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2012.00150 | Hanchen Xie | Hanchen Xie, Mohamed E. Hussein, Aram Galstyan, Wael Abd-Almageed | MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent
Unsupervised Learning Using Mutual Information Maximization | 10 pages, 3 figures, Accepted to WACV2021 | null | null | null | cs.LG cs.CV | http://creativecommons.org/licenses/by/4.0/ | Deep neural networks are powerful, massively parameterized machine learning
models that have been shown to perform well in supervised learning tasks.
However, very large amounts of labeled data are usually needed to train deep
neural networks. Several semi-supervised learning approaches have been proposed
to train neural networks using smaller amounts of labeled data with a large
amount of unlabeled data. The performance of these semi-supervised methods
significantly degrades as the size of labeled data decreases. We introduce
Mutual-information-based Unsupervised & Semi-supervised Concurrent LEarning
(MUSCLE), a hybrid learning approach that uses mutual information to combine
both unsupervised and semi-supervised learning. MUSCLE can be used as a
stand-alone training scheme for neural networks, and can also be incorporated
into other learning approaches. We show that the proposed hybrid model
outperforms state of the art on several standard benchmarks, including
CIFAR-10, CIFAR-100, and Mini-Imagenet. Furthermore, the performance gain
consistently increases with the reduction in the amount of labeled data, as
well as in the presence of bias. We also show that MUSCLE has the potential to
boost the classification performance when used in the fine-tuning phase for a
model pre-trained only on unlabeled data.
| [
{
"created": "Mon, 30 Nov 2020 23:01:04 GMT",
"version": "v1"
}
] | 2020-12-02 | [
[
"Xie",
"Hanchen",
""
],
[
"Hussein",
"Mohamed E.",
""
],
[
"Galstyan",
"Aram",
""
],
[
"Abd-Almageed",
"Wael",
""
]
] | Deep neural networks are powerful, massively parameterized machine learning models that have been shown to perform well in supervised learning tasks. However, very large amounts of labeled data are usually needed to train deep neural networks. Several semi-supervised learning approaches have been proposed to train neural networks using smaller amounts of labeled data with a large amount of unlabeled data. The performance of these semi-supervised methods significantly degrades as the size of labeled data decreases. We introduce Mutual-information-based Unsupervised & Semi-supervised Concurrent LEarning (MUSCLE), a hybrid learning approach that uses mutual information to combine both unsupervised and semi-supervised learning. MUSCLE can be used as a stand-alone training scheme for neural networks, and can also be incorporated into other learning approaches. We show that the proposed hybrid model outperforms state of the art on several standard benchmarks, including CIFAR-10, CIFAR-100, and Mini-Imagenet. Furthermore, the performance gain consistently increases with the reduction in the amount of labeled data, as well as in the presence of bias. We also show that MUSCLE has the potential to boost the classification performance when used in the fine-tuning phase for a model pre-trained only on unlabeled data. |
2404.08985 | Yijiang Liu | Yijiang Liu, Rongyu Zhang, Huanrui Yang, Kurt Keutzer, Yuan Du, Li Du,
Shanghang Zhang | Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient
Finetuning | 13 pages, 5 figures | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have demonstrated significant potential in
performing multiple tasks in multimedia applications, ranging from content
generation to interactive entertainment, and artistic creation. However, the
diversity of downstream tasks in multitask scenarios presents substantial
adaptation challenges for LLMs. While traditional methods often succumb to
knowledge confusion on their monolithic dense models, Mixture-of-Experts (MoE)
has been emerged as a promising solution with its sparse architecture for
effective task decoupling. Inspired by the principles of human cognitive
neuroscience, we design a novel framework \texttt{Intuition-MoR1E} that
leverages the inherent semantic clustering of instances to mimic the human
brain to deal with multitask, offering implicit guidance to router for
optimized feature allocation. Moreover, we introduce cutting-edge Rank-1
Experts formulation designed to manage a spectrum of intuitions, demonstrating
enhanced parameter efficiency and effectiveness in multitask LLM finetuning.
Extensive experiments demonstrate that Intuition-MoR1E achieves superior
efficiency and 2.15\% overall accuracy improvement across 14 public datasets
against other state-of-the-art baselines.
| [
{
"created": "Sat, 13 Apr 2024 12:14:58 GMT",
"version": "v1"
}
] | 2024-04-16 | [
[
"Liu",
"Yijiang",
""
],
[
"Zhang",
"Rongyu",
""
],
[
"Yang",
"Huanrui",
""
],
[
"Keutzer",
"Kurt",
""
],
[
"Du",
"Yuan",
""
],
[
"Du",
"Li",
""
],
[
"Zhang",
"Shanghang",
""
]
] | Large Language Models (LLMs) have demonstrated significant potential in performing multiple tasks in multimedia applications, ranging from content generation to interactive entertainment, and artistic creation. However, the diversity of downstream tasks in multitask scenarios presents substantial adaptation challenges for LLMs. While traditional methods often succumb to knowledge confusion on their monolithic dense models, Mixture-of-Experts (MoE) has been emerged as a promising solution with its sparse architecture for effective task decoupling. Inspired by the principles of human cognitive neuroscience, we design a novel framework \texttt{Intuition-MoR1E} that leverages the inherent semantic clustering of instances to mimic the human brain to deal with multitask, offering implicit guidance to router for optimized feature allocation. Moreover, we introduce cutting-edge Rank-1 Experts formulation designed to manage a spectrum of intuitions, demonstrating enhanced parameter efficiency and effectiveness in multitask LLM finetuning. Extensive experiments demonstrate that Intuition-MoR1E achieves superior efficiency and 2.15\% overall accuracy improvement across 14 public datasets against other state-of-the-art baselines. |
2004.00517 | Christoph G\"unther | Christoph G\"unther, Michael G\"unther, Daniel G\"unther | Tracing Contacts to Control the COVID-19 Pandemic | 5 pages, no figures | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The control of the COVID-19 pandemic requires a considerable reduction of
contacts mostly achieved by imposing movement control up to the level of
enforced quarantine. This has lead to a collapse of substantial parts of the
economy. Carriers of the disease are infectious roughly 3 days after exposure
to the virus. First symptoms occur later or not at all. As a consequence
tracing the contacts of people identified as carriers is essential for
controlling the pandemic. This tracing must work everywhere, in particular
indoors, where people are closest to each other. Furthermore, it should respect
people's privacy. The present paper presents a method to enable a thorough
traceability with very little risk on privacy. In our opinion, the latter
capabilities are necessary to control the pandemic during a future relaunch of
our economy.
| [
{
"created": "Wed, 1 Apr 2020 15:40:48 GMT",
"version": "v1"
}
] | 2020-04-02 | [
[
"Günther",
"Christoph",
""
],
[
"Günther",
"Michael",
""
],
[
"Günther",
"Daniel",
""
]
] | The control of the COVID-19 pandemic requires a considerable reduction of contacts mostly achieved by imposing movement control up to the level of enforced quarantine. This has lead to a collapse of substantial parts of the economy. Carriers of the disease are infectious roughly 3 days after exposure to the virus. First symptoms occur later or not at all. As a consequence tracing the contacts of people identified as carriers is essential for controlling the pandemic. This tracing must work everywhere, in particular indoors, where people are closest to each other. Furthermore, it should respect people's privacy. The present paper presents a method to enable a thorough traceability with very little risk on privacy. In our opinion, the latter capabilities are necessary to control the pandemic during a future relaunch of our economy. |
2407.16889 | Modan Tailleur | Modan Tailleur (LS2N), Pierre Aumond (UMRAE), Vincent Tourre (AAU),
Mathieu Lagrange (LS2N) | Towards better visualizations of urban sound environments: insights from
interviews | null | INTERNOISE 2024, Aug 2024, Nantes (France), France | null | null | cs.CY cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Urban noise maps and noise visualizations traditionally provide macroscopic
representations of noise levels across cities. However, those representations
fail at accurately gauging the sound perception associated with these sound
environments, as perception highly depends on the sound sources involved. This
paper aims at analyzing the need for the representations of sound sources, by
identifying the urban stakeholders for whom such representations are assumed to
be of importance. Through spoken interviews with various urban stakeholders, we
have gained insight into current practices, the strengths and weaknesses of
existing tools and the relevance of incorporating sound sources into existing
urban sound environment representations. Three distinct use of sound source
representations emerged in this study: 1) noise-related complaints for
industrials and specialized citizens, 2) soundscape quality assessment for
citizens, and 3) guidance for urban planners. Findings also reveal diverse
perspectives for the use of visualizations, which should use indicators adapted
to the target audience, and enable data accessibility.
| [
{
"created": "Tue, 11 Jun 2024 07:39:48 GMT",
"version": "v1"
}
] | 2024-07-25 | [
[
"Tailleur",
"Modan",
"",
"LS2N"
],
[
"Aumond",
"Pierre",
"",
"UMRAE"
],
[
"Tourre",
"Vincent",
"",
"AAU"
],
[
"Lagrange",
"Mathieu",
"",
"LS2N"
]
] | Urban noise maps and noise visualizations traditionally provide macroscopic representations of noise levels across cities. However, those representations fail at accurately gauging the sound perception associated with these sound environments, as perception highly depends on the sound sources involved. This paper aims at analyzing the need for the representations of sound sources, by identifying the urban stakeholders for whom such representations are assumed to be of importance. Through spoken interviews with various urban stakeholders, we have gained insight into current practices, the strengths and weaknesses of existing tools and the relevance of incorporating sound sources into existing urban sound environment representations. Three distinct use of sound source representations emerged in this study: 1) noise-related complaints for industrials and specialized citizens, 2) soundscape quality assessment for citizens, and 3) guidance for urban planners. Findings also reveal diverse perspectives for the use of visualizations, which should use indicators adapted to the target audience, and enable data accessibility. |
2301.09310 | Jinho Lee | Seongyeon Park, Hajin Kim, Tanveer Ahmad, Nauman Ahmed, Zaid Al-Ars,
H. Peter Hofstee, Youngsok Kim, and Jinho Lee | SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence
Alignment on GPUs | Published at IPDPS'22 | null | null | null | cs.DB cs.DC | http://creativecommons.org/licenses/by/4.0/ | Sequence alignment forms an important backbone in many sequencing
applications. A commonly used strategy for sequence alignment is an approximate
string matching with a two-dimensional dynamic programming approach. Although
some prior work has been conducted on GPU acceleration of a sequence alignment,
we identify several shortcomings that limit exploiting the full computational
capability of modern GPUs. This paper presents SaLoBa, a GPU-accelerated
sequence alignment library focused on seed extension. Based on the analysis of
previous work with real-world sequencing data, we propose techniques to exploit
the data locality and improve workload balancing. The experimental results
reveal that SaLoBa significantly improves the seed extension kernel compared to
state-of-the-art GPU-based methods.
| [
{
"created": "Mon, 23 Jan 2023 08:14:40 GMT",
"version": "v1"
}
] | 2023-01-24 | [
[
"Park",
"Seongyeon",
""
],
[
"Kim",
"Hajin",
""
],
[
"Ahmad",
"Tanveer",
""
],
[
"Ahmed",
"Nauman",
""
],
[
"Al-Ars",
"Zaid",
""
],
[
"Hofstee",
"H. Peter",
""
],
[
"Kim",
"Youngsok",
""
],
[
"Lee",
"Jinho",
""
]
] | Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SaLoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve workload balancing. The experimental results reveal that SaLoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods. |
2102.08868 | Fartash Faghri | Fartash Faghri, Sven Gowal, Cristina Vasconcelos, David J. Fleet,
Fabian Pedregosa, Nicolas Le Roux | Bridging the Gap Between Adversarial Robustness and Optimization Bias | New CIFAR-10 experiments and Fourier attack variations | null | null | null | cs.LG cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We demonstrate that the choice of optimizer, neural network architecture, and
regularizer significantly affect the adversarial robustness of linear neural
networks, providing guarantees without the need for adversarial training. To
this end, we revisit a known result linking maximally robust classifiers and
minimum norm solutions, and combine it with recent results on the implicit bias
of optimizers. First, we show that, under certain conditions, it is possible to
achieve both perfect standard accuracy and a certain degree of robustness,
simply by training an overparametrized model using the implicit bias of the
optimization. In that regime, there is a direct relationship between the type
of the optimizer and the attack to which the model is robust. To the best of
our knowledge, this work is the first to study the impact of optimization
methods such as sign gradient descent and proximal methods on adversarial
robustness. Second, we characterize the robustness of linear convolutional
models, showing that they resist attacks subject to a constraint on the
Fourier-$\ell_\infty$ norm. To illustrate these findings we design a novel
Fourier-$\ell_\infty$ attack that finds adversarial examples with controllable
frequencies. We evaluate Fourier-$\ell_\infty$ robustness of
adversarially-trained deep CIFAR-10 models from the standard RobustBench
benchmark and visualize adversarial perturbations.
| [
{
"created": "Wed, 17 Feb 2021 16:58:04 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Jun 2021 15:27:16 GMT",
"version": "v2"
}
] | 2021-06-08 | [
[
"Faghri",
"Fartash",
""
],
[
"Gowal",
"Sven",
""
],
[
"Vasconcelos",
"Cristina",
""
],
[
"Fleet",
"David J.",
""
],
[
"Pedregosa",
"Fabian",
""
],
[
"Roux",
"Nicolas Le",
""
]
] | We demonstrate that the choice of optimizer, neural network architecture, and regularizer significantly affect the adversarial robustness of linear neural networks, providing guarantees without the need for adversarial training. To this end, we revisit a known result linking maximally robust classifiers and minimum norm solutions, and combine it with recent results on the implicit bias of optimizers. First, we show that, under certain conditions, it is possible to achieve both perfect standard accuracy and a certain degree of robustness, simply by training an overparametrized model using the implicit bias of the optimization. In that regime, there is a direct relationship between the type of the optimizer and the attack to which the model is robust. To the best of our knowledge, this work is the first to study the impact of optimization methods such as sign gradient descent and proximal methods on adversarial robustness. Second, we characterize the robustness of linear convolutional models, showing that they resist attacks subject to a constraint on the Fourier-$\ell_\infty$ norm. To illustrate these findings we design a novel Fourier-$\ell_\infty$ attack that finds adversarial examples with controllable frequencies. We evaluate Fourier-$\ell_\infty$ robustness of adversarially-trained deep CIFAR-10 models from the standard RobustBench benchmark and visualize adversarial perturbations. |
2110.02369 | Karl Stratos | Wenzheng Zhang, Wenyue Hua, Karl Stratos | EntQA: Entity Linking as Question Answering | ICLR 2022 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A conventional approach to entity linking is to first find mentions in a
given document and then infer their underlying entities in the knowledge base.
A well-known limitation of this approach is that it requires finding mentions
without knowing their entities, which is unnatural and difficult. We present a
new model that does not suffer from this limitation called EntQA, which stands
for Entity linking as Question Answering. EntQA first proposes candidate
entities with a fast retrieval module, and then scrutinizes the document to
find mentions of each candidate with a powerful reader module. Our approach
combines progress in entity linking with that in open-domain question answering
and capitalizes on pretrained models for dense entity retrieval and reading
comprehension. Unlike in previous works, we do not rely on a mention-candidates
dictionary or large-scale weak supervision. EntQA achieves strong results on
the GERBIL benchmarking platform.
| [
{
"created": "Tue, 5 Oct 2021 21:39:57 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Mar 2022 21:53:43 GMT",
"version": "v2"
}
] | 2022-03-09 | [
[
"Zhang",
"Wenzheng",
""
],
[
"Hua",
"Wenyue",
""
],
[
"Stratos",
"Karl",
""
]
] | A conventional approach to entity linking is to first find mentions in a given document and then infer their underlying entities in the knowledge base. A well-known limitation of this approach is that it requires finding mentions without knowing their entities, which is unnatural and difficult. We present a new model that does not suffer from this limitation called EntQA, which stands for Entity linking as Question Answering. EntQA first proposes candidate entities with a fast retrieval module, and then scrutinizes the document to find mentions of each candidate with a powerful reader module. Our approach combines progress in entity linking with that in open-domain question answering and capitalizes on pretrained models for dense entity retrieval and reading comprehension. Unlike in previous works, we do not rely on a mention-candidates dictionary or large-scale weak supervision. EntQA achieves strong results on the GERBIL benchmarking platform. |
1609.02191 | Chuang Wang | Chuang Wang and Yue M. Lu | Online Learning for Sparse PCA in High Dimensions: Exact Dynamics and
Phase Transitions | 5 pages | null | null | null | cs.IT cond-mat.dis-nn math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the dynamics of an online algorithm for learning a sparse leading
eigenvector from samples generated from a spiked covariance model. This
algorithm combines the classical Oja's method for online PCA with an
element-wise nonlinearity at each iteration to promote sparsity. In the
high-dimensional limit, the joint empirical measure of the underlying sparse
eigenvector and its estimate provided by the algorithm is shown to converge
weakly to a deterministic, measure-valued process. This scaling limit is
characterized as the unique solution of a nonlinear PDE, and it provides exact
information regarding the asymptotic performance of the algorithm. For example,
performance metrics such as the cosine similarity and the misclassification
rate in sparse support recovery can be obtained by examining the limiting
dynamics. A steady-state analysis of the nonlinear PDE also reveals an
interesting phase transition phenomenon. Although our analysis is asymptotic in
nature, numerical simulations show that the theoretical predictions are
accurate for moderate signal dimensions.
| [
{
"created": "Wed, 7 Sep 2016 20:55:38 GMT",
"version": "v1"
}
] | 2016-09-09 | [
[
"Wang",
"Chuang",
""
],
[
"Lu",
"Yue M.",
""
]
] | We study the dynamics of an online algorithm for learning a sparse leading eigenvector from samples generated from a spiked covariance model. This algorithm combines the classical Oja's method for online PCA with an element-wise nonlinearity at each iteration to promote sparsity. In the high-dimensional limit, the joint empirical measure of the underlying sparse eigenvector and its estimate provided by the algorithm is shown to converge weakly to a deterministic, measure-valued process. This scaling limit is characterized as the unique solution of a nonlinear PDE, and it provides exact information regarding the asymptotic performance of the algorithm. For example, performance metrics such as the cosine similarity and the misclassification rate in sparse support recovery can be obtained by examining the limiting dynamics. A steady-state analysis of the nonlinear PDE also reveals an interesting phase transition phenomenon. Although our analysis is asymptotic in nature, numerical simulations show that the theoretical predictions are accurate for moderate signal dimensions. |
2210.01800 | Fengdi Che | Fengdi Che, Xiru Zhu, Doina Precup, David Meger, and Gregory Dudek | Bayesian Q-learning With Imperfect Expert Demonstrations | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Guided exploration with expert demonstrations improves data efficiency for
reinforcement learning, but current algorithms often overuse expert
information. We propose a novel algorithm to speed up Q-learning with the help
of a limited amount of imperfect expert demonstrations. The algorithm avoids
excessive reliance on expert data by relaxing the optimal expert assumption and
gradually reducing the usage of uninformative expert data. Experimentally, we
evaluate our approach on a sparse-reward chain environment and six more
complicated Atari games with delayed rewards. With the proposed methods, we can
achieve better results than Deep Q-learning from Demonstrations (Hester et al.,
2017) in most environments.
| [
{
"created": "Sat, 1 Oct 2022 17:38:19 GMT",
"version": "v1"
}
] | 2022-10-06 | [
[
"Che",
"Fengdi",
""
],
[
"Zhu",
"Xiru",
""
],
[
"Precup",
"Doina",
""
],
[
"Meger",
"David",
""
],
[
"Dudek",
"Gregory",
""
]
] | Guided exploration with expert demonstrations improves data efficiency for reinforcement learning, but current algorithms often overuse expert information. We propose a novel algorithm to speed up Q-learning with the help of a limited amount of imperfect expert demonstrations. The algorithm avoids excessive reliance on expert data by relaxing the optimal expert assumption and gradually reducing the usage of uninformative expert data. Experimentally, we evaluate our approach on a sparse-reward chain environment and six more complicated Atari games with delayed rewards. With the proposed methods, we can achieve better results than Deep Q-learning from Demonstrations (Hester et al., 2017) in most environments. |
2104.00148 | Gonzalo M\'endez Dr | Gonzalo Gabriel M\'endez, Luis Gal\'arraga and Katherine Chiluiza | Showing Academic Performance Predictions during Term Planning: Effects
on Students' Decisions, Behaviors, and Preferences | 17 pages | null | 10.1145/3411764 | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | Course selection is a crucial activity for students as it directly impacts
their workload and performance. It is also time-consuming, prone to
subjectivity, and often carried out based on incomplete information. This task
can, nevertheless, be assisted with computational tools, for instance, by
predicting performance based on historical data. We investigate the effects of
showing grade predictions to students through an interactive visualization
tool. A qualitative study suggests that in the presence of predictions,
students may focus too much on maximizing their performance, to the detriment
of other factors such as the workload. A follow-up quantitative study explored
whether these effects are mitigated by changing how predictions are conveyed.
Our observations suggest the presence of a framing effect that induces students
to put more effort into course selection when faced with more specific
predictions. We discuss these and other findings and outline considerations for
designing better data-driven course selection tools.
| [
{
"created": "Wed, 31 Mar 2021 22:32:21 GMT",
"version": "v1"
}
] | 2021-04-02 | [
[
"Méndez",
"Gonzalo Gabriel",
""
],
[
"Galárraga",
"Luis",
""
],
[
"Chiluiza",
"Katherine",
""
]
] | Course selection is a crucial activity for students as it directly impacts their workload and performance. It is also time-consuming, prone to subjectivity, and often carried out based on incomplete information. This task can, nevertheless, be assisted with computational tools, for instance, by predicting performance based on historical data. We investigate the effects of showing grade predictions to students through an interactive visualization tool. A qualitative study suggests that in the presence of predictions, students may focus too much on maximizing their performance, to the detriment of other factors such as the workload. A follow-up quantitative study explored whether these effects are mitigated by changing how predictions are conveyed. Our observations suggest the presence of a framing effect that induces students to put more effort into course selection when faced with more specific predictions. We discuss these and other findings and outline considerations for designing better data-driven course selection tools. |
1705.09180 | Jingjin Yu | Shuai D. Han, Nicholas M. Stiffler, Athansios Krontiris, Kostas E.
Bekris and Jingjin Yu | High-Quality Tabletop Rearrangement with Overhand Grasps: Hardness
Results and Fast Methods | Updated manuscript | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies the underlying combinatorial structure of a class of
object rearrangement problems, which appear frequently in applications. The
problems involve multiple, similar-geometry objects placed on a flat,
horizontal surface, where a robot can approach them from above and perform
pick-and-place operations to rearrange them. The paper considers both the case
where the start and goal object poses overlap, and where they do not. For
overlapping poses, the primary objective is to minimize the number of
pick-and-place actions and then to minimize the distance traveled by the
end-effector. For the non-overlapping case, the objective is solely to minimize
the end-effector distance. While such problems do not involve all the
complexities of general rearrangement, they remain computationally hard
challenges in both cases. This is shown through two-way reductions between
well-understood, hard combinatorial challenges and these rearrangement
problems. The benefit of the reduction is that there are well studied
algorithms for solving these well-established combinatorial challenges. These
algorithms can be very efficient in practice despite the hardness results. The
paper builds on these reduction results to propose an algorithmic pipeline for
dealing with the rearrangement problems. Experimental evaluation shows that the
proposed pipeline achieves high-quality paths with regards to the optimization
objectives. Furthermore, it exhibits highly desirable scalability as the number
of objects increases in both the overlapping and non-overlapping setups.
| [
{
"created": "Thu, 25 May 2017 13:54:27 GMT",
"version": "v1"
},
{
"created": "Sun, 11 Jun 2017 14:08:43 GMT",
"version": "v2"
},
{
"created": "Tue, 20 Jun 2017 20:21:47 GMT",
"version": "v3"
}
] | 2017-06-22 | [
[
"Han",
"Shuai D.",
""
],
[
"Stiffler",
"Nicholas M.",
""
],
[
"Krontiris",
"Athansios",
""
],
[
"Bekris",
"Kostas E.",
""
],
[
"Yu",
"Jingjin",
""
]
] | This paper studies the underlying combinatorial structure of a class of object rearrangement problems, which appear frequently in applications. The problems involve multiple, similar-geometry objects placed on a flat, horizontal surface, where a robot can approach them from above and perform pick-and-place operations to rearrange them. The paper considers both the case where the start and goal object poses overlap, and where they do not. For overlapping poses, the primary objective is to minimize the number of pick-and-place actions and then to minimize the distance traveled by the end-effector. For the non-overlapping case, the objective is solely to minimize the end-effector distance. While such problems do not involve all the complexities of general rearrangement, they remain computationally hard challenges in both cases. This is shown through two-way reductions between well-understood, hard combinatorial challenges and these rearrangement problems. The benefit of the reduction is that there are well studied algorithms for solving these well-established combinatorial challenges. These algorithms can be very efficient in practice despite the hardness results. The paper builds on these reduction results to propose an algorithmic pipeline for dealing with the rearrangement problems. Experimental evaluation shows that the proposed pipeline achieves high-quality paths with regards to the optimization objectives. Furthermore, it exhibits highly desirable scalability as the number of objects increases in both the overlapping and non-overlapping setups. |
2105.10859 | Dipika Singhania | Dipika Singhania, Rahul Rahaman, Angela Yao | Coarse to Fine Multi-Resolution Temporal Convolutional Network | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Temporal convolutional networks (TCNs) are a commonly used architecture for
temporal video segmentation. TCNs however, tend to suffer from
over-segmentation errors and require additional refinement modules to ensure
smoothness and temporal coherency. In this work, we propose a novel temporal
encoder-decoder to tackle the problem of sequence fragmentation. In particular,
the decoder follows a coarse-to-fine structure with an implicit ensemble of
multiple temporal resolutions. The ensembling produces smoother segmentations
that are more accurate and better-calibrated, bypassing the need for additional
refinement modules. In addition, we enhance our training with a
multi-resolution feature-augmentation strategy to promote robustness to varying
temporal resolutions. Finally, to support our architecture and encourage
further sequence coherency, we propose an action loss that penalizes
misclassifications at the video level. Experiments show that our stand-alone
architecture, together with our novel feature-augmentation strategy and new
loss, outperforms the state-of-the-art on three temporal video segmentation
benchmarks.
| [
{
"created": "Sun, 23 May 2021 06:07:40 GMT",
"version": "v1"
}
] | 2021-05-25 | [
[
"Singhania",
"Dipika",
""
],
[
"Rahaman",
"Rahul",
""
],
[
"Yao",
"Angela",
""
]
] | Temporal convolutional networks (TCNs) are a commonly used architecture for temporal video segmentation. TCNs however, tend to suffer from over-segmentation errors and require additional refinement modules to ensure smoothness and temporal coherency. In this work, we propose a novel temporal encoder-decoder to tackle the problem of sequence fragmentation. In particular, the decoder follows a coarse-to-fine structure with an implicit ensemble of multiple temporal resolutions. The ensembling produces smoother segmentations that are more accurate and better-calibrated, bypassing the need for additional refinement modules. In addition, we enhance our training with a multi-resolution feature-augmentation strategy to promote robustness to varying temporal resolutions. Finally, to support our architecture and encourage further sequence coherency, we propose an action loss that penalizes misclassifications at the video level. Experiments show that our stand-alone architecture, together with our novel feature-augmentation strategy and new loss, outperforms the state-of-the-art on three temporal video segmentation benchmarks. |
1805.12308 | Luliang Jia | Luliang Jia, Yuhua Xu, Youming Sun, Shuo Feng, and Alagan Anpalagan | Stackelberg Game Approaches for Anti-jamming Defence in Wireless
Networks | 8 pages, 6figures, to appear in IEEE Wireless Communications | null | null | null | cs.GT cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article investigates the anti-jamming communications problem in wireless
networks from a Stackelberg game perspective. By exploring and analyzing the
inherent characteristics of the anti-jamming problem, we present and discuss
some technical challenges and fundamental requirements to address them. To be
specific, the adversarial characteristic, incomplete information constraints,
dynamics, uncertainty, dense deployment, and heterogeneous feature bring
technical challenges to anti-jamming communications in wireless networks. Then,
for the purpose of improving system performance, four requirements for
anti-jamming communications are presented and discussed. Following the
advantages of the Stackelberg game model in anti-jamming field, we formulate an
anti-jamming decision-making framework based on the Stackelberg game for
anti-jamming defence in wireless networks. Moreover, two preliminary case
studies are presented and discussed for better understanding of the
anti-jamming Stackelberg game problem. Finally, some future research directions
are also provided.
| [
{
"created": "Thu, 31 May 2018 03:28:57 GMT",
"version": "v1"
}
] | 2018-06-01 | [
[
"Jia",
"Luliang",
""
],
[
"Xu",
"Yuhua",
""
],
[
"Sun",
"Youming",
""
],
[
"Feng",
"Shuo",
""
],
[
"Anpalagan",
"Alagan",
""
]
] | This article investigates the anti-jamming communications problem in wireless networks from a Stackelberg game perspective. By exploring and analyzing the inherent characteristics of the anti-jamming problem, we present and discuss some technical challenges and fundamental requirements to address them. To be specific, the adversarial characteristic, incomplete information constraints, dynamics, uncertainty, dense deployment, and heterogeneous feature bring technical challenges to anti-jamming communications in wireless networks. Then, for the purpose of improving system performance, four requirements for anti-jamming communications are presented and discussed. Following the advantages of the Stackelberg game model in anti-jamming field, we formulate an anti-jamming decision-making framework based on the Stackelberg game for anti-jamming defence in wireless networks. Moreover, two preliminary case studies are presented and discussed for better understanding of the anti-jamming Stackelberg game problem. Finally, some future research directions are also provided. |
1902.10895 | Jordan Malof | Wei Hu, Kyle Bradbury, Jordan M. Malof, Boning Li, Bohao Huang, Artem
Streltsov, K. Sydny Fujita, and Ben Hoen | What you get is not always what you see: pitfalls in solar array
assessment using overhead imagery | 25 pages | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Effective integration planning for small, distributed solar photovoltaic (PV)
arrays into electric power grids requires access to high quality data: the
location and power capacity of individual solar PV arrays. Unfortunately,
national databases of small-scale solar PV do not exist; those that do are
limited in their spatial resolution, typically aggregated up to state or
national levels. While several promising approaches for solar PV detection have
been published, strategies for evaluating the performance of these models are
often highly heterogeneous from study to study. The resulting comparison of
these methods for practical applications for energy assessments becomes
challenging and may imply that the reported performance evaluations are overly
optimistic. The heterogeneity comes in many forms, each of which we explore in
this work: the level of spatial aggregation, the validation of ground truth,
inconsistencies in the training and validation datasets, and the degree of
diversity of the locations and sensors from which the training and validation
data originate. For each, we discuss emerging practices from the literature to
address them or suggest directions of future research. As part of our
investigation, we evaluate solar PV identification performance in two large
regions. Our findings suggest that traditional performance evaluation of the
automated identification of solar PV from satellite imagery may be optimistic
due to common limitations in the validation process. The takeaways from this
work are intended to inform and catalyze the large-scale practical application
of automated solar PV assessment techniques by energy researchers and
professionals.
| [
{
"created": "Thu, 28 Feb 2019 05:10:08 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Jul 2022 22:09:37 GMT",
"version": "v2"
}
] | 2022-07-27 | [
[
"Hu",
"Wei",
""
],
[
"Bradbury",
"Kyle",
""
],
[
"Malof",
"Jordan M.",
""
],
[
"Li",
"Boning",
""
],
[
"Huang",
"Bohao",
""
],
[
"Streltsov",
"Artem",
""
],
[
"Fujita",
"K. Sydny",
""
],
[
"Hoen",
"Ben",
""
]
] | Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations are overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the level of spatial aggregation, the validation of ground truth, inconsistencies in the training and validation datasets, and the degree of diversity of the locations and sensors from which the training and validation data originate. For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions. Our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals. |
2302.09419 | Ce Zhou | Ce Zhou (1), Qian Li (2), Chen Li (2), Jun Yu (3), Yixin Liu (3),
Guangjing Wang (1), Kai Zhang (3), Cheng Ji (2), Qiben Yan (1), Lifang He
(3), Hao Peng (2), Jianxin Li (2), Jia Wu (4), Ziwei Liu (5), Pengtao Xie
(6), Caiming Xiong (7), Jian Pei (8), Philip S. Yu (9), Lichao Sun (3) ((1)
Michigan State University, (2) Beihang University, (3) Lehigh University, (4)
Macquarie University, (5) Nanyang Technological University, (6) University of
California San Diego, (7) Salesforce AI Research, (8) Duke University, (9)
University of Illinois at Chicago) | A Comprehensive Survey on Pretrained Foundation Models: A History from
BERT to ChatGPT | 99 pages, 16 figures | null | null | null | cs.AI cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pretrained Foundation Models (PFMs) are regarded as the foundation for
various downstream tasks with different data modalities. A PFM (e.g., BERT,
ChatGPT, and GPT-4) is trained on large-scale data which provides a reasonable
parameter initialization for a wide range of downstream applications. BERT
learns bidirectional encoder representations from Transformers, which are
trained on large datasets as contextual language models. Similarly, the
generative pretrained transformer (GPT) method employs Transformers as the
feature extractor and is trained using an autoregressive paradigm on large
datasets. Recently, ChatGPT shows promising success on large language models,
which applies an autoregressive language model with zero shot or few shot
prompting. The remarkable achievements of PFM have brought significant
breakthroughs to various fields of AI. Numerous studies have proposed different
methods, raising the demand for an updated survey. This study provides a
comprehensive review of recent research advancements, challenges, and
opportunities for PFMs in text, image, graph, as well as other data modalities.
The review covers the basic components and existing pretraining methods used in
natural language processing, computer vision, and graph learning. Additionally,
it explores advanced PFMs used for different data modalities and unified PFMs
that consider data quality and quantity. The review also discusses research
related to the fundamentals of PFMs, such as model efficiency and compression,
security, and privacy. Finally, the study provides key implications, future
research directions, challenges, and open problems in the field of PFMs.
Overall, this survey aims to shed light on the research of the PFMs on
scalability, security, logical reasoning ability, cross-domain learning
ability, and the user-friendly interactive ability for artificial general
intelligence.
| [
{
"created": "Sat, 18 Feb 2023 20:51:09 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Mar 2023 14:44:09 GMT",
"version": "v2"
},
{
"created": "Mon, 1 May 2023 07:48:05 GMT",
"version": "v3"
}
] | 2023-05-02 | [
[
"Zhou",
"Ce",
""
],
[
"Li",
"Qian",
""
],
[
"Li",
"Chen",
""
],
[
"Yu",
"Jun",
""
],
[
"Liu",
"Yixin",
""
],
[
"Wang",
"Guangjing",
""
],
[
"Zhang",
"Kai",
""
],
[
"Ji",
"Cheng",
""
],
[
"Yan",
"Qiben",
""
],
[
"He",
"Lifang",
""
],
[
"Peng",
"Hao",
""
],
[
"Li",
"Jianxin",
""
],
[
"Wu",
"Jia",
""
],
[
"Liu",
"Ziwei",
""
],
[
"Xie",
"Pengtao",
""
],
[
"Xiong",
"Caiming",
""
],
[
"Pei",
"Jian",
""
],
[
"Yu",
"Philip S.",
""
],
[
"Sun",
"Lichao",
""
]
] | Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on large-scale data which provides a reasonable parameter initialization for a wide range of downstream applications. BERT learns bidirectional encoder representations from Transformers, which are trained on large datasets as contextual language models. Similarly, the generative pretrained transformer (GPT) method employs Transformers as the feature extractor and is trained using an autoregressive paradigm on large datasets. Recently, ChatGPT shows promising success on large language models, which applies an autoregressive language model with zero shot or few shot prompting. The remarkable achievements of PFM have brought significant breakthroughs to various fields of AI. Numerous studies have proposed different methods, raising the demand for an updated survey. This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities. The review covers the basic components and existing pretraining methods used in natural language processing, computer vision, and graph learning. Additionally, it explores advanced PFMs used for different data modalities and unified PFMs that consider data quality and quantity. The review also discusses research related to the fundamentals of PFMs, such as model efficiency and compression, security, and privacy. Finally, the study provides key implications, future research directions, challenges, and open problems in the field of PFMs. Overall, this survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, and the user-friendly interactive ability for artificial general intelligence. |
2006.13286 | Chao Zhang | Chao Zhang, Yuanwei Liu, Zhijin Qin and Zhiguo Ding | Semi-Grant-Free NOMA: A Stochastic Geometry Model | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Grant-free (GF) transmission holds promise in terms of low latency
communication by directly transmitting messages without waiting for any
permissions. However, collision situations may frequently happen when limited
spectrum is occupied by numerous GF users. The non-orthogonal multiple access
(NOMA) technique can be a promising solution to achieve massive connectivity
and fewer collisions for GF transmission by multiplexing users in power domain.
We utilize a semi-grant-free (semi-GF) NOMA scheme for enhancing network
connectivity and spectral efficiency by enabling grant-based (GB) and GF users
to share the same spectrum resources. With the aid of semi-GF protocols, uplink
NOMA networks are investigated by invoking stochastic geometry techniques. We
propose a novel \textit{dynamic protocol} to interpret which part of the GF
users are paired in NOMA transmissions via transmitting various channel quality
thresholds by an added handshake. We utilize open-loop protocol with a fixed
average threshold as the benchmark to investigate performance improvement. It
is observed that dynamic protocol provides more accurate channel quality
thresholds than open-loop protocol, thereby the interference from the GF users
is reduced to a large extent. We analyze the outage performance and diversity
gains under two protocols. Numerical results demonstrate that dynamic protocol
is capable of enhancing the outage performance than open-loop protocol.
| [
{
"created": "Tue, 23 Jun 2020 19:32:48 GMT",
"version": "v1"
}
] | 2020-06-25 | [
[
"Zhang",
"Chao",
""
],
[
"Liu",
"Yuanwei",
""
],
[
"Qin",
"Zhijin",
""
],
[
"Ding",
"Zhiguo",
""
]
] | Grant-free (GF) transmission holds promise in terms of low latency communication by directly transmitting messages without waiting for any permissions. However, collision situations may frequently happen when limited spectrum is occupied by numerous GF users. The non-orthogonal multiple access (NOMA) technique can be a promising solution to achieve massive connectivity and fewer collisions for GF transmission by multiplexing users in power domain. We utilize a semi-grant-free (semi-GF) NOMA scheme for enhancing network connectivity and spectral efficiency by enabling grant-based (GB) and GF users to share the same spectrum resources. With the aid of semi-GF protocols, uplink NOMA networks are investigated by invoking stochastic geometry techniques. We propose a novel \textit{dynamic protocol} to interpret which part of the GF users are paired in NOMA transmissions via transmitting various channel quality thresholds by an added handshake. We utilize open-loop protocol with a fixed average threshold as the benchmark to investigate performance improvement. It is observed that dynamic protocol provides more accurate channel quality thresholds than open-loop protocol, thereby the interference from the GF users is reduced to a large extent. We analyze the outage performance and diversity gains under two protocols. Numerical results demonstrate that dynamic protocol is capable of enhancing the outage performance than open-loop protocol. |
1206.4914 | Mario Alejandro Castrillon | Mario A. Castrillon, Damian A. Morero, and Mario R. Hueda | Joint Demapping and Decoding for DQPSK Optical Coherent Receivers | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a low-complexity joint demapper-decoder scheme for coherent
optical receivers with DQPSK modulation. The new technique reduces to 0.7dB the
gap between QPSK and DQPSK in 100Gb/s coherent optical systems.
| [
{
"created": "Thu, 21 Jun 2012 15:25:05 GMT",
"version": "v1"
}
] | 2012-06-22 | [
[
"Castrillon",
"Mario A.",
""
],
[
"Morero",
"Damian A.",
""
],
[
"Hueda",
"Mario R.",
""
]
] | We present a low-complexity joint demapper-decoder scheme for coherent optical receivers with DQPSK modulation. The new technique reduces to 0.7dB the gap between QPSK and DQPSK in 100Gb/s coherent optical systems. |
2001.01049 | Ziling Heng | Ziling Heng, Cunsheng Ding, Weiqiong Wang | Optimal Binary Linear Codes from Maximal Arcs | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The binary Hamming codes with parameters $[2^m-1, 2^m-1-m, 3]$ are perfect.
Their extended codes have parameters $[2^m, 2^m-1-m, 4]$ and are
distance-optimal. The first objective of this paper is to construct a class of
binary linear codes with parameters $[2^{m+s}+2^s-2^m,2^{m+s}+2^s-2^m-2m-2,4]$,
which have better information rates than the class of extended binary Hamming
codes, and are also distance-optimal. The second objective is to construct a
class of distance-optimal binary codes with parameters $[2^m+2, 2^m-2m, 6]$.
Both classes of binary linear codes have new parameters.
| [
{
"created": "Sat, 4 Jan 2020 07:02:18 GMT",
"version": "v1"
}
] | 2020-01-07 | [
[
"Heng",
"Ziling",
""
],
[
"Ding",
"Cunsheng",
""
],
[
"Wang",
"Weiqiong",
""
]
] | The binary Hamming codes with parameters $[2^m-1, 2^m-1-m, 3]$ are perfect. Their extended codes have parameters $[2^m, 2^m-1-m, 4]$ and are distance-optimal. The first objective of this paper is to construct a class of binary linear codes with parameters $[2^{m+s}+2^s-2^m,2^{m+s}+2^s-2^m-2m-2,4]$, which have better information rates than the class of extended binary Hamming codes, and are also distance-optimal. The second objective is to construct a class of distance-optimal binary codes with parameters $[2^m+2, 2^m-2m, 6]$. Both classes of binary linear codes have new parameters. |
2104.08638 | Priyanka Bose | Priyanka Bose, Dipanjan Das, Yanju Chen, Yu Feng, Christopher Kruegel,
Giovanni Vigna | SAILFISH: Vetting Smart Contract State-Inconsistency Bugs in Seconds | null | IEEE Symposium on Security & Privacy, May 2022 | null | null | cs.CR cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents SAILFISH, a scalable system for automatically finding
state-inconsistency bugs in smart contracts. To make the analysis tractable, we
introduce a hybrid approach that includes (i) a light-weight exploration phase
that dramatically reduces the number of instructions to analyze, and (ii) a
precise refinement phase based on symbolic evaluation guided by our novel
value-summary analysis, which generates extra constraints to over-approximate
the side effects of whole-program execution, thereby ensuring the precision of
the symbolic evaluation. We developed a prototype of SAILFISH and evaluated its
ability to detect two state-inconsistency flaws, viz., reentrancy and
transaction order dependence (TOD) in Ethereum smart contracts. Further, we
present detection rules for other kinds of smart contract flaws that SAILFISH
can be extended to detect.
Our experiments demonstrate the efficiency of our hybrid approach as well as
the benefit of the value summary analysis. In particular, we show that S
SAILFISH outperforms five state-of-the-art smart contract analyzers (SECURITY,
MYTHRIL, OYENTE, SEREUM and VANDAL ) in terms of performance, and precision. In
total, SAILFISH discovered 47 previously unknown vulnerable smart contracts out
of 89,853 smart contracts from ETHERSCAN .
| [
{
"created": "Sat, 17 Apr 2021 20:21:07 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Dec 2021 04:23:57 GMT",
"version": "v2"
}
] | 2021-12-14 | [
[
"Bose",
"Priyanka",
""
],
[
"Das",
"Dipanjan",
""
],
[
"Chen",
"Yanju",
""
],
[
"Feng",
"Yu",
""
],
[
"Kruegel",
"Christopher",
""
],
[
"Vigna",
"Giovanni",
""
]
] | This paper presents SAILFISH, a scalable system for automatically finding state-inconsistency bugs in smart contracts. To make the analysis tractable, we introduce a hybrid approach that includes (i) a light-weight exploration phase that dramatically reduces the number of instructions to analyze, and (ii) a precise refinement phase based on symbolic evaluation guided by our novel value-summary analysis, which generates extra constraints to over-approximate the side effects of whole-program execution, thereby ensuring the precision of the symbolic evaluation. We developed a prototype of SAILFISH and evaluated its ability to detect two state-inconsistency flaws, viz., reentrancy and transaction order dependence (TOD) in Ethereum smart contracts. Further, we present detection rules for other kinds of smart contract flaws that SAILFISH can be extended to detect. Our experiments demonstrate the efficiency of our hybrid approach as well as the benefit of the value summary analysis. In particular, we show that S SAILFISH outperforms five state-of-the-art smart contract analyzers (SECURITY, MYTHRIL, OYENTE, SEREUM and VANDAL ) in terms of performance, and precision. In total, SAILFISH discovered 47 previously unknown vulnerable smart contracts out of 89,853 smart contracts from ETHERSCAN . |
1811.11553 | Michael Alcorn | Michael A. Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai,
Wei-Shinn Ku, Anh Nguyen | Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses
of Familiar Objects | Poster at the 2019 Conference on Computer Vision and Pattern
Recognition | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite excellent performance on stationary test sets, deep neural networks
(DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including
natural, non-adversarial ones, which are common in real-world settings. In this
paper, we present a framework for discovering DNN failures that harnesses 3D
renderers and 3D models. That is, we estimate the parameters of a 3D renderer
that cause a target DNN to misbehave in response to the rendered image. Using
our framework and a self-assembled dataset of 3D objects, we investigate the
vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For
objects that are readily recognized by DNNs in their canonical poses, DNNs
incorrectly classify 97% of their pose space. In addition, DNNs are highly
sensitive to slight pose perturbations. Importantly, adversarial poses transfer
across models and datasets. We find that 99.9% and 99.4% of the poses
misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image
classifiers trained on the same ImageNet dataset, respectively, and 75.5%
transfer to the YOLOv3 object detector trained on MS COCO.
| [
{
"created": "Wed, 28 Nov 2018 13:39:27 GMT",
"version": "v1"
},
{
"created": "Sun, 13 Jan 2019 23:55:45 GMT",
"version": "v2"
},
{
"created": "Thu, 18 Apr 2019 13:54:20 GMT",
"version": "v3"
}
] | 2019-04-19 | [
[
"Alcorn",
"Michael A.",
""
],
[
"Li",
"Qi",
""
],
[
"Gong",
"Zhitao",
""
],
[
"Wang",
"Chengfei",
""
],
[
"Mai",
"Long",
""
],
[
"Ku",
"Wei-Shinn",
""
],
[
"Nguyen",
"Anh",
""
]
] | Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO. |
2212.05891 | Jia-Rui Lin | Zhe Zheng, Bo-Rui Kang, Qi-Tian Yuan, Yu-Cheng Zhou, Xin-Zheng Lu,
Jia-Rui Lin | Text Mining-Based Patent Analysis for Automated Rule Checking in AEC | null | null | null | null | cs.IR cs.CL cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Automated rule checking (ARC), which is expected to promote the efficiency of
the compliance checking process in the architecture, engineering, and
construction (AEC) industry, is gaining increasing attention. Throwing light on
the ARC application hotspots and forecasting its trends are useful to the
related research and drive innovations. Therefore, this study takes the patents
from the database of the Derwent Innovations Index database (DII) and China
national knowledge infrastructure (CNKI) as data sources and then carried out a
three-step analysis including (1) quantitative characteristics (i.e., annual
distribution analysis) of patents, (2) identification of ARC topics using a
latent Dirichlet allocation (LDA) and, (3) SNA-based co-occurrence analysis of
ARC topics. The results show that the research hotspots and trends of Chinese
and English patents are different. The contributions of this study have three
aspects: (1) an approach to a comprehensive analysis of patents by integrating
multiple text mining methods (i.e., SNA and LDA) is introduced ; (2) the
application hotspots and development trends of ARC are reviewed based on patent
analysis; and (3) a signpost for technological development and innovation of
ARC is provided.
| [
{
"created": "Mon, 12 Dec 2022 13:48:38 GMT",
"version": "v1"
}
] | 2022-12-13 | [
[
"Zheng",
"Zhe",
""
],
[
"Kang",
"Bo-Rui",
""
],
[
"Yuan",
"Qi-Tian",
""
],
[
"Zhou",
"Yu-Cheng",
""
],
[
"Lu",
"Xin-Zheng",
""
],
[
"Lin",
"Jia-Rui",
""
]
] | Automated rule checking (ARC), which is expected to promote the efficiency of the compliance checking process in the architecture, engineering, and construction (AEC) industry, is gaining increasing attention. Throwing light on the ARC application hotspots and forecasting its trends are useful to the related research and drive innovations. Therefore, this study takes the patents from the database of the Derwent Innovations Index database (DII) and China national knowledge infrastructure (CNKI) as data sources and then carried out a three-step analysis including (1) quantitative characteristics (i.e., annual distribution analysis) of patents, (2) identification of ARC topics using a latent Dirichlet allocation (LDA) and, (3) SNA-based co-occurrence analysis of ARC topics. The results show that the research hotspots and trends of Chinese and English patents are different. The contributions of this study have three aspects: (1) an approach to a comprehensive analysis of patents by integrating multiple text mining methods (i.e., SNA and LDA) is introduced ; (2) the application hotspots and development trends of ARC are reviewed based on patent analysis; and (3) a signpost for technological development and innovation of ARC is provided. |
2201.09051 | Marco Virgolin | Marco Virgolin and Saverio Fracaros | On the Robustness of Sparse Counterfactual Explanations to Adverse
Perturbations | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Counterfactual explanations (CEs) are a powerful means for understanding how
decisions made by algorithms can be changed. Researchers have proposed a number
of desiderata that CEs should meet to be practically useful, such as requiring
minimal effort to enact, or complying with causal models. We consider a further
aspect to improve the usability of CEs: robustness to adverse perturbations,
which may naturally happen due to unfortunate circumstances. Since CEs
typically prescribe a sparse form of intervention (i.e., only a subset of the
features should be changed), we study the effect of addressing robustness
separately for the features that are recommended to be changed and those that
are not. Our definitions are workable in that they can be incorporated as
penalty terms in the loss functions that are used for discovering CEs. To
experiment with robustness, we create and release code where five data sets
(commonly used in the field of fair and explainable machine learning) have been
enriched with feature-specific annotations that can be used to sample
meaningful perturbations. Our experiments show that CEs are often not robust
and, if adverse perturbations take place (even if not worst-case), the
intervention they prescribe may require a much larger cost than anticipated, or
even become impossible. However, accounting for robustness in the search
process, which can be done rather easily, allows discovering robust CEs
systematically. Robust CEs make additional intervention to contrast
perturbations much less costly than non-robust CEs. We also find that
robustness is easier to achieve for the features to change, posing an important
point of consideration for the choice of what counterfactual explanation is
best for the user. Our code is available at:
https://github.com/marcovirgolin/robust-counterfactuals.
| [
{
"created": "Sat, 22 Jan 2022 13:57:45 GMT",
"version": "v1"
},
{
"created": "Thu, 3 Mar 2022 09:33:49 GMT",
"version": "v2"
},
{
"created": "Fri, 23 Sep 2022 15:00:08 GMT",
"version": "v3"
}
] | 2022-09-26 | [
[
"Virgolin",
"Marco",
""
],
[
"Fracaros",
"Saverio",
""
]
] | Counterfactual explanations (CEs) are a powerful means for understanding how decisions made by algorithms can be changed. Researchers have proposed a number of desiderata that CEs should meet to be practically useful, such as requiring minimal effort to enact, or complying with causal models. We consider a further aspect to improve the usability of CEs: robustness to adverse perturbations, which may naturally happen due to unfortunate circumstances. Since CEs typically prescribe a sparse form of intervention (i.e., only a subset of the features should be changed), we study the effect of addressing robustness separately for the features that are recommended to be changed and those that are not. Our definitions are workable in that they can be incorporated as penalty terms in the loss functions that are used for discovering CEs. To experiment with robustness, we create and release code where five data sets (commonly used in the field of fair and explainable machine learning) have been enriched with feature-specific annotations that can be used to sample meaningful perturbations. Our experiments show that CEs are often not robust and, if adverse perturbations take place (even if not worst-case), the intervention they prescribe may require a much larger cost than anticipated, or even become impossible. However, accounting for robustness in the search process, which can be done rather easily, allows discovering robust CEs systematically. Robust CEs make additional intervention to contrast perturbations much less costly than non-robust CEs. We also find that robustness is easier to achieve for the features to change, posing an important point of consideration for the choice of what counterfactual explanation is best for the user. Our code is available at: https://github.com/marcovirgolin/robust-counterfactuals. |
1106.1516 | Francesco De Pellegrini Dr. | Francesco De Pellegrini, Karina Gomez, Daniele Miorandi and Imrich
Chlamtac | Distributed Wake-Up Scheduling for Energy Saving in Wireless Networks | 13 pages, 4 figures | null | null | null | cs.NI cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A customary solution to reduce the energy consumption of wireless
communication devices is to periodically put the radio into low-power sleep
mode. A relevant problem is to schedule the wake-up of nodes in such a way as
to ensure proper coordination among devices, respecting delay constraints while
still saving energy. In this paper, we introduce a simple algebraic
characterization of the problem of periodic wake-up scheduling under both
energy consumption and delay constraints. We demonstrate that the general
problem of wake-up times coordination is equivalent to integer factorization
and discuss the implications on the design of efficient scheduling algorithms.
We then propose simple polynomial time heuristic algorithms that can be
implemented in a distributed fashion and present a message complexity of the
order of the number of links in the network. Numerical results are provided in
order to assess the performance of the proposed techniques when applied to
wireless sensor networks.
| [
{
"created": "Wed, 8 Jun 2011 08:12:19 GMT",
"version": "v1"
}
] | 2011-06-09 | [
[
"De Pellegrini",
"Francesco",
""
],
[
"Gomez",
"Karina",
""
],
[
"Miorandi",
"Daniele",
""
],
[
"Chlamtac",
"Imrich",
""
]
] | A customary solution to reduce the energy consumption of wireless communication devices is to periodically put the radio into low-power sleep mode. A relevant problem is to schedule the wake-up of nodes in such a way as to ensure proper coordination among devices, respecting delay constraints while still saving energy. In this paper, we introduce a simple algebraic characterization of the problem of periodic wake-up scheduling under both energy consumption and delay constraints. We demonstrate that the general problem of wake-up times coordination is equivalent to integer factorization and discuss the implications on the design of efficient scheduling algorithms. We then propose simple polynomial time heuristic algorithms that can be implemented in a distributed fashion and present a message complexity of the order of the number of links in the network. Numerical results are provided in order to assess the performance of the proposed techniques when applied to wireless sensor networks. |
2303.18087 | Esther Rolf | Esther Rolf | Evaluation Challenges for Geospatial ML | ICLR 2023 Workshop on Machine Learning for Remote Sensing | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As geospatial machine learning models and maps derived from their predictions
are increasingly used for downstream analyses in science and policy, it is
imperative to evaluate their accuracy and applicability. Geospatial machine
learning has key distinctions from other learning paradigms, and as such, the
correct way to measure performance of spatial machine learning outputs has been
a topic of debate. In this paper, I delineate unique challenges of model
evaluation for geospatial machine learning with global or remotely sensed
datasets, culminating in concrete takeaways to improve evaluations of
geospatial model performance.
| [
{
"created": "Fri, 31 Mar 2023 14:24:06 GMT",
"version": "v1"
}
] | 2023-04-03 | [
[
"Rolf",
"Esther",
""
]
] | As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate their accuracy and applicability. Geospatial machine learning has key distinctions from other learning paradigms, and as such, the correct way to measure performance of spatial machine learning outputs has been a topic of debate. In this paper, I delineate unique challenges of model evaluation for geospatial machine learning with global or remotely sensed datasets, culminating in concrete takeaways to improve evaluations of geospatial model performance. |
2305.18665 | Arshdeep Singh | Arshdeep Singh, Haohe Liu, Mark D. Plumbley | E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural
Networks | Accepted in Internoise 2023 conference | null | null | null | cs.SD cs.AI eess.AS eess.SP | http://creativecommons.org/licenses/by/4.0/ | Sounds carry an abundance of information about activities and events in our
everyday environment, such as traffic noise, road works, music, or people
talking. Recent machine learning methods, such as convolutional neural networks
(CNNs), have been shown to be able to automatically recognize sound activities,
a task known as audio tagging. One such method, pre-trained audio neural
networks (PANNs), provides a neural network which has been pre-trained on over
500 sound classes from the publicly available AudioSet dataset, and can be used
as a baseline or starting point for other tasks. However, the existing PANNs
model has a high computational complexity and large storage requirement. This
could limit the potential for deploying PANNs on resource-constrained devices,
such as on-the-edge sound sensors, and could lead to high energy consumption if
many such devices were deployed. In this paper, we reduce the computational
complexity and memory requirement of the PANNs model by taking a pruning
approach to eliminate redundant parameters from the PANNs model. The resulting
Efficient PANNs (E-PANNs) model, which requires 36\% less computations and 70\%
less memory, also slightly improves the sound recognition (audio tagging)
performance. The code for the E-PANNs model has been released under an open
source license.
| [
{
"created": "Tue, 30 May 2023 00:08:55 GMT",
"version": "v1"
}
] | 2023-05-31 | [
[
"Singh",
"Arshdeep",
""
],
[
"Liu",
"Haohe",
""
],
[
"Plumbley",
"Mark D.",
""
]
] | Sounds carry an abundance of information about activities and events in our everyday environment, such as traffic noise, road works, music, or people talking. Recent machine learning methods, such as convolutional neural networks (CNNs), have been shown to be able to automatically recognize sound activities, a task known as audio tagging. One such method, pre-trained audio neural networks (PANNs), provides a neural network which has been pre-trained on over 500 sound classes from the publicly available AudioSet dataset, and can be used as a baseline or starting point for other tasks. However, the existing PANNs model has a high computational complexity and large storage requirement. This could limit the potential for deploying PANNs on resource-constrained devices, such as on-the-edge sound sensors, and could lead to high energy consumption if many such devices were deployed. In this paper, we reduce the computational complexity and memory requirement of the PANNs model by taking a pruning approach to eliminate redundant parameters from the PANNs model. The resulting Efficient PANNs (E-PANNs) model, which requires 36\% less computations and 70\% less memory, also slightly improves the sound recognition (audio tagging) performance. The code for the E-PANNs model has been released under an open source license. |
2002.09587 | Zhanyu Wang | Zhanyu Wang and Jean Honorio | The Sample Complexity of Meta Sparse Regression | null | Artificial Intelligence and Statistics (AISTATS), 2021 | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the meta-learning problem in sparse linear regression
with infinite tasks. We assume that the learner can access several similar
tasks. The goal of the learner is to transfer knowledge from the prior tasks to
a similar but novel task. For p parameters, size of the support set k , and l
samples per task, we show that T \in O (( k log(p) ) /l ) tasks are sufficient
in order to recover the common support of all tasks. With the recovered
support, we can greatly reduce the sample complexity for estimating the
parameter of the novel task, i.e., l \in O (1) with respect to T and p . We
also prove that our rates are minimax optimal. A key difference between
meta-learning and the classical multi-task learning, is that meta-learning
focuses only on the recovery of the parameters of the novel task, while
multi-task learning estimates the parameter of all tasks, which requires l to
grow with T . Instead, our efficient meta-learning estimator allows for l to be
constant with respect to T (i.e., few-shot learning).
| [
{
"created": "Sat, 22 Feb 2020 00:59:53 GMT",
"version": "v1"
},
{
"created": "Sun, 21 Jun 2020 18:35:21 GMT",
"version": "v2"
}
] | 2021-02-19 | [
[
"Wang",
"Zhanyu",
""
],
[
"Honorio",
"Jean",
""
]
] | This paper addresses the meta-learning problem in sparse linear regression with infinite tasks. We assume that the learner can access several similar tasks. The goal of the learner is to transfer knowledge from the prior tasks to a similar but novel task. For p parameters, size of the support set k , and l samples per task, we show that T \in O (( k log(p) ) /l ) tasks are sufficient in order to recover the common support of all tasks. With the recovered support, we can greatly reduce the sample complexity for estimating the parameter of the novel task, i.e., l \in O (1) with respect to T and p . We also prove that our rates are minimax optimal. A key difference between meta-learning and the classical multi-task learning, is that meta-learning focuses only on the recovery of the parameters of the novel task, while multi-task learning estimates the parameter of all tasks, which requires l to grow with T . Instead, our efficient meta-learning estimator allows for l to be constant with respect to T (i.e., few-shot learning). |
2109.03702 | Ziyue Zhang | Ziyue Zhang, Shuai Jiang, Congzhentao Huang, Richard YiDa Xu | Unsupervised clothing change adaptive person ReID | 9 pages | null | 10.1109/LSP.2021.3134195 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clothing changes and lack of data labels are both crucial challenges in
person ReID. For the former challenge, people may occur multiple times at
different locations wearing different clothing. However, most of the current
person ReID research works focus on the benchmarks in which a person's clothing
is kept the same all the time. For the last challenge, some researchers try to
make model learn information from a labeled dataset as a source to an unlabeled
dataset. Whereas purely unsupervised training is less used. In this paper, we
aim to solve both problems at the same time. We design a novel unsupervised
model, Sync-Person-Cloud ReID, to solve the unsupervised clothing change person
ReID problem. We developer a purely unsupervised clothing change person ReID
pipeline with person sync augmentation operation and same person feature
restriction. The person sync augmentation is to supply additional same person
resources. These same person's resources can be used as part supervised input
by same person feature restriction. The extensive experiments on clothing
change ReID datasets show the out-performance of our methods.
| [
{
"created": "Wed, 8 Sep 2021 15:08:10 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Sep 2021 14:42:00 GMT",
"version": "v2"
}
] | 2022-02-09 | [
[
"Zhang",
"Ziyue",
""
],
[
"Jiang",
"Shuai",
""
],
[
"Huang",
"Congzhentao",
""
],
[
"Xu",
"Richard YiDa",
""
]
] | Clothing changes and lack of data labels are both crucial challenges in person ReID. For the former challenge, people may occur multiple times at different locations wearing different clothing. However, most of the current person ReID research works focus on the benchmarks in which a person's clothing is kept the same all the time. For the last challenge, some researchers try to make model learn information from a labeled dataset as a source to an unlabeled dataset. Whereas purely unsupervised training is less used. In this paper, we aim to solve both problems at the same time. We design a novel unsupervised model, Sync-Person-Cloud ReID, to solve the unsupervised clothing change person ReID problem. We developer a purely unsupervised clothing change person ReID pipeline with person sync augmentation operation and same person feature restriction. The person sync augmentation is to supply additional same person resources. These same person's resources can be used as part supervised input by same person feature restriction. The extensive experiments on clothing change ReID datasets show the out-performance of our methods. |
1807.05543 | Arman Ahmadian | Arman Ahmadian, and Hyuncheol Park | Maximizing Ergodic Throughput in Wireless Powered Communication Networks | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper considers a single-antenna wirelesspowered communication network
(WPCN) over a flat-fading channel. We show that, by using our probabilistic
harvestand-transmit (PHAT) strategy, which requires the knowledge of
instantaneous full channel state information (CSI) and fading probability
distribution, the ergodic throughput of this system may be greatly increased
relative to that achieved by the harvestthen-transmit (HTT) protocol. To do so,
instead of dividing every frame to the uplink (UL) and downlink (DL), the
channel is allocated to the UL wireless information transmission (WIT) and DL
wireless power transfer (WPT) based on the estimated channel power gain. In
other words, based on the fading probability distribution, we will derive some
thresholds that determine the association of a frame to the DL WPT or UL WIT.
More specifically, if the channel gain falls below or goes over these
thresholds, the channel will be allocated to WPT or WIT. Simulation results
verify the performance of our proposed scheme.
| [
{
"created": "Sun, 15 Jul 2018 13:21:35 GMT",
"version": "v1"
}
] | 2018-07-17 | [
[
"Ahmadian",
"Arman",
""
],
[
"Park",
"Hyuncheol",
""
]
] | This paper considers a single-antenna wirelesspowered communication network (WPCN) over a flat-fading channel. We show that, by using our probabilistic harvestand-transmit (PHAT) strategy, which requires the knowledge of instantaneous full channel state information (CSI) and fading probability distribution, the ergodic throughput of this system may be greatly increased relative to that achieved by the harvestthen-transmit (HTT) protocol. To do so, instead of dividing every frame to the uplink (UL) and downlink (DL), the channel is allocated to the UL wireless information transmission (WIT) and DL wireless power transfer (WPT) based on the estimated channel power gain. In other words, based on the fading probability distribution, we will derive some thresholds that determine the association of a frame to the DL WPT or UL WIT. More specifically, if the channel gain falls below or goes over these thresholds, the channel will be allocated to WPT or WIT. Simulation results verify the performance of our proposed scheme. |
2109.14812 | Sajad Meisami | Sajad Meisami, Mohammad Beheshti-Atashgah, Mohammad Reza Aref | Using Blockchain to Achieve Decentralized Privacy In IoT Healthcare | 6 pages | International Journal on Cybernetics & Informatics (IJCI) Vol. 12,
No.2, April 2023, Page 97-108 | 10.5121/ijci.2023.120208 | null | cs.CR | http://creativecommons.org/licenses/by-nc-sa/4.0/ | With the advent of the Internet of Things (IoT), e-health has become one of
the main topics of research. Due to the sensitivity of patient information,
patient privacy seems challenging. Nowadays, patient data is usually stored in
the cloud in healthcare programs, making it difficult for users to have enough
control over their data. The recent increment in announced cases of security
and surveillance breaches compromising patients' privacy call into question the
conventional model, in which third-parties gather and control immense amounts
of patients' Healthcare data. In this work, we try to resolve the issues
mentioned above by using blockchain technology. We propose a blockchain-based
protocol suitable for e-health applications that does not require trust in a
third party and provides an efficient privacy-preserving access control
mechanism. Transactions in our proposed system, unlike Bitcoin, are not
entirely financial, and we do not use conventional methods for consensus
operations in blockchain like Proof of Work (PoW). It is not suitable for IoT
applications because IoT devices have resources-constraints. Usage of
appropriate consensus method helps us to increase network security and
efficiency, as well as reducing network cost, i.e., bandwidth and processor
usage. Finally, we provide security and privacy analysis of our proposed
protocol.
| [
{
"created": "Thu, 30 Sep 2021 02:30:09 GMT",
"version": "v1"
}
] | 2023-04-04 | [
[
"Meisami",
"Sajad",
""
],
[
"Beheshti-Atashgah",
"Mohammad",
""
],
[
"Aref",
"Mohammad Reza",
""
]
] | With the advent of the Internet of Things (IoT), e-health has become one of the main topics of research. Due to the sensitivity of patient information, patient privacy seems challenging. Nowadays, patient data is usually stored in the cloud in healthcare programs, making it difficult for users to have enough control over their data. The recent increment in announced cases of security and surveillance breaches compromising patients' privacy call into question the conventional model, in which third-parties gather and control immense amounts of patients' Healthcare data. In this work, we try to resolve the issues mentioned above by using blockchain technology. We propose a blockchain-based protocol suitable for e-health applications that does not require trust in a third party and provides an efficient privacy-preserving access control mechanism. Transactions in our proposed system, unlike Bitcoin, are not entirely financial, and we do not use conventional methods for consensus operations in blockchain like Proof of Work (PoW). It is not suitable for IoT applications because IoT devices have resources-constraints. Usage of appropriate consensus method helps us to increase network security and efficiency, as well as reducing network cost, i.e., bandwidth and processor usage. Finally, we provide security and privacy analysis of our proposed protocol. |
1705.08503 | Fionn Murtagh | Fionn Murtagh | The Geometry and Topology of Data and Information for Analytics of
Processes and Behaviours: Building on Bourdieu and Addressing New Societal
Challenges | 16 pages, 7 figures | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We begin by summarizing the relevance and importance of inductive analytics
based on the geometry and topology of data and information. Contemporary issues
are then discussed. These include how sampling data for representativity is
increasingly to be questioned. While we can always avail of analytics from a
"bag of tools and techniques", in the application of machine learning and
predictive analytics, nonetheless we present the case for Bourdieu and
Benz\'ecri-based science of data, as follows. This is to construct bridges
between data sources and position-taking, and decision-making. There is summary
presentation of a few case studies, illustrating and exemplifying application
domains.
| [
{
"created": "Mon, 15 May 2017 22:44:53 GMT",
"version": "v1"
}
] | 2017-05-25 | [
[
"Murtagh",
"Fionn",
""
]
] | We begin by summarizing the relevance and importance of inductive analytics based on the geometry and topology of data and information. Contemporary issues are then discussed. These include how sampling data for representativity is increasingly to be questioned. While we can always avail of analytics from a "bag of tools and techniques", in the application of machine learning and predictive analytics, nonetheless we present the case for Bourdieu and Benz\'ecri-based science of data, as follows. This is to construct bridges between data sources and position-taking, and decision-making. There is summary presentation of a few case studies, illustrating and exemplifying application domains. |
1507.08396 | Shuangyin Li | Shuangyin Li, Jiefei Li, Guan Huang, Ruiyang Tan, and Rong Pan | Tag-Weighted Topic Model For Large-scale Semi-Structured Documents | null | null | null | null | cs.CL cs.IR cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To date, there have been massive Semi-Structured Documents (SSDs) during the
evolution of the Internet. These SSDs contain both unstructured features (e.g.,
plain text) and metadata (e.g., tags). Most previous works focused on modeling
the unstructured text, and recently, some other methods have been proposed to
model the unstructured text with specific tags. To build a general model for
SSDs remains an important problem in terms of both model fitness and
efficiency. We propose a novel method to model the SSDs by a so-called
Tag-Weighted Topic Model (TWTM). TWTM is a framework that leverages both the
tags and words information, not only to learn the document-topic and topic-word
distributions, but also to infer the tag-topic distributions for text mining
tasks. We present an efficient variational inference method with an EM
algorithm for estimating the model parameters. Meanwhile, we propose three
large-scale solutions for our model under the MapReduce distributed computing
platform for modeling large-scale SSDs. The experimental results show the
effectiveness, efficiency and the robustness by comparing our model with the
state-of-the-art methods in document modeling, tags prediction and text
classification. We also show the performance of the three distributed solutions
in terms of time and accuracy on document modeling.
| [
{
"created": "Thu, 30 Jul 2015 06:44:37 GMT",
"version": "v1"
}
] | 2015-07-31 | [
[
"Li",
"Shuangyin",
""
],
[
"Li",
"Jiefei",
""
],
[
"Huang",
"Guan",
""
],
[
"Tan",
"Ruiyang",
""
],
[
"Pan",
"Rong",
""
]
] | To date, there have been massive Semi-Structured Documents (SSDs) during the evolution of the Internet. These SSDs contain both unstructured features (e.g., plain text) and metadata (e.g., tags). Most previous works focused on modeling the unstructured text, and recently, some other methods have been proposed to model the unstructured text with specific tags. To build a general model for SSDs remains an important problem in terms of both model fitness and efficiency. We propose a novel method to model the SSDs by a so-called Tag-Weighted Topic Model (TWTM). TWTM is a framework that leverages both the tags and words information, not only to learn the document-topic and topic-word distributions, but also to infer the tag-topic distributions for text mining tasks. We present an efficient variational inference method with an EM algorithm for estimating the model parameters. Meanwhile, we propose three large-scale solutions for our model under the MapReduce distributed computing platform for modeling large-scale SSDs. The experimental results show the effectiveness, efficiency and the robustness by comparing our model with the state-of-the-art methods in document modeling, tags prediction and text classification. We also show the performance of the three distributed solutions in terms of time and accuracy on document modeling. |
2109.09476 | Junya Morita | Junya Morita, Thanakit Pitakchokchai, Giri Basanta Raj, Yusuke
Yamamoto, Hiroyasu Yuhashi and Teppei Koguchi | Regulating Ruminative Web-browsing Based on the Counterbalance Modeling
Approach | null | Frontiers in Artificial Intelligence, 2022 | 10.3389/frai.2022.741610 | null | cs.HC cs.AI | http://creativecommons.org/licenses/by/4.0/ | Even though the web environment facilitates daily life, emotional problems
caused by its incompatibility with human cognition are becoming increasingly
serious. To alleviate negative emotions during web use, we developed a browser
extension that presents memorized product images to users, in the form of web
advertisements. This system utilizes the cognitive architecture Adaptive
Control of Thought-Rational (ACT-R) as a model of memory and emotion. A heart
rate sensor modulates the ACT-R model parameters: The emotional states of the
model are synchronized or counterbalanced with the physiological state of the
user. An experiment demonstrates that the counterbalance model suppresses
negative ruminative web browsing. The authors claim that this approach is
advantageous in terms of explainability.
| [
{
"created": "Mon, 20 Sep 2021 12:31:03 GMT",
"version": "v1"
}
] | 2022-08-16 | [
[
"Morita",
"Junya",
""
],
[
"Pitakchokchai",
"Thanakit",
""
],
[
"Raj",
"Giri Basanta",
""
],
[
"Yamamoto",
"Yusuke",
""
],
[
"Yuhashi",
"Hiroyasu",
""
],
[
"Koguchi",
"Teppei",
""
]
] | Even though the web environment facilitates daily life, emotional problems caused by its incompatibility with human cognition are becoming increasingly serious. To alleviate negative emotions during web use, we developed a browser extension that presents memorized product images to users, in the form of web advertisements. This system utilizes the cognitive architecture Adaptive Control of Thought-Rational (ACT-R) as a model of memory and emotion. A heart rate sensor modulates the ACT-R model parameters: The emotional states of the model are synchronized or counterbalanced with the physiological state of the user. An experiment demonstrates that the counterbalance model suppresses negative ruminative web browsing. The authors claim that this approach is advantageous in terms of explainability. |
2401.04592 | Mihael Arcan | Mihael Arcan, David-Paul Niland and Fionn Delahunty | An Assessment on Comprehending Mental Health through Large Language
Models | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Mental health challenges pose considerable global burdens on individuals and
communities. Recent data indicates that more than 20% of adults may encounter
at least one mental disorder in their lifetime. On the one hand, the
advancements in large language models have facilitated diverse applications,
yet a significant research gap persists in understanding and enhancing the
potential of large language models within the domain of mental health. On the
other hand, across various applications, an outstanding question involves the
capacity of large language models to comprehend expressions of human mental
health conditions in natural language. This study presents an initial
evaluation of large language models in addressing this gap. Due to this, we
compare the performance of Llama-2 and ChatGPT with classical Machine as well
as Deep learning models. Our results on the DAIC-WOZ dataset show that
transformer-based models, like BERT or XLNet, outperform the large language
models.
| [
{
"created": "Tue, 9 Jan 2024 14:50:04 GMT",
"version": "v1"
},
{
"created": "Fri, 2 Feb 2024 09:36:58 GMT",
"version": "v2"
}
] | 2024-02-05 | [
[
"Arcan",
"Mihael",
""
],
[
"Niland",
"David-Paul",
""
],
[
"Delahunty",
"Fionn",
""
]
] | Mental health challenges pose considerable global burdens on individuals and communities. Recent data indicates that more than 20% of adults may encounter at least one mental disorder in their lifetime. On the one hand, the advancements in large language models have facilitated diverse applications, yet a significant research gap persists in understanding and enhancing the potential of large language models within the domain of mental health. On the other hand, across various applications, an outstanding question involves the capacity of large language models to comprehend expressions of human mental health conditions in natural language. This study presents an initial evaluation of large language models in addressing this gap. Due to this, we compare the performance of Llama-2 and ChatGPT with classical Machine as well as Deep learning models. Our results on the DAIC-WOZ dataset show that transformer-based models, like BERT or XLNet, outperform the large language models. |
2304.11205 | Camille Coti | Camille Coti and Kevin Huck and Allen D. Malony | STaKTAU: profiling HPC applications' operating system usage | null | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | This paper presents a approach for measuring the time spent by HPC
applications in the operating system's kernel. We use the SystemTap interface
to insert timers before and after system calls, and take advantage of its
stability to design a tool that can be used with multiple versions of the
kernel. We evaluate its performance overhead, using an OS-intensive
mini-benchmark and a raytracing mini app.
| [
{
"created": "Fri, 21 Apr 2023 18:27:57 GMT",
"version": "v1"
}
] | 2023-04-25 | [
[
"Coti",
"Camille",
""
],
[
"Huck",
"Kevin",
""
],
[
"Malony",
"Allen D.",
""
]
] | This paper presents a approach for measuring the time spent by HPC applications in the operating system's kernel. We use the SystemTap interface to insert timers before and after system calls, and take advantage of its stability to design a tool that can be used with multiple versions of the kernel. We evaluate its performance overhead, using an OS-intensive mini-benchmark and a raytracing mini app. |
2109.05927 | Mohammad Masiur Rahaman | Mohammad Masiur Rahaman | An open-source implementation of a phase-field model for brittle
fracture using Gridap in Julia | null | null | null | null | cs.CE physics.app-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article proposes an open-source implementation of a phase-field model
for brittle fracture using a recently developed finite element toolbox, Gridap
in Julia. The present work exploits the advantages of both the phase-field
model and Gridap toolbox for simulating fracture in brittle materials. On one
hand, the use of the phase-field model, which is a continuum approach and uses
a diffuse representation of sharp cracks, enables the proposed implementation
to overcome such well-known drawbacks of the discrete approach for predicting
complex crack paths as the need for re-meshing, enrichment of finite element
shape functions and an explicit tracking of the crack surfaces. On the other
hand, the use of Gridap makes the proposed implementation very compact and
user-friendly that requires low memory usage, and provides a high degree of
flexibility to the users in defining weak forms of partial differential
equations. A test on a notched beam under symmetric three-point bending and a
set of tests on a notched beam with three holes under asymmetric three-point
bending is considered to demonstrate how the proposed Gridap based phase-field
Julia code can be used to simulate fracture in brittle materials.
| [
{
"created": "Fri, 10 Sep 2021 03:02:59 GMT",
"version": "v1"
}
] | 2021-09-14 | [
[
"Rahaman",
"Mohammad Masiur",
""
]
] | This article proposes an open-source implementation of a phase-field model for brittle fracture using a recently developed finite element toolbox, Gridap in Julia. The present work exploits the advantages of both the phase-field model and Gridap toolbox for simulating fracture in brittle materials. On one hand, the use of the phase-field model, which is a continuum approach and uses a diffuse representation of sharp cracks, enables the proposed implementation to overcome such well-known drawbacks of the discrete approach for predicting complex crack paths as the need for re-meshing, enrichment of finite element shape functions and an explicit tracking of the crack surfaces. On the other hand, the use of Gridap makes the proposed implementation very compact and user-friendly that requires low memory usage, and provides a high degree of flexibility to the users in defining weak forms of partial differential equations. A test on a notched beam under symmetric three-point bending and a set of tests on a notched beam with three holes under asymmetric three-point bending is considered to demonstrate how the proposed Gridap based phase-field Julia code can be used to simulate fracture in brittle materials. |
2003.06713 | Rodrigo Nogueira | Rodrigo Nogueira, Zhiying Jiang, Jimmy Lin | Document Ranking with a Pretrained Sequence-to-Sequence Model | null | null | null | null | cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work proposes a novel adaptation of a pretrained sequence-to-sequence
model to the task of document ranking. Our approach is fundamentally different
from a commonly-adopted classification-based formulation of ranking, based on
encoder-only pretrained transformer architectures such as BERT. We show how a
sequence-to-sequence model can be trained to generate relevance labels as
"target words", and how the underlying logits of these target words can be
interpreted as relevance probabilities for ranking. On the popular MS MARCO
passage ranking task, experimental results show that our approach is at least
on par with previous classification-based models and can surpass them with
larger, more-recent models. On the test collection from the TREC 2004 Robust
Track, we demonstrate a zero-shot transfer-based approach that outperforms
previous state-of-the-art models requiring in-dataset cross-validation.
Furthermore, we find that our approach significantly outperforms an
encoder-only model in a data-poor regime (i.e., with few training examples). We
investigate this observation further by varying target words to probe the
model's use of latent knowledge.
| [
{
"created": "Sat, 14 Mar 2020 22:29:50 GMT",
"version": "v1"
}
] | 2020-03-17 | [
[
"Nogueira",
"Rodrigo",
""
],
[
"Jiang",
"Zhiying",
""
],
[
"Lin",
"Jimmy",
""
]
] | This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted classification-based formulation of ranking, based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-to-sequence model can be trained to generate relevance labels as "target words", and how the underlying logits of these target words can be interpreted as relevance probabilities for ranking. On the popular MS MARCO passage ranking task, experimental results show that our approach is at least on par with previous classification-based models and can surpass them with larger, more-recent models. On the test collection from the TREC 2004 Robust Track, we demonstrate a zero-shot transfer-based approach that outperforms previous state-of-the-art models requiring in-dataset cross-validation. Furthermore, we find that our approach significantly outperforms an encoder-only model in a data-poor regime (i.e., with few training examples). We investigate this observation further by varying target words to probe the model's use of latent knowledge. |
1401.5697 | Evgeniy Gabrilovich | Evgeniy Gabrilovich, Shaul Markovitch | Wikipedia-based Semantic Interpretation for Natural Language Processing | null | Journal Of Artificial Intelligence Research, Volume 34, pages
443-498, 2009 | 10.1613/jair.2669 | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Adequate representation of natural language semantics requires access to vast
amounts of common sense and domain-specific world knowledge. Prior work in the
field was based on purely statistical techniques that did not make use of
background knowledge, on limited lexicographic knowledge bases such as WordNet,
or on huge manual efforts such as the CYC project. Here we propose a novel
method, called Explicit Semantic Analysis (ESA), for fine-grained semantic
interpretation of unrestricted natural language texts. Our method represents
meaning in a high-dimensional space of concepts derived from Wikipedia, the
largest encyclopedia in existence. We explicitly represent the meaning of any
text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our
method on text categorization and on computing the degree of semantic
relatedness between fragments of natural language text. Using ESA results in
significant improvements over the previous state of the art in both tasks.
Importantly, due to the use of natural concepts, the ESA model is easy to
explain to human users.
| [
{
"created": "Wed, 15 Jan 2014 05:21:01 GMT",
"version": "v1"
}
] | 2014-01-23 | [
[
"Gabrilovich",
"Evgeniy",
""
],
[
"Markovitch",
"Shaul",
""
]
] | Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users. |
1604.04326 | Stephan Zheng | Stephan Zheng, Yang Song, Thomas Leung, Ian Goodfellow | Improving the Robustness of Deep Neural Networks via Stability Training | Published in CVPR 2016 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we address the issue of output instability of deep neural
networks: small perturbations in the visual input can significantly distort the
feature embeddings and output of a neural network. Such instability affects
many deep architectures with state-of-the-art performance on a wide range of
computer vision tasks. We present a general stability training method to
stabilize deep networks against small input distortions that result from
various types of common image processing, such as compression, rescaling, and
cropping. We validate our method by stabilizing the state-of-the-art Inception
architecture against these types of distortions. In addition, we demonstrate
that our stabilized model gives robust state-of-the-art performance on
large-scale near-duplicate detection, similar-image ranking, and classification
on noisy datasets.
| [
{
"created": "Fri, 15 Apr 2016 01:15:18 GMT",
"version": "v1"
}
] | 2016-04-18 | [
[
"Zheng",
"Stephan",
""
],
[
"Song",
"Yang",
""
],
[
"Leung",
"Thomas",
""
],
[
"Goodfellow",
"Ian",
""
]
] | In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network. Such instability affects many deep architectures with state-of-the-art performance on a wide range of computer vision tasks. We present a general stability training method to stabilize deep networks against small input distortions that result from various types of common image processing, such as compression, rescaling, and cropping. We validate our method by stabilizing the state-of-the-art Inception architecture against these types of distortions. In addition, we demonstrate that our stabilized model gives robust state-of-the-art performance on large-scale near-duplicate detection, similar-image ranking, and classification on noisy datasets. |
2111.07226 | Kostis Kaffes | Kostis Kaffes and Neeraja J. Yadwadkar and Christos Kozyrakis | Practical Scheduling for Real-World Serverless Computing | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Serverless computing has seen rapid growth due to the ease-of-use and
cost-efficiency it provides. However, function scheduling, a critical component
of serverless systems, has been overlooked. In this paper, we take a
first-principles approach toward designing a scheduler that caters to the
unique characteristics of serverless functions as seen in real-world
deployments. We first create a taxonomy of scheduling policies along three
dimensions. Next, we use simulation to explore the scheduling policy space for
the function characteristics in a 14-day trace of Azure functions and conclude
that frequently used features such as late binding and random load balancing
are sub-optimal for common execution time distributions and load ranges. We use
these insights to design Hermes, a scheduler for serverless functions with
three key characteristics. First, to avoid head-of-line blocking due to high
function execution time variability, Hermes uses a combination of early binding
and processor sharing for scheduling at individual worker machines. Second,
Hermes uses a hybrid load balancing approach that improves consolidation at low
load while employing least-loaded balancing at high load to retain high
performance. Third, Hermes is both load and locality-aware, reducing the number
of cold starts compared to pure load-based policies. We implement Hermes for
Apache OpenWhisk and demonstrate that, for the case of the function patterns
observed both in the Azure and in other real-world traces, it achieves up to
85% lower function slowdown and 60% higher throughput compared to existing
policies.
| [
{
"created": "Sun, 14 Nov 2021 02:55:48 GMT",
"version": "v1"
}
] | 2021-11-16 | [
[
"Kaffes",
"Kostis",
""
],
[
"Yadwadkar",
"Neeraja J.",
""
],
[
"Kozyrakis",
"Christos",
""
]
] | Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it provides. However, function scheduling, a critical component of serverless systems, has been overlooked. In this paper, we take a first-principles approach toward designing a scheduler that caters to the unique characteristics of serverless functions as seen in real-world deployments. We first create a taxonomy of scheduling policies along three dimensions. Next, we use simulation to explore the scheduling policy space for the function characteristics in a 14-day trace of Azure functions and conclude that frequently used features such as late binding and random load balancing are sub-optimal for common execution time distributions and load ranges. We use these insights to design Hermes, a scheduler for serverless functions with three key characteristics. First, to avoid head-of-line blocking due to high function execution time variability, Hermes uses a combination of early binding and processor sharing for scheduling at individual worker machines. Second, Hermes uses a hybrid load balancing approach that improves consolidation at low load while employing least-loaded balancing at high load to retain high performance. Third, Hermes is both load and locality-aware, reducing the number of cold starts compared to pure load-based policies. We implement Hermes for Apache OpenWhisk and demonstrate that, for the case of the function patterns observed both in the Azure and in other real-world traces, it achieves up to 85% lower function slowdown and 60% higher throughput compared to existing policies. |
1706.05059 | Victor Dalmau | Victor Dalmau | Conjunctions of Among Constraints | 15 pages plus appendix | null | null | null | cs.AI cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many existing global constraints can be encoded as a conjunction of among
constraints. An among constraint holds if the number of the variables in its
scope whose value belongs to a prespecified set, which we call its range, is
within some given bounds. It is known that domain filtering algorithms can
benefit from reasoning about the interaction of among constraints so that
values can be filtered out taking into consideration several among constraints
simultaneously. The present pa- per embarks into a systematic investigation on
the circumstances under which it is possible to obtain efficient and complete
domain filtering algorithms for conjunctions of among constraints. We start by
observing that restrictions on both the scope and the range of the among
constraints are necessary to obtain meaningful results. Then, we derive a
domain flow-based filtering algorithm and present several applications. In
particular, it is shown that the algorithm unifies and generalizes several
previous existing results.
| [
{
"created": "Thu, 15 Jun 2017 19:51:52 GMT",
"version": "v1"
}
] | 2017-06-19 | [
[
"Dalmau",
"Victor",
""
]
] | Many existing global constraints can be encoded as a conjunction of among constraints. An among constraint holds if the number of the variables in its scope whose value belongs to a prespecified set, which we call its range, is within some given bounds. It is known that domain filtering algorithms can benefit from reasoning about the interaction of among constraints so that values can be filtered out taking into consideration several among constraints simultaneously. The present pa- per embarks into a systematic investigation on the circumstances under which it is possible to obtain efficient and complete domain filtering algorithms for conjunctions of among constraints. We start by observing that restrictions on both the scope and the range of the among constraints are necessary to obtain meaningful results. Then, we derive a domain flow-based filtering algorithm and present several applications. In particular, it is shown that the algorithm unifies and generalizes several previous existing results. |
2310.13570 | Alexandros Xenos | Alexandros Xenos, Themos Stafylakis, Ioannis Patras and Georgios
Tzimiropoulos | A Simple Baseline for Knowledge-Based Visual Question Answering | Accepted at EMNLP 2023 (camera-ready version) | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | This paper is on the problem of Knowledge-Based Visual Question Answering
(KB-VQA). Recent works have emphasized the significance of incorporating both
explicit (through external databases) and implicit (through LLMs) knowledge to
answer questions requiring external knowledge effectively. A common limitation
of such approaches is that they consist of relatively complicated pipelines and
often heavily rely on accessing GPT-3 API. Our main contribution in this paper
is to propose a much simpler and readily reproducible pipeline which, in a
nutshell, is based on efficient in-context learning by prompting LLaMA (1 and
2) using question-informative captions as contextual information. Contrary to
recent approaches, our method is training-free, does not require access to
external databases or APIs, and yet achieves state-of-the-art accuracy on the
OK-VQA and A-OK-VQA datasets. Finally, we perform several ablation studies to
understand important aspects of our method. Our code is publicly available at
https://github.com/alexandrosXe/ASimple-Baseline-For-Knowledge-Based-VQA
| [
{
"created": "Fri, 20 Oct 2023 15:08:17 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Oct 2023 13:24:25 GMT",
"version": "v2"
}
] | 2023-10-25 | [
[
"Xenos",
"Alexandros",
""
],
[
"Stafylakis",
"Themos",
""
],
[
"Patras",
"Ioannis",
""
],
[
"Tzimiropoulos",
"Georgios",
""
]
] | This paper is on the problem of Knowledge-Based Visual Question Answering (KB-VQA). Recent works have emphasized the significance of incorporating both explicit (through external databases) and implicit (through LLMs) knowledge to answer questions requiring external knowledge effectively. A common limitation of such approaches is that they consist of relatively complicated pipelines and often heavily rely on accessing GPT-3 API. Our main contribution in this paper is to propose a much simpler and readily reproducible pipeline which, in a nutshell, is based on efficient in-context learning by prompting LLaMA (1 and 2) using question-informative captions as contextual information. Contrary to recent approaches, our method is training-free, does not require access to external databases or APIs, and yet achieves state-of-the-art accuracy on the OK-VQA and A-OK-VQA datasets. Finally, we perform several ablation studies to understand important aspects of our method. Our code is publicly available at https://github.com/alexandrosXe/ASimple-Baseline-For-Knowledge-Based-VQA |
1601.00184 | Asaf Shabtai | Ben Feher, Lior Sidi, Asaf Shabtai, Rami Puzis | The Security of WebRTC | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | WebRTC is an API that allows users to share streaming information, whether it
is text, sound, video or files. It is supported by all major browsers and has a
flexible underlying infrastructure. In this study we review current WebRTC
structure and security in the contexts of communication disruption,
modification and eavesdropping. In addition, we examine WebRTC security in a
few representative scenarios, setting up and simulating real WebRTC
environments and attacks.
| [
{
"created": "Sat, 2 Jan 2016 15:59:55 GMT",
"version": "v1"
}
] | 2016-01-05 | [
[
"Feher",
"Ben",
""
],
[
"Sidi",
"Lior",
""
],
[
"Shabtai",
"Asaf",
""
],
[
"Puzis",
"Rami",
""
]
] | WebRTC is an API that allows users to share streaming information, whether it is text, sound, video or files. It is supported by all major browsers and has a flexible underlying infrastructure. In this study we review current WebRTC structure and security in the contexts of communication disruption, modification and eavesdropping. In addition, we examine WebRTC security in a few representative scenarios, setting up and simulating real WebRTC environments and attacks. |
0909.0685 | Chris Giannella | Joel W. Branch, Chris Giannella, Boleslaw Szymanski, Ran Wolff, Hillol
Kargupta | In-Network Outlier Detection in Wireless Sensor Networks | Extended version of a paper appearing in the Int'l Conference on
Distributed Computing Systems 2006 | Knowledge and Information Systems 34(1) January, 2013, pp. 23-54 | 10.1007/s10115-011-0474-5 | null | cs.DB cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To address the problem of unsupervised outlier detection in wireless sensor
networks, we develop an approach that (1) is flexible with respect to the
outlier definition, (2) computes the result in-network to reduce both bandwidth
and energy usage,(3) only uses single hop communication thus permitting very
simple node failure detection and message reliability assurance mechanisms
(e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data.
We examine performance using simulation with real sensor data streams. Our
results demonstrate that our approach is accurate and imposes a reasonable
communication load and level of power consumption.
| [
{
"created": "Thu, 3 Sep 2009 15:26:38 GMT",
"version": "v1"
}
] | 2013-05-15 | [
[
"Branch",
"Joel W.",
""
],
[
"Giannella",
"Chris",
""
],
[
"Szymanski",
"Boleslaw",
""
],
[
"Wolff",
"Ran",
""
],
[
"Kargupta",
"Hillol",
""
]
] | To address the problem of unsupervised outlier detection in wireless sensor networks, we develop an approach that (1) is flexible with respect to the outlier definition, (2) computes the result in-network to reduce both bandwidth and energy usage,(3) only uses single hop communication thus permitting very simple node failure detection and message reliability assurance mechanisms (e.g., carrier-sense), and (4) seamlessly accommodates dynamic updates to data. We examine performance using simulation with real sensor data streams. Our results demonstrate that our approach is accurate and imposes a reasonable communication load and level of power consumption. |
2005.10217 | Zhiguo Ding | Z. Ding and R. Schober and H. V. Poor | Unveiling the Importance of SIC in NOMA Systems: Part II: New Results
and Future Directions | null | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In most existing works on non-orthogonal multiple access (NOMA), the decoding
order of successive interference cancellation (SIC) is prefixed and based on
either the users' channel conditions or their quality of service (QoS)
requirements. A recent work on NOMA assisted semi-grant-free transmission
showed that the use of a more sophisticated hybrid SIC scheme can yield
significant performance improvements. This letter illustrates how the concept
of hybrid SIC can be generalized and applied to different NOMA applications. We
first use NOMA assisted mobile edge computing (MEC) as an example to illustrate
the benefits of hybrid SIC, where new results for delay and energy minimization
are presented. Then, future directions for generalizing hybrid SIC with
adaptive decoding order selection as well as its promising applications are
discussed.
| [
{
"created": "Wed, 20 May 2020 17:29:21 GMT",
"version": "v1"
}
] | 2020-05-21 | [
[
"Ding",
"Z.",
""
],
[
"Schober",
"R.",
""
],
[
"Poor",
"H. V.",
""
]
] | In most existing works on non-orthogonal multiple access (NOMA), the decoding order of successive interference cancellation (SIC) is prefixed and based on either the users' channel conditions or their quality of service (QoS) requirements. A recent work on NOMA assisted semi-grant-free transmission showed that the use of a more sophisticated hybrid SIC scheme can yield significant performance improvements. This letter illustrates how the concept of hybrid SIC can be generalized and applied to different NOMA applications. We first use NOMA assisted mobile edge computing (MEC) as an example to illustrate the benefits of hybrid SIC, where new results for delay and energy minimization are presented. Then, future directions for generalizing hybrid SIC with adaptive decoding order selection as well as its promising applications are discussed. |
2211.06720 | Shubham Varma | Rupali Patil, Bhairav Narkhede, Shubham Varma, Shreyans Suraliya,
Ninad Mehendale | Auto Lead Extraction and Digitization of ECG Paper Records using cGAN | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Purpose: An Electrocardiogram (ECG) is the simplest and fastest bio-medical
test that is used to detect any heart-related disease. ECG signals are
generally stored in paper form, which makes it difficult to store and analyze
the data. While capturing ECG leads from paper ECG records, a lot of background
information is also captured, which results in incorrect data interpretation.
Methods: We propose a deep learning-based model for individually extracting
all 12 leads from 12-lead ECG images captured using a camera. To simplify the
analysis of the ECG and the calculation of complex parameters, we also propose
a method to convert the paper ECG format into a storable digital format. The
You Only Look Once, Version 3 (YOLOv3) algorithm has been used to extract the
leads present in the image. These leads are then passed on to another deep
learning model which separates the ECG signal and background from the
single-lead image. After that, vertical scanning is performed on the ECG signal
to convert it into a 1-Dimensional (1D) digital form. To perform the task of
digitalization, we used the pix-2-pix deep learning model and binarized the ECG
signals.
Results: Our proposed method was able to achieve an accuracy of 97.4 %.
Conclusion: The information on the paper ECG fades away over time. Hence, the
digitized ECG signals make it possible to store the records and access them
anytime. This proves highly beneficial for heart patients who require frequent
ECG reports. The stored data can also be useful for research purposes, as this
data can be used to develop computer algorithms that are capable of analyzing
the data.
| [
{
"created": "Sat, 12 Nov 2022 18:36:29 GMT",
"version": "v1"
}
] | 2022-11-15 | [
[
"Patil",
"Rupali",
""
],
[
"Narkhede",
"Bhairav",
""
],
[
"Varma",
"Shubham",
""
],
[
"Suraliya",
"Shreyans",
""
],
[
"Mehendale",
"Ninad",
""
]
] | Purpose: An Electrocardiogram (ECG) is the simplest and fastest bio-medical test that is used to detect any heart-related disease. ECG signals are generally stored in paper form, which makes it difficult to store and analyze the data. While capturing ECG leads from paper ECG records, a lot of background information is also captured, which results in incorrect data interpretation. Methods: We propose a deep learning-based model for individually extracting all 12 leads from 12-lead ECG images captured using a camera. To simplify the analysis of the ECG and the calculation of complex parameters, we also propose a method to convert the paper ECG format into a storable digital format. The You Only Look Once, Version 3 (YOLOv3) algorithm has been used to extract the leads present in the image. These leads are then passed on to another deep learning model which separates the ECG signal and background from the single-lead image. After that, vertical scanning is performed on the ECG signal to convert it into a 1-Dimensional (1D) digital form. To perform the task of digitalization, we used the pix-2-pix deep learning model and binarized the ECG signals. Results: Our proposed method was able to achieve an accuracy of 97.4 %. Conclusion: The information on the paper ECG fades away over time. Hence, the digitized ECG signals make it possible to store the records and access them anytime. This proves highly beneficial for heart patients who require frequent ECG reports. The stored data can also be useful for research purposes, as this data can be used to develop computer algorithms that are capable of analyzing the data. |
2302.00861 | Jiaxiang Dong | Jiaxiang Dong, Haixu Wu, Haoran Zhang, Li Zhang, Jianmin Wang,
Mingsheng Long | SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Time series analysis is widely used in extensive areas. Recently, to reduce
labeling expenses and benefit various tasks, self-supervised pre-training has
attracted immense interest. One mainstream paradigm is masked modeling, which
successfully pre-trains deep models by learning to reconstruct the masked
content based on the unmasked part. However, since the semantic information of
time series is mainly contained in temporal variations, the standard way of
randomly masking a portion of time points will seriously ruin vital temporal
variations of time series, making the reconstruction task too difficult to
guide representation learning. We thus present SimMTM, a Simple pre-training
framework for Masked Time-series Modeling. By relating masked modeling to
manifold learning, SimMTM proposes to recover masked time points by the
weighted aggregation of multiple neighbors outside the manifold, which eases
the reconstruction task by assembling ruined but complementary temporal
variations from multiple masked series. SimMTM further learns to uncover the
local structure of the manifold, which is helpful for masked modeling.
Experimentally, SimMTM achieves state-of-the-art fine-tuning performance
compared to the most advanced time series pre-training methods in two canonical
time series analysis tasks: forecasting and classification, covering both in-
and cross-domain settings.
| [
{
"created": "Thu, 2 Feb 2023 04:12:29 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Feb 2023 05:25:58 GMT",
"version": "v2"
},
{
"created": "Fri, 26 May 2023 14:29:09 GMT",
"version": "v3"
},
{
"created": "Mon, 23 Oct 2023 13:02:38 GMT",
"version": "v4"
}
] | 2023-10-24 | [
[
"Dong",
"Jiaxiang",
""
],
[
"Wu",
"Haixu",
""
],
[
"Zhang",
"Haoran",
""
],
[
"Zhang",
"Li",
""
],
[
"Wang",
"Jianmin",
""
],
[
"Long",
"Mingsheng",
""
]
] | Time series analysis is widely used in extensive areas. Recently, to reduce labeling expenses and benefit various tasks, self-supervised pre-training has attracted immense interest. One mainstream paradigm is masked modeling, which successfully pre-trains deep models by learning to reconstruct the masked content based on the unmasked part. However, since the semantic information of time series is mainly contained in temporal variations, the standard way of randomly masking a portion of time points will seriously ruin vital temporal variations of time series, making the reconstruction task too difficult to guide representation learning. We thus present SimMTM, a Simple pre-training framework for Masked Time-series Modeling. By relating masked modeling to manifold learning, SimMTM proposes to recover masked time points by the weighted aggregation of multiple neighbors outside the manifold, which eases the reconstruction task by assembling ruined but complementary temporal variations from multiple masked series. SimMTM further learns to uncover the local structure of the manifold, which is helpful for masked modeling. Experimentally, SimMTM achieves state-of-the-art fine-tuning performance compared to the most advanced time series pre-training methods in two canonical time series analysis tasks: forecasting and classification, covering both in- and cross-domain settings. |
2304.00363 | Pablo Gamallo | Miguel Cavadas and Pablo Gamallo | Automatic Authorship Attribution in the Work of Tirso de Molina | 20 pages, 2 figures | Recent Advances in Digital Humanities: Romance Language
Applications, Peter Lang Edition, 2022, DOI 10.3726/b19920. ISBN
978-3-631-81147-4 | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Automatic Authorship Attribution (AAA) is the result of applying tools and
techniques from Digital Humanities to authorship attribution studies. Through a
quantitative and statistical approach this discipline can draw further
conclusions about renowned authorship issues which traditional critics have
been dealing with for centuries, opening a new door to style comparison. The
aim of this paper is to prove the potential of these tools and techniques by
testing the authorship of five comedies traditionally attributed to Spanish
playwright Tirso de Molina (1579-1648): La ninfa del cielo, El burlador de
Sevilla, Tan largo me lo fiais, La mujer por fuerza and El condenado por
desconfiado. To accomplish this purpose some experiments concerning clustering
analysis by Stylo package from R and four distance measures are carried out on
a corpus built with plays by Tirso, Andres de Claramonte (c. 1560-1626),
Antonio Mira de Amescua (1577-1644) and Luis Velez de Guevara (1579-1644). The
results obtained point to the denial of all the attributions to Tirso except
for the case of La mujer por fuerza.
| [
{
"created": "Sat, 1 Apr 2023 18:05:14 GMT",
"version": "v1"
}
] | 2023-04-04 | [
[
"Cavadas",
"Miguel",
""
],
[
"Gamallo",
"Pablo",
""
]
] | Automatic Authorship Attribution (AAA) is the result of applying tools and techniques from Digital Humanities to authorship attribution studies. Through a quantitative and statistical approach this discipline can draw further conclusions about renowned authorship issues which traditional critics have been dealing with for centuries, opening a new door to style comparison. The aim of this paper is to prove the potential of these tools and techniques by testing the authorship of five comedies traditionally attributed to Spanish playwright Tirso de Molina (1579-1648): La ninfa del cielo, El burlador de Sevilla, Tan largo me lo fiais, La mujer por fuerza and El condenado por desconfiado. To accomplish this purpose some experiments concerning clustering analysis by Stylo package from R and four distance measures are carried out on a corpus built with plays by Tirso, Andres de Claramonte (c. 1560-1626), Antonio Mira de Amescua (1577-1644) and Luis Velez de Guevara (1579-1644). The results obtained point to the denial of all the attributions to Tirso except for the case of La mujer por fuerza. |
2211.08704 | Sicheng Mo | Sicheng Mo, Fangzhou Mu, Yin Li | A Simple Transformer-Based Model for Ego4D Natural Language Queries
Challenge | 5 pages, 2 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | This report describes Badgers@UW-Madison, our submission to the Ego4D Natural
Language Queries (NLQ) Challenge. Our solution inherits the point-based event
representation from our prior work on temporal action localization, and
develops a Transformer-based model for video grounding. Further, our solution
integrates several strong video features including SlowFast, Omnivore and
EgoVLP. Without bells and whistles, our submission based on a single model
achieves 12.64% Mean R@1 and is ranked 2nd on the public leaderboard.
Meanwhile, our method garners 28.45% (18.03%) R@5 at tIoU=0.3 (0.5), surpassing
the top-ranked solution by up to 5.5 absolute percentage points.
| [
{
"created": "Wed, 16 Nov 2022 06:33:37 GMT",
"version": "v1"
}
] | 2022-11-17 | [
[
"Mo",
"Sicheng",
""
],
[
"Mu",
"Fangzhou",
""
],
[
"Li",
"Yin",
""
]
] | This report describes Badgers@UW-Madison, our submission to the Ego4D Natural Language Queries (NLQ) Challenge. Our solution inherits the point-based event representation from our prior work on temporal action localization, and develops a Transformer-based model for video grounding. Further, our solution integrates several strong video features including SlowFast, Omnivore and EgoVLP. Without bells and whistles, our submission based on a single model achieves 12.64% Mean R@1 and is ranked 2nd on the public leaderboard. Meanwhile, our method garners 28.45% (18.03%) R@5 at tIoU=0.3 (0.5), surpassing the top-ranked solution by up to 5.5 absolute percentage points. |
2208.10817 | Hsien-Chin Lin | Hsien-Chin Lin, Christian Geishauser, Shutong Feng, Nurul Lubis, Carel
van Niekerk, Michael Heck, and Milica Ga\v{s}i\'c | GenTUS: Simulating User Behaviour and Language in Task-oriented
Dialogues with Generative Transformers | Accepted as a long paper to SIGDial 2022 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | User simulators (USs) are commonly used to train task-oriented dialogue
systems (DSs) via reinforcement learning. The interactions often take place on
semantic level for efficiency, but there is still a gap from semantic actions
to natural language, which causes a mismatch between training and deployment
environment. Incorporating a natural language generation (NLG) module with USs
during training can partly deal with this problem. However, since the policy
and NLG of USs are optimised separately, these simulated user utterances may
not be natural enough in a given context. In this work, we propose a generative
transformer-based user simulator (GenTUS). GenTUS consists of an
encoder-decoder structure, which means it can optimise both the user policy and
natural language generation jointly. GenTUS generates both semantic actions and
natural language utterances, preserving interpretability and enhancing language
variation. In addition, by representing the inputs and outputs as word
sequences and by using a large pre-trained language model we can achieve
generalisability in feature representation. We evaluate GenTUS with automatic
metrics and human evaluation. Our results show that GenTUS generates more
natural language and is able to transfer to an unseen ontology in a zero-shot
fashion. In addition, its behaviour can be further shaped with reinforcement
learning opening the door to training specialised user simulators.
| [
{
"created": "Tue, 23 Aug 2022 09:01:17 GMT",
"version": "v1"
}
] | 2022-08-24 | [
[
"Lin",
"Hsien-Chin",
""
],
[
"Geishauser",
"Christian",
""
],
[
"Feng",
"Shutong",
""
],
[
"Lubis",
"Nurul",
""
],
[
"van Niekerk",
"Carel",
""
],
[
"Heck",
"Michael",
""
],
[
"Gašić",
"Milica",
""
]
] | User simulators (USs) are commonly used to train task-oriented dialogue systems (DSs) via reinforcement learning. The interactions often take place on semantic level for efficiency, but there is still a gap from semantic actions to natural language, which causes a mismatch between training and deployment environment. Incorporating a natural language generation (NLG) module with USs during training can partly deal with this problem. However, since the policy and NLG of USs are optimised separately, these simulated user utterances may not be natural enough in a given context. In this work, we propose a generative transformer-based user simulator (GenTUS). GenTUS consists of an encoder-decoder structure, which means it can optimise both the user policy and natural language generation jointly. GenTUS generates both semantic actions and natural language utterances, preserving interpretability and enhancing language variation. In addition, by representing the inputs and outputs as word sequences and by using a large pre-trained language model we can achieve generalisability in feature representation. We evaluate GenTUS with automatic metrics and human evaluation. Our results show that GenTUS generates more natural language and is able to transfer to an unseen ontology in a zero-shot fashion. In addition, its behaviour can be further shaped with reinforcement learning opening the door to training specialised user simulators. |
2406.08946 | Enrico Ferrentino | Lorenzo Pagliara, Vincenzo Petrone, Enrico Ferrentino, Pasquale
Chiacchio | Human-Robot Interface for Teleoperated Robotized Planetary Sample
Collection and Assembly | null | 2023 IEEE 10th International Workshop on Metrology for AeroSpace
(MetroAeroSpace), Milan, Italy, 2023, pp. 171-176 | 10.1109/MetroAeroSpace57412.2023.10189984 | null | cs.RO cs.HC cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As human space exploration evolves toward longer voyages farther from our
home planet, in-situ resource utilization (ISRU) becomes increasingly
important. Haptic teleoperations are one of the technologies by which such
activities can be carried out remotely by humans, whose expertise is still
necessary for complex activities. In order to perform precision tasks with
effectiveness, the operator must experience ease of use and accuracy. The same
features are demanded to reduce the complexity of the training procedures and
the associated learning time for operators without a specific background in
robotic teleoperations. Haptic teleoperation systems, that allow for a natural
feeling of forces, need to cope with the trade-off between accurate movements
and workspace extension. Clearly, both of them are required for typical ISRU
tasks. In this work, we develop a new concept of operations and suitable
human-robot interfaces to achieve sample collection and assembly with ease of
use and accuracy. In the proposed operational concept, the teleoperation space
is extended by executing automated trajectories, offline planned at the control
station. In three different experimental scenarios, we validate the end-to-end
system involving the control station and the robotic asset, by assessing the
contribution of haptics to mission success, the system robustness to consistent
delays, and the ease of training new operators.
| [
{
"created": "Thu, 13 Jun 2024 09:17:10 GMT",
"version": "v1"
}
] | 2024-06-14 | [
[
"Pagliara",
"Lorenzo",
""
],
[
"Petrone",
"Vincenzo",
""
],
[
"Ferrentino",
"Enrico",
""
],
[
"Chiacchio",
"Pasquale",
""
]
] | As human space exploration evolves toward longer voyages farther from our home planet, in-situ resource utilization (ISRU) becomes increasingly important. Haptic teleoperations are one of the technologies by which such activities can be carried out remotely by humans, whose expertise is still necessary for complex activities. In order to perform precision tasks with effectiveness, the operator must experience ease of use and accuracy. The same features are demanded to reduce the complexity of the training procedures and the associated learning time for operators without a specific background in robotic teleoperations. Haptic teleoperation systems, that allow for a natural feeling of forces, need to cope with the trade-off between accurate movements and workspace extension. Clearly, both of them are required for typical ISRU tasks. In this work, we develop a new concept of operations and suitable human-robot interfaces to achieve sample collection and assembly with ease of use and accuracy. In the proposed operational concept, the teleoperation space is extended by executing automated trajectories, offline planned at the control station. In three different experimental scenarios, we validate the end-to-end system involving the control station and the robotic asset, by assessing the contribution of haptics to mission success, the system robustness to consistent delays, and the ease of training new operators. |
1203.4364 | Marilyne Rosselle | Marilyne Rosselle | Teacher Module in an Assistance Tool - Adaptating a device to a teaching
context and and teacher's preferences | 6 pages, 3 figures. This article is a long version of the one edited
in ICALT'2012 | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This communication presents the genesis and the implementation of a teacher
module, which is included in an Assistance Tool (AT). The teacher module is
based on a teacher model for which we did a thorough analysis of the state of
the art. The aim of the AT is to help a teacher to design pedagogical devices.
Teachers can formulate their needs (assistance in the design) and the AT can
relieve them from repetitive tasks related to the deployment of a teaching
device (assistance in the deployment).
| [
{
"created": "Tue, 20 Mar 2012 10:01:37 GMT",
"version": "v1"
}
] | 2013-03-12 | [
[
"Rosselle",
"Marilyne",
""
]
] | This communication presents the genesis and the implementation of a teacher module, which is included in an Assistance Tool (AT). The teacher module is based on a teacher model for which we did a thorough analysis of the state of the art. The aim of the AT is to help a teacher to design pedagogical devices. Teachers can formulate their needs (assistance in the design) and the AT can relieve them from repetitive tasks related to the deployment of a teaching device (assistance in the deployment). |
2311.03534 | Anurag Koul | Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron
Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan Molu,
Miro Dudik, John Langford, Alex Lamb | PcLast: Discovering Plannable Continuous Latent States | Accepted at ICML 2024 | null | null | null | cs.LG cs.AI cs.RO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Goal-conditioned planning benefits from learned low-dimensional
representations of rich observations. While compact latent representations
typically learned from variational autoencoders or inverse dynamics enable
goal-conditioned decision making, they ignore state reachability, hampering
their performance. In this paper, we learn a representation that associates
reachable states together for effective planning and goal-conditioned policy
learning. We first learn a latent representation with multi-step inverse
dynamics (to remove distracting information), and then transform this
representation to associate reachable states together in $\ell_2$ space. Our
proposals are rigorously tested in various simulation testbeds. Numerical
results in reward-based settings show significant improvements in sampling
efficiency. Further, in reward-free settings this approach yields layered state
abstractions that enable computationally efficient hierarchical planning for
reaching ad hoc goals with zero additional samples.
| [
{
"created": "Mon, 6 Nov 2023 21:16:37 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Jun 2024 03:32:58 GMT",
"version": "v2"
}
] | 2024-06-12 | [
[
"Koul",
"Anurag",
""
],
[
"Sujit",
"Shivakanth",
""
],
[
"Chen",
"Shaoru",
""
],
[
"Evans",
"Ben",
""
],
[
"Wu",
"Lili",
""
],
[
"Xu",
"Byron",
""
],
[
"Chari",
"Rajan",
""
],
[
"Islam",
"Riashat",
""
],
[
"Seraj",
"Raihan",
""
],
[
"Efroni",
"Yonathan",
""
],
[
"Molu",
"Lekan",
""
],
[
"Dudik",
"Miro",
""
],
[
"Langford",
"John",
""
],
[
"Lamb",
"Alex",
""
]
] | Goal-conditioned planning benefits from learned low-dimensional representations of rich observations. While compact latent representations typically learned from variational autoencoders or inverse dynamics enable goal-conditioned decision making, they ignore state reachability, hampering their performance. In this paper, we learn a representation that associates reachable states together for effective planning and goal-conditioned policy learning. We first learn a latent representation with multi-step inverse dynamics (to remove distracting information), and then transform this representation to associate reachable states together in $\ell_2$ space. Our proposals are rigorously tested in various simulation testbeds. Numerical results in reward-based settings show significant improvements in sampling efficiency. Further, in reward-free settings this approach yields layered state abstractions that enable computationally efficient hierarchical planning for reaching ad hoc goals with zero additional samples. |
2401.16422 | Eliot Shekhtman | Eliot Shekhtman and Sarah Dean | Strategic Usage in a Multi-Learner Setting | 18 pages, 9 figures | null | null | null | cs.LG cs.GT | http://creativecommons.org/licenses/by/4.0/ | Real-world systems often involve some pool of users choosing between a set of
services. With the increase in popularity of online learning algorithms, these
services can now self-optimize, leveraging data collected on users to maximize
some reward such as service quality. On the flipside, users may strategically
choose which services to use in order to pursue their own reward functions, in
the process wielding power over which services can see and use their data.
Extensive prior research has been conducted on the effects of strategic users
in single-service settings, with strategic behavior manifesting in the
manipulation of observable features to achieve a desired classification;
however, this can often be costly or unattainable for users and fails to
capture the full behavior of multi-service dynamic systems. As such, we analyze
a setting in which strategic users choose among several available services in
order to pursue positive classifications, while services seek to minimize loss
functions on their observations. We focus our analysis on realizable settings,
and show that naive retraining can still lead to oscillation even if all users
are observed at different times; however, if this retraining uses memory of
past observations, convergent behavior can be guaranteed for certain loss
function classes. We provide results obtained from synthetic and real-world
data to empirically validate our theoretical findings.
| [
{
"created": "Mon, 29 Jan 2024 18:59:22 GMT",
"version": "v1"
},
{
"created": "Fri, 8 Mar 2024 21:01:08 GMT",
"version": "v2"
}
] | 2024-03-12 | [
[
"Shekhtman",
"Eliot",
""
],
[
"Dean",
"Sarah",
""
]
] | Real-world systems often involve some pool of users choosing between a set of services. With the increase in popularity of online learning algorithms, these services can now self-optimize, leveraging data collected on users to maximize some reward such as service quality. On the flipside, users may strategically choose which services to use in order to pursue their own reward functions, in the process wielding power over which services can see and use their data. Extensive prior research has been conducted on the effects of strategic users in single-service settings, with strategic behavior manifesting in the manipulation of observable features to achieve a desired classification; however, this can often be costly or unattainable for users and fails to capture the full behavior of multi-service dynamic systems. As such, we analyze a setting in which strategic users choose among several available services in order to pursue positive classifications, while services seek to minimize loss functions on their observations. We focus our analysis on realizable settings, and show that naive retraining can still lead to oscillation even if all users are observed at different times; however, if this retraining uses memory of past observations, convergent behavior can be guaranteed for certain loss function classes. We provide results obtained from synthetic and real-world data to empirically validate our theoretical findings. |
2310.13016 | Sengul Dogan | Turker Tuncer and Sengul Dogan and Mehmet Baygin and Prabal Datta
Barua and Abdul Hafeez-Baig and Ru-San Tan and Subrata Chakraborty and U.
Rajendra Acharya | Solving the multiplication problem of a large language model system
using a graph-based method | 9 pages, 3 figures | null | null | null | cs.OH cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The generative pre-trained transformer (GPT)-based chatbot software ChatGPT
possesses excellent natural language processing capabilities but is inadequate
for solving arithmetic problems, especially multiplication. Its GPT structure
uses a computational graph for multiplication, which has limited accuracy
beyond simple multiplication operations. We developed a graph-based
multiplication algorithm that emulated human-like numerical operations by
incorporating a 10k operator, where k represents the maximum power to base 10
of the larger of two input numbers. Our proposed algorithm attained 100%
accuracy for 1,000,000 large number multiplication tasks, effectively solving
the multiplication challenge of GPT-based and other large language models. Our
work highlights the importance of blending simple human insights into the
design of artificial intelligence algorithms. Keywords: Graph-based
multiplication; ChatGPT; Multiplication problem
| [
{
"created": "Wed, 18 Oct 2023 08:02:00 GMT",
"version": "v1"
}
] | 2023-10-23 | [
[
"Tuncer",
"Turker",
""
],
[
"Dogan",
"Sengul",
""
],
[
"Baygin",
"Mehmet",
""
],
[
"Barua",
"Prabal Datta",
""
],
[
"Hafeez-Baig",
"Abdul",
""
],
[
"Tan",
"Ru-San",
""
],
[
"Chakraborty",
"Subrata",
""
],
[
"Acharya",
"U. Rajendra",
""
]
] | The generative pre-trained transformer (GPT)-based chatbot software ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems, especially multiplication. Its GPT structure uses a computational graph for multiplication, which has limited accuracy beyond simple multiplication operations. We developed a graph-based multiplication algorithm that emulated human-like numerical operations by incorporating a 10k operator, where k represents the maximum power to base 10 of the larger of two input numbers. Our proposed algorithm attained 100% accuracy for 1,000,000 large number multiplication tasks, effectively solving the multiplication challenge of GPT-based and other large language models. Our work highlights the importance of blending simple human insights into the design of artificial intelligence algorithms. Keywords: Graph-based multiplication; ChatGPT; Multiplication problem |
2108.05774 | Kuldeep Singh | Anson Bastos, Kuldeep Singh, Abhishek Nadgeri, Saeedeh Shekarpour,
Isaiah Onando Mulang, Johannes Hoffart | HopfE: Knowledge Graph Representation Learning using Inverse Hopf
Fibrations | CIKM 2021 : 30th ACM International Conference on Information and
Knowledge Management (full paper) | null | null | null | cs.IR cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Recently, several Knowledge Graph Embedding (KGE) approaches have been
devised to represent entities and relations in dense vector space and employed
in downstream tasks such as link prediction. A few KGE techniques address
interpretability, i.e., mapping the connectivity patterns of the relations
(i.e., symmetric/asymmetric, inverse, and composition) to a geometric
interpretation such as rotations. Other approaches model the representations in
higher dimensional space such as four-dimensional space (4D) to enhance the
ability to infer the connectivity patterns (i.e., expressiveness). However,
modeling relation and entity in a 4D space often comes at the cost of
interpretability. This paper proposes HopfE, a novel KGE approach aiming to
achieve the interpretability of inferred relations in the four-dimensional
space. We first model the structural embeddings in 3D Euclidean space and view
the relation operator as an SO(3) rotation. Next, we map the entity embedding
vector from a 3D space to a 4D hypersphere using the inverse Hopf Fibration, in
which we embed the semantic information from the KG ontology. Thus, HopfE
considers the structural and semantic properties of the entities without losing
expressivity and interpretability. Our empirical results on four well-known
benchmarks achieve state-of-the-art performance for the KG completion task.
| [
{
"created": "Thu, 12 Aug 2021 14:34:02 GMT",
"version": "v1"
}
] | 2021-08-13 | [
[
"Bastos",
"Anson",
""
],
[
"Singh",
"Kuldeep",
""
],
[
"Nadgeri",
"Abhishek",
""
],
[
"Shekarpour",
"Saeedeh",
""
],
[
"Mulang",
"Isaiah Onando",
""
],
[
"Hoffart",
"Johannes",
""
]
] | Recently, several Knowledge Graph Embedding (KGE) approaches have been devised to represent entities and relations in dense vector space and employed in downstream tasks such as link prediction. A few KGE techniques address interpretability, i.e., mapping the connectivity patterns of the relations (i.e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations. Other approaches model the representations in higher dimensional space such as four-dimensional space (4D) to enhance the ability to infer the connectivity patterns (i.e., expressiveness). However, modeling relation and entity in a 4D space often comes at the cost of interpretability. This paper proposes HopfE, a novel KGE approach aiming to achieve the interpretability of inferred relations in the four-dimensional space. We first model the structural embeddings in 3D Euclidean space and view the relation operator as an SO(3) rotation. Next, we map the entity embedding vector from a 3D space to a 4D hypersphere using the inverse Hopf Fibration, in which we embed the semantic information from the KG ontology. Thus, HopfE considers the structural and semantic properties of the entities without losing expressivity and interpretability. Our empirical results on four well-known benchmarks achieve state-of-the-art performance for the KG completion task. |
2208.04286 | Pilhyeon Lee | Sungpil Kho, Pilhyeon Lee, Wonyoung Lee, Minsong Ki, Hyeran Byun | Exploiting Shape Cues for Weakly Supervised Semantic Segmentation | Accepted by Pattern Recognition. The first two authors contributed
equally | Pattern Recognition 132 (2022): 108953 | 10.1016/j.patcog.2022.108953 | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise
class predictions with only image-level labels for training. To this end,
previous methods adopt the common pipeline: they generate pseudo masks from
class activation maps (CAMs) and use such masks to supervise segmentation
networks. However, it is challenging to derive comprehensive pseudo masks that
cover the whole extent of objects due to the local property of CAMs, i.e., they
tend to focus solely on small discriminative object parts. In this paper, we
associate the locality of CAMs with the texture-biased property of
convolutional neural networks (CNNs). Accordingly, we propose to exploit shape
information to supplement the texture-biased CNN features, thereby encouraging
mask predictions to be not only comprehensive but also well-aligned with object
boundaries. We further refine the predictions in an online fashion with a novel
refinement method that takes into account both the class and the color
affinities, in order to generate reliable pseudo masks to supervise the model.
Importantly, our model is end-to-end trained within a single-stage framework
and therefore efficient in terms of the training cost. Through extensive
experiments on PASCAL VOC 2012, we validate the effectiveness of our method in
producing precise and shape-aligned segmentation results. Specifically, our
model surpasses the existing state-of-the-art single-stage approaches by large
margins. What is more, it also achieves a new state-of-the-art performance over
multi-stage approaches, when adopted in a simple two-stage pipeline without
bells and whistles.
| [
{
"created": "Mon, 8 Aug 2022 17:25:31 GMT",
"version": "v1"
}
] | 2022-08-09 | [
[
"Kho",
"Sungpil",
""
],
[
"Lee",
"Pilhyeon",
""
],
[
"Lee",
"Wonyoung",
""
],
[
"Ki",
"Minsong",
""
],
[
"Byun",
"Hyeran",
""
]
] | Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise class predictions with only image-level labels for training. To this end, previous methods adopt the common pipeline: they generate pseudo masks from class activation maps (CAMs) and use such masks to supervise segmentation networks. However, it is challenging to derive comprehensive pseudo masks that cover the whole extent of objects due to the local property of CAMs, i.e., they tend to focus solely on small discriminative object parts. In this paper, we associate the locality of CAMs with the texture-biased property of convolutional neural networks (CNNs). Accordingly, we propose to exploit shape information to supplement the texture-biased CNN features, thereby encouraging mask predictions to be not only comprehensive but also well-aligned with object boundaries. We further refine the predictions in an online fashion with a novel refinement method that takes into account both the class and the color affinities, in order to generate reliable pseudo masks to supervise the model. Importantly, our model is end-to-end trained within a single-stage framework and therefore efficient in terms of the training cost. Through extensive experiments on PASCAL VOC 2012, we validate the effectiveness of our method in producing precise and shape-aligned segmentation results. Specifically, our model surpasses the existing state-of-the-art single-stage approaches by large margins. What is more, it also achieves a new state-of-the-art performance over multi-stage approaches, when adopted in a simple two-stage pipeline without bells and whistles. |
2004.03073 | Anastasios Petropoulos | Anastasios Petropoulos, Irem Boybat, Manuel Le Gallo, Evangelos
Eleftheriou, Abu Sebastian and Theodore Antonakopoulos | Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing | 5 pages, 4 figures, accepted for publication at ISCAS 2020 | null | null | null | cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In-memory computing is an emerging non-von Neumann computing paradigm where
certain computational tasks are performed in memory by exploiting the physical
attributes of the memory devices. Memristive devices such as phase-change
memory (PCM), where information is stored in terms of their conductance levels,
are especially well suited for in-memory computing. In particular, memristive
devices, when organized in a crossbar configuration can be used to perform
matrix-vector multiply operations by exploiting Kirchhoff's circuit laws. To
explore the feasibility of such in-memory computing cores in applications such
as deep learning as well as for system-level architectural exploration, it is
highly desirable to develop an accurate hardware emulator that captures the key
physical attributes of the memristive devices. Here, we present one such
emulator for PCM and experimentally validate it using measurements from a PCM
prototype chip. Moreover, we present an application of the emulator for neural
network inference where our emulator can capture the conductance evolution of
approximately 400,000 PCM devices remarkably well.
| [
{
"created": "Tue, 7 Apr 2020 01:53:56 GMT",
"version": "v1"
}
] | 2020-04-08 | [
[
"Petropoulos",
"Anastasios",
""
],
[
"Boybat",
"Irem",
""
],
[
"Gallo",
"Manuel Le",
""
],
[
"Eleftheriou",
"Evangelos",
""
],
[
"Sebastian",
"Abu",
""
],
[
"Antonakopoulos",
"Theodore",
""
]
] | In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where information is stored in terms of their conductance levels, are especially well suited for in-memory computing. In particular, memristive devices, when organized in a crossbar configuration can be used to perform matrix-vector multiply operations by exploiting Kirchhoff's circuit laws. To explore the feasibility of such in-memory computing cores in applications such as deep learning as well as for system-level architectural exploration, it is highly desirable to develop an accurate hardware emulator that captures the key physical attributes of the memristive devices. Here, we present one such emulator for PCM and experimentally validate it using measurements from a PCM prototype chip. Moreover, we present an application of the emulator for neural network inference where our emulator can capture the conductance evolution of approximately 400,000 PCM devices remarkably well. |
2010.13464 | Moritz Beller | Moritz Beller, Chu-Pan Wong, Johannes Bader, Andrew Scott, Mateusz
Machalica, Satish Chandra, Erik Meijer | What It Would Take to Use Mutation Testing in Industry--A Study at
Facebook | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditionally, mutation testing generates an abundance of small deviations of
a program, called mutants. At industrial systems the scale and size of
Facebook's, doing this is infeasible. We should not create mutants that the
test suite would likely fail on or that give no actionable signal to
developers. To tackle this problem, in this paper, we semi-automatically learn
error-inducing patterns from a corpus of common Java coding errors and from
changes that caused operational anomalies at Facebook specifically. We combine
the mutations with instrumentation that measures which tests exactly visited
the mutated piece of code. Results on more than 15,000 generated mutants show
that more than half of the generated mutants survive Facebook's rigorous test
suite of unit, integration, and system tests. Moreover, in a case study with 26
developers, all but two found information of automatically detected test holes
interesting in principle. As such, almost half of the 26 would actually act on
the mutant presented to them by adapting an existing or creating a new test.
The others did not for a variety of reasons often outside the scope of mutation
testing. It remains a practical challenge how we can include such external
information to increase the true actionability rate on mutants.
| [
{
"created": "Mon, 26 Oct 2020 10:03:58 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Oct 2020 06:35:50 GMT",
"version": "v2"
},
{
"created": "Wed, 27 Jan 2021 16:43:42 GMT",
"version": "v3"
}
] | 2021-01-28 | [
[
"Beller",
"Moritz",
""
],
[
"Wong",
"Chu-Pan",
""
],
[
"Bader",
"Johannes",
""
],
[
"Scott",
"Andrew",
""
],
[
"Machalica",
"Mateusz",
""
],
[
"Chandra",
"Satish",
""
],
[
"Meijer",
"Erik",
""
]
] | Traditionally, mutation testing generates an abundance of small deviations of a program, called mutants. At industrial systems the scale and size of Facebook's, doing this is infeasible. We should not create mutants that the test suite would likely fail on or that give no actionable signal to developers. To tackle this problem, in this paper, we semi-automatically learn error-inducing patterns from a corpus of common Java coding errors and from changes that caused operational anomalies at Facebook specifically. We combine the mutations with instrumentation that measures which tests exactly visited the mutated piece of code. Results on more than 15,000 generated mutants show that more than half of the generated mutants survive Facebook's rigorous test suite of unit, integration, and system tests. Moreover, in a case study with 26 developers, all but two found information of automatically detected test holes interesting in principle. As such, almost half of the 26 would actually act on the mutant presented to them by adapting an existing or creating a new test. The others did not for a variety of reasons often outside the scope of mutation testing. It remains a practical challenge how we can include such external information to increase the true actionability rate on mutants. |
1909.13548 | Sai Dayapule | Fan Yao, Kathy Ngyugen, Sai Santosh Dayapule, Jingxin Wu, Bingqian Lu,
Suresh Subramaniam, and Guru Venkataramani | HolDCSim: A Holistic Simulator for Data Centers | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cloud computing based systems, that span data centers, are commonly deployed
to offer high performance for user service requests. As data centers continue
to expand, computer architects and system designers are facing many challenges
on how to balance resource utilization efficiency, server and network
performance, energy consumption and quality-of-service (QoS) demands from the
users. To develop effective data center management policies, it becomes
essential to have an in-depth understanding and synergistic control of the
various sub-components inside large scale computing systems, that include both
computation and communication resources. In this paper, we propose HolDCSim, a
light-weight, holistic, extensible, event-driven data center simulation
platform that effectively models both server and network architectures.
HolDCSim can be used in a variety of data center system studies including
job/task scheduling, resource provisioning, global and local server farm power
management, and network and server performance analysis. We demonstrate the
design of our simulation infrastructure, and illustrate the usefulness of our
framework with several case studies that analyze server/network performance and
energy efficiency. We also perform validation on real machines to verify our
simulator.
| [
{
"created": "Mon, 30 Sep 2019 09:24:40 GMT",
"version": "v1"
},
{
"created": "Mon, 7 Oct 2019 14:13:21 GMT",
"version": "v2"
}
] | 2019-10-08 | [
[
"Yao",
"Fan",
""
],
[
"Ngyugen",
"Kathy",
""
],
[
"Dayapule",
"Sai Santosh",
""
],
[
"Wu",
"Jingxin",
""
],
[
"Lu",
"Bingqian",
""
],
[
"Subramaniam",
"Suresh",
""
],
[
"Venkataramani",
"Guru",
""
]
] | Cloud computing based systems, that span data centers, are commonly deployed to offer high performance for user service requests. As data centers continue to expand, computer architects and system designers are facing many challenges on how to balance resource utilization efficiency, server and network performance, energy consumption and quality-of-service (QoS) demands from the users. To develop effective data center management policies, it becomes essential to have an in-depth understanding and synergistic control of the various sub-components inside large scale computing systems, that include both computation and communication resources. In this paper, we propose HolDCSim, a light-weight, holistic, extensible, event-driven data center simulation platform that effectively models both server and network architectures. HolDCSim can be used in a variety of data center system studies including job/task scheduling, resource provisioning, global and local server farm power management, and network and server performance analysis. We demonstrate the design of our simulation infrastructure, and illustrate the usefulness of our framework with several case studies that analyze server/network performance and energy efficiency. We also perform validation on real machines to verify our simulator. |
2310.15829 | Corentin Kervadec | Corentin Kervadec, Francesca Franzon and Marco Baroni | Unnatural language processing: How do language models handle
machine-generated prompts? | Findings of EMNLP 2023 Camera-Ready | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Language model prompt optimization research has shown that semantically and
grammatically well-formed manually crafted prompts are routinely outperformed
by automatically generated token sequences with no apparent meaning or
syntactic structure, including sequences of vectors from a model's embedding
space. We use machine-generated prompts to probe how models respond to input
that is not composed of natural language expressions. We study the behavior of
models of different sizes in multiple semantic tasks in response to both
continuous and discrete machine-generated prompts, and compare it to the
behavior in response to human-generated natural-language prompts. Even when
producing a similar output, machine-generated and human prompts trigger
different response patterns through the network processing pathways, including
different perplexities, different attention and output entropy distributions,
and different unit activation profiles. We provide preliminary insight into the
nature of the units activated by different prompt types, suggesting that only
natural language prompts recruit a genuinely linguistic circuit.
| [
{
"created": "Tue, 24 Oct 2023 13:32:20 GMT",
"version": "v1"
}
] | 2023-10-25 | [
[
"Kervadec",
"Corentin",
""
],
[
"Franzon",
"Francesca",
""
],
[
"Baroni",
"Marco",
""
]
] | Language model prompt optimization research has shown that semantically and grammatically well-formed manually crafted prompts are routinely outperformed by automatically generated token sequences with no apparent meaning or syntactic structure, including sequences of vectors from a model's embedding space. We use machine-generated prompts to probe how models respond to input that is not composed of natural language expressions. We study the behavior of models of different sizes in multiple semantic tasks in response to both continuous and discrete machine-generated prompts, and compare it to the behavior in response to human-generated natural-language prompts. Even when producing a similar output, machine-generated and human prompts trigger different response patterns through the network processing pathways, including different perplexities, different attention and output entropy distributions, and different unit activation profiles. We provide preliminary insight into the nature of the units activated by different prompt types, suggesting that only natural language prompts recruit a genuinely linguistic circuit. |
2312.04140 | Ryota Maeda | Ryota Maeda, Shinsaku Hiura | Polarimetric Light Transport Analysis for Specular Inter-reflection | Accepted to IEEE Transactions on Computational Imaging (TCI) | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Polarization is well known for its ability to decompose diffuse and specular
reflections. However, the existing decomposition methods only focus on direct
reflection and overlook multiple reflections, especially specular
inter-reflection. In this paper, we propose a novel decomposition method for
handling specular inter-reflection of metal objects by using a unique
polarimetric feature: the rotation direction of linear polarization. This
rotation direction serves as a discriminative factor between direct and
inter-reflection on specular surfaces. To decompose the reflectance components,
we actively rotate the linear polarization of incident light and analyze the
rotation direction of the reflected light. We evaluate our method using both
synthetic and real data, demonstrating its effectiveness in decomposing
specular inter-reflections of metal objects. Furthermore, we demonstrate that
our method can be combined with other decomposition methods for a detailed
analysis of light transport. As a practical application, we show its
effectiveness in improving the accuracy of 3D measurement against strong
specular inter-reflection.
| [
{
"created": "Thu, 7 Dec 2023 08:55:28 GMT",
"version": "v1"
},
{
"created": "Wed, 15 May 2024 16:24:54 GMT",
"version": "v2"
}
] | 2024-05-16 | [
[
"Maeda",
"Ryota",
""
],
[
"Hiura",
"Shinsaku",
""
]
] | Polarization is well known for its ability to decompose diffuse and specular reflections. However, the existing decomposition methods only focus on direct reflection and overlook multiple reflections, especially specular inter-reflection. In this paper, we propose a novel decomposition method for handling specular inter-reflection of metal objects by using a unique polarimetric feature: the rotation direction of linear polarization. This rotation direction serves as a discriminative factor between direct and inter-reflection on specular surfaces. To decompose the reflectance components, we actively rotate the linear polarization of incident light and analyze the rotation direction of the reflected light. We evaluate our method using both synthetic and real data, demonstrating its effectiveness in decomposing specular inter-reflections of metal objects. Furthermore, we demonstrate that our method can be combined with other decomposition methods for a detailed analysis of light transport. As a practical application, we show its effectiveness in improving the accuracy of 3D measurement against strong specular inter-reflection. |
2001.10298 | Amer Krivo\v{s}ija | Maike Buchin and Nicole Funk and Amer Krivo\v{s}ija | On the complexity of the middle curve problem | null | null | null | null | cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For a set of curves, Ahn et al. introduced the notion of a middle curve and
gave algorithms computing these with run time exponential in the number of
curves. Here we study the computational complexity of this problem: we show
that it is NP-complete and give approximation algorithms.
| [
{
"created": "Tue, 28 Jan 2020 12:54:16 GMT",
"version": "v1"
}
] | 2020-01-29 | [
[
"Buchin",
"Maike",
""
],
[
"Funk",
"Nicole",
""
],
[
"Krivošija",
"Amer",
""
]
] | For a set of curves, Ahn et al. introduced the notion of a middle curve and gave algorithms computing these with run time exponential in the number of curves. Here we study the computational complexity of this problem: we show that it is NP-complete and give approximation algorithms. |
2106.15846 | Zhiyuan Wen | Wen Zhiyuan, Cao Jiannong, Yang Ruosong, Liu Shuaiqi, Shen Jiaxing | Automatically Select Emotion for Response via Personality-affected
Emotion Transition | Accepted by Findings of ACL-IJCNLP 2021 | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To provide consistent emotional interaction with users, dialog systems should
be capable to automatically select appropriate emotions for responses like
humans. However, most existing works focus on rendering specified emotions in
responses or empathetically respond to the emotion of users, yet the individual
difference in emotion expression is overlooked. This may lead to inconsistent
emotional expressions and disinterest users. To tackle this issue, we propose
to equip the dialog system with personality and enable it to automatically
select emotions in responses by simulating the emotion transition of humans in
conversation. In detail, the emotion of the dialog system is transitioned from
its preceding emotion in context. The transition is triggered by the preceding
dialog context and affected by the specified personality trait. To achieve
this, we first model the emotion transition in the dialog system as the
variation between the preceding emotion and the response emotion in the
Valence-Arousal-Dominance (VAD) emotion space. Then, we design neural networks
to encode the preceding dialog context and the specified personality traits to
compose the variation. Finally, the emotion for response is selected from the
sum of the preceding emotion and the variation. We construct a dialog dataset
with emotion and personality labels and conduct emotion prediction tasks for
evaluation. Experimental results validate the effectiveness of the
personality-affected emotion transition.
| [
{
"created": "Wed, 30 Jun 2021 07:00:42 GMT",
"version": "v1"
}
] | 2021-07-01 | [
[
"Zhiyuan",
"Wen",
""
],
[
"Jiannong",
"Cao",
""
],
[
"Ruosong",
"Yang",
""
],
[
"Shuaiqi",
"Liu",
""
],
[
"Jiaxing",
"Shen",
""
]
] | To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans. However, most existing works focus on rendering specified emotions in responses or empathetically respond to the emotion of users, yet the individual difference in emotion expression is overlooked. This may lead to inconsistent emotional expressions and disinterest users. To tackle this issue, we propose to equip the dialog system with personality and enable it to automatically select emotions in responses by simulating the emotion transition of humans in conversation. In detail, the emotion of the dialog system is transitioned from its preceding emotion in context. The transition is triggered by the preceding dialog context and affected by the specified personality trait. To achieve this, we first model the emotion transition in the dialog system as the variation between the preceding emotion and the response emotion in the Valence-Arousal-Dominance (VAD) emotion space. Then, we design neural networks to encode the preceding dialog context and the specified personality traits to compose the variation. Finally, the emotion for response is selected from the sum of the preceding emotion and the variation. We construct a dialog dataset with emotion and personality labels and conduct emotion prediction tasks for evaluation. Experimental results validate the effectiveness of the personality-affected emotion transition. |
2302.12910 | Jia Shen | Jia Tracy Shen, Dongwon Lee | Imputing Knowledge Tracing Data with Subject-Based Training via LSTM
Variational Autoencoders Frameworks | Accepted by AAAI2023 AI4ED Workshop | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The issue of missing data poses a great challenge on boosting performance and
application of deep learning models in the {\em Knowledge Tracing} (KT)
problem. However, there has been the lack of understanding on the issue in the
literature. %are not sufficient studies tackling this problem. In this work, to
address this challenge, we adopt a subject-based training method to split and
impute data by student IDs instead of row number splitting which we call
non-subject based training. The benefit of subject-based training can retain
the complete sequence for each student and hence achieve efficient training.
Further, we leverage two existing deep generative frameworks, namely
variational Autoencoders (VAE) and Longitudinal Variational Autoencoders (LVAE)
frameworks and build LSTM kernels into them to form LSTM-VAE and LSTM LVAE
(noted as VAE and LVAE for simplicity) models to generate quality data. In
LVAE, a Gaussian Process (GP) model is trained to disentangle the correlation
between the subject (i.e., student) descriptor information (e.g., age, gender)
and the latent space. The paper finally compare the model performance between
training the original data and training the data imputed with generated data
from non-subject based model VAE-NS and subject-based training models (i.e.,
VAE and LVAE). We demonstrate that the generated data from LSTM-VAE and
LSTM-LVAE can boost the original model performance by about 50%. Moreover, the
original model just needs 10% more student data to surpass the original
performance if the prediction model is small and 50\% more data if the
prediction model is large with our proposed frameworks.
| [
{
"created": "Fri, 24 Feb 2023 21:56:03 GMT",
"version": "v1"
}
] | 2023-02-28 | [
[
"Shen",
"Jia Tracy",
""
],
[
"Lee",
"Dongwon",
""
]
] | The issue of missing data poses a great challenge on boosting performance and application of deep learning models in the {\em Knowledge Tracing} (KT) problem. However, there has been the lack of understanding on the issue in the literature. %are not sufficient studies tackling this problem. In this work, to address this challenge, we adopt a subject-based training method to split and impute data by student IDs instead of row number splitting which we call non-subject based training. The benefit of subject-based training can retain the complete sequence for each student and hence achieve efficient training. Further, we leverage two existing deep generative frameworks, namely variational Autoencoders (VAE) and Longitudinal Variational Autoencoders (LVAE) frameworks and build LSTM kernels into them to form LSTM-VAE and LSTM LVAE (noted as VAE and LVAE for simplicity) models to generate quality data. In LVAE, a Gaussian Process (GP) model is trained to disentangle the correlation between the subject (i.e., student) descriptor information (e.g., age, gender) and the latent space. The paper finally compare the model performance between training the original data and training the data imputed with generated data from non-subject based model VAE-NS and subject-based training models (i.e., VAE and LVAE). We demonstrate that the generated data from LSTM-VAE and LSTM-LVAE can boost the original model performance by about 50%. Moreover, the original model just needs 10% more student data to surpass the original performance if the prediction model is small and 50\% more data if the prediction model is large with our proposed frameworks. |
2312.14925 | Timo Kaufmann | Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke H\"ullermeier | A Survey of Reinforcement Learning from Human Feedback | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Reinforcement learning from human feedback (RLHF) is a variant of
reinforcement learning (RL) that learns from human feedback instead of relying
on an engineered reward function. Building on prior work on the related setting
of preference-based reinforcement learning (PbRL), it stands at the
intersection of artificial intelligence and human-computer interaction. This
positioning offers a promising avenue to enhance the performance and
adaptability of intelligent systems while also improving the alignment of their
objectives with human values. The training of large language models (LLMs) has
impressively demonstrated this potential in recent years, where RLHF played a
decisive role in directing the model's capabilities toward human objectives.
This article provides a comprehensive overview of the fundamentals of RLHF,
exploring the intricate dynamics between RL agents and human input. While
recent focus has been on RLHF for LLMs, our survey adopts a broader
perspective, examining the diverse applications and wide-ranging impact of the
technique. We delve into the core principles that underpin RLHF, shedding light
on the symbiotic relationship between algorithms and human feedback, and
discuss the main research trends in the field. By synthesizing the current
landscape of RLHF research, this article aims to provide researchers as well as
practitioners with a comprehensive understanding of this rapidly growing field
of research.
| [
{
"created": "Fri, 22 Dec 2023 18:58:06 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Apr 2024 17:59:01 GMT",
"version": "v2"
}
] | 2024-05-01 | [
[
"Kaufmann",
"Timo",
""
],
[
"Weng",
"Paul",
""
],
[
"Bengs",
"Viktor",
""
],
[
"Hüllermeier",
"Eyke",
""
]
] | Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning (RL) that learns from human feedback instead of relying on an engineered reward function. Building on prior work on the related setting of preference-based reinforcement learning (PbRL), it stands at the intersection of artificial intelligence and human-computer interaction. This positioning offers a promising avenue to enhance the performance and adaptability of intelligent systems while also improving the alignment of their objectives with human values. The training of large language models (LLMs) has impressively demonstrated this potential in recent years, where RLHF played a decisive role in directing the model's capabilities toward human objectives. This article provides a comprehensive overview of the fundamentals of RLHF, exploring the intricate dynamics between RL agents and human input. While recent focus has been on RLHF for LLMs, our survey adopts a broader perspective, examining the diverse applications and wide-ranging impact of the technique. We delve into the core principles that underpin RLHF, shedding light on the symbiotic relationship between algorithms and human feedback, and discuss the main research trends in the field. By synthesizing the current landscape of RLHF research, this article aims to provide researchers as well as practitioners with a comprehensive understanding of this rapidly growing field of research. |
1911.03852 | Amir Gholami | Zhen Dong, Zhewei Yao, Yaohui Cai, Daiyaan Arfeen, Amir Gholami,
Michael W. Mahoney, Kurt Keutzer | HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks | null | NeurIPS 2020 paper, link:
https://proceedings.neurips.cc/paper/2020/file/d77c703536718b95308130ff2e5cf9ee-Supplemental.pdf | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Quantization is an effective method for reducing memory footprint and
inference time of Neural Networks, e.g., for efficient inference in the cloud,
especially at the edge. However, ultra low precision quantization could lead to
significant degradation in model generalization. A promising method to address
this is to perform mixed-precision quantization, where more sensitive layers
are kept at higher precision. However, the search space for a mixed-precision
quantization is exponential in the number of layers. Recent work has proposed
HAWQ, a novel Hessian based framework, with the aim of reducing this
exponential search space by using second-order information. While promising,
this prior work has three major limitations: (i) HAWQV1 only uses the top
Hessian eigenvalue as a measure of sensitivity and do not consider the rest of
the Hessian spectrum; (ii) HAWQV1 approach only provides relative sensitivity
of different layers and therefore requires a manual selection of the
mixed-precision setting; and (iii) HAWQV1 does not consider mixed-precision
activation quantization. Here, we present HAWQV2 which addresses these
shortcomings. For (i), we perform a theoretical analysis showing that a better
sensitivity metric is to compute the average of all of the Hessian eigenvalues.
For (ii), we develop a Pareto frontier based method for selecting the exact bit
precision of different layers without any manual selection. For (iii), we
extend the Hessian analysis to mixed-precision activation quantization. We have
found this to be very beneficial for object detection. We show that HAWQV2
achieves new state-of-the-art results for a wide range of tasks.
| [
{
"created": "Sun, 10 Nov 2019 04:46:17 GMT",
"version": "v1"
}
] | 2021-05-11 | [
[
"Dong",
"Zhen",
""
],
[
"Yao",
"Zhewei",
""
],
[
"Cai",
"Yaohui",
""
],
[
"Arfeen",
"Daiyaan",
""
],
[
"Gholami",
"Amir",
""
],
[
"Mahoney",
"Michael W.",
""
],
[
"Keutzer",
"Kurt",
""
]
] | Quantization is an effective method for reducing memory footprint and inference time of Neural Networks, e.g., for efficient inference in the cloud, especially at the edge. However, ultra low precision quantization could lead to significant degradation in model generalization. A promising method to address this is to perform mixed-precision quantization, where more sensitive layers are kept at higher precision. However, the search space for a mixed-precision quantization is exponential in the number of layers. Recent work has proposed HAWQ, a novel Hessian based framework, with the aim of reducing this exponential search space by using second-order information. While promising, this prior work has three major limitations: (i) HAWQV1 only uses the top Hessian eigenvalue as a measure of sensitivity and do not consider the rest of the Hessian spectrum; (ii) HAWQV1 approach only provides relative sensitivity of different layers and therefore requires a manual selection of the mixed-precision setting; and (iii) HAWQV1 does not consider mixed-precision activation quantization. Here, we present HAWQV2 which addresses these shortcomings. For (i), we perform a theoretical analysis showing that a better sensitivity metric is to compute the average of all of the Hessian eigenvalues. For (ii), we develop a Pareto frontier based method for selecting the exact bit precision of different layers without any manual selection. For (iii), we extend the Hessian analysis to mixed-precision activation quantization. We have found this to be very beneficial for object detection. We show that HAWQV2 achieves new state-of-the-art results for a wide range of tasks. |
1907.00483 | Amit Kumar Jaiswal | Amit Kumar Jaiswal, Haiming Liu and Ingo Frommholz | Effects of Foraging in Personalized Content-based Image Recommendation | Accepted in Proceedings of the the 2nd International Workshop on
Explainable Recommendation and Search (EARS) at SIGIR 2019 | null | null | null | cs.IR cs.HC cs.MM cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A major challenge of recommender systems is to help users locating
interesting items. Personalized recommender systems have become very popular as
they attempt to predetermine the needs of users and provide them with
recommendations to personalize their navigation. However, few studies have
addressed the question of what drives the users' attention to specific content
within the collection and what influences the selection of interesting items.
To this end, we employ the lens of Information Foraging Theory (IFT) to image
recommendation to demonstrate how the user could utilize visual bookmarks to
locate interesting images. We investigate a personalized content-based image
recommendation system to understand what affects user attention by reinforcing
visual attention cues based on IFT. We further find that visual bookmarks
(cues) lead to a stronger scent of the recommended image collection. Our
evaluation is based on the Pinterest image collection.
| [
{
"created": "Sun, 30 Jun 2019 22:16:32 GMT",
"version": "v1"
},
{
"created": "Sat, 20 Jul 2019 12:43:53 GMT",
"version": "v2"
}
] | 2019-07-23 | [
[
"Jaiswal",
"Amit Kumar",
""
],
[
"Liu",
"Haiming",
""
],
[
"Frommholz",
"Ingo",
""
]
] | A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the needs of users and provide them with recommendations to personalize their navigation. However, few studies have addressed the question of what drives the users' attention to specific content within the collection and what influences the selection of interesting items. To this end, we employ the lens of Information Foraging Theory (IFT) to image recommendation to demonstrate how the user could utilize visual bookmarks to locate interesting images. We investigate a personalized content-based image recommendation system to understand what affects user attention by reinforcing visual attention cues based on IFT. We further find that visual bookmarks (cues) lead to a stronger scent of the recommended image collection. Our evaluation is based on the Pinterest image collection. |
1509.06983 | Marc Hellmuth | Marc Hellmuth, Adrian Fritz, Nicolas Wieseke and Peter F. Stadler | Techniques for the Cograph Editing Problem: Module Merge is equivalent
to Editing P4s | null | null | null | null | cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cographs are graphs in which no four vertices induce a simple connected path
$P_4$. Cograph editing is to find for a given graph $G = (V,E)$ a set of at
most $k$ edge additions and deletions that transform $G$ into a cograph. This
combinatorial optimization problem is NP-hard. It has, recently found
applications in the context of phylogenetics, hence good heuristics are of
practical importance.
It is well-known that the cograph editing problem can be solved independently
on the so-called strong prime modules of the modular decomposition of $G$. We
show here that editing the induced $P_4$'s of a given graph is equivalent to
resolving strong prime modules by means of a newly defined merge operation on
the submodules. This observation leads to a new exact algorithm for the cograph
editing problem that can be used as a starting point for the construction of
novel heuristics.
| [
{
"created": "Wed, 23 Sep 2015 13:54:15 GMT",
"version": "v1"
},
{
"created": "Thu, 24 Sep 2015 09:34:29 GMT",
"version": "v2"
}
] | 2015-09-25 | [
[
"Hellmuth",
"Marc",
""
],
[
"Fritz",
"Adrian",
""
],
[
"Wieseke",
"Nicolas",
""
],
[
"Stadler",
"Peter F.",
""
]
] | Cographs are graphs in which no four vertices induce a simple connected path $P_4$. Cograph editing is to find for a given graph $G = (V,E)$ a set of at most $k$ edge additions and deletions that transform $G$ into a cograph. This combinatorial optimization problem is NP-hard. It has, recently found applications in the context of phylogenetics, hence good heuristics are of practical importance. It is well-known that the cograph editing problem can be solved independently on the so-called strong prime modules of the modular decomposition of $G$. We show here that editing the induced $P_4$'s of a given graph is equivalent to resolving strong prime modules by means of a newly defined merge operation on the submodules. This observation leads to a new exact algorithm for the cograph editing problem that can be used as a starting point for the construction of novel heuristics. |
2401.06080 | Rui Zheng | Binghai Wang, Rui Zheng, Lu Chen, Yan Liu, Shihan Dou, Caishuang
Huang, Wei Shen, Senjie Jin, Enyu Zhou, Chenyu Shi, Songyang Gao, Nuo Xu,
Yuhao Zhou, Xiaoran Fan, Zhiheng Xi, Jun Zhao, Xiao Wang, Tao Ji, Hang Yan,
Lixing Shen, Zhan Chen, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan
Wu, Yu-Gang Jiang | Secrets of RLHF in Large Language Models Part II: Reward Modeling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement Learning from Human Feedback (RLHF) has become a crucial
technology for aligning language models with human values and intentions,
enabling models to produce more helpful and harmless responses. Reward models
are trained as proxies for human preferences to drive reinforcement learning
optimization. While reward models are often considered central to achieving
high performance, they face the following challenges in practical applications:
(1) Incorrect and ambiguous preference pairs in the dataset may hinder the
reward model from accurately capturing human intent. (2) Reward models trained
on data from a specific distribution often struggle to generalize to examples
outside that distribution and are not suitable for iterative RLHF training.
In this report, we attempt to address these two issues. (1) From a data
perspective, we propose a method to measure the strength of preferences within
the data, based on a voting mechanism of multiple reward models. Experimental
results confirm that data with varying preference strengths have different
impacts on reward model performance. We introduce a series of novel methods to
mitigate the influence of incorrect and ambiguous preferences in the dataset
and fully leverage high-quality preference data. (2) From an algorithmic
standpoint, we introduce contrastive learning to enhance the ability of reward
models to distinguish between chosen and rejected responses, thereby improving
model generalization. Furthermore, we employ meta-learning to enable the reward
model to maintain the ability to differentiate subtle differences in
out-of-distribution samples, and this approach can be utilized for iterative
RLHF optimization.
| [
{
"created": "Thu, 11 Jan 2024 17:56:59 GMT",
"version": "v1"
},
{
"created": "Fri, 12 Jan 2024 09:46:10 GMT",
"version": "v2"
}
] | 2024-01-15 | [
[
"Wang",
"Binghai",
""
],
[
"Zheng",
"Rui",
""
],
[
"Chen",
"Lu",
""
],
[
"Liu",
"Yan",
""
],
[
"Dou",
"Shihan",
""
],
[
"Huang",
"Caishuang",
""
],
[
"Shen",
"Wei",
""
],
[
"Jin",
"Senjie",
""
],
[
"Zhou",
"Enyu",
""
],
[
"Shi",
"Chenyu",
""
],
[
"Gao",
"Songyang",
""
],
[
"Xu",
"Nuo",
""
],
[
"Zhou",
"Yuhao",
""
],
[
"Fan",
"Xiaoran",
""
],
[
"Xi",
"Zhiheng",
""
],
[
"Zhao",
"Jun",
""
],
[
"Wang",
"Xiao",
""
],
[
"Ji",
"Tao",
""
],
[
"Yan",
"Hang",
""
],
[
"Shen",
"Lixing",
""
],
[
"Chen",
"Zhan",
""
],
[
"Gui",
"Tao",
""
],
[
"Zhang",
"Qi",
""
],
[
"Qiu",
"Xipeng",
""
],
[
"Huang",
"Xuanjing",
""
],
[
"Wu",
"Zuxuan",
""
],
[
"Jiang",
"Yu-Gang",
""
]
] | Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization. |
1508.03261 | He Sun | Yin Tat Lee and He Sun | Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time | 22 pages. A preliminary version of this paper is to appear in
proceedings of the 56th Annual IEEE Symposium on Foundations of Computer
Science (FOCS 2015) | null | null | null | cs.DS cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the first almost-linear time algorithm for constructing
linear-sized spectral sparsification for graphs. This improves all previous
constructions of linear-sized spectral sparsification, which requires
$\Omega(n^2)$ time.
A key ingredient in our algorithm is a novel combination of two techniques
used in literature for constructing spectral sparsification: Random sampling by
effective resistance, and adaptive constructions based on barrier functions.
| [
{
"created": "Thu, 13 Aug 2015 16:24:28 GMT",
"version": "v1"
}
] | 2015-08-14 | [
[
"Lee",
"Yin Tat",
""
],
[
"Sun",
"He",
""
]
] | We present the first almost-linear time algorithm for constructing linear-sized spectral sparsification for graphs. This improves all previous constructions of linear-sized spectral sparsification, which requires $\Omega(n^2)$ time. A key ingredient in our algorithm is a novel combination of two techniques used in literature for constructing spectral sparsification: Random sampling by effective resistance, and adaptive constructions based on barrier functions. |
2207.02802 | Qianglong Chen | Qianglong Chen, Xiangji Zeng, Jiangang Zhu, Yin Zhang, Bojia Lin, Yang
Yang, Daxin Jiang | Rethinking the Value of Gazetteer in Chinese Named Entity Recognition | Accepted by NLPCC 2022 | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Gazetteer is widely used in Chinese named entity recognition (NER) to enhance
span boundary detection and type classification. However, to further understand
the generalizability and effectiveness of gazetteers, the NLP community still
lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper,
we first re-examine the effectiveness several common practices of the
gazetteer-enhanced NER models and carry out a series of detailed analysis to
evaluate the relationship between the model performance and the gazetteer
characteristics, which can guide us to build a more suitable gazetteer. The
findings of this paper are as follows: (1) the gazetteer improves most of the
situations that the traditional NER model datasets are difficult to learn. (2)
the performance of model greatly benefits from the high-quality pre-trained
lexeme embeddings. (3) a good gazetteer should cover more entities that can be
matched in both the training set and testing set.
| [
{
"created": "Wed, 6 Jul 2022 16:45:25 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Jul 2022 09:13:26 GMT",
"version": "v2"
}
] | 2022-07-19 | [
[
"Chen",
"Qianglong",
""
],
[
"Zeng",
"Xiangji",
""
],
[
"Zhu",
"Jiangang",
""
],
[
"Zhang",
"Yin",
""
],
[
"Lin",
"Bojia",
""
],
[
"Yang",
"Yang",
""
],
[
"Jiang",
"Daxin",
""
]
] | Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we first re-examine the effectiveness several common practices of the gazetteer-enhanced NER models and carry out a series of detailed analysis to evaluate the relationship between the model performance and the gazetteer characteristics, which can guide us to build a more suitable gazetteer. The findings of this paper are as follows: (1) the gazetteer improves most of the situations that the traditional NER model datasets are difficult to learn. (2) the performance of model greatly benefits from the high-quality pre-trained lexeme embeddings. (3) a good gazetteer should cover more entities that can be matched in both the training set and testing set. |
2311.00800 | AmirHosein Fadaei | Amir Hosein Fadaei, Mohammad-Reza A. Dehaqani | Beyond still images: Temporal features and input variance resilience | 13 pages, 9 figures | null | 10.1038/s41598-024-66346-w | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditionally, vision models have predominantly relied on spatial features
extracted from static images, deviating from the continuous stream of
spatiotemporal features processed by the brain in natural vision. While
numerous video-understanding models have emerged, incorporating videos into
image-understanding models with spatiotemporal features has been limited.
Drawing inspiration from natural vision, which exhibits remarkable resilience
to input changes, our research focuses on the development of a brain-inspired
model for vision understanding trained with videos. Our findings demonstrate
that models that train on videos instead of still images and include temporal
features become more resilient to various alternations on input media.
| [
{
"created": "Wed, 1 Nov 2023 19:34:45 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Feb 2024 15:41:08 GMT",
"version": "v2"
}
] | 2024-07-18 | [
[
"Fadaei",
"Amir Hosein",
""
],
[
"Dehaqani",
"Mohammad-Reza A.",
""
]
] | Traditionally, vision models have predominantly relied on spatial features extracted from static images, deviating from the continuous stream of spatiotemporal features processed by the brain in natural vision. While numerous video-understanding models have emerged, incorporating videos into image-understanding models with spatiotemporal features has been limited. Drawing inspiration from natural vision, which exhibits remarkable resilience to input changes, our research focuses on the development of a brain-inspired model for vision understanding trained with videos. Our findings demonstrate that models that train on videos instead of still images and include temporal features become more resilient to various alternations on input media. |
0810.1248 | Ali Parandehgheibi | Ali ParandehGheibi, Atilla Eryilmaz, Asuman Ozdaglar, Muriel Medard | Resource Allocation in Multiple Access Channels | 5 pages, In proc. of ACSSC 2007 | null | null | null | cs.IT cs.NI math.IT math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of rate allocation in a Gaussian multiple-access
channel, with the goal of maximizing a utility function over transmission
rates. In contrast to the literature which focuses on linear utility functions,
we study general concave utility functions. We present a gradient projection
algorithm for this problem. Since the constraint set of the problem is
described by exponentially many constraints, methods that use exact projections
are computationally intractable. Therefore, we develop a new method that uses
approximate projections. We use the polymatroid structure of the capacity
region to show that the approximate projection can be implemented by a
recursive algorithm in time polynomial in the number of users. We further
propose another algorithm for implementing the approximate projections using
rate-splitting and show improved bounds on its convergence time.
| [
{
"created": "Tue, 7 Oct 2008 17:29:52 GMT",
"version": "v1"
}
] | 2008-10-08 | [
[
"ParandehGheibi",
"Ali",
""
],
[
"Eryilmaz",
"Atilla",
""
],
[
"Ozdaglar",
"Asuman",
""
],
[
"Medard",
"Muriel",
""
]
] | We consider the problem of rate allocation in a Gaussian multiple-access channel, with the goal of maximizing a utility function over transmission rates. In contrast to the literature which focuses on linear utility functions, we study general concave utility functions. We present a gradient projection algorithm for this problem. Since the constraint set of the problem is described by exponentially many constraints, methods that use exact projections are computationally intractable. Therefore, we develop a new method that uses approximate projections. We use the polymatroid structure of the capacity region to show that the approximate projection can be implemented by a recursive algorithm in time polynomial in the number of users. We further propose another algorithm for implementing the approximate projections using rate-splitting and show improved bounds on its convergence time. |
2407.16395 | Valderi Leithardt Valderi | Pedro Costa, Valderi Leithardt | Prisec II -- A Comprehensive Model for IoT Security: Cryptographic
Algorithms and Cloud Integration | 8 pages | IEEE Latam Transactions 2024 | null | null | cs.CR cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study addresses the critical issue of ensuring data security and
efficiency in interconnected devices, especially in IoT environments. The
objective is to design and implement a model using cryptographic algorithms to
enhance data security in 5G networks. Challenges arise from the limited
computational capabilities of IoT devices, which require the analysis and
selection of cryptographic algorithms to achieve efficient data transmission.
This study proposes a model that includes four levels of security, each
employing different levels of encryption to provide better data security.
Finally, cloud computing optimizes processing efficiency and resource
utilization to improve data transmission.
| [
{
"created": "Tue, 23 Jul 2024 11:35:24 GMT",
"version": "v1"
}
] | 2024-07-24 | [
[
"Costa",
"Pedro",
""
],
[
"Leithardt",
"Valderi",
""
]
] | This study addresses the critical issue of ensuring data security and efficiency in interconnected devices, especially in IoT environments. The objective is to design and implement a model using cryptographic algorithms to enhance data security in 5G networks. Challenges arise from the limited computational capabilities of IoT devices, which require the analysis and selection of cryptographic algorithms to achieve efficient data transmission. This study proposes a model that includes four levels of security, each employing different levels of encryption to provide better data security. Finally, cloud computing optimizes processing efficiency and resource utilization to improve data transmission. |
0906.0426 | Li Li | Li Li, Yudong Chen, Yi Zhang | A Mixed-Fractal Model for Network Traffic | null | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this short paper, we propose a new multi-fractal flow model, aiming to
provide a possible explanation for the crossover phenomena that appear in the
estimation of Hurst exponent for network traffic. It is shown that crossover
occurs if the network flow consists of several components with different Hurst
components. Our results indicate that this model might be useful in network
traffic modeling and simulation.
| [
{
"created": "Tue, 2 Jun 2009 06:41:19 GMT",
"version": "v1"
}
] | 2009-06-03 | [
[
"Li",
"Li",
""
],
[
"Chen",
"Yudong",
""
],
[
"Zhang",
"Yi",
""
]
] | In this short paper, we propose a new multi-fractal flow model, aiming to provide a possible explanation for the crossover phenomena that appear in the estimation of Hurst exponent for network traffic. It is shown that crossover occurs if the network flow consists of several components with different Hurst components. Our results indicate that this model might be useful in network traffic modeling and simulation. |
2009.00548 | Philipp Meschenmoser | Philipp Meschenmoser, Juri F. Buchm\"uller, Daniel Seebacher, Martin
Wikelski and Daniel A. Keim | MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on
Multiple Scales | IEEE VAST 2020 - Proceedings of IEEE Conference on Visual Analytics
Science and Technology (VAST), 2020 | null | null | null | cs.HC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Segmenting biologging time series of animals on multiple temporal scales is
an essential step that requires complex techniques with careful
parameterization and possibly cross-domain expertise. Yet, there is a lack of
visual-interactive tools that strongly support such multi-scale segmentation.
To close this gap, we present our MultiSegVA platform for interactively
defining segmentation techniques and parameters on multiple temporal scales.
MultiSegVA primarily contributes tailored, visual-interactive means and visual
analytics paradigms for segmenting unlabeled time series on multiple scales.
Further, to flexibly compose the multi-scale segmentation, the platform
contributes a new visual query language that links a variety of segmentation
techniques. To illustrate our approach, we present a domain-oriented set of
segmentation techniques derived in collaboration with movement ecologists. We
demonstrate the applicability and usefulness of MultiSegVA in two real-world
use cases from movement ecology, related to behavior analysis after
environment-aware segmentation, and after progressive clustering. Expert
feedback from movement ecologists shows the effectiveness of tailored
visual-interactive means and visual analytics paradigms at segmenting
multi-scale data, enabling them to perform semantically meaningful analyses. A
third use case demonstrates that MultiSegVA is generalizable to other domains.
| [
{
"created": "Tue, 1 Sep 2020 16:27:08 GMT",
"version": "v1"
},
{
"created": "Wed, 2 Sep 2020 08:22:29 GMT",
"version": "v2"
}
] | 2020-09-03 | [
[
"Meschenmoser",
"Philipp",
""
],
[
"Buchmüller",
"Juri F.",
""
],
[
"Seebacher",
"Daniel",
""
],
[
"Wikelski",
"Martin",
""
],
[
"Keim",
"Daniel A.",
""
]
] | Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales. MultiSegVA primarily contributes tailored, visual-interactive means and visual analytics paradigms for segmenting unlabeled time series on multiple scales. Further, to flexibly compose the multi-scale segmentation, the platform contributes a new visual query language that links a variety of segmentation techniques. To illustrate our approach, we present a domain-oriented set of segmentation techniques derived in collaboration with movement ecologists. We demonstrate the applicability and usefulness of MultiSegVA in two real-world use cases from movement ecology, related to behavior analysis after environment-aware segmentation, and after progressive clustering. Expert feedback from movement ecologists shows the effectiveness of tailored visual-interactive means and visual analytics paradigms at segmenting multi-scale data, enabling them to perform semantically meaningful analyses. A third use case demonstrates that MultiSegVA is generalizable to other domains. |
1602.03031 | Can Alkan | Atalay M. Ileri, Halil I. Ozercan, Alper Gundogdu, Ahmet K. Senol, M.
Yusuf Ozkaya, Can Alkan | Coinami: A Cryptocurrency with DNA Sequence Alignment as Proof-of-work | null | null | null | null | cs.CE cs.CR q-bio.GN | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rate of growth of the amount of data generated using the high throughput
sequencing (HTS) platforms now exceeds the growth stipulated by Moore's Law.
The HTS data is expected to surpass those of other "big data" domains such as
astronomy, before the year 2025. In addition to sequencing genomes for research
purposes, genome and exome sequencing in clinical settings will be a routine
part of health care. The analysis of such large amounts of data, however, is
not without computational challenges. This burden is even more increased due to
the periodic updates to reference genomes, which typically require re-analysis
of existing data. Here we propose Coin-Application Mediator Interface (Coinami)
to distribute the workload for mapping reads to reference genomes using a
volunteer grid computer approach similar to Berkeley Open Infrastructure for
Network Computing (BOINC). However, since HTS read mapping requires substantial
computational resources and fast analysis turnout is desired, Coinami uses the
HTS read mapping as proof-of-work to generate valid blocks to main its own
cryptocurrency system, which may help motivate volunteers to dedicate more
resources. The Coinami protocol includes mechanisms to ensure that jobs
performed by volunteers are correct, and provides genomic data privacy. The
prototype implementation of Coinami is available at http://coinami.github.io/.
| [
{
"created": "Tue, 9 Feb 2016 15:23:38 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Feb 2016 11:19:35 GMT",
"version": "v2"
}
] | 2016-02-22 | [
[
"Ileri",
"Atalay M.",
""
],
[
"Ozercan",
"Halil I.",
""
],
[
"Gundogdu",
"Alper",
""
],
[
"Senol",
"Ahmet K.",
""
],
[
"Ozkaya",
"M. Yusuf",
""
],
[
"Alkan",
"Can",
""
]
] | Rate of growth of the amount of data generated using the high throughput sequencing (HTS) platforms now exceeds the growth stipulated by Moore's Law. The HTS data is expected to surpass those of other "big data" domains such as astronomy, before the year 2025. In addition to sequencing genomes for research purposes, genome and exome sequencing in clinical settings will be a routine part of health care. The analysis of such large amounts of data, however, is not without computational challenges. This burden is even more increased due to the periodic updates to reference genomes, which typically require re-analysis of existing data. Here we propose Coin-Application Mediator Interface (Coinami) to distribute the workload for mapping reads to reference genomes using a volunteer grid computer approach similar to Berkeley Open Infrastructure for Network Computing (BOINC). However, since HTS read mapping requires substantial computational resources and fast analysis turnout is desired, Coinami uses the HTS read mapping as proof-of-work to generate valid blocks to main its own cryptocurrency system, which may help motivate volunteers to dedicate more resources. The Coinami protocol includes mechanisms to ensure that jobs performed by volunteers are correct, and provides genomic data privacy. The prototype implementation of Coinami is available at http://coinami.github.io/. |
2006.03377 | Emil Bj\"ornson | Emil Bj\"ornson, \"Ozgecan \"Ozdogan, Erik G. Larsson | Reconfigurable Intelligent Surfaces: Three Myths and Two Critical
Questions | To appear in IEEE Communications Magazine, 7 pages, 6 figures | IEEE Communications Magazine, vol. 58, no. 12, pp. 90-96, December
2020 | 10.1109/MCOM.001.2000407 | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The search for physical-layer technologies that can play a key role in
beyond-5G systems has started. One option is reconfigurable intelligent
surfaces (RIS), which can collect wireless signals from a transmitter and
passively beamform them towards the receiver. The technology has exciting
prospects and is quickly gaining traction in the communication community, but
in the current hype we have witnessed how several myths and overstatements are
spreading in the literature. In this article, we take a neutral look at the RIS
technology. We first review the fundamentals and then explain specific features
that can be easily misinterpreted. In particular, we debunk three myths: 1)
Current network technology can only control the transmitter and receiver, not
the environment in between; 2) A better asymptotic array gain is achieved than
with conventional beamforming; 3) The pathloss is the same as with anomalous
mirrors. To inspire further research, we conclude by identifying two critical
questions that must be answered for RIS to become a successful technology: 1)
What is a convincing use case for RIS?; 2) How can we estimate channels and
control an RIS in real time?
| [
{
"created": "Fri, 5 Jun 2020 11:25:32 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Oct 2020 14:01:49 GMT",
"version": "v2"
}
] | 2021-01-05 | [
[
"Björnson",
"Emil",
""
],
[
"Özdogan",
"Özgecan",
""
],
[
"Larsson",
"Erik G.",
""
]
] | The search for physical-layer technologies that can play a key role in beyond-5G systems has started. One option is reconfigurable intelligent surfaces (RIS), which can collect wireless signals from a transmitter and passively beamform them towards the receiver. The technology has exciting prospects and is quickly gaining traction in the communication community, but in the current hype we have witnessed how several myths and overstatements are spreading in the literature. In this article, we take a neutral look at the RIS technology. We first review the fundamentals and then explain specific features that can be easily misinterpreted. In particular, we debunk three myths: 1) Current network technology can only control the transmitter and receiver, not the environment in between; 2) A better asymptotic array gain is achieved than with conventional beamforming; 3) The pathloss is the same as with anomalous mirrors. To inspire further research, we conclude by identifying two critical questions that must be answered for RIS to become a successful technology: 1) What is a convincing use case for RIS?; 2) How can we estimate channels and control an RIS in real time? |
2005.07174 | Elena Kochkina | Elena Kochkina and Maria Liakata | Estimating predictive uncertainty for rumour verification models | Accepted to the Annual Conference of the Association for
Computational Linguistics (ACL) 2020 | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | The inability to correctly resolve rumours circulating online can have
harmful real-world consequences. We present a method for incorporating model
and data uncertainty estimates into natural language processing models for
automatic rumour verification. We show that these estimates can be used to
filter out model predictions likely to be erroneous, so that these difficult
instances can be prioritised by a human fact-checker. We propose two methods
for uncertainty-based instance rejection, supervised and unsupervised. We also
show how uncertainty estimates can be used to interpret model performance as a
rumour unfolds.
| [
{
"created": "Thu, 14 May 2020 17:42:25 GMT",
"version": "v1"
}
] | 2020-05-15 | [
[
"Kochkina",
"Elena",
""
],
[
"Liakata",
"Maria",
""
]
] | The inability to correctly resolve rumours circulating online can have harmful real-world consequences. We present a method for incorporating model and data uncertainty estimates into natural language processing models for automatic rumour verification. We show that these estimates can be used to filter out model predictions likely to be erroneous, so that these difficult instances can be prioritised by a human fact-checker. We propose two methods for uncertainty-based instance rejection, supervised and unsupervised. We also show how uncertainty estimates can be used to interpret model performance as a rumour unfolds. |
1506.09061 | Darryl Hill | Prosenjit Bose, Darryl Hill, and Michiel Smid | Improved Spanning Ratio for Low Degree Plane Spanners | 39 pages, appendix has been integrated into the main paper | null | null | null | cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe an algorithm that builds a plane spanner with a maximum degree of
8 and a spanning ratio of approximately 4.414 with respect to the complete
graph. This is the best currently known spanning ratio for a plane spanner with
a maximum degree of less than 14.
| [
{
"created": "Tue, 30 Jun 2015 12:35:02 GMT",
"version": "v1"
},
{
"created": "Thu, 2 Jul 2015 18:19:53 GMT",
"version": "v2"
}
] | 2015-07-03 | [
[
"Bose",
"Prosenjit",
""
],
[
"Hill",
"Darryl",
""
],
[
"Smid",
"Michiel",
""
]
] | We describe an algorithm that builds a plane spanner with a maximum degree of 8 and a spanning ratio of approximately 4.414 with respect to the complete graph. This is the best currently known spanning ratio for a plane spanner with a maximum degree of less than 14. |
2208.08759 | Benjamin Doerr | Benjamin Doerr and Zhongdi Qu | Runtime Analysis for the NSGA-II: Provable Speed-Ups From Crossover | Extended version of a paper that appears in the proceedings of AAAI
2023 | null | null | null | cs.NE cs.AI cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Very recently, the first mathematical runtime analyses for the NSGA-II, the
most common multi-objective evolutionary algorithm, have been conducted.
Continuing this research direction, we prove that the NSGA-II optimizes the
OneJumpZeroJump benchmark asymptotically faster when crossover is employed.
Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt,
this is the first time such an advantage of crossover is proven for the
NSGA-II. Our arguments can be transferred to single-objective optimization.
They then prove that crossover can speed up the $(\mu+1)$ genetic algorithm in
a different way and more pronounced than known before. Our experiments confirm
the added value of crossover and show that the observed advantages are even
larger than what our proofs can guarantee.
| [
{
"created": "Thu, 18 Aug 2022 10:41:44 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Mar 2023 08:58:10 GMT",
"version": "v2"
}
] | 2023-03-16 | [
[
"Doerr",
"Benjamin",
""
],
[
"Qu",
"Zhongdi",
""
]
] | Very recently, the first mathematical runtime analyses for the NSGA-II, the most common multi-objective evolutionary algorithm, have been conducted. Continuing this research direction, we prove that the NSGA-II optimizes the OneJumpZeroJump benchmark asymptotically faster when crossover is employed. Together with a parallel independent work by Dang, Opris, Salehi, and Sudholt, this is the first time such an advantage of crossover is proven for the NSGA-II. Our arguments can be transferred to single-objective optimization. They then prove that crossover can speed up the $(\mu+1)$ genetic algorithm in a different way and more pronounced than known before. Our experiments confirm the added value of crossover and show that the observed advantages are even larger than what our proofs can guarantee. |
2111.01625 | Xutian Deng | Xutian Deng, Yiting Chen, Fei Chen and Miao Li | Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and
Guided Explorations | null | null | 10.1109/ROBIO54168.2021.9739464 | null | cs.RO cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical ultrasound has become a routine examination approach nowadays and is
widely adopted for different medical applications, so it is desired to have a
robotic ultrasound system to perform the ultrasound scanning autonomously.
However, the ultrasound scanning skill is considerably complex, which highly
depends on the experience of the ultrasound physician. In this paper, we
propose a learning-based approach to learn the robotic ultrasound scanning
skills from human demonstrations. First, the robotic ultrasound scanning skill
is encapsulated into a high-dimensional multi-modal model, which takes the
ultrasound images, the pose/position of the probe and the contact force into
account. Second, we leverage the power of imitation learning to train the
multi-modal model with the training data collected from the demonstrations of
experienced ultrasound physicians. Finally, a post-optimization procedure with
guided explorations is proposed to further improve the performance of the
learned model. Robotic experiments are conducted to validate the advantages of
our proposed framework and the learned models.
| [
{
"created": "Tue, 2 Nov 2021 14:38:09 GMT",
"version": "v1"
}
] | 2023-07-27 | [
[
"Deng",
"Xutian",
""
],
[
"Chen",
"Yiting",
""
],
[
"Chen",
"Fei",
""
],
[
"Li",
"Miao",
""
]
] | Medical ultrasound has become a routine examination approach nowadays and is widely adopted for different medical applications, so it is desired to have a robotic ultrasound system to perform the ultrasound scanning autonomously. However, the ultrasound scanning skill is considerably complex, which highly depends on the experience of the ultrasound physician. In this paper, we propose a learning-based approach to learn the robotic ultrasound scanning skills from human demonstrations. First, the robotic ultrasound scanning skill is encapsulated into a high-dimensional multi-modal model, which takes the ultrasound images, the pose/position of the probe and the contact force into account. Second, we leverage the power of imitation learning to train the multi-modal model with the training data collected from the demonstrations of experienced ultrasound physicians. Finally, a post-optimization procedure with guided explorations is proposed to further improve the performance of the learned model. Robotic experiments are conducted to validate the advantages of our proposed framework and the learned models. |
2001.11595 | Matteo Pirotta | Jian Qian, Ronan Fruit, Matteo Pirotta, Alessandro Lazaric | Concentration Inequalities for Multinoulli Random Variables | Tutorial at ALT'19 on Regret Minimization in Infinite-Horizon Finite
Markov Decision Processes | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate concentration inequalities for Dirichlet and Multinomial
random variables.
| [
{
"created": "Thu, 30 Jan 2020 22:44:15 GMT",
"version": "v1"
}
] | 2020-02-03 | [
[
"Qian",
"Jian",
""
],
[
"Fruit",
"Ronan",
""
],
[
"Pirotta",
"Matteo",
""
],
[
"Lazaric",
"Alessandro",
""
]
] | We investigate concentration inequalities for Dirichlet and Multinomial random variables. |
2403.08245 | Shawn Tan | Shawn Tan, Yikang Shen, Rameswar Panda, Aaron Courville | Scattered Mixture-of-Experts Implementation | null | null | null | null | cs.LG cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present ScatterMoE, an implementation of Sparse Mixture-of-Experts (SMoE)
on GPUs. ScatterMoE builds upon existing implementations, and overcoming some
of the limitations to improve inference and training speed, and memory
footprint. This implementation achieves this by avoiding padding and making
excessive copies of the input. We introduce ParallelLinear, the main component
we use to build our implementation and the various kernels used to speed up the
operation. We benchmark our implementation against Megablocks, and show that it
enables a higher throughput and lower memory footprint. We also show how
ParallelLinear enables extension of the Mixture-of-Experts concept by
demonstrating with an implementation of Mixture of Attention.
| [
{
"created": "Wed, 13 Mar 2024 05:00:23 GMT",
"version": "v1"
}
] | 2024-03-14 | [
[
"Tan",
"Shawn",
""
],
[
"Shen",
"Yikang",
""
],
[
"Panda",
"Rameswar",
""
],
[
"Courville",
"Aaron",
""
]
] | We present ScatterMoE, an implementation of Sparse Mixture-of-Experts (SMoE) on GPUs. ScatterMoE builds upon existing implementations, and overcoming some of the limitations to improve inference and training speed, and memory footprint. This implementation achieves this by avoiding padding and making excessive copies of the input. We introduce ParallelLinear, the main component we use to build our implementation and the various kernels used to speed up the operation. We benchmark our implementation against Megablocks, and show that it enables a higher throughput and lower memory footprint. We also show how ParallelLinear enables extension of the Mixture-of-Experts concept by demonstrating with an implementation of Mixture of Attention. |
2206.08422 | Henning U. Voss | Henning U. Voss | Real-time motion amplification on mobile devices | Supplemental data at https://doi.org/10.6084/m9.figshare.20084981.v2.
Changes to v1: Inclusion of offline video processing | null | null | null | cs.GR cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A simple motion amplification algorithm suitable for real-time applications
on mobile devices, including smartphones, is presented. It is based on motion
enhancement by moving average differencing (MEMAD), a temporal high-pass filter
for video streams. MEMAD can amplify small moving objects or subtle motion in
larger objects. It is computationally sufficiently simple to be implemented in
real time on smartphones. In the specific implementation as an Android phone
app, MEMAD is demonstrated on examples chosen such as to motivate applications
in the engineering, biological, and medical sciences.
| [
{
"created": "Thu, 16 Jun 2022 19:48:00 GMT",
"version": "v1"
},
{
"created": "Wed, 10 May 2023 13:34:50 GMT",
"version": "v2"
}
] | 2023-05-11 | [
[
"Voss",
"Henning U.",
""
]
] | A simple motion amplification algorithm suitable for real-time applications on mobile devices, including smartphones, is presented. It is based on motion enhancement by moving average differencing (MEMAD), a temporal high-pass filter for video streams. MEMAD can amplify small moving objects or subtle motion in larger objects. It is computationally sufficiently simple to be implemented in real time on smartphones. In the specific implementation as an Android phone app, MEMAD is demonstrated on examples chosen such as to motivate applications in the engineering, biological, and medical sciences. |
2204.05169 | Vishal Sunder | Vishal Sunder, Samuel Thomas, Hong-Kwang J. Kuo, Jatin Ganhotra, Brian
Kingsbury, Eric Fosler-Lussier | Towards End-to-End Integration of Dialog History for Improved Spoken
Language Understanding | 5 pages, 1 figure | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Dialog history plays an important role in spoken language understanding (SLU)
performance in a dialog system. For end-to-end (E2E) SLU, previous work has
used dialog history in text form, which makes the model dependent on a cascaded
automatic speech recognizer (ASR). This rescinds the benefits of an E2E system
which is intended to be compact and robust to ASR errors. In this paper, we
propose a hierarchical conversation model that is capable of directly using
dialog history in speech form, making it fully E2E. We also distill semantic
knowledge from the available gold conversation transcripts by jointly training
a similar text-based conversation model with an explicit tying of acoustic and
semantic embeddings. We also propose a novel technique that we call DropFrame
to deal with the long training time incurred by adding dialog history in an E2E
manner. On the HarperValleyBank dialog dataset, our E2E history integration
outperforms a history independent baseline by 7.7% absolute F1 score on the
task of dialog action recognition. Our model performs competitively with the
state-of-the-art history based cascaded baseline, but uses 48% fewer
parameters. In the absence of gold transcripts to fine-tune an ASR model, our
model outperforms this baseline by a significant margin of 10% absolute F1
score.
| [
{
"created": "Mon, 11 Apr 2022 14:56:05 GMT",
"version": "v1"
}
] | 2022-04-12 | [
[
"Sunder",
"Vishal",
""
],
[
"Thomas",
"Samuel",
""
],
[
"Kuo",
"Hong-Kwang J.",
""
],
[
"Ganhotra",
"Jatin",
""
],
[
"Kingsbury",
"Brian",
""
],
[
"Fosler-Lussier",
"Eric",
""
]
] | Dialog history plays an important role in spoken language understanding (SLU) performance in a dialog system. For end-to-end (E2E) SLU, previous work has used dialog history in text form, which makes the model dependent on a cascaded automatic speech recognizer (ASR). This rescinds the benefits of an E2E system which is intended to be compact and robust to ASR errors. In this paper, we propose a hierarchical conversation model that is capable of directly using dialog history in speech form, making it fully E2E. We also distill semantic knowledge from the available gold conversation transcripts by jointly training a similar text-based conversation model with an explicit tying of acoustic and semantic embeddings. We also propose a novel technique that we call DropFrame to deal with the long training time incurred by adding dialog history in an E2E manner. On the HarperValleyBank dialog dataset, our E2E history integration outperforms a history independent baseline by 7.7% absolute F1 score on the task of dialog action recognition. Our model performs competitively with the state-of-the-art history based cascaded baseline, but uses 48% fewer parameters. In the absence of gold transcripts to fine-tune an ASR model, our model outperforms this baseline by a significant margin of 10% absolute F1 score. |
2408.05184 | Denis Kokosinskii | Denis Kokosinskii, Mikhail Kuklin, Nikolay Arefyev | Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic
Change Modeling | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | This paper describes our solution of the first subtask from the AXOLOTL-24
shared task on Semantic Change Modeling. The goal of this subtask is to
distribute a given set of usages of a polysemous word from a newer time period
between senses of this word from an older time period and clusters representing
gained senses of this word. We propose and experiment with three new methods
solving this task. Our methods achieve SOTA results according to both official
metrics of the first substask. Additionally, we develop a model that can tell
if a given word usage is not described by any of the provided sense
definitions. This model serves as a component in one of our methods, but can
potentially be useful on its own.
| [
{
"created": "Fri, 9 Aug 2024 17:15:54 GMT",
"version": "v1"
}
] | 2024-08-12 | [
[
"Kokosinskii",
"Denis",
""
],
[
"Kuklin",
"Mikhail",
""
],
[
"Arefyev",
"Nikolay",
""
]
] | This paper describes our solution of the first subtask from the AXOLOTL-24 shared task on Semantic Change Modeling. The goal of this subtask is to distribute a given set of usages of a polysemous word from a newer time period between senses of this word from an older time period and clusters representing gained senses of this word. We propose and experiment with three new methods solving this task. Our methods achieve SOTA results according to both official metrics of the first substask. Additionally, we develop a model that can tell if a given word usage is not described by any of the provided sense definitions. This model serves as a component in one of our methods, but can potentially be useful on its own. |
1212.3631 | Pablo Sprechmann | Pablo Sprechmann, Alex M. Bronstein and Guillermo Sapiro | Learning efficient sparse and low rank models | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parsimony, including sparsity and low rank, has been shown to successfully
model data in numerous machine learning and signal processing tasks.
Traditionally, such modeling approaches rely on an iterative algorithm that
minimizes an objective function with parsimony-promoting terms. The inherently
sequential structure and data-dependent complexity and latency of iterative
optimization constitute a major limitation in many applications requiring
real-time performance or involving large-scale data. Another limitation
encountered by these modeling techniques is the difficulty of their inclusion
in discriminative learning scenarios. In this work, we propose to move the
emphasis from the model to the pursuit algorithm, and develop a process-centric
view of parsimonious modeling, in which a learned deterministic
fixed-complexity pursuit process is used in lieu of iterative optimization. We
show a principled way to construct learnable pursuit process architectures for
structured sparse and robust low rank models, derived from the iteration of
proximal descent algorithms. These architectures learn to approximate the exact
parsimonious representation at a fraction of the complexity of the standard
optimization methods. We also show that appropriate training regimes allow to
naturally extend parsimonious models to discriminative settings.
State-of-the-art results are demonstrated on several challenging problems in
image and audio processing with several orders of magnitude speedup compared to
the exact optimization algorithms.
| [
{
"created": "Fri, 14 Dec 2012 22:50:44 GMT",
"version": "v1"
}
] | 2012-12-18 | [
[
"Sprechmann",
"Pablo",
""
],
[
"Bronstein",
"Alex M.",
""
],
[
"Sapiro",
"Guillermo",
""
]
] | Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with parsimony-promoting terms. The inherently sequential structure and data-dependent complexity and latency of iterative optimization constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Another limitation encountered by these modeling techniques is the difficulty of their inclusion in discriminative learning scenarios. In this work, we propose to move the emphasis from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling, in which a learned deterministic fixed-complexity pursuit process is used in lieu of iterative optimization. We show a principled way to construct learnable pursuit process architectures for structured sparse and robust low rank models, derived from the iteration of proximal descent algorithms. These architectures learn to approximate the exact parsimonious representation at a fraction of the complexity of the standard optimization methods. We also show that appropriate training regimes allow to naturally extend parsimonious models to discriminative settings. State-of-the-art results are demonstrated on several challenging problems in image and audio processing with several orders of magnitude speedup compared to the exact optimization algorithms. |
2205.14440 | Leye Wang | Xiao Han, Leye Wang, Junjie Wu, Yuncong Yang | Large-Scale Privacy-Preserving Network Embedding against Private Link
Inference Attacks | null | null | null | null | cs.LG cs.AI cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network embedding represents network nodes by a low-dimensional informative
vector. While it is generally effective for various downstream tasks, it may
leak some private information of networks, such as hidden private links. In
this work, we address a novel problem of privacy-preserving network embedding
against private link inference attacks. Basically, we propose to perturb the
original network by adding or removing links, and expect the embedding
generated on the perturbed network can leak little information about private
links but hold high utility for various downstream tasks. Towards this goal, we
first propose general measurements to quantify privacy gain and utility loss
incurred by candidate network perturbations; we then design a PPNE framework to
identify the optimal perturbation solution with the best privacy-utility
trade-off in an iterative way. Furthermore, we propose many techniques to
accelerate PPNE and ensure its scalability. For instance, as the skip-gram
embedding methods including DeepWalk and LINE can be seen as matrix
factorization with closed form embedding results, we devise efficient privacy
gain and utility loss approximation methods to avoid the repetitive
time-consuming embedding training for every candidate network perturbation in
each iteration. Experiments on real-life network datasets (with up to millions
of nodes) verify that PPNE outperforms baselines by sacrificing less utility
and obtaining higher privacy protection.
| [
{
"created": "Sat, 28 May 2022 13:59:39 GMT",
"version": "v1"
}
] | 2022-05-31 | [
[
"Han",
"Xiao",
""
],
[
"Wang",
"Leye",
""
],
[
"Wu",
"Junjie",
""
],
[
"Yang",
"Yuncong",
""
]
] | Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private information of networks, such as hidden private links. In this work, we address a novel problem of privacy-preserving network embedding against private link inference attacks. Basically, we propose to perturb the original network by adding or removing links, and expect the embedding generated on the perturbed network can leak little information about private links but hold high utility for various downstream tasks. Towards this goal, we first propose general measurements to quantify privacy gain and utility loss incurred by candidate network perturbations; we then design a PPNE framework to identify the optimal perturbation solution with the best privacy-utility trade-off in an iterative way. Furthermore, we propose many techniques to accelerate PPNE and ensure its scalability. For instance, as the skip-gram embedding methods including DeepWalk and LINE can be seen as matrix factorization with closed form embedding results, we devise efficient privacy gain and utility loss approximation methods to avoid the repetitive time-consuming embedding training for every candidate network perturbation in each iteration. Experiments on real-life network datasets (with up to millions of nodes) verify that PPNE outperforms baselines by sacrificing less utility and obtaining higher privacy protection. |
2007.00328 | Hui Li | Hui Li, Xiao-Jun Wu, Tariq Durrani | NestFuse: An Infrared and Visible Image Fusion Architecture based on
Nest Connection and Spatial/Channel Attention Models | 12 pages, 13 figures, 6 tables. IEEE Transactions on Instrumentation
and Measurement | null | 10.1109/TIM.2020.3005230 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose a novel method for infrared and visible image fusion
where we develop nest connection-based network and spatial/channel attention
models. The nest connection-based network can preserve significant amounts of
information from input data in a multi-scale perspective. The approach
comprises three key elements: encoder, fusion strategy and decoder
respectively. In our proposed fusion strategy, spatial attention models and
channel attention models are developed that describe the importance of each
spatial position and of each channel with deep features. Firstly, the source
images are fed into the encoder to extract multi-scale deep features. The novel
fusion strategy is then developed to fuse these features for each scale.
Finally, the fused image is reconstructed by the nest connection-based decoder.
Experiments are performed on publicly available datasets. These exhibit that
our proposed approach has better fusion performance than other state-of-the-art
methods. This claim is justified through both subjective and objective
evaluation. The code of our fusion method is available at
https://github.com/hli1221/imagefusion-nestfuse
| [
{
"created": "Wed, 1 Jul 2020 08:46:23 GMT",
"version": "v1"
},
{
"created": "Sat, 11 Jul 2020 06:31:34 GMT",
"version": "v2"
}
] | 2020-07-14 | [
[
"Li",
"Hui",
""
],
[
"Wu",
"Xiao-Jun",
""
],
[
"Durrani",
"Tariq",
""
]
] | In this paper we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. The nest connection-based network can preserve significant amounts of information from input data in a multi-scale perspective. The approach comprises three key elements: encoder, fusion strategy and decoder respectively. In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features. Firstly, the source images are fed into the encoder to extract multi-scale deep features. The novel fusion strategy is then developed to fuse these features for each scale. Finally, the fused image is reconstructed by the nest connection-based decoder. Experiments are performed on publicly available datasets. These exhibit that our proposed approach has better fusion performance than other state-of-the-art methods. This claim is justified through both subjective and objective evaluation. The code of our fusion method is available at https://github.com/hli1221/imagefusion-nestfuse |
2006.03423 | Kieran Chin-Cheong | Kieran Chin-Cheong, Thomas Sutter and Julia E. Vogt | Generation of Differentially Private Heterogeneous Electronic Health
Records | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Electronic Health Records (EHRs) are commonly used by the machine learning
community for research on problems specifically related to health care and
medicine. EHRs have the advantages that they can be easily distributed and
contain many features useful for e.g. classification problems. What makes EHR
data sets different from typical machine learning data sets is that they are
often very sparse, due to their high dimensionality, and often contain
heterogeneous (mixed) data types. Furthermore, the data sets deal with
sensitive information, which limits the distribution of any models learned
using them, due to privacy concerns. For these reasons, using EHR data in
practice presents a real challenge. In this work, we explore using Generative
Adversarial Networks to generate synthetic, heterogeneous EHRs with the goal of
using these synthetic records in place of existing data sets for downstream
classification tasks. We will further explore applying differential privacy
(DP) preserving optimization in order to produce DP synthetic EHR data sets,
which provide rigorous privacy guarantees, and are therefore shareable and
usable in the real world. The performance (measured by AUROC, AUPRC and
accuracy) of our model's synthetic, heterogeneous data is very close to the
original data set (within 3 - 5% of the baseline) for the non-DP model when
tested in a binary classification task. Using strong $(1, 10^{-5})$ DP, our
model still produces data useful for machine learning tasks, albeit incurring a
roughly 17% performance penalty in our tested classification task. We
additionally perform a sub-population analysis and find that our model does not
introduce any bias into the synthetic EHR data compared to the baseline in
either male/female populations, or the 0-18, 19-50 and 51+ age groups in terms
of classification performance for either the non-DP or DP variant.
| [
{
"created": "Fri, 5 Jun 2020 13:21:46 GMT",
"version": "v1"
}
] | 2020-06-08 | [
[
"Chin-Cheong",
"Kieran",
""
],
[
"Sutter",
"Thomas",
""
],
[
"Vogt",
"Julia E.",
""
]
] | Electronic Health Records (EHRs) are commonly used by the machine learning community for research on problems specifically related to health care and medicine. EHRs have the advantages that they can be easily distributed and contain many features useful for e.g. classification problems. What makes EHR data sets different from typical machine learning data sets is that they are often very sparse, due to their high dimensionality, and often contain heterogeneous (mixed) data types. Furthermore, the data sets deal with sensitive information, which limits the distribution of any models learned using them, due to privacy concerns. For these reasons, using EHR data in practice presents a real challenge. In this work, we explore using Generative Adversarial Networks to generate synthetic, heterogeneous EHRs with the goal of using these synthetic records in place of existing data sets for downstream classification tasks. We will further explore applying differential privacy (DP) preserving optimization in order to produce DP synthetic EHR data sets, which provide rigorous privacy guarantees, and are therefore shareable and usable in the real world. The performance (measured by AUROC, AUPRC and accuracy) of our model's synthetic, heterogeneous data is very close to the original data set (within 3 - 5% of the baseline) for the non-DP model when tested in a binary classification task. Using strong $(1, 10^{-5})$ DP, our model still produces data useful for machine learning tasks, albeit incurring a roughly 17% performance penalty in our tested classification task. We additionally perform a sub-population analysis and find that our model does not introduce any bias into the synthetic EHR data compared to the baseline in either male/female populations, or the 0-18, 19-50 and 51+ age groups in terms of classification performance for either the non-DP or DP variant. |
1504.04930 | Wentao Huang | Wentao Huang and Michael Langberg and Joerg Kliewer | Connecting Multiple-unicast and Network Error Correction: Reduction and
Unachievability | ISIT 2015. arXiv admin note: text overlap with arXiv:1410.1905 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that solving a multiple-unicast network coding problem can be reduced
to solving a single-unicast network error correction problem, where an
adversary may jam at most a single edge in the network. Specifically, we
present an efficient reduction that maps a multiple-unicast network coding
instance to a network error correction instance while preserving feasibility.
The reduction holds for both the zero probability of error model and the
vanishing probability of error model. Previous reductions are restricted to the
zero-error case. As an application of the reduction, we present a constructive
example showing that the single-unicast network error correction capacity may
not be achievable, a result of separate interest.
| [
{
"created": "Mon, 20 Apr 2015 04:02:35 GMT",
"version": "v1"
}
] | 2015-04-21 | [
[
"Huang",
"Wentao",
""
],
[
"Langberg",
"Michael",
""
],
[
"Kliewer",
"Joerg",
""
]
] | We show that solving a multiple-unicast network coding problem can be reduced to solving a single-unicast network error correction problem, where an adversary may jam at most a single edge in the network. Specifically, we present an efficient reduction that maps a multiple-unicast network coding instance to a network error correction instance while preserving feasibility. The reduction holds for both the zero probability of error model and the vanishing probability of error model. Previous reductions are restricted to the zero-error case. As an application of the reduction, we present a constructive example showing that the single-unicast network error correction capacity may not be achievable, a result of separate interest. |
2401.07745 | Doris Yan | Mi Yan, Jiazhao Zhang, Yan Zhu, He Wang | MaskClustering: View Consensus based Mask Graph Clustering for
Open-Vocabulary 3D Instance Segmentation | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Open-vocabulary 3D instance segmentation is cutting-edge for its ability to
segment 3D instances without predefined categories. However, progress in 3D
lags behind its 2D counterpart due to limited annotated 3D data. To address
this, recent works first generate 2D open-vocabulary masks through 2D models
and then merge them into 3D instances based on metrics calculated between two
neighboring frames. In contrast to these local metrics, we propose a novel
metric, view consensus rate, to enhance the utilization of multi-view
observations. The key insight is that two 2D masks should be deemed part of the
same 3D instance if a significant number of other 2D masks from different views
contain both these two masks. Using this metric as edge weight, we construct a
global mask graph where each mask is a node. Through iterative clustering of
masks showing high view consensus, we generate a series of clusters, each
representing a distinct 3D instance. Notably, our model is training-free.
Through extensive experiments on publicly available datasets, including
ScanNet++, ScanNet200 and MatterPort3D, we demonstrate that our method achieves
state-of-the-art performance in open-vocabulary 3D instance segmentation. Our
project page is at https://pku-epic.github.io/MaskClustering.
| [
{
"created": "Mon, 15 Jan 2024 14:56:15 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Apr 2024 15:30:23 GMT",
"version": "v2"
}
] | 2024-04-11 | [
[
"Yan",
"Mi",
""
],
[
"Zhang",
"Jiazhao",
""
],
[
"Zhu",
"Yan",
""
],
[
"Wang",
"He",
""
]
] | Open-vocabulary 3D instance segmentation is cutting-edge for its ability to segment 3D instances without predefined categories. However, progress in 3D lags behind its 2D counterpart due to limited annotated 3D data. To address this, recent works first generate 2D open-vocabulary masks through 2D models and then merge them into 3D instances based on metrics calculated between two neighboring frames. In contrast to these local metrics, we propose a novel metric, view consensus rate, to enhance the utilization of multi-view observations. The key insight is that two 2D masks should be deemed part of the same 3D instance if a significant number of other 2D masks from different views contain both these two masks. Using this metric as edge weight, we construct a global mask graph where each mask is a node. Through iterative clustering of masks showing high view consensus, we generate a series of clusters, each representing a distinct 3D instance. Notably, our model is training-free. Through extensive experiments on publicly available datasets, including ScanNet++, ScanNet200 and MatterPort3D, we demonstrate that our method achieves state-of-the-art performance in open-vocabulary 3D instance segmentation. Our project page is at https://pku-epic.github.io/MaskClustering. |
2405.19074 | Dipam Goswami Mr. | Dipam Goswami, Albin Soutif--Cormerais, Yuyang Liu, Sandesh Kamath,
Bart{\l}omiej Twardowski, Joost van de Weijer | Resurrecting Old Classes with New Data for Exemplar-Free Continual
Learning | Accepted at CVPR 2024 | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Continual learning methods are known to suffer from catastrophic forgetting,
a phenomenon that is particularly hard to counter for methods that do not store
exemplars of previous tasks. Therefore, to reduce potential drift in the
feature extractor, existing exemplar-free methods are typically evaluated in
settings where the first task is significantly larger than subsequent tasks.
Their performance drops drastically in more challenging settings starting with
a smaller first task. To address this problem of feature drift estimation for
exemplar-free methods, we propose to adversarially perturb the current samples
such that their embeddings are close to the old class prototypes in the old
model embedding space. We then estimate the drift in the embedding space from
the old to the new model using the perturbed images and compensate the
prototypes accordingly. We exploit the fact that adversarial samples are
transferable from the old to the new feature space in a continual learning
setting. The generation of these images is simple and computationally cheap. We
demonstrate in our experiments that the proposed approach better tracks the
movement of prototypes in embedding space and outperforms existing methods on
several standard continual learning benchmarks as well as on fine-grained
datasets. Code is available at https://github.com/dipamgoswami/ADC.
| [
{
"created": "Wed, 29 May 2024 13:31:42 GMT",
"version": "v1"
}
] | 2024-05-30 | [
[
"Goswami",
"Dipam",
""
],
[
"Soutif--Cormerais",
"Albin",
""
],
[
"Liu",
"Yuyang",
""
],
[
"Kamath",
"Sandesh",
""
],
[
"Twardowski",
"Bartłomiej",
""
],
[
"van de Weijer",
"Joost",
""
]
] | Continual learning methods are known to suffer from catastrophic forgetting, a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore, to reduce potential drift in the feature extractor, existing exemplar-free methods are typically evaluated in settings where the first task is significantly larger than subsequent tasks. Their performance drops drastically in more challenging settings starting with a smaller first task. To address this problem of feature drift estimation for exemplar-free methods, we propose to adversarially perturb the current samples such that their embeddings are close to the old class prototypes in the old model embedding space. We then estimate the drift in the embedding space from the old to the new model using the perturbed images and compensate the prototypes accordingly. We exploit the fact that adversarial samples are transferable from the old to the new feature space in a continual learning setting. The generation of these images is simple and computationally cheap. We demonstrate in our experiments that the proposed approach better tracks the movement of prototypes in embedding space and outperforms existing methods on several standard continual learning benchmarks as well as on fine-grained datasets. Code is available at https://github.com/dipamgoswami/ADC. |
2312.01330 | Asaf Shabtai | Roy Peled, Eran Aizikovich, Edan Habler, Yuval Elovici, Asaf Shabtai | Evaluating the Security of Satellite Systems | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Satellite systems are facing an ever-increasing amount of cybersecurity
threats as their role in communications, navigation, and other services
expands. Recent papers have examined attacks targeting satellites and space
systems; however, they did not comprehensively analyze the threats to
satellites and systematically identify adversarial techniques across the attack
lifecycle. This paper presents a comprehensive taxonomy of adversarial tactics,
techniques, and procedures explicitly targeting LEO satellites. First, we
analyze the space ecosystem including the ground, space, Communication, and
user segments, highlighting their architectures, functions, and
vulnerabilities. Then, we examine the threat landscape, including adversary
types, and capabilities, and survey historical and recent attacks such as
jamming, spoofing, and supply chain. Finally, we propose a novel extension of
the MITRE ATT&CK framework to categorize satellite attack techniques across the
adversary lifecycle from reconnaissance to impact. The taxonomy is demonstrated
by modeling high-profile incidents, including the Viasat attack that disrupted
Ukraine's communications. The taxonomy provides the foundation for the
development of defenses against emerging cyber risks to space assets. The
proposed threat model will advance research in the space domain and contribute
to the security of the space domain against sophisticated attacks.
| [
{
"created": "Sun, 3 Dec 2023 09:38:28 GMT",
"version": "v1"
}
] | 2023-12-05 | [
[
"Peled",
"Roy",
""
],
[
"Aizikovich",
"Eran",
""
],
[
"Habler",
"Edan",
""
],
[
"Elovici",
"Yuval",
""
],
[
"Shabtai",
"Asaf",
""
]
] | Satellite systems are facing an ever-increasing amount of cybersecurity threats as their role in communications, navigation, and other services expands. Recent papers have examined attacks targeting satellites and space systems; however, they did not comprehensively analyze the threats to satellites and systematically identify adversarial techniques across the attack lifecycle. This paper presents a comprehensive taxonomy of adversarial tactics, techniques, and procedures explicitly targeting LEO satellites. First, we analyze the space ecosystem including the ground, space, Communication, and user segments, highlighting their architectures, functions, and vulnerabilities. Then, we examine the threat landscape, including adversary types, and capabilities, and survey historical and recent attacks such as jamming, spoofing, and supply chain. Finally, we propose a novel extension of the MITRE ATT&CK framework to categorize satellite attack techniques across the adversary lifecycle from reconnaissance to impact. The taxonomy is demonstrated by modeling high-profile incidents, including the Viasat attack that disrupted Ukraine's communications. The taxonomy provides the foundation for the development of defenses against emerging cyber risks to space assets. The proposed threat model will advance research in the space domain and contribute to the security of the space domain against sophisticated attacks. |
2102.09032 | Karl B\"ackstr\"om | Karl B\"ackstr\"om, Ivan Walulya, Marina Papatriantafilou, Philippas
Tsigas | Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and
Stable Convergence | 13 pages, 10 figures. Accepted in the 35th IEEE International
Parallel & Distributed Processing Symposium | null | null | null | cs.DC cs.DS | http://creativecommons.org/licenses/by/4.0/ | Stochastic gradient descent (SGD) is an essential element in Machine Learning
(ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including
synchronization-free algorithms, e.g. HOGWILD!, have received interest in
certain contexts, due to reduced overhead compared to synchronous
parallelization. Despite that they induce staleness and inconsistency, they
have shown speedup for problems satisfying smooth, strongly convex targets, and
gradient sparsity. Recent works take important steps towards understanding the
potential of parallel SGD for problems not conforming to these strong
assumptions, in particular for deep learning (DL). There is however a gap in
current literature in understanding when AsyncSGD algorithms are useful in
practice, and in particular how mechanisms for synchronization and consistency
play a role. We focus on the impact of consistency-preserving non-blocking
synchronization in SGD convergence, and in sensitivity to hyper-parameter
tuning. We propose Leashed-SGD, an extensible algorithmic framework of
consistency-preserving implementations of AsyncSGD, employing lock-free
synchronization, effectively balancing throughput and latency. We argue
analytically about the dynamics of the algorithms, memory consumption, the
threads' progress over time, and the expected contention. We provide a
comprehensive empirical evaluation, validating the analytical claims,
benchmarking the proposed Leashed-SGD framework, and comparing to baselines for
training multilayer perceptrons (MLP) and convolutional neural networks (CNN).
We observe the crucial impact of contention, staleness and consistency and show
how Leashed-SGD provides significant improvements in stability as well as
wall-clock time to convergence (from 20-80% up to 4x improvements) compared to
the standard lock-based AsyncSGD algorithm and HOGWILD!, while reducing the
overall memory footprint.
| [
{
"created": "Wed, 17 Feb 2021 21:24:44 GMT",
"version": "v1"
}
] | 2021-02-19 | [
[
"Bäckström",
"Karl",
""
],
[
"Walulya",
"Ivan",
""
],
[
"Papatriantafilou",
"Marina",
""
],
[
"Tsigas",
"Philippas",
""
]
] | Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free algorithms, e.g. HOGWILD!, have received interest in certain contexts, due to reduced overhead compared to synchronous parallelization. Despite that they induce staleness and inconsistency, they have shown speedup for problems satisfying smooth, strongly convex targets, and gradient sparsity. Recent works take important steps towards understanding the potential of parallel SGD for problems not conforming to these strong assumptions, in particular for deep learning (DL). There is however a gap in current literature in understanding when AsyncSGD algorithms are useful in practice, and in particular how mechanisms for synchronization and consistency play a role. We focus on the impact of consistency-preserving non-blocking synchronization in SGD convergence, and in sensitivity to hyper-parameter tuning. We propose Leashed-SGD, an extensible algorithmic framework of consistency-preserving implementations of AsyncSGD, employing lock-free synchronization, effectively balancing throughput and latency. We argue analytically about the dynamics of the algorithms, memory consumption, the threads' progress over time, and the expected contention. We provide a comprehensive empirical evaluation, validating the analytical claims, benchmarking the proposed Leashed-SGD framework, and comparing to baselines for training multilayer perceptrons (MLP) and convolutional neural networks (CNN). We observe the crucial impact of contention, staleness and consistency and show how Leashed-SGD provides significant improvements in stability as well as wall-clock time to convergence (from 20-80% up to 4x improvements) compared to the standard lock-based AsyncSGD algorithm and HOGWILD!, while reducing the overall memory footprint. |
2407.08028 | Bingjie Tang | Bingjie Tang, Iretiayo Akinola, Jie Xu, Bowen Wen, Ankur Handa, Karl
Van Wyk, Dieter Fox, Gaurav S. Sukhatme, Fabio Ramos, Yashraj Narang | AutoMate: Specialist and Generalist Assembly Policies over Diverse
Geometries | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robotic assembly for high-mixture settings requires adaptivity to diverse
parts and poses, which is an open challenge. Meanwhile, in other areas of
robotics, large models and sim-to-real have led to tremendous progress.
Inspired by such work, we present AutoMate, a learning framework and system
that consists of 4 parts: 1) a dataset of 100 assemblies compatible with
simulation and the real world, along with parallelized simulation environments
for policy learning, 2) a novel simulation-based approach for learning
specialist (i.e., part-specific) policies and generalist (i.e., unified)
assembly policies, 3) demonstrations of specialist policies that individually
solve 80 assemblies with 80% or higher success rates in simulation, as well as
a generalist policy that jointly solves 20 assemblies with an 80%+ success
rate, and 4) zero-shot sim-to-real transfer that achieves similar (or better)
performance than simulation, including on perception-initialized assembly. The
key methodological takeaway is that a union of diverse algorithms from
manufacturing engineering, character animation, and time-series analysis
provides a generic and robust solution for a diverse range of robotic assembly
problems. To our knowledge, AutoMate provides the first simulation-based
framework for learning specialist and generalist policies over a wide range of
assemblies, as well as the first system demonstrating zero-shot sim-to-real
transfer over such a range. For videos and additional details, please see our
project website: https://bingjietang718.github.io/automate/
| [
{
"created": "Wed, 10 Jul 2024 20:11:29 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Aug 2024 01:01:45 GMT",
"version": "v2"
}
] | 2024-08-02 | [
[
"Tang",
"Bingjie",
""
],
[
"Akinola",
"Iretiayo",
""
],
[
"Xu",
"Jie",
""
],
[
"Wen",
"Bowen",
""
],
[
"Handa",
"Ankur",
""
],
[
"Van Wyk",
"Karl",
""
],
[
"Fox",
"Dieter",
""
],
[
"Sukhatme",
"Gaurav S.",
""
],
[
"Ramos",
"Fabio",
""
],
[
"Narang",
"Yashraj",
""
]
] | Robotic assembly for high-mixture settings requires adaptivity to diverse parts and poses, which is an open challenge. Meanwhile, in other areas of robotics, large models and sim-to-real have led to tremendous progress. Inspired by such work, we present AutoMate, a learning framework and system that consists of 4 parts: 1) a dataset of 100 assemblies compatible with simulation and the real world, along with parallelized simulation environments for policy learning, 2) a novel simulation-based approach for learning specialist (i.e., part-specific) policies and generalist (i.e., unified) assembly policies, 3) demonstrations of specialist policies that individually solve 80 assemblies with 80% or higher success rates in simulation, as well as a generalist policy that jointly solves 20 assemblies with an 80%+ success rate, and 4) zero-shot sim-to-real transfer that achieves similar (or better) performance than simulation, including on perception-initialized assembly. The key methodological takeaway is that a union of diverse algorithms from manufacturing engineering, character animation, and time-series analysis provides a generic and robust solution for a diverse range of robotic assembly problems. To our knowledge, AutoMate provides the first simulation-based framework for learning specialist and generalist policies over a wide range of assemblies, as well as the first system demonstrating zero-shot sim-to-real transfer over such a range. For videos and additional details, please see our project website: https://bingjietang718.github.io/automate/ |
2204.03456 | Rafael Rego Drumond | Lukas Brinkmeyer and Rafael Rego Drumond and Johannes Burchert and
Lars Schmidt-Thieme | Few-Shot Forecasting of Time-Series with Heterogeneous Channels | Under review. Equal contribution (Brinkmeyer and Rego Drumond) | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Learning complex time series forecasting models usually requires a large
amount of data, as each model is trained from scratch for each task/data set.
Leveraging learning experience with similar datasets is a well-established
technique for classification problems called few-shot classification. However,
existing approaches cannot be applied to time-series forecasting because i)
multivariate time-series datasets have different channels and ii) forecasting
is principally different from classification. In this paper we formalize the
problem of few-shot forecasting of time-series with heterogeneous channels for
the first time. Extending recent work on heterogeneous attributes in vector
data, we develop a model composed of permutation-invariant deep set-blocks
which incorporate a temporal embedding. We assemble the first meta-dataset of
40 multivariate time-series datasets and show through experiments that our
model provides a good generalization, outperforming baselines carried over from
simpler scenarios that either fail to learn across tasks or miss temporal
information.
| [
{
"created": "Thu, 7 Apr 2022 14:02:15 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Aug 2022 14:27:13 GMT",
"version": "v2"
}
] | 2022-08-19 | [
[
"Brinkmeyer",
"Lukas",
""
],
[
"Drumond",
"Rafael Rego",
""
],
[
"Burchert",
"Johannes",
""
],
[
"Schmidt-Thieme",
"Lars",
""
]
] | Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for classification problems called few-shot classification. However, existing approaches cannot be applied to time-series forecasting because i) multivariate time-series datasets have different channels and ii) forecasting is principally different from classification. In this paper we formalize the problem of few-shot forecasting of time-series with heterogeneous channels for the first time. Extending recent work on heterogeneous attributes in vector data, we develop a model composed of permutation-invariant deep set-blocks which incorporate a temporal embedding. We assemble the first meta-dataset of 40 multivariate time-series datasets and show through experiments that our model provides a good generalization, outperforming baselines carried over from simpler scenarios that either fail to learn across tasks or miss temporal information. |
2306.01457 | Stefan Arnold | Stefan Arnold, Dilara Yesilbas, Sven Weinzierl | Driving Context into Text-to-Text Privatization | null | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | \textit{Metric Differential Privacy} enables text-to-text privatization by
adding calibrated noise to the vector of a word derived from an embedding space
and projecting this noisy vector back to a discrete vocabulary using a nearest
neighbor search. Since words are substituted without context, this mechanism is
expected to fall short at finding substitutes for words with ambiguous
meanings, such as \textit{'bank'}. To account for these ambiguous words, we
leverage a sense embedding and incorporate a sense disambiguation step prior to
noise injection. We encompass our modification to the privatization mechanism
with an estimation of privacy and utility. For word sense disambiguation on the
\textit{Words in Context} dataset, we demonstrate a substantial increase in
classification accuracy by $6.05\%$.
| [
{
"created": "Fri, 2 Jun 2023 11:33:06 GMT",
"version": "v1"
}
] | 2023-06-05 | [
[
"Arnold",
"Stefan",
""
],
[
"Yesilbas",
"Dilara",
""
],
[
"Weinzierl",
"Sven",
""
]
] | \textit{Metric Differential Privacy} enables text-to-text privatization by adding calibrated noise to the vector of a word derived from an embedding space and projecting this noisy vector back to a discrete vocabulary using a nearest neighbor search. Since words are substituted without context, this mechanism is expected to fall short at finding substitutes for words with ambiguous meanings, such as \textit{'bank'}. To account for these ambiguous words, we leverage a sense embedding and incorporate a sense disambiguation step prior to noise injection. We encompass our modification to the privatization mechanism with an estimation of privacy and utility. For word sense disambiguation on the \textit{Words in Context} dataset, we demonstrate a substantial increase in classification accuracy by $6.05\%$. |
2202.10019 | Md. Rafat Rahman Tushar | Ismot Sadik Peyas, Zahid Hasan, Md. Rafat Rahman Tushar, Al Musabbir,
Raisa Mehjabin Azni, Shahnewaz Siddique | Autonomous Warehouse Robot using Deep Q-Learning | TENCON 2021 | null | 10.1109/TENCON54134.2021.9707256 | null | cs.RO cs.AI cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In warehouses, specialized agents need to navigate, avoid obstacles and
maximize the use of space in the warehouse environment. Due to the
unpredictability of these environments, reinforcement learning approaches can
be applied to complete these tasks. In this paper, we propose using Deep
Reinforcement Learning (DRL) to address the robot navigation and obstacle
avoidance problem and traditional Q-learning with minor variations to maximize
the use of space for product placement. We first investigate the problem for
the single robot case. Next, based on the single robot model, we extend our
system to the multi-robot case. We use a strategic variation of Q-tables to
perform multi-agent Q-learning. We successfully test the performance of our
model in a 2D simulation environment for both the single and multi-robot cases.
| [
{
"created": "Mon, 21 Feb 2022 07:16:51 GMT",
"version": "v1"
}
] | 2022-02-22 | [
[
"Peyas",
"Ismot Sadik",
""
],
[
"Hasan",
"Zahid",
""
],
[
"Tushar",
"Md. Rafat Rahman",
""
],
[
"Musabbir",
"Al",
""
],
[
"Azni",
"Raisa Mehjabin",
""
],
[
"Siddique",
"Shahnewaz",
""
]
] | In warehouses, specialized agents need to navigate, avoid obstacles and maximize the use of space in the warehouse environment. Due to the unpredictability of these environments, reinforcement learning approaches can be applied to complete these tasks. In this paper, we propose using Deep Reinforcement Learning (DRL) to address the robot navigation and obstacle avoidance problem and traditional Q-learning with minor variations to maximize the use of space for product placement. We first investigate the problem for the single robot case. Next, based on the single robot model, we extend our system to the multi-robot case. We use a strategic variation of Q-tables to perform multi-agent Q-learning. We successfully test the performance of our model in a 2D simulation environment for both the single and multi-robot cases. |
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