT",
"version": "v2"
},
{
"created": "Fri, 17 Dec 2021 17:52:23 GMT",
"version": "v3"
}
] |
2021-12-20
|
[
[
"Huang",
"Xiang",
""
],
[
"Lutz",
"Jack H.",
""
],
[
"Migunov",
"Andrei N.",
""
]
] |
In this paper, we develop the elements of the theory of algorithmic randomness in continuous-time Markov chains (CTMCs). Our main contribution is a rigorous, useful notion of what it means for an $\textit{ individual trajectory }$ of a CTMC to be ${ \textit random }$. CTMCs have discrete state spaces and operate in continuous time. This, together with the fact that trajectories may or may not halt, presents challenges not encountered in more conventional developments of algorithmic randomness. Although we formulate algorithmic randomness in the general context of CTMCs, we are primarily interested in the $\textit{ computational }$ power of stochastic chemical reaction networks, which are special cases of CTMCs. This leads us to embrace situations in which the long-term behavior of a network depends essentially on its initial state and hence to eschew assumptions that are frequently made in Markov chain theory to avoid such dependencies. After defining the randomness of trajectories in terms of a new kind of martingale (algorithmic betting strategy), we prove equivalent characterizations in terms of constructive measure theory and Kolmogorov complexity. As a preliminary application, we prove that, in any stochastic chemical reaction network, $\textit{ every }$ random trajectory with bounded molecular counts has the $\textit{ non-Zeno property }$ that infinitely many reactions do not occur in any finite interval of time.
|
2306.12725
|
Senbao Shi
|
Senbao Shi, Zhenran Xu, Baotian Hu, Min Zhang
|
Generative Multimodal Entity Linking
|
Accepted by LREC-COLING 2024
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multimodal Entity Linking (MEL) is the task of mapping mentions with
multimodal contexts to the referent entities from a knowledge base. Existing
MEL methods mainly focus on designing complex multimodal interaction mechanisms
and require fine-tuning all model parameters, which can be prohibitively costly
and difficult to scale in the era of Large Language Models (LLMs). In this
work, we propose GEMEL, a Generative Multimodal Entity Linking framework based
on LLMs, which directly generates target entity names. We keep the vision and
language model frozen and only train a feature mapper to enable cross-modality
interactions. To adapt LLMs to the MEL task, we leverage the in-context
learning capability of LLMs by retrieving multimodal instances as
demonstrations. Extensive experiments show that, with only ~0.3% of the model
parameters fine-tuned, GEMEL achieves state-of-the-art results on two
well-established MEL datasets (7.7% accuracy gains on WikiDiverse and 8.8%
accuracy gains on WikiMEL). The performance gain stems from mitigating the
popularity bias of LLM predictions and disambiguating less common entities
effectively. Further analysis verifies the generality and scalability of GEMEL.
Our framework is compatible with any off-the-shelf language model, paving the
way towards an efficient and general solution for utilizing LLMs in the MEL
task. Our code is available at https://github.com/HITsz-TMG/GEMEL.
|
[
{
"created": "Thu, 22 Jun 2023 07:57:19 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Aug 2023 05:12:12 GMT",
"version": "v2"
},
{
"created": "Tue, 19 Mar 2024 12:30:53 GMT",
"version": "v3"
},
{
"created": "Wed, 20 Mar 2024 01:30:41 GMT",
"version": "v4"
}
] |
2024-03-21
|
[
[
"Shi",
"Senbao",
""
],
[
"Xu",
"Zhenran",
""
],
[
"Hu",
"Baotian",
""
],
[
"Zhang",
"Min",
""
]
] |
Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base. Existing MEL methods mainly focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters, which can be prohibitively costly and difficult to scale in the era of Large Language Models (LLMs). In this work, we propose GEMEL, a Generative Multimodal Entity Linking framework based on LLMs, which directly generates target entity names. We keep the vision and language model frozen and only train a feature mapper to enable cross-modality interactions. To adapt LLMs to the MEL task, we leverage the in-context learning capability of LLMs by retrieving multimodal instances as demonstrations. Extensive experiments show that, with only ~0.3% of the model parameters fine-tuned, GEMEL achieves state-of-the-art results on two well-established MEL datasets (7.7% accuracy gains on WikiDiverse and 8.8% accuracy gains on WikiMEL). The performance gain stems from mitigating the popularity bias of LLM predictions and disambiguating less common entities effectively. Further analysis verifies the generality and scalability of GEMEL. Our framework is compatible with any off-the-shelf language model, paving the way towards an efficient and general solution for utilizing LLMs in the MEL task. Our code is available at https://github.com/HITsz-TMG/GEMEL.
|
1602.01648
|
Maiara F. Bollauf
|
Maiara F. Bollauf and Ram Zamir
|
Uniformity Properties of Construction C
|
5 pages, 1 figure, submitted to ISIT 2016
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Construction C (also known as Forney's multi-level code formula) forms a
Euclidean code for the additive white Gaussian noise (AWGN) channel from $L$
binary code components. If the component codes are linear, then the minimum
distance is the same for all the points, although the kissing number may vary.
In fact, while in the single level ($L=1$) case it reduces to lattice
Construction A, a multi-level Construction C is in general not a lattice. We
show that the two-level ($L=2$) case is special: a two-level Construction C
satisfies Forney's definition for a geometrically uniform constellation.
Specifically, every point sees the same configuration of neighbors, up to a
reflection of the coordinates in which the lower level code is equal to 1. In
contrast, for three levels and up ($L\geq 3$), we construct examples where the
distance spectrum varies between the points, hence the constellation is not
geometrically uniform.
|
[
{
"created": "Thu, 4 Feb 2016 11:53:31 GMT",
"version": "v1"
},
{
"created": "Mon, 9 May 2016 15:46:00 GMT",
"version": "v2"
}
] |
2016-05-10
|
[
[
"Bollauf",
"Maiara F.",
""
],
[
"Zamir",
"Ram",
""
]
] |
Construction C (also known as Forney's multi-level code formula) forms a Euclidean code for the additive white Gaussian noise (AWGN) channel from $L$ binary code components. If the component codes are linear, then the minimum distance is the same for all the points, although the kissing number may vary. In fact, while in the single level ($L=1$) case it reduces to lattice Construction A, a multi-level Construction C is in general not a lattice. We show that the two-level ($L=2$) case is special: a two-level Construction C satisfies Forney's definition for a geometrically uniform constellation. Specifically, every point sees the same configuration of neighbors, up to a reflection of the coordinates in which the lower level code is equal to 1. In contrast, for three levels and up ($L\geq 3$), we construct examples where the distance spectrum varies between the points, hence the constellation is not geometrically uniform.
|
2009.14794
|
Valerii Likhosherstov
|
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou
Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz
Mohiuddin, Lukasz Kaiser, David Belanger, Lucy Colwell, Adrian Weller
|
Rethinking Attention with Performers
|
Published as a conference paper + oral presentation at ICLR 2021. 38
pages. See
https://github.com/google-research/google-research/tree/master/protein_lm for
protein language model code, and
https://github.com/google-research/google-research/tree/master/performer for
Performer code. See
https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html
for Google AI Blog
| null | null | null |
cs.LG cs.CL stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce Performers, Transformer architectures which can estimate regular
(softmax) full-rank-attention Transformers with provable accuracy, but using
only linear (as opposed to quadratic) space and time complexity, without
relying on any priors such as sparsity or low-rankness. To approximate softmax
attention-kernels, Performers use a novel Fast Attention Via positive
Orthogonal Random features approach (FAVOR+), which may be of independent
interest for scalable kernel methods. FAVOR+ can be also used to efficiently
model kernelizable attention mechanisms beyond softmax. This representational
power is crucial to accurately compare softmax with other kernels for the first
time on large-scale tasks, beyond the reach of regular Transformers, and
investigate optimal attention-kernels. Performers are linear architectures
fully compatible with regular Transformers and with strong theoretical
guarantees: unbiased or nearly-unbiased estimation of the attention matrix,
uniform convergence and low estimation variance. We tested Performers on a rich
set of tasks stretching from pixel-prediction through text models to protein
sequence modeling. We demonstrate competitive results with other examined
efficient sparse and dense attention methods, showcasing effectiveness of the
novel attention-learning paradigm leveraged by Performers.
|
[
{
"created": "Wed, 30 Sep 2020 17:09:09 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Feb 2021 21:40:24 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Mar 2021 16:26:47 GMT",
"version": "v3"
},
{
"created": "Sat, 19 Nov 2022 12:45:21 GMT",
"version": "v4"
}
] |
2022-11-22
|
[
[
"Choromanski",
"Krzysztof",
""
],
[
"Likhosherstov",
"Valerii",
""
],
[
"Dohan",
"David",
""
],
[
"Song",
"Xingyou",
""
],
[
"Gane",
"Andreea",
""
],
[
"Sarlos",
"Tamas",
""
],
[
"Hawkins",
"Peter",
""
],
[
"Davis",
"Jared",
""
],
[
"Mohiuddin",
"Afroz",
""
],
[
"Kaiser",
"Lukasz",
""
],
[
"Belanger",
"David",
""
],
[
"Colwell",
"Lucy",
""
],
[
"Weller",
"Adrian",
""
]
] |
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness. To approximate softmax attention-kernels, Performers use a novel Fast Attention Via positive Orthogonal Random features approach (FAVOR+), which may be of independent interest for scalable kernel methods. FAVOR+ can be also used to efficiently model kernelizable attention mechanisms beyond softmax. This representational power is crucial to accurately compare softmax with other kernels for the first time on large-scale tasks, beyond the reach of regular Transformers, and investigate optimal attention-kernels. Performers are linear architectures fully compatible with regular Transformers and with strong theoretical guarantees: unbiased or nearly-unbiased estimation of the attention matrix, uniform convergence and low estimation variance. We tested Performers on a rich set of tasks stretching from pixel-prediction through text models to protein sequence modeling. We demonstrate competitive results with other examined efficient sparse and dense attention methods, showcasing effectiveness of the novel attention-learning paradigm leveraged by Performers.
|
2011.07805
|
Guoqiang Wu
|
Guoqiang Wu, Jun Zhu
|
Multi-label classification: do Hamming loss and subset accuracy really
conflict with each other?
|
To Appear in NeurIPS 2020
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Various evaluation measures have been developed for multi-label
classification, including Hamming Loss (HL), Subset Accuracy (SA) and Ranking
Loss (RL). However, there is a gap between empirical results and the existing
theories: 1) an algorithm often empirically performs well on some measure(s)
while poorly on others, while a formal theoretical analysis is lacking; and 2)
in small label space cases, the algorithms optimizing HL often have comparable
or even better performance on the SA measure than those optimizing SA directly,
while existing theoretical results show that SA and HL are conflicting
measures. This paper provides an attempt to fill up this gap by analyzing the
learning guarantees of the corresponding learning algorithms on both SA and HL
measures. We show that when a learning algorithm optimizes HL with its
surrogate loss, it enjoys an error bound for the HL measure independent of $c$
(the number of labels), while the bound for the SA measure depends on at most
$O(c)$. On the other hand, when directly optimizing SA with its surrogate loss,
it has learning guarantees that depend on $O(\sqrt{c})$ for both HL and SA
measures. This explains the observation that when the label space is not large,
optimizing HL with its surrogate loss can have promising performance for SA. We
further show that our techniques are applicable to analyze the learning
guarantees of algorithms on other measures, such as RL. Finally, the
theoretical analyses are supported by experimental results.
|
[
{
"created": "Mon, 16 Nov 2020 09:13:16 GMT",
"version": "v1"
}
] |
2020-11-17
|
[
[
"Wu",
"Guoqiang",
""
],
[
"Zhu",
"Jun",
""
]
] |
Various evaluation measures have been developed for multi-label classification, including Hamming Loss (HL), Subset Accuracy (SA) and Ranking Loss (RL). However, there is a gap between empirical results and the existing theories: 1) an algorithm often empirically performs well on some measure(s) while poorly on others, while a formal theoretical analysis is lacking; and 2) in small label space cases, the algorithms optimizing HL often have comparable or even better performance on the SA measure than those optimizing SA directly, while existing theoretical results show that SA and HL are conflicting measures. This paper provides an attempt to fill up this gap by analyzing the learning guarantees of the corresponding learning algorithms on both SA and HL measures. We show that when a learning algorithm optimizes HL with its surrogate loss, it enjoys an error bound for the HL measure independent of $c$ (the number of labels), while the bound for the SA measure depends on at most $O(c)$. On the other hand, when directly optimizing SA with its surrogate loss, it has learning guarantees that depend on $O(\sqrt{c})$ for both HL and SA measures. This explains the observation that when the label space is not large, optimizing HL with its surrogate loss can have promising performance for SA. We further show that our techniques are applicable to analyze the learning guarantees of algorithms on other measures, such as RL. Finally, the theoretical analyses are supported by experimental results.
|
1509.04199
|
Eric Sopena
|
Eric Sopena (LaBRI)
|
i-MARK: A New Subtraction Division Game
|
A few typos have been corrected, including the statement of Theorem 8
| null | null | null |
cs.DM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Given two finite sets of integers $S\subseteq\NNN\setminus\{0\}$ and
$D\subseteq\NNN\setminus\{0,1\}$,the impartial combinatorial game $\IMARK(S,D)$
is played on a heap of tokens. From a heap of $n$ tokens, each player can
moveeither to a heap of $n-s$ tokens for some $s\in S$, or to a heap of $n/d$
tokensfor some $d\in D$ if $d$ divides $n$.Such games can be considered as an
integral variant of \MARK-type games, introduced by Elwyn Berlekamp and Joe
Buhlerand studied by Aviezri Fraenkel and Alan Guo, for which it is allowed to
move from a heap of $n$ tokensto a heap of $\lfloor n/d\rfloor$ tokens for any
$d\in D$.Under normal convention, it is observed that the Sprague-Grundy
sequence of the game $\IMARK(S,D)$ is aperiodic for any sets $S$ and
$D$.However, we prove that, in many cases, this sequence is almost periodic and
that the set of winning positions is periodic.Moreover, in all these cases, the
Sprague-Grundy value of a heap of $n$ tokens can be computed in time $O(\log
n)$.We also prove that, under mis\`ere convention, the outcome sequence of
these games is purely periodic.
|
[
{
"created": "Mon, 14 Sep 2015 16:57:57 GMT",
"version": "v1"
},
{
"created": "Mon, 9 Nov 2015 15:11:52 GMT",
"version": "v2"
}
] |
2015-11-10
|
[
[
"Sopena",
"Eric",
"",
"LaBRI"
]
] |
Given two finite sets of integers $S\subseteq\NNN\setminus\{0\}$ and $D\subseteq\NNN\setminus\{0,1\}$,the impartial combinatorial game $\IMARK(S,D)$ is played on a heap of tokens. From a heap of $n$ tokens, each player can moveeither to a heap of $n-s$ tokens for some $s\in S$, or to a heap of $n/d$ tokensfor some $d\in D$ if $d$ divides $n$.Such games can be considered as an integral variant of \MARK-type games, introduced by Elwyn Berlekamp and Joe Buhlerand studied by Aviezri Fraenkel and Alan Guo, for which it is allowed to move from a heap of $n$ tokensto a heap of $\lfloor n/d\rfloor$ tokens for any $d\in D$.Under normal convention, it is observed that the Sprague-Grundy sequence of the game $\IMARK(S,D)$ is aperiodic for any sets $S$ and $D$.However, we prove that, in many cases, this sequence is almost periodic and that the set of winning positions is periodic.Moreover, in all these cases, the Sprague-Grundy value of a heap of $n$ tokens can be computed in time $O(\log n)$.We also prove that, under mis\`ere convention, the outcome sequence of these games is purely periodic.
|
2407.11585
|
Tian Shilong
|
Shilong Tian, Hong Chen, Chengtao Lv, Yu Liu, Jinyang Guo, Xianglong
Liu, Shengxi Li, Hao Yang, Tao Xie
|
QVD: Post-training Quantization for Video Diffusion Models
|
accepted by ACMMM2024
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, video diffusion models (VDMs) have garnered significant attention
due to their notable advancements in generating coherent and realistic video
content. However, processing multiple frame features concurrently, coupled with
the considerable model size, results in high latency and extensive memory
consumption, hindering their broader application. Post-training quantization
(PTQ) is an effective technique to reduce memory footprint and improve
computational efficiency. Unlike image diffusion, we observe that the temporal
features, which are integrated into all frame features, exhibit pronounced
skewness. Furthermore, we investigate significant inter-channel disparities and
asymmetries in the activation of video diffusion models, resulting in low
coverage of quantization levels by individual channels and increasing the
challenge of quantization. To address these issues, we introduce the first PTQ
strategy tailored for video diffusion models, dubbed QVD. Specifically, we
propose the High Temporal Discriminability Quantization (HTDQ) method, designed
for temporal features, which retains the high discriminability of quantized
features, providing precise temporal guidance for all video frames. In
addition, we present the Scattered Channel Range Integration (SCRI) method
which aims to improve the coverage of quantization levels across individual
channels. Experimental validations across various models, datasets, and
bit-width settings demonstrate the effectiveness of our QVD in terms of diverse
metrics. In particular, we achieve near-lossless performance degradation on
W8A8, outperforming the current methods by 205.12 in FVD.
|
[
{
"created": "Tue, 16 Jul 2024 10:47:27 GMT",
"version": "v1"
},
{
"created": "Wed, 17 Jul 2024 05:27:04 GMT",
"version": "v2"
}
] |
2024-07-18
|
[
[
"Tian",
"Shilong",
""
],
[
"Chen",
"Hong",
""
],
[
"Lv",
"Chengtao",
""
],
[
"Liu",
"Yu",
""
],
[
"Guo",
"Jinyang",
""
],
[
"Liu",
"Xianglong",
""
],
[
"Li",
"Shengxi",
""
],
[
"Yang",
"Hao",
""
],
[
"Xie",
"Tao",
""
]
] |
Recently, video diffusion models (VDMs) have garnered significant attention due to their notable advancements in generating coherent and realistic video content. However, processing multiple frame features concurrently, coupled with the considerable model size, results in high latency and extensive memory consumption, hindering their broader application. Post-training quantization (PTQ) is an effective technique to reduce memory footprint and improve computational efficiency. Unlike image diffusion, we observe that the temporal features, which are integrated into all frame features, exhibit pronounced skewness. Furthermore, we investigate significant inter-channel disparities and asymmetries in the activation of video diffusion models, resulting in low coverage of quantization levels by individual channels and increasing the challenge of quantization. To address these issues, we introduce the first PTQ strategy tailored for video diffusion models, dubbed QVD. Specifically, we propose the High Temporal Discriminability Quantization (HTDQ) method, designed for temporal features, which retains the high discriminability of quantized features, providing precise temporal guidance for all video frames. In addition, we present the Scattered Channel Range Integration (SCRI) method which aims to improve the coverage of quantization levels across individual channels. Experimental validations across various models, datasets, and bit-width settings demonstrate the effectiveness of our QVD in terms of diverse metrics. In particular, we achieve near-lossless performance degradation on W8A8, outperforming the current methods by 205.12 in FVD.
|
2009.02717
|
Suhail Sherif
|
Arkadev Chattopadhyay, Ankit Garg, Suhail Sherif
|
Towards Stronger Counterexamples to the Log-Approximate-Rank Conjecture
| null | null | null | null |
cs.CC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We give improved separations for the query complexity analogue of the
log-approximate-rank conjecture i.e. we show that there are a plethora of total
Boolean functions on $n$ input bits, each of which has approximate Fourier
sparsity at most $O(n^3)$ and randomized parity decision tree complexity
$\Theta(n)$. This improves upon the recent work of Chattopadhyay, Mande and
Sherif (JACM '20) both qualitatively (in terms of designing a large number of
examples) and quantitatively (improving the gap from quartic to cubic). We
leave open the problem of proving a randomized communication complexity lower
bound for XOR compositions of our examples. A linear lower bound would lead to
new and improved refutations of the log-approximate-rank conjecture. Moreover,
if any of these compositions had even a sub-linear cost randomized
communication protocol, it would demonstrate that randomized parity decision
tree complexity does not lift to randomized communication complexity in general
(with the XOR gadget).
|
[
{
"created": "Sun, 6 Sep 2020 11:57:33 GMT",
"version": "v1"
}
] |
2020-09-08
|
[
[
"Chattopadhyay",
"Arkadev",
""
],
[
"Garg",
"Ankit",
""
],
[
"Sherif",
"Suhail",
""
]
] |
We give improved separations for the query complexity analogue of the log-approximate-rank conjecture i.e. we show that there are a plethora of total Boolean functions on $n$ input bits, each of which has approximate Fourier sparsity at most $O(n^3)$ and randomized parity decision tree complexity $\Theta(n)$. This improves upon the recent work of Chattopadhyay, Mande and Sherif (JACM '20) both qualitatively (in terms of designing a large number of examples) and quantitatively (improving the gap from quartic to cubic). We leave open the problem of proving a randomized communication complexity lower bound for XOR compositions of our examples. A linear lower bound would lead to new and improved refutations of the log-approximate-rank conjecture. Moreover, if any of these compositions had even a sub-linear cost randomized communication protocol, it would demonstrate that randomized parity decision tree complexity does not lift to randomized communication complexity in general (with the XOR gadget).
|
2105.06723
|
Amrita Suresh
|
Benedikt Bollig, Alain Finkel, Amrita Suresh
|
Bounded Reachability Problems are Decidable in FIFO Machines
| null |
Logical Methods in Computer Science, Volume 18, Issue 1 (January
20, 2022) lmcs:7485
|
10.46298/lmcs-18(1:19)2022
| null |
cs.LO cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
The undecidability of basic decision problems for general FIFO machines such
as reachability and unboundedness is well-known. In this paper, we provide an
underapproximation for the general model by considering only runs that are
input-bounded (i.e. the sequence of messages sent through a particular channel
belongs to a given bounded language). We prove, by reducing this model to a
counter machine with restricted zero tests, that the rational-reachability
problem (and by extension, control-state reachability, unboundedness, deadlock,
etc.) is decidable. This class of machines subsumes input-letter-bounded
machines, flat machines, linear FIFO nets, and monogeneous machines, for which
some of these problems were already shown to be decidable. These theoretical
results can form the foundations to build a tool to verify general FIFO
machines based on the analysis of input-bounded machines.
|
[
{
"created": "Fri, 14 May 2021 09:13:33 GMT",
"version": "v1"
},
{
"created": "Sun, 14 Nov 2021 23:27:44 GMT",
"version": "v2"
},
{
"created": "Wed, 8 Dec 2021 14:52:09 GMT",
"version": "v3"
},
{
"created": "Wed, 19 Jan 2022 16:00:43 GMT",
"version": "v4"
}
] |
2023-06-22
|
[
[
"Bollig",
"Benedikt",
""
],
[
"Finkel",
"Alain",
""
],
[
"Suresh",
"Amrita",
""
]
] |
The undecidability of basic decision problems for general FIFO machines such as reachability and unboundedness is well-known. In this paper, we provide an underapproximation for the general model by considering only runs that are input-bounded (i.e. the sequence of messages sent through a particular channel belongs to a given bounded language). We prove, by reducing this model to a counter machine with restricted zero tests, that the rational-reachability problem (and by extension, control-state reachability, unboundedness, deadlock, etc.) is decidable. This class of machines subsumes input-letter-bounded machines, flat machines, linear FIFO nets, and monogeneous machines, for which some of these problems were already shown to be decidable. These theoretical results can form the foundations to build a tool to verify general FIFO machines based on the analysis of input-bounded machines.
|
1603.06065
|
Sungkyun Chang
|
Sungkyun Chang and Kyogu Lee
|
A pairwise approach to simultaneous onset/offset detection for singing
voice using correntropy
|
2014 IEEE International Conference on Acoustics, Speech and Signal
Processing, 5 pages, 5 figures
| null |
10.1109/ICASSP.2014.6853672
| null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a novelmethod to search for precise locations of
paired note onset and offset in a singing voice signal. In comparison with the
existing onset detection algorithms,our approach differs in two key respects.
First, we employ Correntropy, a generalized correlation function inspired from
Reyni's entropy, as a detection function to capture the instantaneous flux
while preserving insensitiveness to outliers. Next, a novel peak picking
algorithm is specially designed for this detection function. By calculating the
fitness of a pre-defined inverse hyperbolic kernel to a detection function, it
is possible to find an onset and its corresponding offset simultaneously.
Experimental results show that the proposed method achieves performance
significantly better than or comparable to other state-of-the-art techniques
for onset detection in singing voice.
|
[
{
"created": "Sat, 19 Mar 2016 08:45:21 GMT",
"version": "v1"
}
] |
2020-10-29
|
[
[
"Chang",
"Sungkyun",
""
],
[
"Lee",
"Kyogu",
""
]
] |
In this paper, we propose a novelmethod to search for precise locations of paired note onset and offset in a singing voice signal. In comparison with the existing onset detection algorithms,our approach differs in two key respects. First, we employ Correntropy, a generalized correlation function inspired from Reyni's entropy, as a detection function to capture the instantaneous flux while preserving insensitiveness to outliers. Next, a novel peak picking algorithm is specially designed for this detection function. By calculating the fitness of a pre-defined inverse hyperbolic kernel to a detection function, it is possible to find an onset and its corresponding offset simultaneously. Experimental results show that the proposed method achieves performance significantly better than or comparable to other state-of-the-art techniques for onset detection in singing voice.
|
1807.00686
|
Ting Yao
|
Ting Yao and Xue Li
|
YH Technologies at ActivityNet Challenge 2018
|
Rank 2 in both Temporal Activity Detection Task & Kinetics Task @
ActivityNet 2018. arXiv admin note: substantial text overlap with
arXiv:1710.08011 by other authors
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This notebook paper presents an overview and comparative analysis of our
systems designed for the following five tasks in ActivityNet Challenge 2018:
temporal action proposals, temporal action localization, dense-captioning
events in videos, trimmed action recognition, and spatio-temporal action
localization.
|
[
{
"created": "Fri, 29 Jun 2018 07:49:08 GMT",
"version": "v1"
}
] |
2018-07-03
|
[
[
"Yao",
"Ting",
""
],
[
"Li",
"Xue",
""
]
] |
This notebook paper presents an overview and comparative analysis of our systems designed for the following five tasks in ActivityNet Challenge 2018: temporal action proposals, temporal action localization, dense-captioning events in videos, trimmed action recognition, and spatio-temporal action localization.
|
2309.01940
|
Lingyue Fu Miss
|
Lingyue Fu, Huacan Chai, Shuang Luo, Kounianhua Du, Weiming Zhang,
Longteng Fan, Jiayi Lei, Renting Rui, Jianghao Lin, Yuchen Fang, Yifan Liu,
Jingkuan Wang, Siyuan Qi, Kangning Zhang, Weinan Zhang, Yong Yu
|
CodeApex: A Bilingual Programming Evaluation Benchmark for Large
Language Models
|
33pages
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the emergence of Large Language Models (LLMs), there has been a
significant improvement in the programming capabilities of models, attracting
growing attention from researchers. Evaluating the programming capabilities of
LLMs is crucial as it reflects the multifaceted abilities of LLMs, and it has
numerous downstream applications. In this paper, we propose CodeApex, a
bilingual benchmark dataset focusing on the programming comprehension, code
generation, and code correction abilities of LLMs. Programming comprehension
task tests LLMs on multiple-choice exam questions covering conceptual
understanding, commonsense reasoning, and multi-hop reasoning. The code
generation task evaluates LLMs through completing C++ functions based on
provided descriptions and prototypes. The code correction task asks LLMs to fix
real-world erroneous code segments with different error messages. We evaluate
12 widely used LLMs, including both general-purpose and specialized models.
GPT-4 exhibits the best programming capabilities, achieving approximate
accuracy of 69%, 54%, and 66% on the three tasks, respectively. Compared to
human performance, there is still significant room for improvement in LLM
programming. We hope that CodeApex can serve as a reference for evaluating the
coding capabilities of LLMs, further promoting their development and growth.
|
[
{
"created": "Tue, 5 Sep 2023 04:12:01 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Sep 2023 15:36:11 GMT",
"version": "v2"
},
{
"created": "Sun, 10 Sep 2023 13:32:38 GMT",
"version": "v3"
},
{
"created": "Mon, 11 Mar 2024 08:07:28 GMT",
"version": "v4"
}
] |
2024-03-12
|
[
[
"Fu",
"Lingyue",
""
],
[
"Chai",
"Huacan",
""
],
[
"Luo",
"Shuang",
""
],
[
"Du",
"Kounianhua",
""
],
[
"Zhang",
"Weiming",
""
],
[
"Fan",
"Longteng",
""
],
[
"Lei",
"Jiayi",
""
],
[
"Rui",
"Renting",
""
],
[
"Lin",
"Jianghao",
""
],
[
"Fang",
"Yuchen",
""
],
[
"Liu",
"Yifan",
""
],
[
"Wang",
"Jingkuan",
""
],
[
"Qi",
"Siyuan",
""
],
[
"Zhang",
"Kangning",
""
],
[
"Zhang",
"Weinan",
""
],
[
"Yu",
"Yong",
""
]
] |
With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers. Evaluating the programming capabilities of LLMs is crucial as it reflects the multifaceted abilities of LLMs, and it has numerous downstream applications. In this paper, we propose CodeApex, a bilingual benchmark dataset focusing on the programming comprehension, code generation, and code correction abilities of LLMs. Programming comprehension task tests LLMs on multiple-choice exam questions covering conceptual understanding, commonsense reasoning, and multi-hop reasoning. The code generation task evaluates LLMs through completing C++ functions based on provided descriptions and prototypes. The code correction task asks LLMs to fix real-world erroneous code segments with different error messages. We evaluate 12 widely used LLMs, including both general-purpose and specialized models. GPT-4 exhibits the best programming capabilities, achieving approximate accuracy of 69%, 54%, and 66% on the three tasks, respectively. Compared to human performance, there is still significant room for improvement in LLM programming. We hope that CodeApex can serve as a reference for evaluating the coding capabilities of LLMs, further promoting their development and growth.
|
1908.07152
|
Joseph O'Rourke
|
Joseph O'Rourke
|
Unfolding Polyhedra
|
Proceedings 31st Canadian Conference on Computational Geometry, Aug
2019, Edmonton, Alberta. pp. 85-86. The arXiv version updates the proceedings
version by citing a recent result that not every polycube can be
edge-unzipped
| null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Starting with the unsolved "D\"urer's problem" of edge-unfolding a convex
polyhedron to a net, we specialize and generalize (a) the types of cuts
permitted, and (b) the polyhedra shapes, to highlight both advances established
and which problems remain open.
|
[
{
"created": "Tue, 13 Aug 2019 13:49:12 GMT",
"version": "v1"
}
] |
2019-08-21
|
[
[
"O'Rourke",
"Joseph",
""
]
] |
Starting with the unsolved "D\"urer's problem" of edge-unfolding a convex polyhedron to a net, we specialize and generalize (a) the types of cuts permitted, and (b) the polyhedra shapes, to highlight both advances established and which problems remain open.
|
2307.02339
|
Ludwig Mohr
|
Ludwig Mohr, Ismail Geles and Friedrich Fraundorfer
|
GAFAR: Graph-Attention Feature-Augmentation for Registration A Fast and
Light-weight Point Set Registration Algorithm
|
Accepted to the 11th European Conference on Mobile Robots (ECMR2023)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Rigid registration of point clouds is a fundamental problem in computer
vision with many applications from 3D scene reconstruction to geometry capture
and robotics. If a suitable initial registration is available, conventional
methods like ICP and its many variants can provide adequate solutions. In
absence of a suitable initialization and in the presence of a high outlier rate
or in the case of small overlap though the task of rigid registration still
presents great challenges. The advent of deep learning in computer vision has
brought new drive to research on this topic, since it provides the possibility
to learn expressive feature-representations and provide one-shot estimates
instead of depending on time-consuming iterations of conventional robust
methods. Yet, the rotation and permutation invariant nature of point clouds
poses its own challenges to deep learning, resulting in loss of performance and
low generalization capability due to sensitivity to outliers and
characteristics of 3D scans not present during network training. In this work,
we present a novel fast and light-weight network architecture using the
attention mechanism to augment point descriptors at inference time to optimally
suit the registration task of the specific point clouds it is presented with.
Employing a fully-connected graph both within and between point clouds lets the
network reason about the importance and reliability of points for registration,
making our approach robust to outliers, low overlap and unseen data. We test
the performance of our registration algorithm on different registration and
generalization tasks and provide information on runtime and resource
consumption. The code and trained weights are available at
https://github.com/mordecaimalignatius/GAFAR/.
|
[
{
"created": "Wed, 5 Jul 2023 14:50:36 GMT",
"version": "v1"
}
] |
2023-07-06
|
[
[
"Mohr",
"Ludwig",
""
],
[
"Geles",
"Ismail",
""
],
[
"Fraundorfer",
"Friedrich",
""
]
] |
Rigid registration of point clouds is a fundamental problem in computer vision with many applications from 3D scene reconstruction to geometry capture and robotics. If a suitable initial registration is available, conventional methods like ICP and its many variants can provide adequate solutions. In absence of a suitable initialization and in the presence of a high outlier rate or in the case of small overlap though the task of rigid registration still presents great challenges. The advent of deep learning in computer vision has brought new drive to research on this topic, since it provides the possibility to learn expressive feature-representations and provide one-shot estimates instead of depending on time-consuming iterations of conventional robust methods. Yet, the rotation and permutation invariant nature of point clouds poses its own challenges to deep learning, resulting in loss of performance and low generalization capability due to sensitivity to outliers and characteristics of 3D scans not present during network training. In this work, we present a novel fast and light-weight network architecture using the attention mechanism to augment point descriptors at inference time to optimally suit the registration task of the specific point clouds it is presented with. Employing a fully-connected graph both within and between point clouds lets the network reason about the importance and reliability of points for registration, making our approach robust to outliers, low overlap and unseen data. We test the performance of our registration algorithm on different registration and generalization tasks and provide information on runtime and resource consumption. The code and trained weights are available at https://github.com/mordecaimalignatius/GAFAR/.
|
1904.10574
|
Hasan Manzour
|
Hasan Manzour, Simge K\"u\c{c}\"ukyavuz, Ali Shojaie
|
Integer Programming for Learning Directed Acyclic Graphs from Continuous
Data
| null | null | null | null |
cs.LG cs.DM stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning directed acyclic graphs (DAGs) from data is a challenging task both
in theory and in practice, because the number of possible DAGs scales
superexponentially with the number of nodes. In this paper, we study the
problem of learning an optimal DAG from continuous observational data. We cast
this problem in the form of a mathematical programming model which can
naturally incorporate a super-structure in order to reduce the set of possible
candidate DAGs. We use the penalized negative log-likelihood score function
with both $\ell_0$ and $\ell_1$ regularizations and propose a new mixed-integer
quadratic optimization (MIQO) model, referred to as a layered network (LN)
formulation. The LN formulation is a compact model, which enjoys as tight an
optimal continuous relaxation value as the stronger but larger formulations
under a mild condition. Computational results indicate that the proposed
formulation outperforms existing mathematical formulations and scales better
than available algorithms that can solve the same problem with only $\ell_1$
regularization. In particular, the LN formulation clearly outperforms existing
methods in terms of computational time needed to find an optimal DAG in the
presence of a sparse super-structure.
|
[
{
"created": "Tue, 23 Apr 2019 23:58:40 GMT",
"version": "v1"
}
] |
2019-04-25
|
[
[
"Manzour",
"Hasan",
""
],
[
"Küçükyavuz",
"Simge",
""
],
[
"Shojaie",
"Ali",
""
]
] |
Learning directed acyclic graphs (DAGs) from data is a challenging task both in theory and in practice, because the number of possible DAGs scales superexponentially with the number of nodes. In this paper, we study the problem of learning an optimal DAG from continuous observational data. We cast this problem in the form of a mathematical programming model which can naturally incorporate a super-structure in order to reduce the set of possible candidate DAGs. We use the penalized negative log-likelihood score function with both $\ell_0$ and $\ell_1$ regularizations and propose a new mixed-integer quadratic optimization (MIQO) model, referred to as a layered network (LN) formulation. The LN formulation is a compact model, which enjoys as tight an optimal continuous relaxation value as the stronger but larger formulations under a mild condition. Computational results indicate that the proposed formulation outperforms existing mathematical formulations and scales better than available algorithms that can solve the same problem with only $\ell_1$ regularization. In particular, the LN formulation clearly outperforms existing methods in terms of computational time needed to find an optimal DAG in the presence of a sparse super-structure.
|
1511.07608
|
Janne V. Kujala
|
Janne V. Kujala, Tuomas J. Lukka, and Harri Holopainen
|
Picking a Conveyor Clean by an Autonomously Learning Robot
|
6 pages, 8 figures
| null | null | null |
cs.RO cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a research picking prototype related to our company's industrial
waste sorting application. The goal of the prototype is to be as autonomous as
possible and it both calibrates itself and improves its picking with minimal
human intervention. The system learns to pick objects better based on a
feedback sensor in its gripper and uses machine learning to choosing the best
proposal from a random sample produced by simple hard-coded geometric models.
We show experimentally the system improving its picking autonomously by
measuring the pick success rate as function of time. We also show how this
system can pick a conveyor belt clean, depositing 70 out of 80 objects in a
difficult to manipulate pile of novel objects into the correct chute. We
discuss potential improvements and next steps in this direction.
|
[
{
"created": "Tue, 24 Nov 2015 08:35:49 GMT",
"version": "v1"
}
] |
2015-11-25
|
[
[
"Kujala",
"Janne V.",
""
],
[
"Lukka",
"Tuomas J.",
""
],
[
"Holopainen",
"Harri",
""
]
] |
We present a research picking prototype related to our company's industrial waste sorting application. The goal of the prototype is to be as autonomous as possible and it both calibrates itself and improves its picking with minimal human intervention. The system learns to pick objects better based on a feedback sensor in its gripper and uses machine learning to choosing the best proposal from a random sample produced by simple hard-coded geometric models. We show experimentally the system improving its picking autonomously by measuring the pick success rate as function of time. We also show how this system can pick a conveyor belt clean, depositing 70 out of 80 objects in a difficult to manipulate pile of novel objects into the correct chute. We discuss potential improvements and next steps in this direction.
|
1708.04317
|
Tianyang Wang
|
Tianyang Wang, Zhengrui Qin, Michelle Zhu
|
An ELU Network with Total Variation for Image Denoising
|
10 pages, Accepted by the 24th International Conference on Neural
Information Processing (2017)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a novel convolutional neural network (CNN) for
image denoising, which uses exponential linear unit (ELU) as the activation
function. We investigate the suitability by analyzing ELU's connection with
trainable nonlinear reaction diffusion model (TNRD) and residual denoising. On
the other hand, batch normalization (BN) is indispensable for residual
denoising and convergence purpose. However, direct stacking of BN and ELU
degrades the performance of CNN. To mitigate this issue, we design an
innovative combination of activation layer and normalization layer to exploit
and leverage the ELU network, and discuss the corresponding rationale.
Moreover, inspired by the fact that minimizing total variation (TV) can be
applied to image denoising, we propose a TV regularized L2 loss to evaluate the
training effect during the iterations. Finally, we conduct extensive
experiments, showing that our model outperforms some recent and popular
approaches on Gaussian denoising with specific or randomized noise levels for
both gray and color images.
|
[
{
"created": "Mon, 14 Aug 2017 20:47:35 GMT",
"version": "v1"
}
] |
2017-08-16
|
[
[
"Wang",
"Tianyang",
""
],
[
"Qin",
"Zhengrui",
""
],
[
"Zhu",
"Michelle",
""
]
] |
In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function. We investigate the suitability by analyzing ELU's connection with trainable nonlinear reaction diffusion model (TNRD) and residual denoising. On the other hand, batch normalization (BN) is indispensable for residual denoising and convergence purpose. However, direct stacking of BN and ELU degrades the performance of CNN. To mitigate this issue, we design an innovative combination of activation layer and normalization layer to exploit and leverage the ELU network, and discuss the corresponding rationale. Moreover, inspired by the fact that minimizing total variation (TV) can be applied to image denoising, we propose a TV regularized L2 loss to evaluate the training effect during the iterations. Finally, we conduct extensive experiments, showing that our model outperforms some recent and popular approaches on Gaussian denoising with specific or randomized noise levels for both gray and color images.
|
2207.11952
|
Viorica Chifu
|
Cristina Bianca Pop, Viorica Rozina Chifu, Corina Cordea, Emil Stefan
Chifu, Octav Barsan
|
Forecasting the Short-Term Energy Consumption Using Random Forests and
Gradient Boosting
| null |
2021 20th RoEduNet Conference: Networking in Education and
Research (RoEduNet), 2021, pp. 1-6
|
10.1109/RoEduNet54112.2021.9638276
| null |
cs.AI cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
This paper analyzes comparatively the performance of Random Forests and
Gradient Boosting algorithms in the field of forecasting the energy consumption
based on historical data. The two algorithms are applied in order to forecast
the energy consumption individually, and then combined together by using a
Weighted Average Ensemble Method. The comparison among the achieved
experimental results proves that the Weighted Average Ensemble Method provides
more accurate results than each of the two algorithms applied alone.
|
[
{
"created": "Mon, 25 Jul 2022 07:40:25 GMT",
"version": "v1"
}
] |
2022-07-26
|
[
[
"Pop",
"Cristina Bianca",
""
],
[
"Chifu",
"Viorica Rozina",
""
],
[
"Cordea",
"Corina",
""
],
[
"Chifu",
"Emil Stefan",
""
],
[
"Barsan",
"Octav",
""
]
] |
This paper analyzes comparatively the performance of Random Forests and Gradient Boosting algorithms in the field of forecasting the energy consumption based on historical data. The two algorithms are applied in order to forecast the energy consumption individually, and then combined together by using a Weighted Average Ensemble Method. The comparison among the achieved experimental results proves that the Weighted Average Ensemble Method provides more accurate results than each of the two algorithms applied alone.
|
2403.14293
|
Ponkoj Shill
|
Ponkoj Chandra Shill, Md. Azizul Hakim, Muhammad Jahanzeb Khan,
Bashira Akter Anima
|
Human Reactions to Incorrect Answers from Robots
|
6 pages, 6 figures, 1 table, Ro-Man 2024
| null | null | null |
cs.RO cs.SE
|
http://creativecommons.org/publicdomain/zero/1.0/
|
As robots grow more and more integrated into numerous industries, it is
critical to comprehend how humans respond to their failures. This paper
systematically studies how trust dynamics and system design are affected by
human responses to robot failures. The three-stage survey used in the study
provides a thorough understanding of human-robot interactions. While the second
stage concentrates on interaction details, such as robot precision and error
acknowledgment, the first stage collects demographic data and initial levels of
trust. In the last phase, participants' perceptions are examined after the
encounter, and trust dynamics, forgiveness, and propensity to suggest robotic
technologies are evaluated. Results show that participants' trust in robotic
technologies increased significantly when robots acknowledged their errors or
limitations to participants and their willingness to suggest robots for
activities in the future points to a favorable change in perception,
emphasizing the role that direct engagement has in influencing trust dynamics.
By providing useful advice for creating more sympathetic, responsive, and
reliable robotic systems, the study advances the science of human-robot
interaction and promotes a wider adoption of robotic technologies.
|
[
{
"created": "Thu, 21 Mar 2024 11:00:11 GMT",
"version": "v1"
}
] |
2024-07-08
|
[
[
"Shill",
"Ponkoj Chandra",
""
],
[
"Hakim",
"Md. Azizul",
""
],
[
"Khan",
"Muhammad Jahanzeb",
""
],
[
"Anima",
"Bashira Akter",
""
]
] |
As robots grow more and more integrated into numerous industries, it is critical to comprehend how humans respond to their failures. This paper systematically studies how trust dynamics and system design are affected by human responses to robot failures. The three-stage survey used in the study provides a thorough understanding of human-robot interactions. While the second stage concentrates on interaction details, such as robot precision and error acknowledgment, the first stage collects demographic data and initial levels of trust. In the last phase, participants' perceptions are examined after the encounter, and trust dynamics, forgiveness, and propensity to suggest robotic technologies are evaluated. Results show that participants' trust in robotic technologies increased significantly when robots acknowledged their errors or limitations to participants and their willingness to suggest robots for activities in the future points to a favorable change in perception, emphasizing the role that direct engagement has in influencing trust dynamics. By providing useful advice for creating more sympathetic, responsive, and reliable robotic systems, the study advances the science of human-robot interaction and promotes a wider adoption of robotic technologies.
|
1608.04105
|
Timothy Molter
|
Timothy W. Molter and M. Alexander Nugent
|
Machine Learning with Memristors via Thermodynamic RAM
| null |
CNNA 2016, 15th International Workshop on Cellular Nanoscale
Networks and their Applications, Dresden, Germany, 2016, pp. 1-2
| null | null |
cs.ET cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Thermodynamic RAM (kT-RAM) is a neuromemristive co-processor design based on
the theory of AHaH Computing and implemented via CMOS and memristors. The
co-processor is a 2-D array of differential memristor pairs (synapses) that can
be selectively coupled together (neurons) via the digital bit addressing of the
underlying CMOS RAM circuitry. The chip is designed to plug into existing
digital computers and be interacted with via a simple instruction set.
Anti-Hebbian and Hebbian (AHaH) computing forms the theoretical framework from
which a nature-inspired type of computing architecture is built where, unlike
von Neumann architectures, memory and processor are physically combined for
synaptic operations. Through exploitation of AHaH attractor states,
memristor-based circuits converge to attractor basins that represents machine
learning solutions such as unsupervised feature learning, supervised
classification and anomaly detection. Because kT-RAM eliminates the need to
shuttle bits back and forth between memory and processor and can operate at
very low voltage levels, it can significantly surpass CPU, GPU, and FPGA
performance for synaptic integration and learning operations. Here, we present
a memristor technology developed for use in kT-RAM, in particular
bi-directional incremental adaptation of conductance via short low-voltage 1.0
V, 1.0 microsecond pulses.
|
[
{
"created": "Sun, 14 Aug 2016 14:01:10 GMT",
"version": "v1"
}
] |
2017-04-27
|
[
[
"Molter",
"Timothy W.",
""
],
[
"Nugent",
"M. Alexander",
""
]
] |
Thermodynamic RAM (kT-RAM) is a neuromemristive co-processor design based on the theory of AHaH Computing and implemented via CMOS and memristors. The co-processor is a 2-D array of differential memristor pairs (synapses) that can be selectively coupled together (neurons) via the digital bit addressing of the underlying CMOS RAM circuitry. The chip is designed to plug into existing digital computers and be interacted with via a simple instruction set. Anti-Hebbian and Hebbian (AHaH) computing forms the theoretical framework from which a nature-inspired type of computing architecture is built where, unlike von Neumann architectures, memory and processor are physically combined for synaptic operations. Through exploitation of AHaH attractor states, memristor-based circuits converge to attractor basins that represents machine learning solutions such as unsupervised feature learning, supervised classification and anomaly detection. Because kT-RAM eliminates the need to shuttle bits back and forth between memory and processor and can operate at very low voltage levels, it can significantly surpass CPU, GPU, and FPGA performance for synaptic integration and learning operations. Here, we present a memristor technology developed for use in kT-RAM, in particular bi-directional incremental adaptation of conductance via short low-voltage 1.0 V, 1.0 microsecond pulses.
|
1903.10740
|
Juraj Sebej
|
Stavros Konstantinidis, Mitja Mastnak, Juraj Sebej
|
Partitioning a Symmetric Rational Relation into Two Asymmetric Rational
Relations
|
19 pages, 4 figures. Submitted to the 24th International Conference
on Implementation and Application of Automata, July 22-25, 2019, Kosice,
Slovakia
| null | null | null |
cs.FL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider the problem of partitioning effectively a given symmetric (and
irreflexive) rational relation R into two asymmetric rational relations. This
problem is motivated by a recent method of embedding an R-independent language
into one that is maximal R-independent, where the method requires to use an
asymmetric partition of R. We solve the problem when R is realized by a
zero-avoiding transducer (with some bound k): if the absolute value of the
input-output length discrepancy of a computation exceeds k then the length
discrepancy of the computation cannot become zero. This class of relations
properly contains all recognizable, all left synchronous, and all right
synchronous relations. We leave the asymmetric partition problem open when R is
not realized by a zero-avoiding transducer. We also show examples of total
wordorderings for which there is a relation R that cannot be partitioned into
two asymmetric rational relations such that one of them is decreasing with
respect to the given word-ordering.
|
[
{
"created": "Tue, 26 Mar 2019 08:58:25 GMT",
"version": "v1"
}
] |
2019-03-27
|
[
[
"Konstantinidis",
"Stavros",
""
],
[
"Mastnak",
"Mitja",
""
],
[
"Sebej",
"Juraj",
""
]
] |
We consider the problem of partitioning effectively a given symmetric (and irreflexive) rational relation R into two asymmetric rational relations. This problem is motivated by a recent method of embedding an R-independent language into one that is maximal R-independent, where the method requires to use an asymmetric partition of R. We solve the problem when R is realized by a zero-avoiding transducer (with some bound k): if the absolute value of the input-output length discrepancy of a computation exceeds k then the length discrepancy of the computation cannot become zero. This class of relations properly contains all recognizable, all left synchronous, and all right synchronous relations. We leave the asymmetric partition problem open when R is not realized by a zero-avoiding transducer. We also show examples of total wordorderings for which there is a relation R that cannot be partitioned into two asymmetric rational relations such that one of them is decreasing with respect to the given word-ordering.
|
1006.0379
|
Jamshid Abouei
|
J. David Brown, Jamshid Abouei, Konstantinos N. Plataniotis,
Subbarayan Pasupathy
|
Adaptive Demodulation in Differentially Coherent Phase Systems: Design
and Performance Analysis
|
25 pages, 11 Figures, submitted to IEEE Transactions on
Communications, June 1, 2010
| null |
10.1109/TCOMM.2011.051311.100331
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Adaptive Demodulation (ADM) is a newly proposed rate-adaptive system which
operates without requiring Channel State Information (CSI) at the transmitter
(unlike adaptive modulation) by using adaptive decision region boundaries at
the receiver and encoding the data with a rateless code. This paper addresses
the design and performance of an ADM scheme for two common differentially
coherent schemes: M-DPSK (M-ary Differential Phase Shift Keying) and M-DAPSK
(M-ary Differential Amplitude and Phase Shift Keying) operating over AWGN and
Rayleigh fading channels. The optimal method for determining the most reliable
bits for a given differential detection scheme is presented. In addition,
simple (near-optimal) implementations are provided for recovering the most
reliable bits from a received pair of differentially encoded symbols for
systems using 16-DPSK and 16- DAPSK. The new receivers offer the advantages of
a rate-adaptive system, without requiring CSI at the transmitter and a coherent
phase reference at the receiver. Bit error analysis for the ADM system in both
cases is presented along with numerical results of the spectral efficiency for
the rate-adaptive systems operating over a Rayleigh fading channel.
|
[
{
"created": "Wed, 2 Jun 2010 13:56:42 GMT",
"version": "v1"
}
] |
2016-11-17
|
[
[
"Brown",
"J. David",
""
],
[
"Abouei",
"Jamshid",
""
],
[
"Plataniotis",
"Konstantinos N.",
""
],
[
"Pasupathy",
"Subbarayan",
""
]
] |
Adaptive Demodulation (ADM) is a newly proposed rate-adaptive system which operates without requiring Channel State Information (CSI) at the transmitter (unlike adaptive modulation) by using adaptive decision region boundaries at the receiver and encoding the data with a rateless code. This paper addresses the design and performance of an ADM scheme for two common differentially coherent schemes: M-DPSK (M-ary Differential Phase Shift Keying) and M-DAPSK (M-ary Differential Amplitude and Phase Shift Keying) operating over AWGN and Rayleigh fading channels. The optimal method for determining the most reliable bits for a given differential detection scheme is presented. In addition, simple (near-optimal) implementations are provided for recovering the most reliable bits from a received pair of differentially encoded symbols for systems using 16-DPSK and 16- DAPSK. The new receivers offer the advantages of a rate-adaptive system, without requiring CSI at the transmitter and a coherent phase reference at the receiver. Bit error analysis for the ADM system in both cases is presented along with numerical results of the spectral efficiency for the rate-adaptive systems operating over a Rayleigh fading channel.
|
2203.15767
|
Yaxin Hu
|
Yaxin Hu, Yuxiao Qu, Adam Maus, and Bilge Mutlu
|
Polite or Direct? Conversation Design of a Smart Display for Older
Adults Based on Politeness Theory
|
To be published in 2022 CHI Conference on Human Factors in Computing
Systems (CHI '22)
| null | null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Conversational interfaces increasingly rely on human-like dialogue to offer a
natural experience. However, relying on dialogue involving multiple exchanges
for even simple tasks can overburden users, particularly older adults. In this
paper, we explored the use of politeness theory in conversation design to
alleviate this burden and improve user experience. To achieve this goal, we
categorized the voice interaction offered by a smart display application
designed for older adults into seven major speech acts: request, suggest,
instruct, comment, welcome, farewell, and repair. We identified face needs for
each speech act, applied politeness strategies that best address these needs,
and tested the ability of these strategies to shape the perceived politeness of
a voice assistant in an online study ($n=64$). Based on the findings of this
study, we designed direct and polite versions of the system and conducted a
field study ($n=15$) in which participants used each of the versions for five
days at their homes. Based on five factors merged from our qualitative
findings, we identified four distinctive user personas$\unicode{x2013}$socially
oriented follower, socially oriented leader, utility oriented follower, and
utility oriented leader$\unicode{x2013}$that can inform personalized design of
smart displays.
|
[
{
"created": "Tue, 29 Mar 2022 17:26:08 GMT",
"version": "v1"
}
] |
2022-03-30
|
[
[
"Hu",
"Yaxin",
""
],
[
"Qu",
"Yuxiao",
""
],
[
"Maus",
"Adam",
""
],
[
"Mutlu",
"Bilge",
""
]
] |
Conversational interfaces increasingly rely on human-like dialogue to offer a natural experience. However, relying on dialogue involving multiple exchanges for even simple tasks can overburden users, particularly older adults. In this paper, we explored the use of politeness theory in conversation design to alleviate this burden and improve user experience. To achieve this goal, we categorized the voice interaction offered by a smart display application designed for older adults into seven major speech acts: request, suggest, instruct, comment, welcome, farewell, and repair. We identified face needs for each speech act, applied politeness strategies that best address these needs, and tested the ability of these strategies to shape the perceived politeness of a voice assistant in an online study ($n=64$). Based on the findings of this study, we designed direct and polite versions of the system and conducted a field study ($n=15$) in which participants used each of the versions for five days at their homes. Based on five factors merged from our qualitative findings, we identified four distinctive user personas$\unicode{x2013}$socially oriented follower, socially oriented leader, utility oriented follower, and utility oriented leader$\unicode{x2013}$that can inform personalized design of smart displays.
|
2103.07576
|
Annemarie van der Marel
|
Annemarie van der Marel (1), Claire L. O'Connell (1), Sanjay Prasher
(1), Chelsea Carminito (1), Xavier Francis (1), Elizabeth A. Hobson (1) ((1)
Department of Biological Sciences, University of Cincinnati, Cincinnati, OH,
USA)
|
A comparison of low-cost behavioral observation software applications
and recommendations for use
|
23 pages
| null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In the field of animal behavior and behavioral ecology, many standardized
methods to observe animal behavior were established in the last decades. While
the protocols remained similar, behavioral researchers can take advantage of
technological advancements to enter observations directly onto a handheld
computer (phone, tablet, etc.), saving time and potentially increasing fidelity
of recordings. However, we now have the choice between many different platforms
for recording behavioral observations. Our challenge is choosing the most
appropriate platform that fits a particular study question, research design,
budget, and desired amount of preparatory time. Here, we review six low-cost
software applications for handheld computers that are available for real-time
entry of behavioral observations: Animal Behaviour Pro, Animal Observer, BORIS,
CyberTracker, Prim8, and ZooMonitor. We discuss the preliminary decisions that
have to be made about the study design, and we assess the six applications by
providing the advantages and disadvantages of each platform and an overall
application comparison. Our goal is to help researchers make calculated
decisions about what behavioral observation platform is best for their study
system and question.
|
[
{
"created": "Fri, 12 Mar 2021 23:51:30 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Apr 2021 01:34:37 GMT",
"version": "v2"
},
{
"created": "Thu, 16 Sep 2021 15:17:02 GMT",
"version": "v3"
},
{
"created": "Mon, 25 Oct 2021 15:11:16 GMT",
"version": "v4"
}
] |
2021-10-26
|
[
[
"van der Marel",
"Annemarie",
""
],
[
"O'Connell",
"Claire L.",
""
],
[
"Prasher",
"Sanjay",
""
],
[
"Carminito",
"Chelsea",
""
],
[
"Francis",
"Xavier",
""
],
[
"Hobson",
"Elizabeth A.",
""
]
] |
In the field of animal behavior and behavioral ecology, many standardized methods to observe animal behavior were established in the last decades. While the protocols remained similar, behavioral researchers can take advantage of technological advancements to enter observations directly onto a handheld computer (phone, tablet, etc.), saving time and potentially increasing fidelity of recordings. However, we now have the choice between many different platforms for recording behavioral observations. Our challenge is choosing the most appropriate platform that fits a particular study question, research design, budget, and desired amount of preparatory time. Here, we review six low-cost software applications for handheld computers that are available for real-time entry of behavioral observations: Animal Behaviour Pro, Animal Observer, BORIS, CyberTracker, Prim8, and ZooMonitor. We discuss the preliminary decisions that have to be made about the study design, and we assess the six applications by providing the advantages and disadvantages of each platform and an overall application comparison. Our goal is to help researchers make calculated decisions about what behavioral observation platform is best for their study system and question.
|
1801.03106
|
Wolfgang Orthuber
|
Wolfgang Orthuber (Kiel University)
|
Why informatics and general science need a conjoint basic definition of
information
|
15 pages, 4 figures
| null | null | null |
cs.DL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
First the basic definition of information as a selection from a set of
possibilities resp. domain is recalled. This also applies to digital
information. The bits of digital information are parts of number sequences
which represent a selection from a set of possibilities resp. domain. For
faultless conversation sender and receiver of information must have the same
definition of the domain (e.g. of language vocabulary). Up to now the
definition of the domain and of its elements is derived from context and
knowledge. The internet provides an additional important possibility: A link to
a conjoint uniform definition of the domain at unique location on the internet.
The associated basic information structure is called "Domain Vector" (DV) and
has the structure "UL (of the domain definition) plus sequence of numbers". The
"UL" is not only "Uniform Locator" of the domain definition. It also identifies
a certain kind of information for later comparison and search. It can be a
Uniform Resource Locator (URL) or an abbreviated equivalent, e.g. a hierarchic
numeric pointer or a short local pointer to a table with global internet
pointers. The DV structure can be used as general carrier of information which
is language independent and more precise than language. A domain which contains
DVs is called "Domain Space" (DS) and is defined as metric space. This allows
similarity search according to user defined criteria, so that any kind of
definable information can be made comparable and searchable according to user
selected (relevant) and objectifiable (globally uniform) criteria. DS
definitions can be reused in new DS definitions. Their elements, the DVs, are
automatically globally uniformly identified and defined. Obviously such
conjoint definition of comparable information has great potential. It also can
avoid interoperability problems and redundant programming and so save high
costs.
|
[
{
"created": "Mon, 8 Jan 2018 17:25:00 GMT",
"version": "v1"
}
] |
2018-01-11
|
[
[
"Orthuber",
"Wolfgang",
"",
"Kiel University"
]
] |
First the basic definition of information as a selection from a set of possibilities resp. domain is recalled. This also applies to digital information. The bits of digital information are parts of number sequences which represent a selection from a set of possibilities resp. domain. For faultless conversation sender and receiver of information must have the same definition of the domain (e.g. of language vocabulary). Up to now the definition of the domain and of its elements is derived from context and knowledge. The internet provides an additional important possibility: A link to a conjoint uniform definition of the domain at unique location on the internet. The associated basic information structure is called "Domain Vector" (DV) and has the structure "UL (of the domain definition) plus sequence of numbers". The "UL" is not only "Uniform Locator" of the domain definition. It also identifies a certain kind of information for later comparison and search. It can be a Uniform Resource Locator (URL) or an abbreviated equivalent, e.g. a hierarchic numeric pointer or a short local pointer to a table with global internet pointers. The DV structure can be used as general carrier of information which is language independent and more precise than language. A domain which contains DVs is called "Domain Space" (DS) and is defined as metric space. This allows similarity search according to user defined criteria, so that any kind of definable information can be made comparable and searchable according to user selected (relevant) and objectifiable (globally uniform) criteria. DS definitions can be reused in new DS definitions. Their elements, the DVs, are automatically globally uniformly identified and defined. Obviously such conjoint definition of comparable information has great potential. It also can avoid interoperability problems and redundant programming and so save high costs.
|
1806.06180
|
Ganapati Bhat
|
Ganapati Bhat, Suat Gumussoy, Umit Y. Ogras
|
Power-Temperature Stability and Safety Analysis for Multiprocessor
Systems
|
Published in ACM TECS
|
ACM Trans. Embed. Comput. Syst. 16, 5s, Article 145 (September
2017), 19 pages
|
10.1145/3126567
| null |
cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern multiprocessor system-on-chips (SoCs) integrate multiple heterogeneous
cores to achieve high energy efficiency. The power consumption of each core
contributes to an increase in the temperature across the chip floorplan. In
turn, higher temperature increases the leakage power exponentially, and leads
to a positive feedback with nonlinear dynamics. This paper presents a
power-temperature stability and safety analysis technique for multiprocessor
systems. This analysis reveals the conditions under which the power-temperature
trajectory converges to a stable fixed point. We also present a simple formula
to compute the stable fixed point and maximum thermally-safe power consumption
at runtime. Hardware measurements on a state-of-the-art mobile processor show
that our analytical formulation can predict the stable fixed point with an
average error of 2.6%. Hence, our approach can be used at runtime to ensure
thermally safe operation and guard against thermal threats.
|
[
{
"created": "Sat, 16 Jun 2018 04:43:25 GMT",
"version": "v1"
}
] |
2018-06-19
|
[
[
"Bhat",
"Ganapati",
""
],
[
"Gumussoy",
"Suat",
""
],
[
"Ogras",
"Umit Y.",
""
]
] |
Modern multiprocessor system-on-chips (SoCs) integrate multiple heterogeneous cores to achieve high energy efficiency. The power consumption of each core contributes to an increase in the temperature across the chip floorplan. In turn, higher temperature increases the leakage power exponentially, and leads to a positive feedback with nonlinear dynamics. This paper presents a power-temperature stability and safety analysis technique for multiprocessor systems. This analysis reveals the conditions under which the power-temperature trajectory converges to a stable fixed point. We also present a simple formula to compute the stable fixed point and maximum thermally-safe power consumption at runtime. Hardware measurements on a state-of-the-art mobile processor show that our analytical formulation can predict the stable fixed point with an average error of 2.6%. Hence, our approach can be used at runtime to ensure thermally safe operation and guard against thermal threats.
|
1606.07518
|
EPTCS
|
Alexandru Baltag (Institute for logic, Language and Computation.
University of Amsterdam), Nina Gierasimczuk (Institute for Logic, Language
and Computation. University of Amsterdam), Sonja Smets (Institute for Logic,
Language and Computation. University of Amsterdam)
|
On the Solvability of Inductive Problems: A Study in Epistemic Topology
|
In Proceedings TARK 2015, arXiv:1606.07295
|
EPTCS 215, 2016, pp. 81-98
|
10.4204/EPTCS.215.7
| null |
cs.LO cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate the issues of inductive problem-solving and learning by
doxastic agents. We provide topological characterizations of solvability and
learnability, and we use them to prove that AGM-style belief revision is
"universal", i.e., that every solvable problem is solvable by AGM conditioning.
|
[
{
"created": "Fri, 24 Jun 2016 00:30:59 GMT",
"version": "v1"
}
] |
2016-06-27
|
[
[
"Baltag",
"Alexandru",
"",
"Institute for logic, Language and Computation.\n University of Amsterdam"
],
[
"Gierasimczuk",
"Nina",
"",
"Institute for Logic, Language\n and Computation. University of Amsterdam"
],
[
"Smets",
"Sonja",
"",
"Institute for Logic,\n Language and Computation. University of Amsterdam"
]
] |
We investigate the issues of inductive problem-solving and learning by doxastic agents. We provide topological characterizations of solvability and learnability, and we use them to prove that AGM-style belief revision is "universal", i.e., that every solvable problem is solvable by AGM conditioning.
|
1211.6631
|
Onur Ozdemir
|
Onur Ozdemir, Pramod K. Varshney, Wei Su, and Andrew L. Drozd
|
Asymptotic Properties of Likelihood Based Linear Modulation
Classification Systems
|
12 pages double-column, 6 figures, submitted to IEEE Transactions on
Wireless Communications
| null | null | null |
cs.IT math.IT stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The problem of linear modulation classification using likelihood based
methods is considered. Asymptotic properties of most commonly used classifiers
in the literature are derived. These classifiers are based on hybrid likelihood
ratio test (HLRT) and average likelihood ratio test (ALRT), respectively. Both
a single-sensor setting and a multi-sensor setting that uses a distributed
decision fusion approach are analyzed. For a modulation classification system
using a single sensor, it is shown that HLRT achieves asymptotically vanishing
probability of error (Pe) whereas the same result cannot be proven for ALRT. In
a multi-sensor setting using soft decision fusion, conditions are derived under
which Pe vanishes asymptotically. Furthermore, the asymptotic analysis of the
fusion rule that assumes independent sensor decisions is carried out.
|
[
{
"created": "Wed, 28 Nov 2012 15:22:29 GMT",
"version": "v1"
}
] |
2012-11-29
|
[
[
"Ozdemir",
"Onur",
""
],
[
"Varshney",
"Pramod K.",
""
],
[
"Su",
"Wei",
""
],
[
"Drozd",
"Andrew L.",
""
]
] |
The problem of linear modulation classification using likelihood based methods is considered. Asymptotic properties of most commonly used classifiers in the literature are derived. These classifiers are based on hybrid likelihood ratio test (HLRT) and average likelihood ratio test (ALRT), respectively. Both a single-sensor setting and a multi-sensor setting that uses a distributed decision fusion approach are analyzed. For a modulation classification system using a single sensor, it is shown that HLRT achieves asymptotically vanishing probability of error (Pe) whereas the same result cannot be proven for ALRT. In a multi-sensor setting using soft decision fusion, conditions are derived under which Pe vanishes asymptotically. Furthermore, the asymptotic analysis of the fusion rule that assumes independent sensor decisions is carried out.
|
2306.17498
|
Hartmut Koenitz
|
Hartmut Koenitz, Jonathan Barbara, Lissa Holloway-Attaway, Frank Nack,
Mirjam Palosaari Eladhari, Agnes Bakk
|
INDCOR White Paper 0: Interactive Digital Narratives (IDNs) -- A
Solution to the Challenge of Representing Complex Issues
| null | null | null | null |
cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Citizens everywhere have the right to be well-informed. Yet, with the high
complexity of many contemporary issues, such as global warming and migration,
our means of information need to mutually adapt. Narrative has always been at
the core of information exchange - regardless of whether our ancestors sat
around a fire and exchanged stories, or whether we read an article in a
newspaper, or watched a TV news broadcast. Yet, the narrative formats of the
newspaper article, the news broadcast, the documentary, and the textbook are
severely limited when it comes to representing highly complex topics which may
include several competing - and sometimes equally valid - perspectives. Such
complexity contributes to a high level of uncertainty due to a multitude of
factors affecting an outcome. Fortunately, with Interactive Digital Narrative
(IDN), there is a novel media format which can address these challenges. IDNs
can present several different perspectives in the same work, and give audiences
the ability to explore them at will through decision-making. After experiencing
the consequences of their decisions, the audience can replay to revisit and
change these decisions in order to consider their alternatives. IDN works
enable deep personalization and the inclusion of live data. These capabilities
make IDN a 21st century democratic medium, empowering citizens through the
understanding of complex issues. In this white paper, we discuss the challenge
of representing complexity, describe the advantages offered by IDNs, and point
out opportunities and strategies for deployment.
|
[
{
"created": "Fri, 30 Jun 2023 09:16:59 GMT",
"version": "v1"
}
] |
2023-07-03
|
[
[
"Koenitz",
"Hartmut",
""
],
[
"Barbara",
"Jonathan",
""
],
[
"Holloway-Attaway",
"Lissa",
""
],
[
"Nack",
"Frank",
""
],
[
"Eladhari",
"Mirjam Palosaari",
""
],
[
"Bakk",
"Agnes",
""
]
] |
Citizens everywhere have the right to be well-informed. Yet, with the high complexity of many contemporary issues, such as global warming and migration, our means of information need to mutually adapt. Narrative has always been at the core of information exchange - regardless of whether our ancestors sat around a fire and exchanged stories, or whether we read an article in a newspaper, or watched a TV news broadcast. Yet, the narrative formats of the newspaper article, the news broadcast, the documentary, and the textbook are severely limited when it comes to representing highly complex topics which may include several competing - and sometimes equally valid - perspectives. Such complexity contributes to a high level of uncertainty due to a multitude of factors affecting an outcome. Fortunately, with Interactive Digital Narrative (IDN), there is a novel media format which can address these challenges. IDNs can present several different perspectives in the same work, and give audiences the ability to explore them at will through decision-making. After experiencing the consequences of their decisions, the audience can replay to revisit and change these decisions in order to consider their alternatives. IDN works enable deep personalization and the inclusion of live data. These capabilities make IDN a 21st century democratic medium, empowering citizens through the understanding of complex issues. In this white paper, we discuss the challenge of representing complexity, describe the advantages offered by IDNs, and point out opportunities and strategies for deployment.
|
1703.09651
|
Divya Shyam Singh
|
Divya Shyam Singha, G.B.L. Chowdarya, D Roy Mahapatraa
|
Structural Damage Identification Using Artificial Neural Network and
Synthetic data
|
6 pages,6 figures, ISSS conference
| null | null | null |
cs.LG cs.CE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents real-time vibration based identification technique using
measured frequency response functions(FRFs) under random vibration loading.
Artificial Neural Networks (ANNs) are trained to map damage fingerprints to
damage characteristic parameters. Principal component statistical analysis(PCA)
technique was used to tackle the problem of high dimensionality and high noise
of data, which is common for industrial structures. The present study considers
Crack, Rivet hole expansion and redundant uniform mass as damages on the
structure. Frequency response function data after being reduced in size using
PCA is fed to individual neural networks to localize and predict the severity
of damage on the structure. The system of ANNs trained with both numerical and
experimental model data to make the system reliable and robust. The methodology
is applied to a numerical model of stiffened panel structure, where damages are
confined close to the stiffener. The results showed that, in all the cases
considered, it is possible to localize and predict severity of the damage
occurrence with very good accuracy and reliability.
|
[
{
"created": "Mon, 27 Mar 2017 08:54:09 GMT",
"version": "v1"
}
] |
2017-03-29
|
[
[
"Singha",
"Divya Shyam",
""
],
[
"Chowdarya",
"G. B. L.",
""
],
[
"Mahapatraa",
"D Roy",
""
]
] |
This paper presents real-time vibration based identification technique using measured frequency response functions(FRFs) under random vibration loading. Artificial Neural Networks (ANNs) are trained to map damage fingerprints to damage characteristic parameters. Principal component statistical analysis(PCA) technique was used to tackle the problem of high dimensionality and high noise of data, which is common for industrial structures. The present study considers Crack, Rivet hole expansion and redundant uniform mass as damages on the structure. Frequency response function data after being reduced in size using PCA is fed to individual neural networks to localize and predict the severity of damage on the structure. The system of ANNs trained with both numerical and experimental model data to make the system reliable and robust. The methodology is applied to a numerical model of stiffened panel structure, where damages are confined close to the stiffener. The results showed that, in all the cases considered, it is possible to localize and predict severity of the damage occurrence with very good accuracy and reliability.
|
2111.00780
|
Lantao Yu
|
Lantao Yu, Jiaming Song, Yang Song, Stefano Ermon
|
Pseudo-Spherical Contrastive Divergence
|
NeurIPS 2021
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Energy-based models (EBMs) offer flexible distribution parametrization.
However, due to the intractable partition function, they are typically trained
via contrastive divergence for maximum likelihood estimation. In this paper, we
propose pseudo-spherical contrastive divergence (PS-CD) to generalize maximum
likelihood learning of EBMs. PS-CD is derived from the maximization of a family
of strictly proper homogeneous scoring rules, which avoids the computation of
the intractable partition function and provides a generalized family of
learning objectives that include contrastive divergence as a special case.
Moreover, PS-CD allows us to flexibly choose various learning objectives to
train EBMs without additional computational cost or variational minimax
optimization. Theoretical analysis on the proposed method and extensive
experiments on both synthetic data and commonly used image datasets demonstrate
the effectiveness and modeling flexibility of PS-CD, as well as its robustness
to data contamination, thus showing its superiority over maximum likelihood and
$f$-EBMs.
|
[
{
"created": "Mon, 1 Nov 2021 09:17:15 GMT",
"version": "v1"
}
] |
2021-11-02
|
[
[
"Yu",
"Lantao",
""
],
[
"Song",
"Jiaming",
""
],
[
"Song",
"Yang",
""
],
[
"Ermon",
"Stefano",
""
]
] |
Energy-based models (EBMs) offer flexible distribution parametrization. However, due to the intractable partition function, they are typically trained via contrastive divergence for maximum likelihood estimation. In this paper, we propose pseudo-spherical contrastive divergence (PS-CD) to generalize maximum likelihood learning of EBMs. PS-CD is derived from the maximization of a family of strictly proper homogeneous scoring rules, which avoids the computation of the intractable partition function and provides a generalized family of learning objectives that include contrastive divergence as a special case. Moreover, PS-CD allows us to flexibly choose various learning objectives to train EBMs without additional computational cost or variational minimax optimization. Theoretical analysis on the proposed method and extensive experiments on both synthetic data and commonly used image datasets demonstrate the effectiveness and modeling flexibility of PS-CD, as well as its robustness to data contamination, thus showing its superiority over maximum likelihood and $f$-EBMs.
|
1805.12282
|
Huda Khayrallah
|
Huda Khayrallah, Philipp Koehn
|
On the Impact of Various Types of Noise on Neural Machine Translation
|
Please cite as: @InProceedings{khayrallah-koehn:2018:WNMT, author =
{Khayrallah, Huda and Koehn, Philipp}, title = {On the Impact of Various
Types of Noise on Neural Machine Translation}, booktitle = {Proceedings of
the Second Workshop on Neural Machine Translation and Generation}, year =
{2018}, address = {Melbourne}, publisher = {Association for Computational
Linguistics} }
| null |
10.18653/v1/W18-2709
| null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We examine how various types of noise in the parallel training data impact
the quality of neural machine translation systems. We create five types of
artificial noise and analyze how they degrade performance in neural and
statistical machine translation. We find that neural models are generally more
harmed by noise than statistical models. For one especially egregious type of
noise they learn to just copy the input sentence.
|
[
{
"created": "Thu, 31 May 2018 01:33:19 GMT",
"version": "v1"
}
] |
2020-09-14
|
[
[
"Khayrallah",
"Huda",
""
],
[
"Koehn",
"Philipp",
""
]
] |
We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.
|
2303.12766
|
Xin Lai
|
Xin Lai, Yukang Chen, Fanbin Lu, Jianhui Liu, Jiaya Jia
|
Spherical Transformer for LiDAR-based 3D Recognition
|
Accepted to CVPR 2023. Code is available at
https://github.com/dvlab-research/SphereFormer.git
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
LiDAR-based 3D point cloud recognition has benefited various applications.
Without specially considering the LiDAR point distribution, most current
methods suffer from information disconnection and limited receptive field,
especially for the sparse distant points. In this work, we study the
varying-sparsity distribution of LiDAR points and present SphereFormer to
directly aggregate information from dense close points to the sparse distant
ones. We design radial window self-attention that partitions the space into
multiple non-overlapping narrow and long windows. It overcomes the
disconnection issue and enlarges the receptive field smoothly and dramatically,
which significantly boosts the performance of sparse distant points. Moreover,
to fit the narrow and long windows, we propose exponential splitting to yield
fine-grained position encoding and dynamic feature selection to increase model
representation ability. Notably, our method ranks 1st on both nuScenes and
SemanticKITTI semantic segmentation benchmarks with 81.9% and 74.8% mIoU,
respectively. Also, we achieve the 3rd place on nuScenes object detection
benchmark with 72.8% NDS and 68.5% mAP. Code is available at
https://github.com/dvlab-research/SphereFormer.git.
|
[
{
"created": "Wed, 22 Mar 2023 17:30:14 GMT",
"version": "v1"
}
] |
2023-03-23
|
[
[
"Lai",
"Xin",
""
],
[
"Chen",
"Yukang",
""
],
[
"Lu",
"Fanbin",
""
],
[
"Liu",
"Jianhui",
""
],
[
"Jia",
"Jiaya",
""
]
] |
LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse distant points. In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones. We design radial window self-attention that partitions the space into multiple non-overlapping narrow and long windows. It overcomes the disconnection issue and enlarges the receptive field smoothly and dramatically, which significantly boosts the performance of sparse distant points. Moreover, to fit the narrow and long windows, we propose exponential splitting to yield fine-grained position encoding and dynamic feature selection to increase model representation ability. Notably, our method ranks 1st on both nuScenes and SemanticKITTI semantic segmentation benchmarks with 81.9% and 74.8% mIoU, respectively. Also, we achieve the 3rd place on nuScenes object detection benchmark with 72.8% NDS and 68.5% mAP. Code is available at https://github.com/dvlab-research/SphereFormer.git.
|
2303.03398
|
Ronald Caplan
|
Ronald M. Caplan, Miko M. Stulajter, Jon A. Linker
|
Acceleration of a production Solar MHD code with Fortran standard
parallelism: From OpenACC to `do concurrent'
|
10 pages, 2 tables, 4 figures, accepted to the AsHES workshop at
IPDPS 2023
| null | null | null |
cs.MS astro-ph.IM cs.DC cs.PL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There is growing interest in using standard language constructs for
accelerated computing, avoiding the need for (often vendor-specific) external
APIs. These constructs hold the potential to be more portable and much more
`future-proof'. For Fortran codes, the current focus is on the {\tt do
concurrent} (DC) loop. While there have been some successful examples of
GPU-acceleration using DC for benchmark and/or small codes, its widespread
adoption will require demonstrations of its use in full-size applications.
Here, we look at the current capabilities and performance of using DC in a
production application called Magnetohydrodynamic Algorithm outside a Sphere
(MAS). MAS is a state-of-the-art model for studying coronal and heliospheric
dynamics, is over 70,000 lines long, and has previously been ported to GPUs
using MPI+OpenACC. We attempt to eliminate as many of its OpenACC directives as
possible in favor of DC. We show that using the NVIDIA {\tt nvfortran}
compiler's Fortran 202X preview implementation, unified managed memory, and
modified MPI launch methods, we can achieve GPU acceleration across multiple
GPUs without using a single OpenACC directive. However, doing so results in a
slowdown between 1.25x and 3x. We discuss what future improvements are needed
to avoid this loss, and show how we can still retain close
|
[
{
"created": "Sun, 5 Mar 2023 21:37:34 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Mar 2023 20:18:20 GMT",
"version": "v2"
}
] |
2023-03-10
|
[
[
"Caplan",
"Ronald M.",
""
],
[
"Stulajter",
"Miko M.",
""
],
[
"Linker",
"Jon A.",
""
]
] |
There is growing interest in using standard language constructs for accelerated computing, avoiding the need for (often vendor-specific) external APIs. These constructs hold the potential to be more portable and much more `future-proof'. For Fortran codes, the current focus is on the {\tt do concurrent} (DC) loop. While there have been some successful examples of GPU-acceleration using DC for benchmark and/or small codes, its widespread adoption will require demonstrations of its use in full-size applications. Here, we look at the current capabilities and performance of using DC in a production application called Magnetohydrodynamic Algorithm outside a Sphere (MAS). MAS is a state-of-the-art model for studying coronal and heliospheric dynamics, is over 70,000 lines long, and has previously been ported to GPUs using MPI+OpenACC. We attempt to eliminate as many of its OpenACC directives as possible in favor of DC. We show that using the NVIDIA {\tt nvfortran} compiler's Fortran 202X preview implementation, unified managed memory, and modified MPI launch methods, we can achieve GPU acceleration across multiple GPUs without using a single OpenACC directive. However, doing so results in a slowdown between 1.25x and 3x. We discuss what future improvements are needed to avoid this loss, and show how we can still retain close
|
1202.3683
|
Rajendra Shinde
|
Debojyoti Dutta, Michael Kapralov, Ian Post, Rajendra Shinde
|
Optimal bandwidth-aware VM allocation for Infrastructure-as-a-Service
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Infrastructure-as-a-Service (IaaS) providers need to offer richer services to
be competitive while optimizing their resource usage to keep costs down. Richer
service offerings include new resource request models involving bandwidth
guarantees between virtual machines (VMs). Thus we consider the following
problem: given a VM request graph (where nodes are VMs and edges represent
virtual network connectivity between the VMs) and a real data center topology,
find an allocation of VMs to servers that satisfies the bandwidth guarantees
for every virtual network edge---which maps to a path in the physical
network---and minimizes congestion of the network.
Previous work has shown that for arbitrary networks and requests, finding the
optimal embedding satisfying bandwidth requests is $\mathcal{NP}$-hard.
However, in most data center architectures, the routing protocols employed are
based on a spanning tree of the physical network. In this paper, we prove that
the problem remains $\mathcal{NP}$-hard even when the physical network topology
is restricted to be a tree, and the request graph topology is also restricted.
We also present a dynamic programming algorithm for computing the optimal
embedding in a tree network which runs in time $O(3^kn)$, where $n$ is the
number of nodes in the physical topology and $k$ is the size of the request
graph, which is well suited for practical requests which have small $k$. Such
requests form a large class of web-service and enterprise workloads. Also, if
we restrict the requests topology to a clique (all VMs connected to a virtual
switch with uniform bandwidth requirements), we show that the dynamic
programming algorithm can be modified to output the minimum congestion
embedding in time $O(k^2n)$.
|
[
{
"created": "Thu, 16 Feb 2012 20:03:44 GMT",
"version": "v1"
}
] |
2012-02-17
|
[
[
"Dutta",
"Debojyoti",
""
],
[
"Kapralov",
"Michael",
""
],
[
"Post",
"Ian",
""
],
[
"Shinde",
"Rajendra",
""
]
] |
Infrastructure-as-a-Service (IaaS) providers need to offer richer services to be competitive while optimizing their resource usage to keep costs down. Richer service offerings include new resource request models involving bandwidth guarantees between virtual machines (VMs). Thus we consider the following problem: given a VM request graph (where nodes are VMs and edges represent virtual network connectivity between the VMs) and a real data center topology, find an allocation of VMs to servers that satisfies the bandwidth guarantees for every virtual network edge---which maps to a path in the physical network---and minimizes congestion of the network. Previous work has shown that for arbitrary networks and requests, finding the optimal embedding satisfying bandwidth requests is $\mathcal{NP}$-hard. However, in most data center architectures, the routing protocols employed are based on a spanning tree of the physical network. In this paper, we prove that the problem remains $\mathcal{NP}$-hard even when the physical network topology is restricted to be a tree, and the request graph topology is also restricted. We also present a dynamic programming algorithm for computing the optimal embedding in a tree network which runs in time $O(3^kn)$, where $n$ is the number of nodes in the physical topology and $k$ is the size of the request graph, which is well suited for practical requests which have small $k$. Such requests form a large class of web-service and enterprise workloads. Also, if we restrict the requests topology to a clique (all VMs connected to a virtual switch with uniform bandwidth requirements), we show that the dynamic programming algorithm can be modified to output the minimum congestion embedding in time $O(k^2n)$.
|
2303.10062
|
Qiaojie Zheng
|
Qiaojie Zheng, Jiucai Zhang, Amy Zhang, Xiaoli Zhang
|
Confidence-aware 3D Gaze Estimation and Evaluation Metric
|
9 pages 12 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Deep learning appearance-based 3D gaze estimation is gaining popularity due
to its minimal hardware requirements and being free of constraint. Unreliable
and overconfident inferences, however, still limit the adoption of this gaze
estimation method. To address the unreliable and overconfident issues, we
introduce a confidence-aware model that predicts uncertainties together with
gaze angle estimations. We also introduce a novel effectiveness evaluation
method based on the causality between eye feature degradation and the rise in
inference uncertainty to assess the uncertainty estimation. Our
confidence-aware model demonstrates reliable uncertainty estimations while
providing angular estimation accuracies on par with the state-of-the-art.
Compared with the existing statistical uncertainty-angular-error evaluation
metric, the proposed effectiveness evaluation approach can more effectively
judge inferred uncertainties' performance at each prediction.
|
[
{
"created": "Fri, 17 Mar 2023 15:44:44 GMT",
"version": "v1"
}
] |
2023-03-20
|
[
[
"Zheng",
"Qiaojie",
""
],
[
"Zhang",
"Jiucai",
""
],
[
"Zhang",
"Amy",
""
],
[
"Zhang",
"Xiaoli",
""
]
] |
Deep learning appearance-based 3D gaze estimation is gaining popularity due to its minimal hardware requirements and being free of constraint. Unreliable and overconfident inferences, however, still limit the adoption of this gaze estimation method. To address the unreliable and overconfident issues, we introduce a confidence-aware model that predicts uncertainties together with gaze angle estimations. We also introduce a novel effectiveness evaluation method based on the causality between eye feature degradation and the rise in inference uncertainty to assess the uncertainty estimation. Our confidence-aware model demonstrates reliable uncertainty estimations while providing angular estimation accuracies on par with the state-of-the-art. Compared with the existing statistical uncertainty-angular-error evaluation metric, the proposed effectiveness evaluation approach can more effectively judge inferred uncertainties' performance at each prediction.
|
2006.07244
|
Todd Murphey
|
Ana Pervan and Todd D. Murphey
|
Algorithmic Design for Embodied Intelligence in Synthetic Cells
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In nature, biological organisms jointly evolve both their morphology and
their neurological capabilities to improve their chances for survival.
Consequently, task information is encoded in both their brains and their
bodies. In robotics, the development of complex control and planning algorithms
often bears sole responsibility for improving task performance. This dependence
on centralized control can be problematic for systems with computational
limitations, such as mechanical systems and robots on the microscale. In these
cases we need to be able to offload complex computation onto the physical
morphology of the system. To this end, we introduce a methodology for
algorithmically arranging sensing and actuation components into a robot design
while maintaining a low level of design complexity (quantified using a measure
of graph entropy), and a high level of task embodiment (evaluated by analyzing
the Kullback-Leibler divergence between physical executions of the robot and
those of an idealized system). This approach computes an idealized,
unconstrained control policy which is projected onto a limited selection of
sensors and actuators in a given library, resulting in intelligence that is
distributed away from a central processor and instead embodied in the physical
body of a robot. The method is demonstrated by computationally optimizing a
simulated synthetic cell.
|
[
{
"created": "Fri, 12 Jun 2020 14:58:12 GMT",
"version": "v1"
}
] |
2020-06-15
|
[
[
"Pervan",
"Ana",
""
],
[
"Murphey",
"Todd D.",
""
]
] |
In nature, biological organisms jointly evolve both their morphology and their neurological capabilities to improve their chances for survival. Consequently, task information is encoded in both their brains and their bodies. In robotics, the development of complex control and planning algorithms often bears sole responsibility for improving task performance. This dependence on centralized control can be problematic for systems with computational limitations, such as mechanical systems and robots on the microscale. In these cases we need to be able to offload complex computation onto the physical morphology of the system. To this end, we introduce a methodology for algorithmically arranging sensing and actuation components into a robot design while maintaining a low level of design complexity (quantified using a measure of graph entropy), and a high level of task embodiment (evaluated by analyzing the Kullback-Leibler divergence between physical executions of the robot and those of an idealized system). This approach computes an idealized, unconstrained control policy which is projected onto a limited selection of sensors and actuators in a given library, resulting in intelligence that is distributed away from a central processor and instead embodied in the physical body of a robot. The method is demonstrated by computationally optimizing a simulated synthetic cell.
|
2103.10055
|
Yaohui Guo
|
Yaohui Guo, Cong Shi, and X. Jessie Yang
|
Reverse Psychology in Trust-Aware Human-Robot Interaction
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
To facilitate effective human-robot interaction (HRI), trust-aware HRI has
been proposed, wherein the robotic agent explicitly considers the human's trust
during its planning and decision making. The success of trust-aware HRI depends
on the specification of a trust dynamics model and a trust-behavior model. In
this study, we proposed one novel trust-behavior model, namely the reverse
psychology model, and compared it against the commonly used disuse model. We
examined how the two models affect the robot's optimal policy and the
human-robot team performance. Results indicate that the robot will deliberately
"manipulate" the human's trust under the reverse psychology model. To correct
this "manipulative" behavior, we proposed a trust-seeking reward function that
facilitates trust establishment without significantly sacrificing the team
performance.
|
[
{
"created": "Thu, 18 Mar 2021 07:30:58 GMT",
"version": "v1"
}
] |
2021-03-19
|
[
[
"Guo",
"Yaohui",
""
],
[
"Shi",
"Cong",
""
],
[
"Yang",
"X. Jessie",
""
]
] |
To facilitate effective human-robot interaction (HRI), trust-aware HRI has been proposed, wherein the robotic agent explicitly considers the human's trust during its planning and decision making. The success of trust-aware HRI depends on the specification of a trust dynamics model and a trust-behavior model. In this study, we proposed one novel trust-behavior model, namely the reverse psychology model, and compared it against the commonly used disuse model. We examined how the two models affect the robot's optimal policy and the human-robot team performance. Results indicate that the robot will deliberately "manipulate" the human's trust under the reverse psychology model. To correct this "manipulative" behavior, we proposed a trust-seeking reward function that facilitates trust establishment without significantly sacrificing the team performance.
|
1910.08647
|
Pavel Naumov
|
Pavel Naumov and Jia Tao
|
Blameworthiness in Security Games
|
34th AAAI Conference on Artificial Intelligence (AAAI-20), February
7-12, 2020, New York, New York, USA
| null | null | null |
cs.AI cs.GT cs.LO math.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Security games are an example of a successful real-world application of game
theory. The paper defines blameworthiness of the defender and the attacker in
security games using the principle of alternative possibilities and provides a
sound and complete logical system for reasoning about blameworthiness in such
games. Two of the axioms of this system capture the asymmetry of information in
security games.
|
[
{
"created": "Fri, 18 Oct 2019 22:22:35 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Nov 2019 20:50:51 GMT",
"version": "v2"
}
] |
2019-11-13
|
[
[
"Naumov",
"Pavel",
""
],
[
"Tao",
"Jia",
""
]
] |
Security games are an example of a successful real-world application of game theory. The paper defines blameworthiness of the defender and the attacker in security games using the principle of alternative possibilities and provides a sound and complete logical system for reasoning about blameworthiness in such games. Two of the axioms of this system capture the asymmetry of information in security games.
|
2101.06665
|
Farhad Merchant
|
Vinay Saxena, Ankitha Reddy, Jonathan Neudorfer, John Gustafson,
Sangeeth Nambiar, Rainer Leupers, Farhad Merchant
|
Brightening the Optical Flow through Posit Arithmetic
|
To appear in ISQED 2021
| null | null | null |
cs.AR cs.MS
|
http://creativecommons.org/publicdomain/zero/1.0/
|
As new technologies are invented, their commercial viability needs to be
carefully examined along with their technical merits and demerits. The posit
data format, proposed as a drop-in replacement for IEEE 754 float format, is
one such invention that requires extensive theoretical and experimental study
to identify products that can benefit from the advantages of posits for
specific market segments. In this paper, we present an extensive empirical
study of posit-based arithmetic vis-\`a-vis IEEE 754 compliant arithmetic for
the optical flow estimation method called Lucas-Kanade (LuKa). First, we use
SoftPosit and SoftFloat format emulators to perform an empirical error analysis
of the LuKa method. Our study shows that the average error in LuKa with
SoftPosit is an order of magnitude lower than LuKa with SoftFloat. We then
present the integration of the hardware implementation of a posit adder and
multiplier in a RISC-V open-source platform. We make several recommendations,
along with the analysis of LuKa in the RISC-V context, for future generation
platforms incorporating posit arithmetic units.
|
[
{
"created": "Sun, 17 Jan 2021 13:19:10 GMT",
"version": "v1"
}
] |
2021-01-19
|
[
[
"Saxena",
"Vinay",
""
],
[
"Reddy",
"Ankitha",
""
],
[
"Neudorfer",
"Jonathan",
""
],
[
"Gustafson",
"John",
""
],
[
"Nambiar",
"Sangeeth",
""
],
[
"Leupers",
"Rainer",
""
],
[
"Merchant",
"Farhad",
""
]
] |
As new technologies are invented, their commercial viability needs to be carefully examined along with their technical merits and demerits. The posit data format, proposed as a drop-in replacement for IEEE 754 float format, is one such invention that requires extensive theoretical and experimental study to identify products that can benefit from the advantages of posits for specific market segments. In this paper, we present an extensive empirical study of posit-based arithmetic vis-\`a-vis IEEE 754 compliant arithmetic for the optical flow estimation method called Lucas-Kanade (LuKa). First, we use SoftPosit and SoftFloat format emulators to perform an empirical error analysis of the LuKa method. Our study shows that the average error in LuKa with SoftPosit is an order of magnitude lower than LuKa with SoftFloat. We then present the integration of the hardware implementation of a posit adder and multiplier in a RISC-V open-source platform. We make several recommendations, along with the analysis of LuKa in the RISC-V context, for future generation platforms incorporating posit arithmetic units.
|
2301.00153
|
Peter \v{S}vec
|
Peter \v{S}vec, \v{S}tefan Balogh, Martin Homola, J\'an K\v{l}uka
|
Knowledge-Based Dataset for Training PE Malware Detection Models
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Ontologies are a standard for semantic schemata in many knowledge-intensive
domains of human interest. They are now becoming increasingly important also in
areas until very recently dominated by subsymbolic representations and
machine-learning-based data processing. One such area is information security,
and more specifically malware detection. We propose PE Malware Ontology that
offers a reusable semantic schema for Portable Executable (PE, Windows binary
format) malware files. The ontology was inspired by the structure of the data
in the EMBER dataset and it currently covers the data intended for static
malware analysis. With this proposal, we hope to achieve: a) a unified semantic
representation for PE malware datasets that are available or will be published
in the future; (b) applicability of symbolic, neural-symbolic, or otherwise
explainable approaches in the PE Malware domain that may lead to improved
interpretability of results which may now be characterized by the terms defined
in the ontology; and (c)by joint publishing of semantically treated EMBER data,
including fractional datasets, also improved reproducibility of experiments.
|
[
{
"created": "Sat, 31 Dec 2022 08:46:02 GMT",
"version": "v1"
}
] |
2023-01-03
|
[
[
"Švec",
"Peter",
""
],
[
"Balogh",
"Štefan",
""
],
[
"Homola",
"Martin",
""
],
[
"Kľuka",
"Ján",
""
]
] |
Ontologies are a standard for semantic schemata in many knowledge-intensive domains of human interest. They are now becoming increasingly important also in areas until very recently dominated by subsymbolic representations and machine-learning-based data processing. One such area is information security, and more specifically malware detection. We propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE, Windows binary format) malware files. The ontology was inspired by the structure of the data in the EMBER dataset and it currently covers the data intended for static malware analysis. With this proposal, we hope to achieve: a) a unified semantic representation for PE malware datasets that are available or will be published in the future; (b) applicability of symbolic, neural-symbolic, or otherwise explainable approaches in the PE Malware domain that may lead to improved interpretability of results which may now be characterized by the terms defined in the ontology; and (c)by joint publishing of semantically treated EMBER data, including fractional datasets, also improved reproducibility of experiments.
|
2406.05349
|
Thanh Huy Nguyen Mr.
|
Thanh-Huy Nguyen, Thi Kim Ngan Ngo, Mai Anh Vu, Ting-Yuan Tu
|
Blurry-Consistency Segmentation Framework with Selective Stacking on
Differential Interference Contrast 3D Breast Cancer Spheroid
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The ability of three-dimensional (3D) spheroid modeling to study the invasive
behavior of breast cancer cells has drawn increased attention. The deep
learning-based image processing framework is very effective at speeding up the
cell morphological analysis process. Out-of-focus photos taken while capturing
3D cells under several z-slices, however, could negatively impact the deep
learning model. In this work, we created a new algorithm to handle blurry
images while preserving the stacked image quality. Furthermore, we proposed a
unique training architecture that leverages consistency training to help reduce
the bias of the model when dense-slice stacking is applied. Additionally, the
model's stability is increased under the sparse-slice stacking effect by
utilizing the self-training approach. The new blurring stacking technique and
training flow are combined with the suggested architecture and self-training
mechanism to provide an innovative yet easy-to-use framework. Our methods
produced noteworthy experimental outcomes in terms of both quantitative and
qualitative aspects.
|
[
{
"created": "Sat, 8 Jun 2024 04:31:36 GMT",
"version": "v1"
}
] |
2024-06-11
|
[
[
"Nguyen",
"Thanh-Huy",
""
],
[
"Ngo",
"Thi Kim Ngan",
""
],
[
"Vu",
"Mai Anh",
""
],
[
"Tu",
"Ting-Yuan",
""
]
] |
The ability of three-dimensional (3D) spheroid modeling to study the invasive behavior of breast cancer cells has drawn increased attention. The deep learning-based image processing framework is very effective at speeding up the cell morphological analysis process. Out-of-focus photos taken while capturing 3D cells under several z-slices, however, could negatively impact the deep learning model. In this work, we created a new algorithm to handle blurry images while preserving the stacked image quality. Furthermore, we proposed a unique training architecture that leverages consistency training to help reduce the bias of the model when dense-slice stacking is applied. Additionally, the model's stability is increased under the sparse-slice stacking effect by utilizing the self-training approach. The new blurring stacking technique and training flow are combined with the suggested architecture and self-training mechanism to provide an innovative yet easy-to-use framework. Our methods produced noteworthy experimental outcomes in terms of both quantitative and qualitative aspects.
|
1003.3312
|
Secretary Aircc Journal
|
G. G. Md. Nawaz Ali (1), (2), Rajib Chakraborty (2), Md. Shihabul Alam
(2) and Edward Chan (1), ((1) City University of Hong Kong, China and (2)
Khulna University of Engineering & Technology, Bangladesh)
|
An Efficient Approach for Generalized Load Balancing in Multipath Packet
Switched Networks
|
12 Pages, IJCNC Journal 2010
|
International Journal of Computer Networks & Communications 2.2
(2010) 142-153
|
10.5121/ijcnc.2010.2211
| null |
cs.NI
|
http://creativecommons.org/licenses/by-nc-sa/3.0/
|
This paper is a quantitative analysis on packet switched network with a view
to generalize load balancing and determination of appropriate routing algorithm
in multipath environment. Several routing algorithms have been introduced for
routing of packets from source to destination. Some of them route packets
accurately with increased workload and some of them drastically cut down the
workload. A few of them can find out a minimum workload deviation for both UDP
and TCP packets. We simulated these approaches in a well defined simulator,
analyzed and evaluated their performance. After expanding our analysis with
varying weights and number of paths we found that the recently proposed routing
algorithm Mixed Weighted Fair Routing (MWFR) outperforms the existing routing
algorithms by reducing the routing and network overhead and saving the scarce
bandwidth as well as CPU consumption for packet switching networks.
|
[
{
"created": "Wed, 17 Mar 2010 07:15:27 GMT",
"version": "v1"
}
] |
2010-07-15
|
[
[
"Ali",
"G. G. Md. Nawaz",
""
],
[
"Chakraborty",
"Rajib",
""
],
[
"Alam",
"Md. Shihabul",
""
],
[
"Chan",
"Edward",
""
]
] |
This paper is a quantitative analysis on packet switched network with a view to generalize load balancing and determination of appropriate routing algorithm in multipath environment. Several routing algorithms have been introduced for routing of packets from source to destination. Some of them route packets accurately with increased workload and some of them drastically cut down the workload. A few of them can find out a minimum workload deviation for both UDP and TCP packets. We simulated these approaches in a well defined simulator, analyzed and evaluated their performance. After expanding our analysis with varying weights and number of paths we found that the recently proposed routing algorithm Mixed Weighted Fair Routing (MWFR) outperforms the existing routing algorithms by reducing the routing and network overhead and saving the scarce bandwidth as well as CPU consumption for packet switching networks.
|
2202.05998
|
Hangwei Qian
|
Hangwei Qian, Tian Tian, Chunyan Miao
|
What Makes Good Contrastive Learning on Small-Scale Wearable-based
Tasks?
|
Preprint
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Self-supervised learning establishes a new paradigm of learning
representations with much fewer or even no label annotations. Recently there
has been remarkable progress on large-scale contrastive learning models which
require substantial computing resources, yet such models are not practically
optimal for small-scale tasks. To fill the gap, we aim to study contrastive
learning on the wearable-based activity recognition task. Specifically, we
conduct an in-depth study of contrastive learning from both algorithmic-level
and task-level perspectives. For algorithmic-level analysis, we decompose
contrastive models into several key components and conduct rigorous
experimental evaluations to better understand the efficacy and rationale behind
contrastive learning. More importantly, for task-level analysis, we show that
the wearable-based signals bring unique challenges and opportunities to
existing contrastive models, which cannot be readily solved by existing
algorithms. Our thorough empirical studies suggest important practices and shed
light on future research challenges. In the meantime, this paper presents an
open-source PyTorch library \texttt{CL-HAR}, which can serve as a practical
tool for researchers. The library is highly modularized and easy to use, which
opens up avenues for exploring novel contrastive models quickly in the future.
|
[
{
"created": "Sat, 12 Feb 2022 06:10:15 GMT",
"version": "v1"
}
] |
2022-02-15
|
[
[
"Qian",
"Hangwei",
""
],
[
"Tian",
"Tian",
""
],
[
"Miao",
"Chunyan",
""
]
] |
Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-scale contrastive learning models which require substantial computing resources, yet such models are not practically optimal for small-scale tasks. To fill the gap, we aim to study contrastive learning on the wearable-based activity recognition task. Specifically, we conduct an in-depth study of contrastive learning from both algorithmic-level and task-level perspectives. For algorithmic-level analysis, we decompose contrastive models into several key components and conduct rigorous experimental evaluations to better understand the efficacy and rationale behind contrastive learning. More importantly, for task-level analysis, we show that the wearable-based signals bring unique challenges and opportunities to existing contrastive models, which cannot be readily solved by existing algorithms. Our thorough empirical studies suggest important practices and shed light on future research challenges. In the meantime, this paper presents an open-source PyTorch library \texttt{CL-HAR}, which can serve as a practical tool for researchers. The library is highly modularized and easy to use, which opens up avenues for exploring novel contrastive models quickly in the future.
|
2112.11215
|
Yixuan Zhang
|
Nurul Suhaimi, Nutchanon Yongsatianchot, Yixuan Zhang, Anisa Amiji,
Shivani A. Patel, Stacy Marsella, Miso Kim, Jacqueline Griffin, Andrea Parker
|
Examining Older Adults' Information Exposure, Wellbeing, and Adherence
to Protective Measures During the COVID-19 Pandemic
|
3 pages
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Older adults are at greater risk of experiencing negative physical and
psychological impacts of the novel coronavirus 2019 (COVID-19) pandemic. Our
ongoing study is assessing COVID-19 information exposure in adults aged 55 and
above compared to other age groups living in Massachusetts and Georgia. This
work investigates the potential association between information exposure and
wellbeing as well as adherence to COVID-19 protective measures. Our initial
results show that older adults received information related to COVID-19 less
frequently than the middle-aged group, yet they feel more content and less
stressed than the other age groups. Further analysis to identify other
potential confounding variables is addressed.
|
[
{
"created": "Fri, 17 Dec 2021 15:33:13 GMT",
"version": "v1"
}
] |
2021-12-22
|
[
[
"Suhaimi",
"Nurul",
""
],
[
"Yongsatianchot",
"Nutchanon",
""
],
[
"Zhang",
"Yixuan",
""
],
[
"Amiji",
"Anisa",
""
],
[
"Patel",
"Shivani A.",
""
],
[
"Marsella",
"Stacy",
""
],
[
"Kim",
"Miso",
""
],
[
"Griffin",
"Jacqueline",
""
],
[
"Parker",
"Andrea",
""
]
] |
Older adults are at greater risk of experiencing negative physical and psychological impacts of the novel coronavirus 2019 (COVID-19) pandemic. Our ongoing study is assessing COVID-19 information exposure in adults aged 55 and above compared to other age groups living in Massachusetts and Georgia. This work investigates the potential association between information exposure and wellbeing as well as adherence to COVID-19 protective measures. Our initial results show that older adults received information related to COVID-19 less frequently than the middle-aged group, yet they feel more content and less stressed than the other age groups. Further analysis to identify other potential confounding variables is addressed.
|
2402.11131
|
Mahyar Najibi
|
Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Mohammad
Rastegari, Mahyar Najibi
|
Speculative Streaming: Fast LLM Inference without Auxiliary Models
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Speculative decoding is a prominent technique to speed up the inference of a
large target language model based on predictions of an auxiliary draft model.
While effective, in application-specific settings, it often involves
fine-tuning both draft and target models to achieve high acceptance rates. As
the number of downstream tasks grows, these draft models add significant
complexity to inference systems. We propose Speculative Streaming, a
single-model speculative decoding method that fuses drafting into the target
model by changing the fine-tuning objective from next token prediction to
future n-gram prediction. Speculative Streaming speeds up decoding by 1.8 -
3.1X in a diverse set of tasks, such as Summarization, Structured Queries, and
Meaning Representation, without sacrificing generation quality. Additionally,
Speculative Streaming is parameter-efficient. It achieves on-par/higher
speed-ups than Medusa-style architectures while using ~10000X fewer extra
parameters, making it well-suited for resource-constrained devices.
|
[
{
"created": "Fri, 16 Feb 2024 23:36:43 GMT",
"version": "v1"
}
] |
2024-02-20
|
[
[
"Bhendawade",
"Nikhil",
""
],
[
"Belousova",
"Irina",
""
],
[
"Fu",
"Qichen",
""
],
[
"Mason",
"Henry",
""
],
[
"Rastegari",
"Mohammad",
""
],
[
"Najibi",
"Mahyar",
""
]
] |
Speculative decoding is a prominent technique to speed up the inference of a large target language model based on predictions of an auxiliary draft model. While effective, in application-specific settings, it often involves fine-tuning both draft and target models to achieve high acceptance rates. As the number of downstream tasks grows, these draft models add significant complexity to inference systems. We propose Speculative Streaming, a single-model speculative decoding method that fuses drafting into the target model by changing the fine-tuning objective from next token prediction to future n-gram prediction. Speculative Streaming speeds up decoding by 1.8 - 3.1X in a diverse set of tasks, such as Summarization, Structured Queries, and Meaning Representation, without sacrificing generation quality. Additionally, Speculative Streaming is parameter-efficient. It achieves on-par/higher speed-ups than Medusa-style architectures while using ~10000X fewer extra parameters, making it well-suited for resource-constrained devices.
|
2106.14350
|
Boris Kovalerchuk
|
Boris Kovalerchuk, Divya Chandrika Kalla, Bedant Agarwal
|
Deep Learning Image Recognition for Non-images
|
33 pages, 17 figures, 18 tables
| null | null | null |
cs.LG cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Powerful deep learning algorithms open an opportunity for solving non-image
Machine Learning (ML) problems by transforming these problems to into the image
recognition problems. The CPC-R algorithm presented in this chapter converts
non-image data into images by visualizing non-image data. Then deep learning
CNN algorithms solve the learning problems on these images. The design of the
CPC-R algorithm allows preserving all high-dimensional information in 2-D
images. The use of pair values mapping instead of single value mapping used in
the alternative approaches allows encoding each n-D point with 2 times fewer
visual elements. The attributes of an n-D point are divided into pairs of its
values and each pair is visualized as 2-D points in the same 2-D Cartesian
coordinates. Next, grey scale or color intensity values are assigned to each
pair to encode the order of pairs. This is resulted in the heatmap image. The
computational experiments with CPC-R are conducted for different CNN
architectures, and methods to optimize the CPC-R images showing that the
combined CPC-R and deep learning CNN algorithms are able to solve non-image ML
problems reaching high accuracy on the benchmark datasets. This chapter expands
our prior work by adding more experiments to test accuracy of classification,
exploring saliency and informativeness of discovered features to test their
interpretability, and generalizing the approach.
|
[
{
"created": "Mon, 28 Jun 2021 00:36:36 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Feb 2022 22:08:16 GMT",
"version": "v2"
}
] |
2022-02-11
|
[
[
"Kovalerchuk",
"Boris",
""
],
[
"Kalla",
"Divya Chandrika",
""
],
[
"Agarwal",
"Bedant",
""
]
] |
Powerful deep learning algorithms open an opportunity for solving non-image Machine Learning (ML) problems by transforming these problems to into the image recognition problems. The CPC-R algorithm presented in this chapter converts non-image data into images by visualizing non-image data. Then deep learning CNN algorithms solve the learning problems on these images. The design of the CPC-R algorithm allows preserving all high-dimensional information in 2-D images. The use of pair values mapping instead of single value mapping used in the alternative approaches allows encoding each n-D point with 2 times fewer visual elements. The attributes of an n-D point are divided into pairs of its values and each pair is visualized as 2-D points in the same 2-D Cartesian coordinates. Next, grey scale or color intensity values are assigned to each pair to encode the order of pairs. This is resulted in the heatmap image. The computational experiments with CPC-R are conducted for different CNN architectures, and methods to optimize the CPC-R images showing that the combined CPC-R and deep learning CNN algorithms are able to solve non-image ML problems reaching high accuracy on the benchmark datasets. This chapter expands our prior work by adding more experiments to test accuracy of classification, exploring saliency and informativeness of discovered features to test their interpretability, and generalizing the approach.
|
1904.04978
|
Dawei Du
|
Congcong Li, Dawei Du, Libo Zhang, Tiejian Luo, Yanjun Wu, Qi Tian,
Longyin Wen, Siwei Lyu
|
Data Priming Network for Automatic Check-Out
|
Accepted to ACM MM 2019
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automatic Check-Out (ACO) receives increased interests in recent years. An
important component of the ACO system is the visual item counting, which
recognizes the categories and counts of the items chosen by the customers.
However, the training of such a system is challenged by the domain adaptation
problem, in which the training data are images from isolated items while the
testing images are for collections of items. Existing methods solve this
problem with data augmentation using synthesized images, but the image
synthesis leads to unreal images that affect the training process. In this
paper, we propose a new data priming method to solve the domain adaptation
problem. Specifically, we first use pre-augmentation data priming, in which we
remove distracting background from the training images using the coarse-to-fine
strategy and select images with realistic view angles by the pose pruning
method. In the post-augmentation step, we train a data priming network using
detection and counting collaborative learning, and select more reliable images
from testing data to fine-tune the final visual item tallying network.
Experiments on the large scale Retail Product Checkout (RPC) dataset
demonstrate the superiority of the proposed method, i.e., we achieve 80.51%
checkout accuracy compared with 56.68% of the baseline methods. The source
codes can be found in https://isrc.iscas.ac.cn/gitlab/research/acm-mm-2019-ACO.
|
[
{
"created": "Wed, 10 Apr 2019 02:12:48 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Aug 2019 17:12:56 GMT",
"version": "v2"
},
{
"created": "Wed, 7 Aug 2019 03:04:32 GMT",
"version": "v3"
}
] |
2019-08-08
|
[
[
"Li",
"Congcong",
""
],
[
"Du",
"Dawei",
""
],
[
"Zhang",
"Libo",
""
],
[
"Luo",
"Tiejian",
""
],
[
"Wu",
"Yanjun",
""
],
[
"Tian",
"Qi",
""
],
[
"Wen",
"Longyin",
""
],
[
"Lyu",
"Siwei",
""
]
] |
Automatic Check-Out (ACO) receives increased interests in recent years. An important component of the ACO system is the visual item counting, which recognizes the categories and counts of the items chosen by the customers. However, the training of such a system is challenged by the domain adaptation problem, in which the training data are images from isolated items while the testing images are for collections of items. Existing methods solve this problem with data augmentation using synthesized images, but the image synthesis leads to unreal images that affect the training process. In this paper, we propose a new data priming method to solve the domain adaptation problem. Specifically, we first use pre-augmentation data priming, in which we remove distracting background from the training images using the coarse-to-fine strategy and select images with realistic view angles by the pose pruning method. In the post-augmentation step, we train a data priming network using detection and counting collaborative learning, and select more reliable images from testing data to fine-tune the final visual item tallying network. Experiments on the large scale Retail Product Checkout (RPC) dataset demonstrate the superiority of the proposed method, i.e., we achieve 80.51% checkout accuracy compared with 56.68% of the baseline methods. The source codes can be found in https://isrc.iscas.ac.cn/gitlab/research/acm-mm-2019-ACO.
|
2309.06188
|
Mazvydas Gudelis
|
Mazvydas Gudelis, Michal Mackiewicz, Julie Bremner, Sophie Fielding
|
Computer Vision Pipeline for Automated Antarctic Krill Analysis
|
Accepted to MVEO @ BMVC 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
British Antarctic Survey (BAS) researchers launch annual expeditions to the
Antarctic in order to estimate Antarctic Krill biomass and assess the change
from previous years. These comparisons provide insight into the effects of the
current environment on this key component of the marine food chain. In this
work we have developed tools for automating the data collection and analysis
process, using web-based image annotation tools and deep learning image
classification and regression models. We achieve highly accurate krill instance
segmentation results with an average 77.28% AP score, as well as separate
maturity stage and length estimation of krill specimens with 62.99% accuracy
and a 1.98mm length error respectively.
|
[
{
"created": "Tue, 12 Sep 2023 12:54:12 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Oct 2023 11:51:21 GMT",
"version": "v2"
}
] |
2023-10-13
|
[
[
"Gudelis",
"Mazvydas",
""
],
[
"Mackiewicz",
"Michal",
""
],
[
"Bremner",
"Julie",
""
],
[
"Fielding",
"Sophie",
""
]
] |
British Antarctic Survey (BAS) researchers launch annual expeditions to the Antarctic in order to estimate Antarctic Krill biomass and assess the change from previous years. These comparisons provide insight into the effects of the current environment on this key component of the marine food chain. In this work we have developed tools for automating the data collection and analysis process, using web-based image annotation tools and deep learning image classification and regression models. We achieve highly accurate krill instance segmentation results with an average 77.28% AP score, as well as separate maturity stage and length estimation of krill specimens with 62.99% accuracy and a 1.98mm length error respectively.
|
1303.4169
|
Makiko Konoshima
|
Yui Noma, Makiko Konoshima
|
Markov Chain Monte Carlo for Arrangement of Hyperplanes in
Locality-Sensitive Hashing
|
13 pages, 10 figures
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Since Hamming distances can be calculated by bitwise computations, they can
be calculated with less computational load than L2 distances. Similarity
searches can therefore be performed faster in Hamming distance space. The
elements of Hamming distance space are bit strings. On the other hand, the
arrangement of hyperplanes induce the transformation from the feature vectors
into feature bit strings. This transformation method is a type of
locality-sensitive hashing that has been attracting attention as a way of
performing approximate similarity searches at high speed. Supervised learning
of hyperplane arrangements allows us to obtain a method that transforms them
into feature bit strings reflecting the information of labels applied to
higher-dimensional feature vectors. In this p aper, we propose a supervised
learning method for hyperplane arrangements in feature space that uses a Markov
chain Monte Carlo (MCMC) method. We consider the probability density functions
used during learning, and evaluate their performance. We also consider the
sampling method for learning data pairs needed in learning, and we evaluate its
performance. We confirm that the accuracy of this learning method when using a
suitable probability density function and sampling method is greater than the
accuracy of existing learning methods.
|
[
{
"created": "Mon, 18 Mar 2013 07:14:15 GMT",
"version": "v1"
}
] |
2013-03-19
|
[
[
"Noma",
"Yui",
""
],
[
"Konoshima",
"Makiko",
""
]
] |
Since Hamming distances can be calculated by bitwise computations, they can be calculated with less computational load than L2 distances. Similarity searches can therefore be performed faster in Hamming distance space. The elements of Hamming distance space are bit strings. On the other hand, the arrangement of hyperplanes induce the transformation from the feature vectors into feature bit strings. This transformation method is a type of locality-sensitive hashing that has been attracting attention as a way of performing approximate similarity searches at high speed. Supervised learning of hyperplane arrangements allows us to obtain a method that transforms them into feature bit strings reflecting the information of labels applied to higher-dimensional feature vectors. In this p aper, we propose a supervised learning method for hyperplane arrangements in feature space that uses a Markov chain Monte Carlo (MCMC) method. We consider the probability density functions used during learning, and evaluate their performance. We also consider the sampling method for learning data pairs needed in learning, and we evaluate its performance. We confirm that the accuracy of this learning method when using a suitable probability density function and sampling method is greater than the accuracy of existing learning methods.
|
1908.00981
|
Sakib Khan
|
Sakib Mahmud Khan, Mashrur Chowdhury
|
Situation-Aware Left-Turning Connected and Automated Vehicle Operation
at Signalized Intersections
| null | null | null | null |
cs.RO cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One challenging aspect of the Connected and Automated Vehicle (CAV) operation
in mixed traffic is the development of a situation-awareness module for CAVs.
While operating on public roads, CAVs need to assess their surroundings,
especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive
driving behavior, and CAVs expect other non-autonomous entities on the road
will follow the traffic rules or common driving behavior. However, the presence
of aggressive human drivers in the surrounding environment, who may not follow
traffic rules and behave abruptly, can lead to serious safety consequences. In
this paper, we have addressed the CAV and non-CAV interaction by evaluating a
situation-awareness module for left-turning CAV operations in an urban area.
Existing literature does not consider the intent of the following vehicle for a
CAVs left-turning movement, and existing CAV controllers do not assess the
following non-CAVs intents. Based on our simulation study, the situation-aware
CAV controller module reduces up to 27% of the abrupt braking of the following
non-CAVs for scenarios with different opposing through movement compared to the
base scenario with the autonomous vehicle, without considering the following
vehicles intent. The analysis shows that the average travel time reductions for
the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane
are 58%, 52%, and 62%, respectively, for the aggressive human driver following
the CAV if the following vehicles intent is considered by a CAV in making a
left turn at an intersection.
|
[
{
"created": "Fri, 2 Aug 2019 16:44:14 GMT",
"version": "v1"
},
{
"created": "Mon, 16 Nov 2020 19:21:11 GMT",
"version": "v2"
}
] |
2020-11-18
|
[
[
"Khan",
"Sakib Mahmud",
""
],
[
"Chowdhury",
"Mashrur",
""
]
] |
One challenging aspect of the Connected and Automated Vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other non-autonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. In this paper, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. Existing literature does not consider the intent of the following vehicle for a CAVs left-turning movement, and existing CAV controllers do not assess the following non-CAVs intents. Based on our simulation study, the situation-aware CAV controller module reduces up to 27% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicles intent. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are 58%, 52%, and 62%, respectively, for the aggressive human driver following the CAV if the following vehicles intent is considered by a CAV in making a left turn at an intersection.
|
0705.1673
|
Tshilidzi Marwala
|
L. Mdlazi, C.J. Stander, P.S. Heyns and T. Marwala
|
Using artificial intelligence for data reduction in mechanical
engineering
|
6 pages
| null | null | null |
cs.CE cs.AI cs.NE
| null |
In this paper artificial neural networks and support vector machines are used
to reduce the amount of vibration data that is required to estimate the Time
Domain Average of a gear vibration signal. Two models for estimating the time
domain average of a gear vibration signal are proposed. The models are tested
on data from an accelerated gear life test rig. Experimental results indicate
that the required data for calculating the Time Domain Average of a gear
vibration signal can be reduced by up to 75% when the proposed models are
implemented.
|
[
{
"created": "Fri, 11 May 2007 15:49:40 GMT",
"version": "v1"
}
] |
2007-05-23
|
[
[
"Mdlazi",
"L.",
""
],
[
"Stander",
"C. J.",
""
],
[
"Heyns",
"P. S.",
""
],
[
"Marwala",
"T.",
""
]
] |
In this paper artificial neural networks and support vector machines are used to reduce the amount of vibration data that is required to estimate the Time Domain Average of a gear vibration signal. Two models for estimating the time domain average of a gear vibration signal are proposed. The models are tested on data from an accelerated gear life test rig. Experimental results indicate that the required data for calculating the Time Domain Average of a gear vibration signal can be reduced by up to 75% when the proposed models are implemented.
|
1104.4597
|
Thomas Rothvoss
|
Thomas Rothvoss
|
The Entropy Rounding Method in Approximation Algorithms
| null | null | null | null |
cs.DS math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Let A be a matrix, c be any linear objective function and x be a fractional
vector, say an LP solution to some discrete optimization problem. Then a
recurring task in theoretical computer science (and in approximation algorithms
in particular) is to obtain an integral vector y such that Ax is roughly Ay and
c*y exceeds c*x by only a moderate factor.
We give a new randomized rounding procedure for this task, provided that A
has bounded Delta-approximate entropy. This property means that for uniformly
chosen random signs chi(j) in {-1,+1} on any subset of the columns, the outcome
A*chi can be approximately described using a sub-linear number of bits in
expectation.
To achieve this result, we modify well-known techniques from the field of
discrepancy theory, especially we rely on Beck's entropy method, which to the
best of our knowledge has never been used before in the context of
approximation algorithms. Our result can be made constructive using the Bansal
framework based on semidefinite programming.
We demonstrate the versatility of our procedure by rounding fractional
solutions to column-based linear programs for some generalizations of Bin
Packing. For example we obtain a polynomial time OPT + O(log^2 OPT)
approximation for Bin Packing With Rejection and the first AFPTAS for the Train
Delivery problem.
|
[
{
"created": "Sun, 24 Apr 2011 00:48:36 GMT",
"version": "v1"
}
] |
2011-04-26
|
[
[
"Rothvoss",
"Thomas",
""
]
] |
Let A be a matrix, c be any linear objective function and x be a fractional vector, say an LP solution to some discrete optimization problem. Then a recurring task in theoretical computer science (and in approximation algorithms in particular) is to obtain an integral vector y such that Ax is roughly Ay and c*y exceeds c*x by only a moderate factor. We give a new randomized rounding procedure for this task, provided that A has bounded Delta-approximate entropy. This property means that for uniformly chosen random signs chi(j) in {-1,+1} on any subset of the columns, the outcome A*chi can be approximately described using a sub-linear number of bits in expectation. To achieve this result, we modify well-known techniques from the field of discrepancy theory, especially we rely on Beck's entropy method, which to the best of our knowledge has never been used before in the context of approximation algorithms. Our result can be made constructive using the Bansal framework based on semidefinite programming. We demonstrate the versatility of our procedure by rounding fractional solutions to column-based linear programs for some generalizations of Bin Packing. For example we obtain a polynomial time OPT + O(log^2 OPT) approximation for Bin Packing With Rejection and the first AFPTAS for the Train Delivery problem.
|
1609.04259
|
Guillaume Moroz
|
Guillaume Moroz (VEGAS), \'Eric Schost
|
A Fast Algorithm for Computing the Truncated Resultant
| null |
ISSAC '16, Jul 2016, Waterloo, Canada. ACM, Proceedings of the ACM
on International Symposium on Symbolic and Algebraic Computation, pp.341-348,
2016
|
10.1145/2930889.2930931
| null |
cs.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Let P and Q be two polynomials in K[x, y] with degree at most d, where K is a
field. Denoting by R $\in$ K[x] the resultant of P and Q with respect to y, we
present an algorithm to compute R mod x^k in O~(kd) arithmetic operations in K,
where the O~ notation indicates that we omit polylogarithmic factors. This is
an improvement over state-of-the-art algorithms that require to compute R in
O~(d^3) operations before computing its first k coefficients.
|
[
{
"created": "Wed, 14 Sep 2016 13:25:33 GMT",
"version": "v1"
}
] |
2016-09-15
|
[
[
"Moroz",
"Guillaume",
"",
"VEGAS"
],
[
"Schost",
"Éric",
""
]
] |
Let P and Q be two polynomials in K[x, y] with degree at most d, where K is a field. Denoting by R $\in$ K[x] the resultant of P and Q with respect to y, we present an algorithm to compute R mod x^k in O~(kd) arithmetic operations in K, where the O~ notation indicates that we omit polylogarithmic factors. This is an improvement over state-of-the-art algorithms that require to compute R in O~(d^3) operations before computing its first k coefficients.
|
2307.01559
|
Elia Cereda
|
Elia Cereda and Alessandro Giusti and Daniele Palossi
|
Secure Deep Learning-based Distributed Intelligence on Pocket-sized
Drones
|
This paper has been accepted for publication in the EWSN 2023
conference. \copyright 2023 ACM
| null | null | null |
cs.RO cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Palm-sized nano-drones are an appealing class of edge nodes, but their
limited computational resources prevent running large deep-learning models
onboard. Adopting an edge-fog computational paradigm, we can offload part of
the computation to the fog; however, this poses security concerns if the fog
node, or the communication link, can not be trusted. To tackle this concern, we
propose a novel distributed edge-fog execution scheme that validates fog
computation by redundantly executing a random subnetwork aboard our nano-drone.
Compared to a State-of-the-Art visual pose estimation network that entirely
runs onboard, a larger network executed in a distributed way improves the $R^2$
score by +0.19; in case of attack, our approach detects it within 2s with 95%
probability.
|
[
{
"created": "Tue, 4 Jul 2023 08:29:41 GMT",
"version": "v1"
}
] |
2023-07-06
|
[
[
"Cereda",
"Elia",
""
],
[
"Giusti",
"Alessandro",
""
],
[
"Palossi",
"Daniele",
""
]
] |
Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. To tackle this concern, we propose a novel distributed edge-fog execution scheme that validates fog computation by redundantly executing a random subnetwork aboard our nano-drone. Compared to a State-of-the-Art visual pose estimation network that entirely runs onboard, a larger network executed in a distributed way improves the $R^2$ score by +0.19; in case of attack, our approach detects it within 2s with 95% probability.
|
1703.01026
|
Edward Barker
|
Edward W. Barker and Charl J. Ras
|
Unsupervised Basis Function Adaptation for Reinforcement Learning
|
Extended abstract submitted (3 March 2017) for 3rd Multidisciplinary
Conference on Reinforcement Learning and Decision Making (RLDM) 2017
| null | null | null |
cs.AI cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When using reinforcement learning (RL) algorithms to evaluate a policy it is
common, given a large state space, to introduce some form of approximation
architecture for the value function (VF). The exact form of this architecture
can have a significant effect on the accuracy of the VF estimate, however, and
determining a suitable approximation architecture can often be a highly complex
task. Consequently there is a large amount of interest in the potential for
allowing RL algorithms to adaptively generate approximation architectures.
We investigate a method of adapting approximation architectures which uses
feedback regarding the frequency with which an agent has visited certain states
to guide which areas of the state space to approximate with greater detail.
This method is "unsupervised" in the sense that it makes no direct reference to
reward or the VF estimate. We introduce an algorithm based upon this idea which
adapts a state aggregation approximation architecture on-line.
A common method of scoring a VF estimate is to weight the squared Bellman
error of each state-action by the probability of that state-action occurring.
Adopting this scoring method, and assuming $S$ states, we demonstrate
theoretically that - provided (1) the number of cells $X$ in the state
aggregation architecture is of order $\sqrt{S}\log_2{S}\ln{S}$ or greater, (2)
the policy and transition function are close to deterministic, and (3) the
prior for the transition function is uniformly distributed - our algorithm,
used in conjunction with a suitable RL algorithm, can guarantee a score which
is arbitrarily close to zero as $S$ becomes large. It is able to do this
despite having only $O(X \log_2S)$ space complexity and negligible time
complexity. The results take advantage of certain properties of the stationary
distributions of Markov chains.
|
[
{
"created": "Fri, 3 Mar 2017 03:24:03 GMT",
"version": "v1"
}
] |
2017-03-06
|
[
[
"Barker",
"Edward W.",
""
],
[
"Ras",
"Charl J.",
""
]
] |
When using reinforcement learning (RL) algorithms to evaluate a policy it is common, given a large state space, to introduce some form of approximation architecture for the value function (VF). The exact form of this architecture can have a significant effect on the accuracy of the VF estimate, however, and determining a suitable approximation architecture can often be a highly complex task. Consequently there is a large amount of interest in the potential for allowing RL algorithms to adaptively generate approximation architectures. We investigate a method of adapting approximation architectures which uses feedback regarding the frequency with which an agent has visited certain states to guide which areas of the state space to approximate with greater detail. This method is "unsupervised" in the sense that it makes no direct reference to reward or the VF estimate. We introduce an algorithm based upon this idea which adapts a state aggregation approximation architecture on-line. A common method of scoring a VF estimate is to weight the squared Bellman error of each state-action by the probability of that state-action occurring. Adopting this scoring method, and assuming $S$ states, we demonstrate theoretically that - provided (1) the number of cells $X$ in the state aggregation architecture is of order $\sqrt{S}\log_2{S}\ln{S}$ or greater, (2) the policy and transition function are close to deterministic, and (3) the prior for the transition function is uniformly distributed - our algorithm, used in conjunction with a suitable RL algorithm, can guarantee a score which is arbitrarily close to zero as $S$ becomes large. It is able to do this despite having only $O(X \log_2S)$ space complexity and negligible time complexity. The results take advantage of certain properties of the stationary distributions of Markov chains.
|
1903.00902
|
Wendong Wang
|
Wendong Wang, Jianjun Wang
|
Deterministic Analysis of Weighted BPDN With Partially Known Support
Information
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, with the aid of the powerful Restricted Isometry Constant
(RIC), a deterministic (or say non-stochastic) analysis, which includes a
series of sufficient conditions (related to the RIC order) and their resultant
error estimates, is established for the weighted Basis Pursuit De-Noising
(BPDN) to guarantee the robust signal recovery when Partially Known Support
Information (PKSI) of the signal is available. Specifically, the obtained
conditions extend nontrivially the ones induced recently for the traditional
constrained weighted $\ell_{1}$-minimization model to those for its
unconstrained counterpart, i.e., the weighted BPDN. The obtained error
estimates are also comparable to the analogous ones induced previously for the
robust recovery of the signals with PKSI from some constrained models.
Moreover, these results to some degree may well complement the recent
investigation of the weighted BPDN which is based on the stochastic analysis.
|
[
{
"created": "Sun, 3 Mar 2019 13:20:45 GMT",
"version": "v1"
}
] |
2019-03-05
|
[
[
"Wang",
"Wendong",
""
],
[
"Wang",
"Jianjun",
""
]
] |
In this paper, with the aid of the powerful Restricted Isometry Constant (RIC), a deterministic (or say non-stochastic) analysis, which includes a series of sufficient conditions (related to the RIC order) and their resultant error estimates, is established for the weighted Basis Pursuit De-Noising (BPDN) to guarantee the robust signal recovery when Partially Known Support Information (PKSI) of the signal is available. Specifically, the obtained conditions extend nontrivially the ones induced recently for the traditional constrained weighted $\ell_{1}$-minimization model to those for its unconstrained counterpart, i.e., the weighted BPDN. The obtained error estimates are also comparable to the analogous ones induced previously for the robust recovery of the signals with PKSI from some constrained models. Moreover, these results to some degree may well complement the recent investigation of the weighted BPDN which is based on the stochastic analysis.
|
2310.15017
|
Zhongjian Qiao
|
Zhongjian Qiao and Jiafei Lyu and Xiu Li
|
Mind the Model, Not the Agent: The Primacy Bias in Model-based RL
|
Accepted by European Conference on Artificial Intelligence (ECAI)
2024
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The primacy bias in model-free reinforcement learning (MFRL), which refers to
the agent's tendency to overfit early data and lose the ability to learn from
new data, can significantly decrease the performance of MFRL algorithms.
Previous studies have shown that employing simple techniques, such as resetting
the agent's parameters, can substantially alleviate the primacy bias in MFRL.
However, the primacy bias in model-based reinforcement learning (MBRL) remains
unexplored. In this work, we focus on investigating the primacy bias in MBRL.
We begin by observing that resetting the agent's parameters harms its
performance in the context of MBRL. We further find that the primacy bias in
MBRL is more closely related to the primacy bias of the world model instead of
the primacy bias of the agent. Based on this finding, we propose \textit{world
model resetting}, a simple yet effective technique to alleviate the primacy
bias in MBRL. We apply our method to two different MBRL algorithms, MBPO and
DreamerV2. We validate the effectiveness of our method on multiple continuous
control tasks on MuJoCo and DeepMind Control Suite, as well as discrete control
tasks on Atari 100k benchmark. The experimental results show that \textit{world
model resetting} can significantly alleviate the primacy bias in the
model-based setting and improve the algorithm's performance. We also give a
guide on how to perform \textit{world model resetting} effectively.
|
[
{
"created": "Mon, 23 Oct 2023 15:12:20 GMT",
"version": "v1"
},
{
"created": "Sun, 7 Jul 2024 14:32:02 GMT",
"version": "v2"
}
] |
2024-07-09
|
[
[
"Qiao",
"Zhongjian",
""
],
[
"Lyu",
"Jiafei",
""
],
[
"Li",
"Xiu",
""
]
] |
The primacy bias in model-free reinforcement learning (MFRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significantly decrease the performance of MFRL algorithms. Previous studies have shown that employing simple techniques, such as resetting the agent's parameters, can substantially alleviate the primacy bias in MFRL. However, the primacy bias in model-based reinforcement learning (MBRL) remains unexplored. In this work, we focus on investigating the primacy bias in MBRL. We begin by observing that resetting the agent's parameters harms its performance in the context of MBRL. We further find that the primacy bias in MBRL is more closely related to the primacy bias of the world model instead of the primacy bias of the agent. Based on this finding, we propose \textit{world model resetting}, a simple yet effective technique to alleviate the primacy bias in MBRL. We apply our method to two different MBRL algorithms, MBPO and DreamerV2. We validate the effectiveness of our method on multiple continuous control tasks on MuJoCo and DeepMind Control Suite, as well as discrete control tasks on Atari 100k benchmark. The experimental results show that \textit{world model resetting} can significantly alleviate the primacy bias in the model-based setting and improve the algorithm's performance. We also give a guide on how to perform \textit{world model resetting} effectively.
|
2305.09123
|
Weizhao Tang
|
Weizhao Tang, Peiyao Sheng, Ronghao Ni, Pronoy Roy, Xuechao Wang,
Giulia Fanti, and Pramod Viswanath
|
CFT-Forensics: High-Performance Byzantine Accountability for Crash Fault
Tolerant Protocols
| null | null | null | null |
cs.DC cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Crash fault tolerant (CFT) consensus algorithms are commonly used in
scenarios where system components are trusted -- e.g., enterprise settings and
government infrastructure. However, CFT consensus can be broken by even a
single corrupt node. A desirable property in the face of such potential
Byzantine faults is \emph{accountability}: if a corrupt node breaks protocol
and affects consensus safety, it should be possible to identify the culpable
components with cryptographic integrity from the node states. Today, the
best-known protocol for providing accountability to CFT protocols is called
PeerReview; it essentially records a signed transcript of all messages sent
during the CFT protocol. Because PeerReview is agnostic to the underlying CFT
protocol, it incurs high communication and storage overhead. We propose
CFT-Forensics, an accountability framework for CFT protocols. We show that for
a special family of \emph{forensics-compliant} CFT protocols (which includes
widely-used CFT protocols like Raft and multi-Paxos), CFT-Forensics gives
provable accountability guarantees. Under realistic deployment settings, we
show theoretically that CFT-Forensics operates at a fraction of the cost of
PeerReview. We subsequently instantiate CFT-Forensics for Raft, and implement
Raft-Forensics as an extension to the popular nuRaft library. In extensive
experiments, we demonstrate that Raft-Forensics adds low overhead to vanilla
Raft. With 256 byte messages, Raft-Forensics achieves a peak throughput 87.8\%
of vanilla Raft at 46\% higher latency ($+44$ ms). We finally integrate
Raft-Forensics into the open-source central bank digital currency OpenCBDC, and
show that in wide-area network experiments, Raft-Forensics achieves 97.8\% of
the throughput of Raft, with 14.5\% higher latency ($+326$ ms).
|
[
{
"created": "Tue, 16 May 2023 03:09:26 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Nov 2023 20:59:49 GMT",
"version": "v2"
},
{
"created": "Mon, 3 Jun 2024 14:20:12 GMT",
"version": "v3"
}
] |
2024-06-04
|
[
[
"Tang",
"Weizhao",
""
],
[
"Sheng",
"Peiyao",
""
],
[
"Ni",
"Ronghao",
""
],
[
"Roy",
"Pronoy",
""
],
[
"Wang",
"Xuechao",
""
],
[
"Fanti",
"Giulia",
""
],
[
"Viswanath",
"Pramod",
""
]
] |
Crash fault tolerant (CFT) consensus algorithms are commonly used in scenarios where system components are trusted -- e.g., enterprise settings and government infrastructure. However, CFT consensus can be broken by even a single corrupt node. A desirable property in the face of such potential Byzantine faults is \emph{accountability}: if a corrupt node breaks protocol and affects consensus safety, it should be possible to identify the culpable components with cryptographic integrity from the node states. Today, the best-known protocol for providing accountability to CFT protocols is called PeerReview; it essentially records a signed transcript of all messages sent during the CFT protocol. Because PeerReview is agnostic to the underlying CFT protocol, it incurs high communication and storage overhead. We propose CFT-Forensics, an accountability framework for CFT protocols. We show that for a special family of \emph{forensics-compliant} CFT protocols (which includes widely-used CFT protocols like Raft and multi-Paxos), CFT-Forensics gives provable accountability guarantees. Under realistic deployment settings, we show theoretically that CFT-Forensics operates at a fraction of the cost of PeerReview. We subsequently instantiate CFT-Forensics for Raft, and implement Raft-Forensics as an extension to the popular nuRaft library. In extensive experiments, we demonstrate that Raft-Forensics adds low overhead to vanilla Raft. With 256 byte messages, Raft-Forensics achieves a peak throughput 87.8\% of vanilla Raft at 46\% higher latency ($+44$ ms). We finally integrate Raft-Forensics into the open-source central bank digital currency OpenCBDC, and show that in wide-area network experiments, Raft-Forensics achieves 97.8\% of the throughput of Raft, with 14.5\% higher latency ($+326$ ms).
|
2102.10757
|
Yaochen Xie
|
Yaochen Xie, Zhao Xu, Jingtun Zhang, Zhengyang Wang, Shuiwang Ji
|
Self-Supervised Learning of Graph Neural Networks: A Unified Review
|
Accepted by TPAMI. 26 pages, 7 figures
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep models trained in supervised mode have achieved remarkable success on a
variety of tasks. When labeled samples are limited, self-supervised learning
(SSL) is emerging as a new paradigm for making use of large amounts of
unlabeled samples. SSL has achieved promising performance on natural language
and image learning tasks. Recently, there is a trend to extend such success to
graph data using graph neural networks (GNNs). In this survey, we provide a
unified review of different ways of training GNNs using SSL. Specifically, we
categorize SSL methods into contrastive and predictive models. In either
category, we provide a unified framework for methods as well as how these
methods differ in each component under the framework. Our unified treatment of
SSL methods for GNNs sheds light on the similarities and differences of various
methods, setting the stage for developing new methods and algorithms. We also
summarize different SSL settings and the corresponding datasets used in each
setting. To facilitate methodological development and empirical comparison, we
develop a standardized testbed for SSL in GNNs, including implementations of
common baseline methods, datasets, and evaluation metrics.
|
[
{
"created": "Mon, 22 Feb 2021 03:43:45 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Feb 2021 18:12:23 GMT",
"version": "v2"
},
{
"created": "Tue, 23 Mar 2021 22:24:21 GMT",
"version": "v3"
},
{
"created": "Tue, 15 Feb 2022 19:15:32 GMT",
"version": "v4"
},
{
"created": "Mon, 25 Apr 2022 14:44:40 GMT",
"version": "v5"
}
] |
2022-04-26
|
[
[
"Xie",
"Yaochen",
""
],
[
"Xu",
"Zhao",
""
],
[
"Zhang",
"Jingtun",
""
],
[
"Wang",
"Zhengyang",
""
],
[
"Ji",
"Shuiwang",
""
]
] |
Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples. SSL has achieved promising performance on natural language and image learning tasks. Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs). In this survey, we provide a unified review of different ways of training GNNs using SSL. Specifically, we categorize SSL methods into contrastive and predictive models. In either category, we provide a unified framework for methods as well as how these methods differ in each component under the framework. Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms. We also summarize different SSL settings and the corresponding datasets used in each setting. To facilitate methodological development and empirical comparison, we develop a standardized testbed for SSL in GNNs, including implementations of common baseline methods, datasets, and evaluation metrics.
|
1409.0988
|
Matthias W\"ahlisch
|
Michael Frey, Mesut G\"unes
|
Attack of the Ants: Studying Ant Routing Algorithms in Simulation and
Wireless Testbeds
|
Published in: A. F\"orster, C. Sommer, T. Steinbach, M. W\"ahlisch
(Eds.), Proc. of 1st OMNeT++ Community Summit, Hamburg, Germany, September 2,
2014, arXiv:1409.0093, 2014
| null | null |
OMNET/2014/08
|
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Wireless networks are becoming the key building block of our communications
infrastructure. Examples range from cellular networks to ad hoc and sensor
networks in wildlife monitoring and environmental scenarios. With the rise of
the Internet of Things (IoT) millions of physical and virtual objects will
communicate wireless and enhance the daily life. The adaptivity and scalability
of wireless networks in the IoT is one of the most challenging tasks.
Bio-inspired networking algorithms are a way to tackle these issues. In this
paper we present a simulation framework based on OMNeT++ to implement ant
routing algorithms to study and compare them on the algorithmic level and an
approach to run large simulation studies in a comprehensive way.
|
[
{
"created": "Wed, 3 Sep 2014 08:30:36 GMT",
"version": "v1"
}
] |
2014-09-05
|
[
[
"Frey",
"Michael",
""
],
[
"Günes",
"Mesut",
""
]
] |
Wireless networks are becoming the key building block of our communications infrastructure. Examples range from cellular networks to ad hoc and sensor networks in wildlife monitoring and environmental scenarios. With the rise of the Internet of Things (IoT) millions of physical and virtual objects will communicate wireless and enhance the daily life. The adaptivity and scalability of wireless networks in the IoT is one of the most challenging tasks. Bio-inspired networking algorithms are a way to tackle these issues. In this paper we present a simulation framework based on OMNeT++ to implement ant routing algorithms to study and compare them on the algorithmic level and an approach to run large simulation studies in a comprehensive way.
|
1503.02427
|
Mingxuan Wang
|
Mingxuan Wang and Zhengdong Lu and Hang Li and Qun Liu
|
Syntax-based Deep Matching of Short Texts
|
Accepted by IJCAI-2015 as full paper
| null | null | null |
cs.CL cs.LG cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many tasks in natural language processing, ranging from machine translation
to question answering, can be reduced to the problem of matching two sentences
or more generally two short texts. We propose a new approach to the problem,
called Deep Match Tree (DeepMatch$_{tree}$), under a general setting. The
approach consists of two components, 1) a mining algorithm to discover patterns
for matching two short-texts, defined in the product space of dependency trees,
and 2) a deep neural network for matching short texts using the mined patterns,
as well as a learning algorithm to build the network having a sparse structure.
We test our algorithm on the problem of matching a tweet and a response in
social media, a hard matching problem proposed in [Wang et al., 2013], and show
that DeepMatch$_{tree}$ can outperform a number of competitor models including
one without using dependency trees and one based on word-embedding, all with
large margins
|
[
{
"created": "Mon, 9 Mar 2015 11:11:15 GMT",
"version": "v1"
},
{
"created": "Tue, 10 Mar 2015 03:24:58 GMT",
"version": "v2"
},
{
"created": "Thu, 12 Mar 2015 08:31:01 GMT",
"version": "v3"
},
{
"created": "Fri, 24 Apr 2015 04:48:25 GMT",
"version": "v4"
},
{
"created": "Mon, 18 May 2015 13:26:28 GMT",
"version": "v5"
},
{
"created": "Fri, 12 Jun 2015 08:26:01 GMT",
"version": "v6"
}
] |
2015-06-15
|
[
[
"Wang",
"Mingxuan",
""
],
[
"Lu",
"Zhengdong",
""
],
[
"Li",
"Hang",
""
],
[
"Liu",
"Qun",
""
]
] |
Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two short texts. We propose a new approach to the problem, called Deep Match Tree (DeepMatch$_{tree}$), under a general setting. The approach consists of two components, 1) a mining algorithm to discover patterns for matching two short-texts, defined in the product space of dependency trees, and 2) a deep neural network for matching short texts using the mined patterns, as well as a learning algorithm to build the network having a sparse structure. We test our algorithm on the problem of matching a tweet and a response in social media, a hard matching problem proposed in [Wang et al., 2013], and show that DeepMatch$_{tree}$ can outperform a number of competitor models including one without using dependency trees and one based on word-embedding, all with large margins
|
2110.11736
|
Wanchuang Zhu Dr.
|
Wanchuang Zhu, Benjamin Zi Hao Zhao, Simon Luo, Tongliang Liu, Ke Deng
|
MANDERA: Malicious Node Detection in Federated Learning via Ranking
|
17 pages, 11 figures, ICML
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Byzantine attacks hinder the deployment of federated learning algorithms.
Although we know that the benign gradients and Byzantine attacked gradients are
distributed differently, to detect the malicious gradients is challenging due
to (1) the gradient is high-dimensional and each dimension has its unique
distribution and (2) the benign gradients and the attacked gradients are always
mixed (two-sample test methods cannot apply directly). To address the above,
for the first time, we propose MANDERA which is theoretically guaranteed to
efficiently detect all malicious gradients under Byzantine attacks with no
prior knowledge or history about the number of attacked nodes. More
specifically, we transfer the original updating gradient space into a ranking
matrix. By such an operation, the scales of different dimensions of the
gradients in the ranking space become identical. The high-dimensional benign
gradients and the malicious gradients can be easily separated. The
effectiveness of MANDERA is further confirmed by experimentation on four
Byzantine attack implementations (Gaussian, Zero Gradient, Sign Flipping,
Shifted Mean), comparing with state-of-the-art defenses. The experiments cover
both IID and Non-IID datasets.
|
[
{
"created": "Fri, 22 Oct 2021 12:14:16 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Jan 2023 04:24:03 GMT",
"version": "v2"
}
] |
2023-01-18
|
[
[
"Zhu",
"Wanchuang",
""
],
[
"Zhao",
"Benjamin Zi Hao",
""
],
[
"Luo",
"Simon",
""
],
[
"Liu",
"Tongliang",
""
],
[
"Deng",
"Ke",
""
]
] |
Byzantine attacks hinder the deployment of federated learning algorithms. Although we know that the benign gradients and Byzantine attacked gradients are distributed differently, to detect the malicious gradients is challenging due to (1) the gradient is high-dimensional and each dimension has its unique distribution and (2) the benign gradients and the attacked gradients are always mixed (two-sample test methods cannot apply directly). To address the above, for the first time, we propose MANDERA which is theoretically guaranteed to efficiently detect all malicious gradients under Byzantine attacks with no prior knowledge or history about the number of attacked nodes. More specifically, we transfer the original updating gradient space into a ranking matrix. By such an operation, the scales of different dimensions of the gradients in the ranking space become identical. The high-dimensional benign gradients and the malicious gradients can be easily separated. The effectiveness of MANDERA is further confirmed by experimentation on four Byzantine attack implementations (Gaussian, Zero Gradient, Sign Flipping, Shifted Mean), comparing with state-of-the-art defenses. The experiments cover both IID and Non-IID datasets.
|
2104.02392
|
Thomas Steiner
|
Thomas Steiner, Fran\c{c}ois Beaufort
|
Accessing HID Devices on the Web With the WebHID API: How to play the
Chrome Dino Game by Jumping With a Nintendo Joy-Con Controller in One's
Pocket
|
2 pages, accepted at the Developers Track of The Web Conference 2021
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-sa/4.0/
|
In this demonstration, we show how special hardware like Nintendo Joy-Con
controllers can be made accessible from the Web through the new WebHID API.
This novel technology proposal allows developers to write Web drivers in pure
JavaScript that talk to Human Interface Device (HID) devices via the HID
protocol. One such example of a driver has been realized in the project
Joy-Con-WebHID, which allows for fun pastimes like playing the Google Chrome
browser's offline dinosaur game by jumping. This works thanks to the
accelerometers built into Joy-Con controllers whose signals are read out by the
driver and used to control the game character in the browser. A video of the
experience is available.
|
[
{
"created": "Tue, 6 Apr 2021 09:49:53 GMT",
"version": "v1"
}
] |
2021-04-07
|
[
[
"Steiner",
"Thomas",
""
],
[
"Beaufort",
"François",
""
]
] |
In this demonstration, we show how special hardware like Nintendo Joy-Con controllers can be made accessible from the Web through the new WebHID API. This novel technology proposal allows developers to write Web drivers in pure JavaScript that talk to Human Interface Device (HID) devices via the HID protocol. One such example of a driver has been realized in the project Joy-Con-WebHID, which allows for fun pastimes like playing the Google Chrome browser's offline dinosaur game by jumping. This works thanks to the accelerometers built into Joy-Con controllers whose signals are read out by the driver and used to control the game character in the browser. A video of the experience is available.
|
2203.15233
|
I-Chao Shen
|
I-Chao Shen, Yu Ju Chen, Oliver van Kaick, Takeo Igarashi
|
AutoPoly: Predicting a Polygonal Mesh Construction Sequence from a
Silhouette Image
|
8 pages
| null | null | null |
cs.CV cs.CG cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Polygonal modeling is a core task of content creation in Computer Graphics.
The complexity of modeling, in terms of the number and the order of operations
and time required to execute them makes it challenging to learn and execute.
Our goal is to automatically derive a polygonal modeling sequence for a given
target. Then, one can learn polygonal modeling by observing the resulting
sequence and also expedite the modeling process by starting from the
auto-generated result. As a starting point for building a system for 3D
modeling in the future, we tackle the 2D shape modeling problem and present
AutoPoly, a hybrid method that generates a polygonal mesh construction sequence
from a silhouette image. The key idea of our method is the use of the Monte
Carlo tree search (MCTS) algorithm and differentiable rendering to separately
predict sequential topological actions and geometric actions. Our hybrid method
can alter topology, whereas the recently proposed inverse shape estimation
methods using differentiable rendering can only handle a fixed topology. Our
novel reward function encourages MCTS to select topological actions that lead
to a simpler shape without self-intersection. We further designed two deep
learning-based methods to improve the expansion and simulation steps in the
MCTS search process: an $n$-step "future action prediction" network (nFAP-Net)
to generate candidates for potential topological actions, and a shape warping
network (WarpNet) to predict polygonal shapes given the predicted rendered
images and topological actions. We demonstrate the efficiency of our method on
2D polygonal shapes of multiple man-made object categories.
|
[
{
"created": "Tue, 29 Mar 2022 04:48:47 GMT",
"version": "v1"
}
] |
2022-03-30
|
[
[
"Shen",
"I-Chao",
""
],
[
"Chen",
"Yu Ju",
""
],
[
"van Kaick",
"Oliver",
""
],
[
"Igarashi",
"Takeo",
""
]
] |
Polygonal modeling is a core task of content creation in Computer Graphics. The complexity of modeling, in terms of the number and the order of operations and time required to execute them makes it challenging to learn and execute. Our goal is to automatically derive a polygonal modeling sequence for a given target. Then, one can learn polygonal modeling by observing the resulting sequence and also expedite the modeling process by starting from the auto-generated result. As a starting point for building a system for 3D modeling in the future, we tackle the 2D shape modeling problem and present AutoPoly, a hybrid method that generates a polygonal mesh construction sequence from a silhouette image. The key idea of our method is the use of the Monte Carlo tree search (MCTS) algorithm and differentiable rendering to separately predict sequential topological actions and geometric actions. Our hybrid method can alter topology, whereas the recently proposed inverse shape estimation methods using differentiable rendering can only handle a fixed topology. Our novel reward function encourages MCTS to select topological actions that lead to a simpler shape without self-intersection. We further designed two deep learning-based methods to improve the expansion and simulation steps in the MCTS search process: an $n$-step "future action prediction" network (nFAP-Net) to generate candidates for potential topological actions, and a shape warping network (WarpNet) to predict polygonal shapes given the predicted rendered images and topological actions. We demonstrate the efficiency of our method on 2D polygonal shapes of multiple man-made object categories.
|
1812.00898
|
Aishwarya Agrawal
|
Aishwarya Agrawal, Mateusz Malinowski, Felix Hill, Ali Eslami, Oriol
Vinyals, Tejas Kulkarni
|
Generating Diverse Programs with Instruction Conditioned Reinforced
Adversarial Learning
| null | null | null | null |
cs.LG cs.CL cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Advances in Deep Reinforcement Learning have led to agents that perform well
across a variety of sensory-motor domains. In this work, we study the setting
in which an agent must learn to generate programs for diverse scenes
conditioned on a given symbolic instruction. Final goals are specified to our
agent via images of the scenes. A symbolic instruction consistent with the goal
images is used as the conditioning input for our policies. Since a single
instruction corresponds to a diverse set of different but still consistent
end-goal images, the agent needs to learn to generate a distribution over
programs given an instruction. We demonstrate that with simple changes to the
reinforced adversarial learning objective, we can learn instruction conditioned
policies to achieve the corresponding diverse set of goals. Most importantly,
our agent's stochastic policy is shown to more accurately capture the diversity
in the goal distribution than a fixed pixel-based reward function baseline. We
demonstrate the efficacy of our approach on two domains: (1) drawing MNIST
digits with a paint software conditioned on instructions and (2) constructing
scenes in a 3D editor that satisfies a certain instruction.
|
[
{
"created": "Mon, 3 Dec 2018 16:51:35 GMT",
"version": "v1"
}
] |
2018-12-04
|
[
[
"Agrawal",
"Aishwarya",
""
],
[
"Malinowski",
"Mateusz",
""
],
[
"Hill",
"Felix",
""
],
[
"Eslami",
"Ali",
""
],
[
"Vinyals",
"Oriol",
""
],
[
"Kulkarni",
"Tejas",
""
]
] |
Advances in Deep Reinforcement Learning have led to agents that perform well across a variety of sensory-motor domains. In this work, we study the setting in which an agent must learn to generate programs for diverse scenes conditioned on a given symbolic instruction. Final goals are specified to our agent via images of the scenes. A symbolic instruction consistent with the goal images is used as the conditioning input for our policies. Since a single instruction corresponds to a diverse set of different but still consistent end-goal images, the agent needs to learn to generate a distribution over programs given an instruction. We demonstrate that with simple changes to the reinforced adversarial learning objective, we can learn instruction conditioned policies to achieve the corresponding diverse set of goals. Most importantly, our agent's stochastic policy is shown to more accurately capture the diversity in the goal distribution than a fixed pixel-based reward function baseline. We demonstrate the efficacy of our approach on two domains: (1) drawing MNIST digits with a paint software conditioned on instructions and (2) constructing scenes in a 3D editor that satisfies a certain instruction.
|
2311.15210
|
Pingyao Feng
|
Pingyao Feng, Siheng Yi, Qingrui Qu, Zhiwang Yu, Yifei Zhu
|
Topology combined machine learning for consonant recognition
| null | null | null | null |
cs.LG math.ST stat.TH
|
http://creativecommons.org/licenses/by/4.0/
|
In artificial-intelligence-aided signal processing, existing deep learning
models often exhibit a black-box structure, and their validity and
comprehensibility remain elusive. The integration of topological methods,
despite its relatively nascent application, serves a dual purpose of making
models more interpretable as well as extracting structural information from
time-dependent data for smarter learning. Here, we provide a transparent and
broadly applicable methodology, TopCap, to capture the most salient topological
features inherent in time series for machine learning. Rooted in
high-dimensional ambient spaces, TopCap is capable of capturing features rarely
detected in datasets with low intrinsic dimensionality. Applying time-delay
embedding and persistent homology, we obtain descriptors which encapsulate
information such as the vibration of a time series, in terms of its variability
of frequency, amplitude, and average line, demonstrated with simulated data.
This information is then vectorised and fed into multiple machine learning
algorithms such as k-nearest neighbours and support vector machine. Notably, in
classifying voiced and voiceless consonants, TopCap achieves an accuracy
exceeding 96% and is geared towards designing topological convolutional layers
for deep learning of speech and audio signals.
|
[
{
"created": "Sun, 26 Nov 2023 06:53:56 GMT",
"version": "v1"
}
] |
2023-11-28
|
[
[
"Feng",
"Pingyao",
""
],
[
"Yi",
"Siheng",
""
],
[
"Qu",
"Qingrui",
""
],
[
"Yu",
"Zhiwang",
""
],
[
"Zhu",
"Yifei",
""
]
] |
In artificial-intelligence-aided signal processing, existing deep learning models often exhibit a black-box structure, and their validity and comprehensibility remain elusive. The integration of topological methods, despite its relatively nascent application, serves a dual purpose of making models more interpretable as well as extracting structural information from time-dependent data for smarter learning. Here, we provide a transparent and broadly applicable methodology, TopCap, to capture the most salient topological features inherent in time series for machine learning. Rooted in high-dimensional ambient spaces, TopCap is capable of capturing features rarely detected in datasets with low intrinsic dimensionality. Applying time-delay embedding and persistent homology, we obtain descriptors which encapsulate information such as the vibration of a time series, in terms of its variability of frequency, amplitude, and average line, demonstrated with simulated data. This information is then vectorised and fed into multiple machine learning algorithms such as k-nearest neighbours and support vector machine. Notably, in classifying voiced and voiceless consonants, TopCap achieves an accuracy exceeding 96% and is geared towards designing topological convolutional layers for deep learning of speech and audio signals.
|
2205.00479
|
Zhixian Yang
|
Zhixian Yang, Renliang Sun, Xiaojun Wan
|
Nearest Neighbor Knowledge Distillation for Neural Machine Translation
|
Accepted to NAACL 2022 Main Conference
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
k-nearest-neighbor machine translation (NN-MT), proposed by Khandelwal et al.
(2021), has achieved many state-of-the-art results in machine translation
tasks. Although effective, NN-MT requires conducting NN searches through the
large datastore for each decoding step during inference, prohibitively
increasing the decoding cost and thus leading to the difficulty for the
deployment in real-world applications. In this paper, we propose to move the
time-consuming NN search forward to the preprocessing phase, and then introduce
Nearest Neighbor Knowledge Distillation (NN-KD) that trains the base NMT model
to directly learn the knowledge of NN. Distilling knowledge retrieved by NN can
encourage the NMT model to take more reasonable target tokens into
consideration, thus addressing the overcorrection problem. Extensive
experimental results show that, the proposed method achieves consistent
improvement over the state-of-the-art baselines including NN-MT, while
maintaining the same training and decoding speed as the standard NMT model.
|
[
{
"created": "Sun, 1 May 2022 14:30:49 GMT",
"version": "v1"
}
] |
2022-05-03
|
[
[
"Yang",
"Zhixian",
""
],
[
"Sun",
"Renliang",
""
],
[
"Wan",
"Xiaojun",
""
]
] |
k-nearest-neighbor machine translation (NN-MT), proposed by Khandelwal et al. (2021), has achieved many state-of-the-art results in machine translation tasks. Although effective, NN-MT requires conducting NN searches through the large datastore for each decoding step during inference, prohibitively increasing the decoding cost and thus leading to the difficulty for the deployment in real-world applications. In this paper, we propose to move the time-consuming NN search forward to the preprocessing phase, and then introduce Nearest Neighbor Knowledge Distillation (NN-KD) that trains the base NMT model to directly learn the knowledge of NN. Distilling knowledge retrieved by NN can encourage the NMT model to take more reasonable target tokens into consideration, thus addressing the overcorrection problem. Extensive experimental results show that, the proposed method achieves consistent improvement over the state-of-the-art baselines including NN-MT, while maintaining the same training and decoding speed as the standard NMT model.
|
1302.3721
|
Joseph Mellor
|
Joseph Mellor, Jonathan Shapiro
|
Thompson Sampling in Switching Environments with Bayesian Online Change
Point Detection
|
A version will appear in the Sixteenth international conference on
Artificial Intelligence and Statistics (AIStats 2013)
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Thompson Sampling has recently been shown to be optimal in the Bernoulli
Multi-Armed Bandit setting[Kaufmann et al., 2012]. This bandit problem assumes
stationary distributions for the rewards. It is often unrealistic to model the
real world as a stationary distribution. In this paper we derive and evaluate
algorithms using Thompson Sampling for a Switching Multi-Armed Bandit Problem.
We propose a Thompson Sampling strategy equipped with a Bayesian change point
mechanism to tackle this problem. We develop algorithms for a variety of cases
with constant switching rate: when switching occurs all arms change (Global
Switching), switching occurs independently for each arm (Per-Arm Switching),
when the switching rate is known and when it must be inferred from data. This
leads to a family of algorithms we collectively term Change-Point Thompson
Sampling (CTS). We show empirical results of the algorithm in 4 artificial
environments, and 2 derived from real world data; news click-through[Yahoo!,
2011] and foreign exchange data[Dukascopy, 2012], comparing them to some other
bandit algorithms. In real world data CTS is the most effective.
|
[
{
"created": "Fri, 15 Feb 2013 10:48:57 GMT",
"version": "v1"
}
] |
2013-02-18
|
[
[
"Mellor",
"Joseph",
""
],
[
"Shapiro",
"Jonathan",
""
]
] |
Thompson Sampling has recently been shown to be optimal in the Bernoulli Multi-Armed Bandit setting[Kaufmann et al., 2012]. This bandit problem assumes stationary distributions for the rewards. It is often unrealistic to model the real world as a stationary distribution. In this paper we derive and evaluate algorithms using Thompson Sampling for a Switching Multi-Armed Bandit Problem. We propose a Thompson Sampling strategy equipped with a Bayesian change point mechanism to tackle this problem. We develop algorithms for a variety of cases with constant switching rate: when switching occurs all arms change (Global Switching), switching occurs independently for each arm (Per-Arm Switching), when the switching rate is known and when it must be inferred from data. This leads to a family of algorithms we collectively term Change-Point Thompson Sampling (CTS). We show empirical results of the algorithm in 4 artificial environments, and 2 derived from real world data; news click-through[Yahoo!, 2011] and foreign exchange data[Dukascopy, 2012], comparing them to some other bandit algorithms. In real world data CTS is the most effective.
|
2004.05773
|
Isabelle Augenstein
|
Pepa Atanasova and Jakob Grue Simonsen and Christina Lioma and
Isabelle Augenstein
|
Generating Fact Checking Explanations
|
In Proceedings of the 2020 Annual Conference of the Association for
Computational Linguistics (ACL 2020)
| null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most existing work on automated fact checking is concerned with predicting
the veracity of claims based on metadata, social network spread, language used
in claims, and, more recently, evidence supporting or denying claims. A crucial
piece of the puzzle that is still missing is to understand how to automate the
most elaborate part of the process -- generating justifications for verdicts on
claims. This paper provides the first study of how these explanations can be
generated automatically based on available claim context, and how this task can
be modelled jointly with veracity prediction. Our results indicate that
optimising both objectives at the same time, rather than training them
separately, improves the performance of a fact checking system. The results of
a manual evaluation further suggest that the informativeness, coverage and
overall quality of the generated explanations are also improved in the
multi-task model.
|
[
{
"created": "Mon, 13 Apr 2020 05:23:25 GMT",
"version": "v1"
}
] |
2020-04-14
|
[
[
"Atanasova",
"Pepa",
""
],
[
"Simonsen",
"Jakob Grue",
""
],
[
"Lioma",
"Christina",
""
],
[
"Augenstein",
"Isabelle",
""
]
] |
Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process -- generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model.
|
1412.0751
|
John Wieting
|
John Wieting
|
Tiered Clustering to Improve Lexical Entailment
|
Paper for course project for Advanced NLP Spring 2013. 8 pages
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/3.0/
|
Many tasks in Natural Language Processing involve recognizing lexical
entailment. Two different approaches to this problem have been proposed
recently that are quite different from each other. The first is an asymmetric
similarity measure designed to give high scores when the contexts of the
narrower term in the entailment are a subset of those of the broader term. The
second is a supervised approach where a classifier is learned to predict
entailment given a concatenated latent vector representation of the word. Both
of these approaches are vector space models that use a single context vector as
a representation of the word. In this work, I study the effects of clustering
words into senses and using these multiple context vectors to infer entailment
using extensions of these two algorithms. I find that this approach offers some
improvement to these entailment algorithms.
|
[
{
"created": "Tue, 2 Dec 2014 00:53:35 GMT",
"version": "v1"
}
] |
2014-12-03
|
[
[
"Wieting",
"John",
""
]
] |
Many tasks in Natural Language Processing involve recognizing lexical entailment. Two different approaches to this problem have been proposed recently that are quite different from each other. The first is an asymmetric similarity measure designed to give high scores when the contexts of the narrower term in the entailment are a subset of those of the broader term. The second is a supervised approach where a classifier is learned to predict entailment given a concatenated latent vector representation of the word. Both of these approaches are vector space models that use a single context vector as a representation of the word. In this work, I study the effects of clustering words into senses and using these multiple context vectors to infer entailment using extensions of these two algorithms. I find that this approach offers some improvement to these entailment algorithms.
|
2310.03668
|
Iker Garc\'ia-Ferrero
|
Oscar Sainz, Iker Garc\'ia-Ferrero, Rodrigo Agerri, Oier Lopez de
Lacalle, German Rigau, Eneko Agirre
|
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
|
The Twelfth International Conference on Learning Representations -
ICLR 2024
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Large Language Models (LLMs) combined with instruction tuning have made
significant progress when generalizing to unseen tasks. However, they have been
less successful in Information Extraction (IE), lagging behind task-specific
models. Typically, IE tasks are characterized by complex annotation guidelines
that describe the task and give examples to humans. Previous attempts to
leverage such information have failed, even with the largest models, as they
are not able to follow the guidelines out of the box. In this paper, we propose
GoLLIE (Guideline-following Large Language Model for IE), a model able to
improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to
comply with annotation guidelines. Comprehensive evaluation empirically
demonstrates that GoLLIE is able to generalize to and follow unseen guidelines,
outperforming previous attempts at zero-shot information extraction. The
ablation study shows that detailed guidelines are key for good results.
|
[
{
"created": "Thu, 5 Oct 2023 16:43:13 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Oct 2023 17:41:15 GMT",
"version": "v2"
},
{
"created": "Mon, 11 Dec 2023 08:24:40 GMT",
"version": "v3"
},
{
"created": "Wed, 21 Feb 2024 15:51:58 GMT",
"version": "v4"
},
{
"created": "Wed, 6 Mar 2024 16:38:03 GMT",
"version": "v5"
}
] |
2024-03-07
|
[
[
"Sainz",
"Oscar",
""
],
[
"García-Ferrero",
"Iker",
""
],
[
"Agerri",
"Rodrigo",
""
],
[
"de Lacalle",
"Oier Lopez",
""
],
[
"Rigau",
"German",
""
],
[
"Agirre",
"Eneko",
""
]
] |
Large Language Models (LLMs) combined with instruction tuning have made significant progress when generalizing to unseen tasks. However, they have been less successful in Information Extraction (IE), lagging behind task-specific models. Typically, IE tasks are characterized by complex annotation guidelines that describe the task and give examples to humans. Previous attempts to leverage such information have failed, even with the largest models, as they are not able to follow the guidelines out of the box. In this paper, we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines. Comprehensive evaluation empirically demonstrates that GoLLIE is able to generalize to and follow unseen guidelines, outperforming previous attempts at zero-shot information extraction. The ablation study shows that detailed guidelines are key for good results.
|
2204.02684
|
Xinyue Huo
|
Xinyue Huo, Lingxi Xie, Hengtong Hu, Wengang Zhou, Houqiang Li, Qi
Tian
|
Domain-Agnostic Prior for Transfer Semantic Segmentation
|
Accepted by CVPR 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Unsupervised domain adaptation (UDA) is an important topic in the computer
vision community. The key difficulty lies in defining a common property between
the source and target domains so that the source-domain features can align with
the target-domain semantics. In this paper, we present a simple and effective
mechanism that regularizes cross-domain representation learning with a
domain-agnostic prior (DAP) that constrains the features extracted from source
and target domains to align with a domain-agnostic space. In practice, this is
easily implemented as an extra loss term that requires a little extra costs. In
the standard evaluation protocol of transferring synthesized data to real data,
we validate the effectiveness of different types of DAP, especially that
borrowed from a text embedding model that shows favorable performance beyond
the state-of-the-art UDA approaches in terms of segmentation accuracy. Our
research reveals that UDA benefits much from better proxies, possibly from
other data modalities.
|
[
{
"created": "Wed, 6 Apr 2022 09:13:25 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Apr 2022 07:53:49 GMT",
"version": "v2"
}
] |
2022-04-21
|
[
[
"Huo",
"Xinyue",
""
],
[
"Xie",
"Lingxi",
""
],
[
"Hu",
"Hengtong",
""
],
[
"Zhou",
"Wengang",
""
],
[
"Li",
"Houqiang",
""
],
[
"Tian",
"Qi",
""
]
] |
Unsupervised domain adaptation (UDA) is an important topic in the computer vision community. The key difficulty lies in defining a common property between the source and target domains so that the source-domain features can align with the target-domain semantics. In this paper, we present a simple and effective mechanism that regularizes cross-domain representation learning with a domain-agnostic prior (DAP) that constrains the features extracted from source and target domains to align with a domain-agnostic space. In practice, this is easily implemented as an extra loss term that requires a little extra costs. In the standard evaluation protocol of transferring synthesized data to real data, we validate the effectiveness of different types of DAP, especially that borrowed from a text embedding model that shows favorable performance beyond the state-of-the-art UDA approaches in terms of segmentation accuracy. Our research reveals that UDA benefits much from better proxies, possibly from other data modalities.
|
2310.04192
|
Daniel Weber
|
Daniel Weber, Fabian Thomas, Lukas Gerlach, Ruiyi Zhang, Michael
Schwarz
|
Reviving Meltdown 3a
|
published at ESORICS 2023
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Since the initial discovery of Meltdown and Spectre in 2017, different
variants of these attacks have been discovered. One often overlooked variant is
Meltdown 3a, also known as Meltdown-CPL-REG. Even though Meltdown-CPL-REG was
initially discovered in 2018, the available information regarding the
vulnerability is still sparse. In this paper, we analyze Meltdown-CPL-REG on 19
different CPUs from different vendors using an automated tool. We observe that
the impact is more diverse than documented and differs from CPU to CPU.
Surprisingly, while the newest Intel CPUs do not seem affected by
Meltdown-CPL-REG, the newest available AMD CPUs (Zen3+) are still affected by
the vulnerability. Furthermore, given our attack primitive CounterLeak, we show
that besides up-to-date patches, Meltdown-CPL-REG can still be exploited as we
reenable performance-counter-based attacks on cryptographic algorithms, break
KASLR, and mount Spectre attacks. Although Meltdown-CPL-REG is not as powerful
as other transient-execution attacks, its attack surface should not be
underestimated.
|
[
{
"created": "Fri, 6 Oct 2023 12:11:46 GMT",
"version": "v1"
}
] |
2023-10-09
|
[
[
"Weber",
"Daniel",
""
],
[
"Thomas",
"Fabian",
""
],
[
"Gerlach",
"Lukas",
""
],
[
"Zhang",
"Ruiyi",
""
],
[
"Schwarz",
"Michael",
""
]
] |
Since the initial discovery of Meltdown and Spectre in 2017, different variants of these attacks have been discovered. One often overlooked variant is Meltdown 3a, also known as Meltdown-CPL-REG. Even though Meltdown-CPL-REG was initially discovered in 2018, the available information regarding the vulnerability is still sparse. In this paper, we analyze Meltdown-CPL-REG on 19 different CPUs from different vendors using an automated tool. We observe that the impact is more diverse than documented and differs from CPU to CPU. Surprisingly, while the newest Intel CPUs do not seem affected by Meltdown-CPL-REG, the newest available AMD CPUs (Zen3+) are still affected by the vulnerability. Furthermore, given our attack primitive CounterLeak, we show that besides up-to-date patches, Meltdown-CPL-REG can still be exploited as we reenable performance-counter-based attacks on cryptographic algorithms, break KASLR, and mount Spectre attacks. Although Meltdown-CPL-REG is not as powerful as other transient-execution attacks, its attack surface should not be underestimated.
|
2103.11683
|
Qi Shen
|
Qi Shen, Shijun Wu, Yanzhen Zou, Bing Xie
|
Comprehensive Integration of API Usage Patterns
|
11 pages, Accepted to the 29th IEEE/ACM International Conference on
Program Comprehension
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nowadays, developers often reuse existing APIs to implement their programming
tasks. A lot of API usage patterns are mined to help developers learn API usage
rules. However, there are still many missing variables to be synthesized when
developers integrate the patterns into their programming context. To deal with
this issue, we propose a comprehensive approach to integrate API usage patterns
in this paper. We first perform an empirical study by analyzing how API usage
patterns are integrated in real-world projects. We find the expressions for
variable synthesis is often non-trivial and can be divided into 5 syntax types.
Based on the observation, we promote an approach to help developers
interactively complete API usage patterns. Compared to the existing code
completion techniques, our approach can recommend infrequent expressions
accompanied with their real-world usage examples according to the user intent.
The evaluation shows that our approach could assist users to integrate APIs
more efficiently and complete the programming tasks faster than existing works.
|
[
{
"created": "Mon, 22 Mar 2021 09:24:43 GMT",
"version": "v1"
}
] |
2021-03-23
|
[
[
"Shen",
"Qi",
""
],
[
"Wu",
"Shijun",
""
],
[
"Zou",
"Yanzhen",
""
],
[
"Xie",
"Bing",
""
]
] |
Nowadays, developers often reuse existing APIs to implement their programming tasks. A lot of API usage patterns are mined to help developers learn API usage rules. However, there are still many missing variables to be synthesized when developers integrate the patterns into their programming context. To deal with this issue, we propose a comprehensive approach to integrate API usage patterns in this paper. We first perform an empirical study by analyzing how API usage patterns are integrated in real-world projects. We find the expressions for variable synthesis is often non-trivial and can be divided into 5 syntax types. Based on the observation, we promote an approach to help developers interactively complete API usage patterns. Compared to the existing code completion techniques, our approach can recommend infrequent expressions accompanied with their real-world usage examples according to the user intent. The evaluation shows that our approach could assist users to integrate APIs more efficiently and complete the programming tasks faster than existing works.
|
2402.13597
|
Wang Liu
|
Wang Liu, Cunhua Pan, Hong Ren, Jiangzhou Wang, Robert Schober, and
Lajos Hanzo
|
Near-Field Multiuser Beam-Training for Extremely Large-Scale MIMO
Systems
|
submitted to IEEE
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are
capable of improving spectral efficiency by employing far more antennas than
conventional massive MIMO at the base station (BS). However, beam training in
multiuser XL-MIMO systems is challenging. To tackle these issues, we conceive a
three-phase graph neural network (GNN)-based beam training scheme for multiuser
XL-MIMO systems. In the first phase, only far-field wide beams have to be
tested for each user and the GNN is utilized to map the beamforming gain
information of the far-field wide beams to the optimal near-field beam for each
user. In addition, the proposed GNN-based scheme can exploit the
position-correlation between adjacent users for further improvement of the
accuracy of beam training. In the second phase, a beam allocation scheme based
on the probability vectors produced at the outputs of GNNs is proposed to
address the above beam-direction conflicts between users. In the third phase,
the hybrid TBF is designed for further reducing the inter-user interference.
Our simulation results show that the proposed scheme improves the beam training
performance of the benchmarks. Moreover, the performance of the proposed beam
training scheme approaches that of an exhaustive search, despite requiring only
about 7% of the pilot overhead.
|
[
{
"created": "Wed, 21 Feb 2024 07:59:44 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Mar 2024 00:34:49 GMT",
"version": "v2"
}
] |
2024-03-27
|
[
[
"Liu",
"Wang",
""
],
[
"Pan",
"Cunhua",
""
],
[
"Ren",
"Hong",
""
],
[
"Wang",
"Jiangzhou",
""
],
[
"Schober",
"Robert",
""
],
[
"Hanzo",
"Lajos",
""
]
] |
Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam training scheme for multiuser XL-MIMO systems. In the first phase, only far-field wide beams have to be tested for each user and the GNN is utilized to map the beamforming gain information of the far-field wide beams to the optimal near-field beam for each user. In addition, the proposed GNN-based scheme can exploit the position-correlation between adjacent users for further improvement of the accuracy of beam training. In the second phase, a beam allocation scheme based on the probability vectors produced at the outputs of GNNs is proposed to address the above beam-direction conflicts between users. In the third phase, the hybrid TBF is designed for further reducing the inter-user interference. Our simulation results show that the proposed scheme improves the beam training performance of the benchmarks. Moreover, the performance of the proposed beam training scheme approaches that of an exhaustive search, despite requiring only about 7% of the pilot overhead.
|
2003.03220
|
Ozan \c{C}atal
|
Ozan \c{C}atal, Samuel Wauthier, Tim Verbelen, Cedric De Boom, Bart
Dhoedt
|
Deep Active Inference for Autonomous Robot Navigation
|
workshop paper at BAICS at ICLR 2020
| null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Active inference is a theory that underpins the way biological agent's
perceive and act in the real world. At its core, active inference is based on
the principle that the brain is an approximate Bayesian inference engine,
building an internal generative model to drive agents towards minimal surprise.
Although this theory has shown interesting results with grounding in cognitive
neuroscience, its application remains limited to simulations with small,
predefined sensor and state spaces.
In this paper, we leverage recent advances in deep learning to build more
complex generative models that can work without a predefined states space.
State representations are learned end-to-end from real-world, high-dimensional
sensory data such as camera frames. We also show that these generative models
can be used to engage in active inference. To the best of our knowledge this is
the first application of deep active inference for a real-world robot
navigation task.
|
[
{
"created": "Fri, 6 Mar 2020 14:01:01 GMT",
"version": "v1"
}
] |
2020-03-09
|
[
[
"Çatal",
"Ozan",
""
],
[
"Wauthier",
"Samuel",
""
],
[
"Verbelen",
"Tim",
""
],
[
"De Boom",
"Cedric",
""
],
[
"Dhoedt",
"Bart",
""
]
] |
Active inference is a theory that underpins the way biological agent's perceive and act in the real world. At its core, active inference is based on the principle that the brain is an approximate Bayesian inference engine, building an internal generative model to drive agents towards minimal surprise. Although this theory has shown interesting results with grounding in cognitive neuroscience, its application remains limited to simulations with small, predefined sensor and state spaces. In this paper, we leverage recent advances in deep learning to build more complex generative models that can work without a predefined states space. State representations are learned end-to-end from real-world, high-dimensional sensory data such as camera frames. We also show that these generative models can be used to engage in active inference. To the best of our knowledge this is the first application of deep active inference for a real-world robot navigation task.
|
2403.11051
|
Akrati Saxena
|
Mariana Macedo, Akrati Saxena
|
Gender differences in online communication: A case study of Soccer
| null | null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Social media and digital platforms allow us to express our opinions freely
and easily to a vast number of people. In this study, we examine whether there
are gender-based differences in how communication happens via Twitter in regard
to soccer. Soccer is one of the most popular sports, and therefore, on social
media, it engages a diverse audience regardless of their technical knowledge.
We collected Twitter data for three months (March-June) for English and
Portuguese that contains 9.5 million Tweets related to soccer, and only 18.38%
tweets were identified as belonging to women, highlighting a possible gender
gap already in the number of people who participated actively in this topic. We
then conduct a fine-grained text-level and network-level analysis to identify
the gender differences that might exist while communicating on Twitter. Our
results show that women express their emotions more intensely than men,
regardless of the differences in volume. The network generated from Portuguese
has lower homophily than English. However, this difference in homophily does
not impact how females express their emotions and sentiments, suggesting that
these aspects are inherent norms or characteristics of genders. Our study
unveils more gaps through qualitative and quantitative analyses, highlighting
the importance of examining and reporting gender gaps in online communication
to create a more inclusive space where people can openly share their opinions.
|
[
{
"created": "Sun, 17 Mar 2024 01:26:38 GMT",
"version": "v1"
}
] |
2024-03-19
|
[
[
"Macedo",
"Mariana",
""
],
[
"Saxena",
"Akrati",
""
]
] |
Social media and digital platforms allow us to express our opinions freely and easily to a vast number of people. In this study, we examine whether there are gender-based differences in how communication happens via Twitter in regard to soccer. Soccer is one of the most popular sports, and therefore, on social media, it engages a diverse audience regardless of their technical knowledge. We collected Twitter data for three months (March-June) for English and Portuguese that contains 9.5 million Tweets related to soccer, and only 18.38% tweets were identified as belonging to women, highlighting a possible gender gap already in the number of people who participated actively in this topic. We then conduct a fine-grained text-level and network-level analysis to identify the gender differences that might exist while communicating on Twitter. Our results show that women express their emotions more intensely than men, regardless of the differences in volume. The network generated from Portuguese has lower homophily than English. However, this difference in homophily does not impact how females express their emotions and sentiments, suggesting that these aspects are inherent norms or characteristics of genders. Our study unveils more gaps through qualitative and quantitative analyses, highlighting the importance of examining and reporting gender gaps in online communication to create a more inclusive space where people can openly share their opinions.
|
1906.10002
|
Daniel Loureiro
|
Daniel Loureiro and Alipio Jorge
|
LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)
|
Accepted at the SemDeep-5 Workshop in IJCAI 2019. Code and data:
https://github.com/danlou/LMMS
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper describes the LIAAD system that was ranked second place in the
Word-in-Context challenge (WiC) featured in SemDeep-5. Our solution is based on
a novel system for Word Sense Disambiguation (WSD) using contextual embeddings
and full-inventory sense embeddings. We adapt this WSD system, in a
straightforward manner, for the present task of detecting whether the same
sense occurs in a pair of sentences. Additionally, we show that our solution is
able to achieve competitive performance even without using the provided
training or development sets, mitigating potential concerns related to task
overfitting
|
[
{
"created": "Mon, 24 Jun 2019 14:49:05 GMT",
"version": "v1"
}
] |
2019-06-25
|
[
[
"Loureiro",
"Daniel",
""
],
[
"Jorge",
"Alipio",
""
]
] |
This paper describes the LIAAD system that was ranked second place in the Word-in-Context challenge (WiC) featured in SemDeep-5. Our solution is based on a novel system for Word Sense Disambiguation (WSD) using contextual embeddings and full-inventory sense embeddings. We adapt this WSD system, in a straightforward manner, for the present task of detecting whether the same sense occurs in a pair of sentences. Additionally, we show that our solution is able to achieve competitive performance even without using the provided training or development sets, mitigating potential concerns related to task overfitting
|
2309.12673
|
Jerry Yao-Chieh Hu
|
Jerry Yao-Chieh Hu, Donglin Yang, Dennis Wu, Chenwei Xu, Bo-Yu Chen,
Han Liu
|
On Sparse Modern Hopfield Model
|
37 pages, accepted at NeurIPS 2023. [v2] updated to match with
camera-ready version. Code is available at
https://github.com/MAGICS-LAB/SparseModernHopfield
| null | null | null |
cs.LG cs.AI cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce the sparse modern Hopfield model as a sparse extension of the
modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield
model equips a memory-retrieval dynamics whose one-step approximation
corresponds to the sparse attention mechanism. Theoretically, our key
contribution is a principled derivation of a closed-form sparse Hopfield energy
using the convex conjugate of the sparse entropic regularizer. Building upon
this, we derive the sparse memory retrieval dynamics from the sparse energy
function and show its one-step approximation is equivalent to the
sparse-structured attention. Importantly, we provide a sparsity-dependent
memory retrieval error bound which is provably tighter than its dense analog.
The conditions for the benefits of sparsity to arise are therefore identified
and discussed. In addition, we show that the sparse modern Hopfield model
maintains the robust theoretical properties of its dense counterpart, including
rapid fixed point convergence and exponential memory capacity. Empirically, we
use both synthetic and real-world datasets to demonstrate that the sparse
Hopfield model outperforms its dense counterpart in many situations.
|
[
{
"created": "Fri, 22 Sep 2023 07:32:45 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Nov 2023 22:45:39 GMT",
"version": "v2"
}
] |
2023-12-01
|
[
[
"Hu",
"Jerry Yao-Chieh",
""
],
[
"Yang",
"Donglin",
""
],
[
"Wu",
"Dennis",
""
],
[
"Xu",
"Chenwei",
""
],
[
"Chen",
"Bo-Yu",
""
],
[
"Liu",
"Han",
""
]
] |
We introduce the sparse modern Hopfield model as a sparse extension of the modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a memory-retrieval dynamics whose one-step approximation corresponds to the sparse attention mechanism. Theoretically, our key contribution is a principled derivation of a closed-form sparse Hopfield energy using the convex conjugate of the sparse entropic regularizer. Building upon this, we derive the sparse memory retrieval dynamics from the sparse energy function and show its one-step approximation is equivalent to the sparse-structured attention. Importantly, we provide a sparsity-dependent memory retrieval error bound which is provably tighter than its dense analog. The conditions for the benefits of sparsity to arise are therefore identified and discussed. In addition, we show that the sparse modern Hopfield model maintains the robust theoretical properties of its dense counterpart, including rapid fixed point convergence and exponential memory capacity. Empirically, we use both synthetic and real-world datasets to demonstrate that the sparse Hopfield model outperforms its dense counterpart in many situations.
|
2203.00386
|
Zihao Wang
|
Zihao Wang, Wei Liu, Qian He, Xinglong Wu, Zili Yi
|
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Training a text-to-image generator in the general domain (e.g., Dall.e,
CogView) requires huge amounts of paired text-image data, which is too
expensive to collect. In this paper, we propose a self-supervised scheme named
as CLIP-GEN for general text-to-image generation with the language-image priors
extracted with a pre-trained CLIP model. In our approach, we only require a set
of unlabeled images in the general domain to train a text-to-image generator.
Specifically, given an image without text labels, we first extract the
embedding of the image in the united language-vision embedding space with the
image encoder of CLIP. Next, we convert the image into a sequence of discrete
tokens in the VQGAN codebook space (the VQGAN model can be trained with the
unlabeled image dataset in hand). Finally, we train an autoregressive
transformer that maps the image tokens from its unified language-vision
representation. Once trained, the transformer can generate coherent image
tokens based on the text embedding extracted from the text encoder of CLIP upon
an input text. Such a strategy enables us to train a strong and general
text-to-image generator with large text-free image dataset such as ImageNet.
Qualitative and quantitative evaluations verify that our method significantly
outperforms optimization-based text-to-image methods in terms of image quality
while not compromising the text-image matching. Our method can even achieve
comparable performance as flagship supervised models like CogView.
|
[
{
"created": "Tue, 1 Mar 2022 12:11:32 GMT",
"version": "v1"
}
] |
2022-03-02
|
[
[
"Wang",
"Zihao",
""
],
[
"Liu",
"Wei",
""
],
[
"He",
"Qian",
""
],
[
"Wu",
"Xinglong",
""
],
[
"Yi",
"Zili",
""
]
] |
Training a text-to-image generator in the general domain (e.g., Dall.e, CogView) requires huge amounts of paired text-image data, which is too expensive to collect. In this paper, we propose a self-supervised scheme named as CLIP-GEN for general text-to-image generation with the language-image priors extracted with a pre-trained CLIP model. In our approach, we only require a set of unlabeled images in the general domain to train a text-to-image generator. Specifically, given an image without text labels, we first extract the embedding of the image in the united language-vision embedding space with the image encoder of CLIP. Next, we convert the image into a sequence of discrete tokens in the VQGAN codebook space (the VQGAN model can be trained with the unlabeled image dataset in hand). Finally, we train an autoregressive transformer that maps the image tokens from its unified language-vision representation. Once trained, the transformer can generate coherent image tokens based on the text embedding extracted from the text encoder of CLIP upon an input text. Such a strategy enables us to train a strong and general text-to-image generator with large text-free image dataset such as ImageNet. Qualitative and quantitative evaluations verify that our method significantly outperforms optimization-based text-to-image methods in terms of image quality while not compromising the text-image matching. Our method can even achieve comparable performance as flagship supervised models like CogView.
|
1707.03886
|
Amit Dhurandhar
|
Amit Dhurandhar, Vijay Iyengar, Ronny Luss and Karthikeyan Shanmugam
|
A Formal Framework to Characterize Interpretability of Procedures
|
presented at 2017 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2017), Sydney, NSW, Australia
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We provide a novel notion of what it means to be interpretable, looking past
the usual association with human understanding. Our key insight is that
interpretability is not an absolute concept and so we define it relative to a
target model, which may or may not be a human. We define a framework that
allows for comparing interpretable procedures by linking it to important
practical aspects such as accuracy and robustness. We characterize many of the
current state-of-the-art interpretable methods in our framework portraying its
general applicability.
|
[
{
"created": "Wed, 12 Jul 2017 19:42:08 GMT",
"version": "v1"
}
] |
2017-07-14
|
[
[
"Dhurandhar",
"Amit",
""
],
[
"Iyengar",
"Vijay",
""
],
[
"Luss",
"Ronny",
""
],
[
"Shanmugam",
"Karthikeyan",
""
]
] |
We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding. Our key insight is that interpretability is not an absolute concept and so we define it relative to a target model, which may or may not be a human. We define a framework that allows for comparing interpretable procedures by linking it to important practical aspects such as accuracy and robustness. We characterize many of the current state-of-the-art interpretable methods in our framework portraying its general applicability.
|
1801.01612
|
Jaouhar Fattahi
|
Jaouhar Fattahi and Mohamed Mejri
|
Secrecy by Witness-Functions under Equational Theories
|
http://ieeexplore.ieee.org/document/7301205/
|
7th International Conference on Electronics, Computers and
Artificial Intelligence (ECAI), 2015
|
10.1109/ECAI.2015.7301205
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we use the witness-functions to analyze cryptographic
protocols for secrecy under nonempty equational theories. The witness-functions
are safe metrics used to compute security. An analysis with a witness-function
consists in making sure that the security of every atomic message does not
decrease during its lifecycle in the protocol. The analysis gets more difficult
under nonempty equational theories. Indeed, the intruder can take advantage of
the algebraic properties of the cryptographic primitives to derive secrets.
These properties arise from the use of mathematical functions, such as
multiplication, addition, exclusive-or or modular exponentiation in the
cryptosystems and the protocols. Here, we show how to use the witness-functions
under nonempty equational theories and we run an analysis on the
Needham-Schroeder-Lowe protocol under the cipher homomorphism. This analysis
reveals that although this protocol is proved secure under the perfect
encryption assumption, its security collapses under the homomorphic primitives.
We show how the witness-functions help to illustrate an attack scenario on it
and we propose an amended version to fix it.
|
[
{
"created": "Fri, 5 Jan 2018 02:21:14 GMT",
"version": "v1"
}
] |
2018-01-08
|
[
[
"Fattahi",
"Jaouhar",
""
],
[
"Mejri",
"Mohamed",
""
]
] |
In this paper, we use the witness-functions to analyze cryptographic protocols for secrecy under nonempty equational theories. The witness-functions are safe metrics used to compute security. An analysis with a witness-function consists in making sure that the security of every atomic message does not decrease during its lifecycle in the protocol. The analysis gets more difficult under nonempty equational theories. Indeed, the intruder can take advantage of the algebraic properties of the cryptographic primitives to derive secrets. These properties arise from the use of mathematical functions, such as multiplication, addition, exclusive-or or modular exponentiation in the cryptosystems and the protocols. Here, we show how to use the witness-functions under nonempty equational theories and we run an analysis on the Needham-Schroeder-Lowe protocol under the cipher homomorphism. This analysis reveals that although this protocol is proved secure under the perfect encryption assumption, its security collapses under the homomorphic primitives. We show how the witness-functions help to illustrate an attack scenario on it and we propose an amended version to fix it.
|
1402.7015
|
Fabian Pedregosa
|
Fabian Pedregosa (INRIA Saclay - Ile de France, INRIA Paris -
Rocquencourt), Michael Eickenberg (INRIA Saclay - Ile de France, LNAO),
Philippe Ciuciu (INRIA Saclay - Ile de France, NEUROSPIN), Bertrand Thirion
(INRIA Saclay - Ile de France, NEUROSPIN), Alexandre Gramfort (LTCI)
|
Data-driven HRF estimation for encoding and decoding models
|
appears in NeuroImage (2015)
| null |
10.1016/j.neuroimage.2014.09.060
| null |
cs.CE cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite the common usage of a canonical, data-independent, hemodynamic
response function (HRF), it is known that the shape of the HRF varies across
brain regions and subjects. This suggests that a data-driven estimation of this
function could lead to more statistical power when modeling BOLD fMRI data.
However, unconstrained estimation of the HRF can yield highly unstable results
when the number of free parameters is large. We develop a method for the joint
estimation of activation and HRF using a rank constraint causing the estimated
HRF to be equal across events/conditions, yet permitting it to be different
across voxels. Model estimation leads to an optimization problem that we
propose to solve with an efficient quasi-Newton method exploiting fast gradient
computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be
extended to the setting of GLM with separate designs which has been shown to
improve decoding accuracy in brain activity decoding experiments. We compare 10
different HRF modeling methods in terms of encoding and decoding score in two
different datasets. Our results show that the R1-GLM model significantly
outperforms competing methods in both encoding and decoding settings,
positioning it as an attractive method both from the points of view of accuracy
and computational efficiency.
|
[
{
"created": "Thu, 27 Feb 2014 18:50:58 GMT",
"version": "v1"
},
{
"created": "Sun, 6 Apr 2014 06:11:17 GMT",
"version": "v2"
},
{
"created": "Tue, 15 Jul 2014 11:14:00 GMT",
"version": "v3"
},
{
"created": "Mon, 6 Oct 2014 16:39:55 GMT",
"version": "v4"
},
{
"created": "Fri, 31 Oct 2014 13:47:01 GMT",
"version": "v5"
},
{
"created": "Fri, 7 Nov 2014 11:27:19 GMT",
"version": "v6"
}
] |
2014-11-10
|
[
[
"Pedregosa",
"Fabian",
"",
"INRIA Saclay - Ile de France, INRIA Paris -\n Rocquencourt"
],
[
"Eickenberg",
"Michael",
"",
"INRIA Saclay - Ile de France, LNAO"
],
[
"Ciuciu",
"Philippe",
"",
"INRIA Saclay - Ile de France, NEUROSPIN"
],
[
"Thirion",
"Bertrand",
"",
"INRIA Saclay - Ile de France, NEUROSPIN"
],
[
"Gramfort",
"Alexandre",
"",
"LTCI"
]
] |
Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large. We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels. Model estimation leads to an optimization problem that we propose to solve with an efficient quasi-Newton method exploiting fast gradient computations. This model, called GLM with Rank-1 constraint (R1-GLM), can be extended to the setting of GLM with separate designs which has been shown to improve decoding accuracy in brain activity decoding experiments. We compare 10 different HRF modeling methods in terms of encoding and decoding score in two different datasets. Our results show that the R1-GLM model significantly outperforms competing methods in both encoding and decoding settings, positioning it as an attractive method both from the points of view of accuracy and computational efficiency.
|
2312.12891
|
Steven James
|
William Hill, Ireton Liu, Anita De Mello Koch, Damion Harvey, Nishanth
Kumar, George Konidaris, Steven James
|
MinePlanner: A Benchmark for Long-Horizon Planning in Large Minecraft
Worlds
|
Accepted to the 6th ICAPS Workshop on the International Planning
Competition (WIPC 2024)
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a new benchmark for planning tasks based on the Minecraft game.
Our benchmark contains 45 tasks overall, but also provides support for creating
both propositional and numeric instances of new Minecraft tasks automatically.
We benchmark numeric and propositional planning systems on these tasks, with
results demonstrating that state-of-the-art planners are currently incapable of
dealing with many of the challenges advanced by our new benchmark, such as
scaling to instances with thousands of objects. Based on these results, we
identify areas of improvement for future planners. Our framework is made
available at https://github.com/IretonLiu/mine-pddl/.
|
[
{
"created": "Wed, 20 Dec 2023 10:04:39 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Apr 2024 11:22:36 GMT",
"version": "v2"
}
] |
2024-04-30
|
[
[
"Hill",
"William",
""
],
[
"Liu",
"Ireton",
""
],
[
"Koch",
"Anita De Mello",
""
],
[
"Harvey",
"Damion",
""
],
[
"Kumar",
"Nishanth",
""
],
[
"Konidaris",
"George",
""
],
[
"James",
"Steven",
""
]
] |
We propose a new benchmark for planning tasks based on the Minecraft game. Our benchmark contains 45 tasks overall, but also provides support for creating both propositional and numeric instances of new Minecraft tasks automatically. We benchmark numeric and propositional planning systems on these tasks, with results demonstrating that state-of-the-art planners are currently incapable of dealing with many of the challenges advanced by our new benchmark, such as scaling to instances with thousands of objects. Based on these results, we identify areas of improvement for future planners. Our framework is made available at https://github.com/IretonLiu/mine-pddl/.
|
2212.08872
|
Salman Mohebi
|
Salman Mohebi, Andrea Zanella and Michele Zorzi
|
Pilot Reuse in Cell-Free Massive MIMO Systems: A Diverse Clustering
Approach
|
29 pages, 9 figures, submitted to IEEE
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Distributed or Cell-free (CF) massive Multiple-Input, Multiple-Output
(mMIMO), has been recently proposed as an answer to the limitations of the
current network-centric systems in providing high-rate ubiquitous transmission.
The capability of providing uniform service level makes CF mMIMO a potential
technology for beyond-5G and 6G networks. The acquisition of accurate Channel
State Information (CSI) is critical for different CF mMIMO operations. Hence,
an uplink pilot training phase is used to efficiently estimate transmission
channels. The number of available orthogonal pilot signals is limited, and
reusing these pilots will increase co-pilot interference. This causes an
undesirable effect known as pilot contamination that could reduce the system
performance. Hence, a proper pilot reuse strategy is needed to mitigate the
effects of pilot contamination. In this paper, we formulate pilot assignment in
CF mMIMO as a diverse clustering problem and propose an iterative maxima search
scheme to solve it. In this approach, we first form the clusters of User
Equipments (UEs) so that the intra-cluster diversity maximizes and then assign
the same pilots for all UEs in the same cluster. The numerical results show the
proposed techniques' superiority over other methods concerning the achieved
uplink and downlink average and per-user data rate.
|
[
{
"created": "Sat, 17 Dec 2022 13:56:49 GMT",
"version": "v1"
}
] |
2022-12-20
|
[
[
"Mohebi",
"Salman",
""
],
[
"Zanella",
"Andrea",
""
],
[
"Zorzi",
"Michele",
""
]
] |
Distributed or Cell-free (CF) massive Multiple-Input, Multiple-Output (mMIMO), has been recently proposed as an answer to the limitations of the current network-centric systems in providing high-rate ubiquitous transmission. The capability of providing uniform service level makes CF mMIMO a potential technology for beyond-5G and 6G networks. The acquisition of accurate Channel State Information (CSI) is critical for different CF mMIMO operations. Hence, an uplink pilot training phase is used to efficiently estimate transmission channels. The number of available orthogonal pilot signals is limited, and reusing these pilots will increase co-pilot interference. This causes an undesirable effect known as pilot contamination that could reduce the system performance. Hence, a proper pilot reuse strategy is needed to mitigate the effects of pilot contamination. In this paper, we formulate pilot assignment in CF mMIMO as a diverse clustering problem and propose an iterative maxima search scheme to solve it. In this approach, we first form the clusters of User Equipments (UEs) so that the intra-cluster diversity maximizes and then assign the same pilots for all UEs in the same cluster. The numerical results show the proposed techniques' superiority over other methods concerning the achieved uplink and downlink average and per-user data rate.
|
1807.03165
|
Jeremy Kepner
|
Jeremy Kepner, Vijay Gadepally, Hayden Jananthan, Lauren Milechin, Sid
Samsi
|
Sparse Deep Neural Network Exact Solutions
|
8 pages, 10 figures, accepted to IEEE HPEC 2018. arXiv admin note:
text overlap with arXiv:1708.02937
| null |
10.1109/HPEC.2018.8547742
| null |
cs.LG cs.CV cs.NE stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep neural networks (DNNs) have emerged as key enablers of machine learning.
Applying larger DNNs to more diverse applications is an important challenge.
The computations performed during DNN training and inference are dominated by
operations on the weight matrices describing the DNN. As DNNs incorporate more
layers and more neurons per layers, these weight matrices may be required to be
sparse because of memory limitations. Sparse DNNs are one possible approach,
but the underlying theory is in the early stages of development and presents a
number of challenges, including determining the accuracy of inference and
selecting nonzero weights for training. Associative array algebra has been
developed by the big data community to combine and extend database, matrix, and
graph/network concepts for use in large, sparse data problems. Applying this
mathematics to DNNs simplifies the formulation of DNN mathematics and reveals
that DNNs are linear over oscillating semirings. This work uses associative
array DNNs to construct exact solutions and corresponding perturbation models
to the rectified linear unit (ReLU) DNN equations that can be used to construct
test vectors for sparse DNN implementations over various precisions. These
solutions can be used for DNN verification, theoretical explorations of DNN
properties, and a starting point for the challenge of sparse training.
|
[
{
"created": "Fri, 6 Jul 2018 00:47:12 GMT",
"version": "v1"
}
] |
2018-12-17
|
[
[
"Kepner",
"Jeremy",
""
],
[
"Gadepally",
"Vijay",
""
],
[
"Jananthan",
"Hayden",
""
],
[
"Milechin",
"Lauren",
""
],
[
"Samsi",
"Sid",
""
]
] |
Deep neural networks (DNNs) have emerged as key enablers of machine learning. Applying larger DNNs to more diverse applications is an important challenge. The computations performed during DNN training and inference are dominated by operations on the weight matrices describing the DNN. As DNNs incorporate more layers and more neurons per layers, these weight matrices may be required to be sparse because of memory limitations. Sparse DNNs are one possible approach, but the underlying theory is in the early stages of development and presents a number of challenges, including determining the accuracy of inference and selecting nonzero weights for training. Associative array algebra has been developed by the big data community to combine and extend database, matrix, and graph/network concepts for use in large, sparse data problems. Applying this mathematics to DNNs simplifies the formulation of DNN mathematics and reveals that DNNs are linear over oscillating semirings. This work uses associative array DNNs to construct exact solutions and corresponding perturbation models to the rectified linear unit (ReLU) DNN equations that can be used to construct test vectors for sparse DNN implementations over various precisions. These solutions can be used for DNN verification, theoretical explorations of DNN properties, and a starting point for the challenge of sparse training.
|
2402.09164
|
Ruoyu Chen
|
Ruoyu Chen, Hua Zhang, Siyuan Liang, Jingzhi Li, Xiaochun Cao
|
Less is More: Fewer Interpretable Region via Submodular Subset Selection
|
Accepted to ICLR 2024 (Oral)
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image attribution algorithms aim to identify important regions that are
highly relevant to model decisions. Although existing attribution solutions can
effectively assign importance to target elements, they still face the following
challenges: 1) existing attribution methods generate inaccurate small regions
thus misleading the direction of correct attribution, and 2) the model cannot
produce good attribution results for samples with wrong predictions. To address
the above challenges, this paper re-models the above image attribution problem
as a submodular subset selection problem, aiming to enhance model
interpretability using fewer regions. To address the lack of attention to local
regions, we construct a novel submodular function to discover more accurate
small interpretation regions. To enhance the attribution effect for all
samples, we also impose four different constraints on the selection of
sub-regions, i.e., confidence, effectiveness, consistency, and collaboration
scores, to assess the importance of various subsets. Moreover, our theoretical
analysis substantiates that the proposed function is in fact submodular.
Extensive experiments show that the proposed method outperforms SOTA methods on
two face datasets (Celeb-A and VGG-Face2) and one fine-grained dataset
(CUB-200-2011). For correctly predicted samples, the proposed method improves
the Deletion and Insertion scores with an average of 4.9% and 2.5% gain
relative to HSIC-Attribution. For incorrectly predicted samples, our method
achieves gains of 81.0% and 18.4% compared to the HSIC-Attribution algorithm in
the average highest confidence and Insertion score respectively. The code is
released at https://github.com/RuoyuChen10/SMDL-Attribution.
|
[
{
"created": "Wed, 14 Feb 2024 13:30:02 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Feb 2024 03:29:41 GMT",
"version": "v2"
}
] |
2024-03-01
|
[
[
"Chen",
"Ruoyu",
""
],
[
"Zhang",
"Hua",
""
],
[
"Liang",
"Siyuan",
""
],
[
"Li",
"Jingzhi",
""
],
[
"Cao",
"Xiaochun",
""
]
] |
Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face the following challenges: 1) existing attribution methods generate inaccurate small regions thus misleading the direction of correct attribution, and 2) the model cannot produce good attribution results for samples with wrong predictions. To address the above challenges, this paper re-models the above image attribution problem as a submodular subset selection problem, aiming to enhance model interpretability using fewer regions. To address the lack of attention to local regions, we construct a novel submodular function to discover more accurate small interpretation regions. To enhance the attribution effect for all samples, we also impose four different constraints on the selection of sub-regions, i.e., confidence, effectiveness, consistency, and collaboration scores, to assess the importance of various subsets. Moreover, our theoretical analysis substantiates that the proposed function is in fact submodular. Extensive experiments show that the proposed method outperforms SOTA methods on two face datasets (Celeb-A and VGG-Face2) and one fine-grained dataset (CUB-200-2011). For correctly predicted samples, the proposed method improves the Deletion and Insertion scores with an average of 4.9% and 2.5% gain relative to HSIC-Attribution. For incorrectly predicted samples, our method achieves gains of 81.0% and 18.4% compared to the HSIC-Attribution algorithm in the average highest confidence and Insertion score respectively. The code is released at https://github.com/RuoyuChen10/SMDL-Attribution.
|
2103.07241
|
Giovani Guizzo
|
Giovani Guizzo, Federica Sarro, Jens Krinke, Silvia Regina Vergilio
|
Sentinel: A Hyper-Heuristic for the Generation of Mutant Reduction
Strategies
|
in IEEE Transactions on Software Engineering
| null |
10.1109/TSE.2020.3002496
| null |
cs.SE cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mutation testing is an effective approach to evaluate and strengthen software
test suites, but its adoption is currently limited by the mutants' execution
computational cost. Several strategies have been proposed to reduce this cost
(a.k.a. mutation cost reduction strategies), however none of them has proven to
be effective for all scenarios since they often need an ad-hoc manual selection
and configuration depending on the software under test (SUT). In this paper, we
propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed
Sentinel, to automate the generation of optimal cost reduction strategies for
every new SUT. We evaluate Sentinel by carrying out a thorough empirical study
involving 40 releases of 10 open-source real-world software systems and both
baseline and state-of-the-art strategies as a benchmark. We execute a total of
4,800 experiments, and evaluate their results with both quality indicators and
statistical significance tests, following the most recent best practice in the
literature. The results show that strategies generated by Sentinel outperform
the baseline strategies in 95% of the cases always with large effect sizes.
They also obtain statistically significantly better results than
state-of-the-art strategies in 88% of the cases, with large effect sizes for
95% of them. Also, our study reveals that the mutation strategies generated by
Sentinel for a given software version can be used without any loss in quality
for subsequently developed versions in 95% of the cases. These results show
that Sentinel is able to automatically generate mutation strategies that reduce
mutation testing cost without affecting its testing effectiveness (i.e.
mutation score), thus taking off from the tester's shoulders the burden of
manually selecting and configuring strategies for each SUT.
|
[
{
"created": "Fri, 12 Mar 2021 12:38:51 GMT",
"version": "v1"
}
] |
2021-03-15
|
[
[
"Guizzo",
"Giovani",
""
],
[
"Sarro",
"Federica",
""
],
[
"Krinke",
"Jens",
""
],
[
"Vergilio",
"Silvia Regina",
""
]
] |
Mutation testing is an effective approach to evaluate and strengthen software test suites, but its adoption is currently limited by the mutants' execution computational cost. Several strategies have been proposed to reduce this cost (a.k.a. mutation cost reduction strategies), however none of them has proven to be effective for all scenarios since they often need an ad-hoc manual selection and configuration depending on the software under test (SUT). In this paper, we propose a novel multi-objective evolutionary hyper-heuristic approach, dubbed Sentinel, to automate the generation of optimal cost reduction strategies for every new SUT. We evaluate Sentinel by carrying out a thorough empirical study involving 40 releases of 10 open-source real-world software systems and both baseline and state-of-the-art strategies as a benchmark. We execute a total of 4,800 experiments, and evaluate their results with both quality indicators and statistical significance tests, following the most recent best practice in the literature. The results show that strategies generated by Sentinel outperform the baseline strategies in 95% of the cases always with large effect sizes. They also obtain statistically significantly better results than state-of-the-art strategies in 88% of the cases, with large effect sizes for 95% of them. Also, our study reveals that the mutation strategies generated by Sentinel for a given software version can be used without any loss in quality for subsequently developed versions in 95% of the cases. These results show that Sentinel is able to automatically generate mutation strategies that reduce mutation testing cost without affecting its testing effectiveness (i.e. mutation score), thus taking off from the tester's shoulders the burden of manually selecting and configuring strategies for each SUT.
|
1605.04344
|
Ludovic Righetti
|
Brahayam Ponton, Stefan Schaal, Ludovic Righetti
|
On the Effects of Measurement Uncertainty in Optimal Control of Contact
Interactions
|
17 pages, 5 figures - this version is the one published at WAFR 2016
to fulfill the open access requirements of the EU commission, please refer to
the previous version for the complete derivation of the algorithm
| null |
10.1007/978-3-030-43089-4_50
| null |
cs.SY cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Stochastic Optimal Control (SOC) typically considers noise only in the
process model, i.e. unknown disturbances. However, in many robotic applications
involving interaction with the environment, such as locomotion and
manipulation, uncertainty also comes from lack of precise knowledge of the
world, which is not an actual disturbance. We analyze the effects of also
considering noise in the measurement model, by developing a SOC algorithm based
on risk-sensitive control, that includes the dynamics of an observer in such a
way that the control law explicitly depends on the current measurement
uncertainty. In simulation results on a simple 2D manipulator, we have observed
that measurement uncertainty leads to low impedance behaviors, a result in
contrast with the effects of process noise that creates stiff behaviors. This
suggests that taking into account measurement uncertainty could be a
potentially very interesting way to approach problems involving uncertain
contact interactions.
|
[
{
"created": "Fri, 13 May 2016 22:12:10 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Jan 2018 18:01:59 GMT",
"version": "v2"
},
{
"created": "Sat, 5 Jun 2021 19:48:03 GMT",
"version": "v3"
}
] |
2021-06-08
|
[
[
"Ponton",
"Brahayam",
""
],
[
"Schaal",
"Stefan",
""
],
[
"Righetti",
"Ludovic",
""
]
] |
Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications involving interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of precise knowledge of the world, which is not an actual disturbance. We analyze the effects of also considering noise in the measurement model, by developing a SOC algorithm based on risk-sensitive control, that includes the dynamics of an observer in such a way that the control law explicitly depends on the current measurement uncertainty. In simulation results on a simple 2D manipulator, we have observed that measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise that creates stiff behaviors. This suggests that taking into account measurement uncertainty could be a potentially very interesting way to approach problems involving uncertain contact interactions.
|
1611.01148
|
Ted Alcorn
|
John W. Ayers (San Diego State University), Benjamin M. Althouse
(Santa Fe Institute), Eric C. Leas (UC San Diego), Ted Alcorn (Everytown for
Gun Safety), Mark Dredze (Johns Hopkins University)
|
Can Big Media Data Revolutionarize Gun Violence Prevention?
|
Presented at the Data For Good Exchange 2016
| null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The scientific method drives improvements in public health, but a strategy of
obstructionism has impeded scientists from gathering even a minimal amount of
information to address America's gun violence epidemic. We argue that in spite
of a lack of federal investment, large amounts of publicly available data offer
scientists an opportunity to measure a range of firearm-related behaviors.
Given the diversity of available data - including news coverage, social media,
web forums, online advertisements, and Internet searches (to name a few) -
there are ample opportunities for scientists to study everything from trends in
particular types of gun violence to gun-related behaviors (such as purchases
and safety practices) to public understanding of and sentiment towards various
gun violence reduction measures. Science has been sidelined in the gun violence
debate for too long. Scientists must tap the big media data stream and help
resolve this crisis.
|
[
{
"created": "Thu, 3 Nov 2016 19:52:00 GMT",
"version": "v1"
}
] |
2016-11-04
|
[
[
"Ayers",
"John W.",
"",
"San Diego State University"
],
[
"Althouse",
"Benjamin M.",
"",
"Santa Fe Institute"
],
[
"Leas",
"Eric C.",
"",
"UC San Diego"
],
[
"Alcorn",
"Ted",
"",
"Everytown for\n Gun Safety"
],
[
"Dredze",
"Mark",
"",
"Johns Hopkins University"
]
] |
The scientific method drives improvements in public health, but a strategy of obstructionism has impeded scientists from gathering even a minimal amount of information to address America's gun violence epidemic. We argue that in spite of a lack of federal investment, large amounts of publicly available data offer scientists an opportunity to measure a range of firearm-related behaviors. Given the diversity of available data - including news coverage, social media, web forums, online advertisements, and Internet searches (to name a few) - there are ample opportunities for scientists to study everything from trends in particular types of gun violence to gun-related behaviors (such as purchases and safety practices) to public understanding of and sentiment towards various gun violence reduction measures. Science has been sidelined in the gun violence debate for too long. Scientists must tap the big media data stream and help resolve this crisis.
|
1904.00742
|
Renato Krohling
|
Giuliano L. Manso, Helder Knidel, Renato A. Krohling, Jose A. Ventura
|
A smartphone application to detection and classification of coffee leaf
miner and coffee leaf rust
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generally, the identification and classification of plant diseases and/or
pests are performed by an expert . One of the problems facing coffee farmers in
Brazil is crop infestation, particularly by leaf rust Hemileia vastatrix and
leaf miner Leucoptera coffeella. The progression of the diseases and or pests
occurs spatially and temporarily. So, it is very important to automatically
identify the degree of severity. The main goal of this article consists on the
development of a method and its i implementation as an App that allow the
detection of the foliar damages from images of coffee leaf that are captured
using a smartphone, and identify whether it is rust or leaf miner, and in turn
the calculation of its severity degree. The method consists of identifying a
leaf from the image and separates it from the background with the use of a
segmentation algorithm. In the segmentation process, various types of
backgrounds for the image using the HSV and YCbCr color spaces are tested. In
the segmentation of foliar damages, the Otsu algorithm and the iterative
threshold algorithm, in the YCgCr color space, have been used and compared to
k-means. Next, features of the segmented foliar damages are calculated. For the
classification, artificial neural network trained with extreme learning machine
have been used. The results obtained shows the feasibility and effectiveness of
the approach to identify and classify foliar damages, and the automatic
calculation of the severity. The results obtained are very promising according
to experts.
|
[
{
"created": "Tue, 19 Mar 2019 21:45:47 GMT",
"version": "v1"
}
] |
2019-04-02
|
[
[
"Manso",
"Giuliano L.",
""
],
[
"Knidel",
"Helder",
""
],
[
"Krohling",
"Renato A.",
""
],
[
"Ventura",
"Jose A.",
""
]
] |
Generally, the identification and classification of plant diseases and/or pests are performed by an expert . One of the problems facing coffee farmers in Brazil is crop infestation, particularly by leaf rust Hemileia vastatrix and leaf miner Leucoptera coffeella. The progression of the diseases and or pests occurs spatially and temporarily. So, it is very important to automatically identify the degree of severity. The main goal of this article consists on the development of a method and its i implementation as an App that allow the detection of the foliar damages from images of coffee leaf that are captured using a smartphone, and identify whether it is rust or leaf miner, and in turn the calculation of its severity degree. The method consists of identifying a leaf from the image and separates it from the background with the use of a segmentation algorithm. In the segmentation process, various types of backgrounds for the image using the HSV and YCbCr color spaces are tested. In the segmentation of foliar damages, the Otsu algorithm and the iterative threshold algorithm, in the YCgCr color space, have been used and compared to k-means. Next, features of the segmented foliar damages are calculated. For the classification, artificial neural network trained with extreme learning machine have been used. The results obtained shows the feasibility and effectiveness of the approach to identify and classify foliar damages, and the automatic calculation of the severity. The results obtained are very promising according to experts.
|
1502.07979
|
Anastasios Noulas Anastasios Noulas
|
Anastasios Noulas, Blake Shaw, Renaud Lambiotte, Cecilia Mascolo
|
Topological Properties and Temporal Dynamics of Place Networks in Urban
Environments
| null | null | null | null |
cs.SI physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Understanding the spatial networks formed by the trajectories of mobile users
can be beneficial to applications ranging from epidemiology to local search.
Despite the potential for impact in a number of fields, several aspects of
human mobility networks remain largely unexplored due to the lack of
large-scale data at a fine spatiotemporal resolution. Using a longitudinal
dataset from the location-based service Foursquare, we perform an empirical
analysis of the topological properties of place networks and note their
resemblance to online social networks in terms of heavy-tailed degree
distributions, triadic closure mechanisms and the small world property. Unlike
social networks however, place networks present a mixture of connectivity
trends in terms of assortativity that are surprisingly similar to those of the
web graph. We take advantage of additional semantic information to interpret
how nodes that take on functional roles such as `travel hub', or `food spot'
behave in these networks. Finally, motivated by the large volume of new links
appearing in place networks over time, we formulate the classic link prediction
problem in this new domain. We propose a novel variant of gravity models that
brings together three essential elements of inter-place connectivity in urban
environments: network-level interactions, human mobility dynamics, and
geographic distance. We evaluate this model and find it outperforms a number of
baseline predictors and supervised learning algorithms on a task of predicting
new links in a sample of one hundred popular cities.
|
[
{
"created": "Fri, 27 Feb 2015 17:30:16 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Mar 2015 14:03:02 GMT",
"version": "v2"
}
] |
2015-03-18
|
[
[
"Noulas",
"Anastasios",
""
],
[
"Shaw",
"Blake",
""
],
[
"Lambiotte",
"Renaud",
""
],
[
"Mascolo",
"Cecilia",
""
]
] |
Understanding the spatial networks formed by the trajectories of mobile users can be beneficial to applications ranging from epidemiology to local search. Despite the potential for impact in a number of fields, several aspects of human mobility networks remain largely unexplored due to the lack of large-scale data at a fine spatiotemporal resolution. Using a longitudinal dataset from the location-based service Foursquare, we perform an empirical analysis of the topological properties of place networks and note their resemblance to online social networks in terms of heavy-tailed degree distributions, triadic closure mechanisms and the small world property. Unlike social networks however, place networks present a mixture of connectivity trends in terms of assortativity that are surprisingly similar to those of the web graph. We take advantage of additional semantic information to interpret how nodes that take on functional roles such as `travel hub', or `food spot' behave in these networks. Finally, motivated by the large volume of new links appearing in place networks over time, we formulate the classic link prediction problem in this new domain. We propose a novel variant of gravity models that brings together three essential elements of inter-place connectivity in urban environments: network-level interactions, human mobility dynamics, and geographic distance. We evaluate this model and find it outperforms a number of baseline predictors and supervised learning algorithms on a task of predicting new links in a sample of one hundred popular cities.
|
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