id stringlengths 9 10 | submitter stringlengths 1 64 ⌀ | authors stringlengths 4 20.7k | title stringlengths 4 246 | comments stringlengths 1 523 ⌀ | journal-ref stringlengths 4 404 ⌀ | doi stringlengths 11 153 ⌀ | report-no stringlengths 2 254 ⌀ | categories stringlengths 5 98 | license stringclasses 9 values | orig_abstract stringlengths 14 3.35k | versions listlengths 1 60 | update_date stringlengths 10 10 | authors_parsed listlengths 1 1.35k | abstract stringlengths 11 3.34k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2406.08085 | Yiqin Wang | Haoji Zhang, Yiqin Wang, Yansong Tang, Yong Liu, Jiashi Feng, Jifeng
Dai, Xiaojie Jin | Flash-VStream: Memory-Based Real-Time Understanding for Long Video
Streams | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Benefiting from the advancements in large language models and cross-modal
alignment, existing multi-modal video understanding methods have achieved
prominent performance in offline scenario. However, online video streams, as
one of the most common media forms in the real world, have seldom received
attention. Compared to offline videos, the 'dynamic' nature of online video
streams poses challenges for the direct application of existing models and
introduces new problems, such as the storage of extremely long-term
information, interaction between continuous visual content and 'asynchronous'
user questions. Therefore, in this paper we present Flash-VStream, a
video-language model that simulates the memory mechanism of human. Our model is
able to process extremely long video streams in real-time and respond to user
queries simultaneously. Compared to existing models, Flash-VStream achieves
significant reductions in inference latency and VRAM consumption, which is
intimately related to performing understanding of online streaming video. In
addition, given that existing video understanding benchmarks predominantly
concentrate on offline scenario, we propose VStream-QA, a novel question
answering benchmark specifically designed for online video streaming
understanding. Comparisons with popular existing methods on the proposed
benchmark demonstrate the superiority of our method for such challenging
setting. To verify the generalizability of our approach, we further evaluate it
on existing video understanding benchmarks and achieves state-of-the-art
performance in offline scenarios as well. All code, models, and datasets are
available at the https://invinciblewyq.github.io/vstream-page/
| [
{
"created": "Wed, 12 Jun 2024 11:07:55 GMT",
"version": "v1"
},
{
"created": "Sun, 30 Jun 2024 05:39:46 GMT",
"version": "v2"
}
] | 2024-07-02 | [
[
"Zhang",
"Haoji",
""
],
[
"Wang",
"Yiqin",
""
],
[
"Tang",
"Yansong",
""
],
[
"Liu",
"Yong",
""
],
[
"Feng",
"Jiashi",
""
],
[
"Dai",
"Jifeng",
""
],
[
"Jin",
"Xiaojie",
""
]
] | Benefiting from the advancements in large language models and cross-modal alignment, existing multi-modal video understanding methods have achieved prominent performance in offline scenario. However, online video streams, as one of the most common media forms in the real world, have seldom received attention. Compared to offline videos, the 'dynamic' nature of online video streams poses challenges for the direct application of existing models and introduces new problems, such as the storage of extremely long-term information, interaction between continuous visual content and 'asynchronous' user questions. Therefore, in this paper we present Flash-VStream, a video-language model that simulates the memory mechanism of human. Our model is able to process extremely long video streams in real-time and respond to user queries simultaneously. Compared to existing models, Flash-VStream achieves significant reductions in inference latency and VRAM consumption, which is intimately related to performing understanding of online streaming video. In addition, given that existing video understanding benchmarks predominantly concentrate on offline scenario, we propose VStream-QA, a novel question answering benchmark specifically designed for online video streaming understanding. Comparisons with popular existing methods on the proposed benchmark demonstrate the superiority of our method for such challenging setting. To verify the generalizability of our approach, we further evaluate it on existing video understanding benchmarks and achieves state-of-the-art performance in offline scenarios as well. All code, models, and datasets are available at the https://invinciblewyq.github.io/vstream-page/ |
2008.10316 | Maximilian B\"other | Thomas Bl\"asius and Maximilian B\"other and Philipp Fischbeck and
Tobias Friedrich and Alina Gries and Falk H\"uffner and Otto Ki{\ss}ig and
Pascal Lenzner and Louise Molitor and Leon Schiller and Armin Wells and Simon
Wietheger | A Strategic Routing Framework and Algorithms for Computing Alternative
Paths | 19 pages, 7 figures, full version of paper accepted at ATMOS 2020 | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional navigation services find the fastest route for a single driver.
Though always using the fastest route seems desirable for every individual,
selfish behavior can have undesirable effects such as higher energy consumption
and avoidable congestion, even leading to higher overall and individual travel
times. In contrast, strategic routing aims at optimizing the traffic for all
agents regarding a global optimization goal. We introduce a framework to
formalize real-world strategic routing scenarios as algorithmic problems and
study one of them, which we call Single Alternative Path (SAP), in detail.
There, we are given an original route between a single origin--destination
pair. The goal is to suggest an alternative route to all agents that optimizes
the overall travel time under the assumption that the agents distribute among
both routes according to a psychological model, for which we introduce the
concept of Pareto-conformity. We show that the SAP problem is NP-complete, even
for such models. Nonetheless, assuming Pareto-conformity, we give multiple
algorithms for different variants of SAP, using multi-criteria shortest path
algorithms as subroutines. Moreover, we prove that several natural models are
in fact Pareto-conform. The implementation of our algorithms serves as a proof
of concept, showing that SAP can be solved in reasonable time even though the
algorithms have exponential running time in the worst case.
| [
{
"created": "Mon, 24 Aug 2020 10:56:41 GMT",
"version": "v1"
}
] | 2020-08-25 | [
[
"Bläsius",
"Thomas",
""
],
[
"Böther",
"Maximilian",
""
],
[
"Fischbeck",
"Philipp",
""
],
[
"Friedrich",
"Tobias",
""
],
[
"Gries",
"Alina",
""
],
[
"Hüffner",
"Falk",
""
],
[
"Kißig",
"Otto",
""
],
[
"Lenzner",
"Pascal",
""
],
[
"Molitor",
"Louise",
""
],
[
"Schiller",
"Leon",
""
],
[
"Wells",
"Armin",
""
],
[
"Wietheger",
"Simon",
""
]
] | Traditional navigation services find the fastest route for a single driver. Though always using the fastest route seems desirable for every individual, selfish behavior can have undesirable effects such as higher energy consumption and avoidable congestion, even leading to higher overall and individual travel times. In contrast, strategic routing aims at optimizing the traffic for all agents regarding a global optimization goal. We introduce a framework to formalize real-world strategic routing scenarios as algorithmic problems and study one of them, which we call Single Alternative Path (SAP), in detail. There, we are given an original route between a single origin--destination pair. The goal is to suggest an alternative route to all agents that optimizes the overall travel time under the assumption that the agents distribute among both routes according to a psychological model, for which we introduce the concept of Pareto-conformity. We show that the SAP problem is NP-complete, even for such models. Nonetheless, assuming Pareto-conformity, we give multiple algorithms for different variants of SAP, using multi-criteria shortest path algorithms as subroutines. Moreover, we prove that several natural models are in fact Pareto-conform. The implementation of our algorithms serves as a proof of concept, showing that SAP can be solved in reasonable time even though the algorithms have exponential running time in the worst case. |
2004.05575 | Koteswar Rao Jerripothula | Koteswar Rao Jerripothula, Jianfei Cai, Jiangbo Lu, Junsong Yuan | Image Co-skeletonization via Co-segmentation | 13 pages, 12 figures, Submitted to IEEE Transactions on Image
Processing (TIP) | null | null | null | cs.CV cs.MM eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in the joint processing of images have certainly shown its
advantages over individual processing. Different from the existing works geared
towards co-segmentation or co-localization, in this paper, we explore a new
joint processing topic: image co-skeletonization, which is defined as joint
skeleton extraction of objects in an image collection. Object skeletonization
in a single natural image is a challenging problem because there is hardly any
prior knowledge about the object. Therefore, we resort to the idea of object
co-skeletonization, hoping that the commonness prior that exists across the
images may help, just as it does for other joint processing problems such as
co-segmentation. We observe that the skeleton can provide good scribbles for
segmentation, and skeletonization, in turn, needs good segmentation. Therefore,
we propose a coupled framework for co-skeletonization and co-segmentation tasks
so that they are well informed by each other, and benefit each other
synergistically. Since it is a new problem, we also construct a benchmark
dataset by annotating nearly 1.8k images spread across 38 categories. Extensive
experiments demonstrate that the proposed method achieves promising results in
all the three possible scenarios of joint-processing: weakly-supervised,
supervised, and unsupervised.
| [
{
"created": "Sun, 12 Apr 2020 09:35:54 GMT",
"version": "v1"
}
] | 2020-04-14 | [
[
"Jerripothula",
"Koteswar Rao",
""
],
[
"Cai",
"Jianfei",
""
],
[
"Lu",
"Jiangbo",
""
],
[
"Yuan",
"Junsong",
""
]
] | Recent advances in the joint processing of images have certainly shown its advantages over individual processing. Different from the existing works geared towards co-segmentation or co-localization, in this paper, we explore a new joint processing topic: image co-skeletonization, which is defined as joint skeleton extraction of objects in an image collection. Object skeletonization in a single natural image is a challenging problem because there is hardly any prior knowledge about the object. Therefore, we resort to the idea of object co-skeletonization, hoping that the commonness prior that exists across the images may help, just as it does for other joint processing problems such as co-segmentation. We observe that the skeleton can provide good scribbles for segmentation, and skeletonization, in turn, needs good segmentation. Therefore, we propose a coupled framework for co-skeletonization and co-segmentation tasks so that they are well informed by each other, and benefit each other synergistically. Since it is a new problem, we also construct a benchmark dataset by annotating nearly 1.8k images spread across 38 categories. Extensive experiments demonstrate that the proposed method achieves promising results in all the three possible scenarios of joint-processing: weakly-supervised, supervised, and unsupervised. |
1910.11141 | Alexey Radul | Alexey Radul, Brian Patton, Dougal Maclaurin, Matthew D. Hoffman and
Rif A. Saurous | Automatically Batching Control-Intensive Programs for Modern
Accelerators | 10 pages; Machine Learning and Systems 2020 | null | null | null | cs.DC cs.LG cs.PL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We present a general approach to batching arbitrary computations for
accelerators such as GPUs. We show orders-of-magnitude speedups using our
method on the No U-Turn Sampler (NUTS), a workhorse algorithm in Bayesian
statistics. The central challenge of batching NUTS and other Markov chain Monte
Carlo algorithms is data-dependent control flow and recursion. We overcome this
by mechanically transforming a single-example implementation into a form that
explicitly tracks the current program point for each batch member, and only
steps forward those in the same place. We present two different batching
algorithms: a simpler, previously published one that inherits recursion from
the host Python, and a more complex, novel one that implemenents recursion
directly and can batch across it. We implement these batching methods as a
general program transformation on Python source. Both the batching system and
the NUTS implementation presented here are available as part of the popular
TensorFlow Probability software package.
| [
{
"created": "Wed, 23 Oct 2019 14:06:18 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Mar 2020 15:56:56 GMT",
"version": "v2"
}
] | 2020-03-13 | [
[
"Radul",
"Alexey",
""
],
[
"Patton",
"Brian",
""
],
[
"Maclaurin",
"Dougal",
""
],
[
"Hoffman",
"Matthew D.",
""
],
[
"Saurous",
"Rif A.",
""
]
] | We present a general approach to batching arbitrary computations for accelerators such as GPUs. We show orders-of-magnitude speedups using our method on the No U-Turn Sampler (NUTS), a workhorse algorithm in Bayesian statistics. The central challenge of batching NUTS and other Markov chain Monte Carlo algorithms is data-dependent control flow and recursion. We overcome this by mechanically transforming a single-example implementation into a form that explicitly tracks the current program point for each batch member, and only steps forward those in the same place. We present two different batching algorithms: a simpler, previously published one that inherits recursion from the host Python, and a more complex, novel one that implemenents recursion directly and can batch across it. We implement these batching methods as a general program transformation on Python source. Both the batching system and the NUTS implementation presented here are available as part of the popular TensorFlow Probability software package. |
1811.06287 | Michael Werman | Levi Offen and Michael Werman | Sketch based Reduced Memory Hough Transform | 5 pages | 2018 25th IEEE International Conference on Image Processing (ICIP) | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes using sketch algorithms to represent the votes in Hough
transforms. Replacing the accumulator array with a sketch (Sketch Hough
Transform - SHT) significantly reduces the memory needed to compute a Hough
transform. We also present a new sketch, Count Median Update, which works
better than known sketch methods for replacing the accumulator array in the
Hough Transform.
| [
{
"created": "Thu, 15 Nov 2018 10:44:35 GMT",
"version": "v1"
}
] | 2018-11-16 | [
[
"Offen",
"Levi",
""
],
[
"Werman",
"Michael",
""
]
] | This paper proposes using sketch algorithms to represent the votes in Hough transforms. Replacing the accumulator array with a sketch (Sketch Hough Transform - SHT) significantly reduces the memory needed to compute a Hough transform. We also present a new sketch, Count Median Update, which works better than known sketch methods for replacing the accumulator array in the Hough Transform. |
2407.17738 | Haoran Zhu | Haoran Zhu, Yifan Zhou, Chang Xu, Ruixiang Zhang, and Wen Yang | Enhancing Fine-grained Object Detection in Aerial Images via Orthogonal
Mapping | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fine-Grained Object Detection (FGOD) is a critical task in high-resolution
aerial image analysis. This letter introduces Orthogonal Mapping (OM), a simple
yet effective method aimed at addressing the challenge of semantic confusion
inherent in FGOD. OM introduces orthogonal constraints in the feature space by
decoupling features from the last layer of the classification branch with a
class-wise orthogonal vector basis. This effectively mitigates semantic
confusion and enhances classification accuracy. Moreover, OM can be seamlessly
integrated into mainstream object detectors. Extensive experiments conducted on
three FGOD datasets (FAIR1M, ShipRSImageNet, and MAR20) demonstrate the
effectiveness and superiority of the proposed approach. Notably, with just one
line of code, OM achieves a 4.08% improvement in mean Average Precision (mAP)
over FCOS on the ShipRSImageNet dataset. Codes are released at
https://github.com/ZhuHaoranEIS/Orthogonal-FGOD.
| [
{
"created": "Thu, 25 Jul 2024 03:26:41 GMT",
"version": "v1"
}
] | 2024-07-26 | [
[
"Zhu",
"Haoran",
""
],
[
"Zhou",
"Yifan",
""
],
[
"Xu",
"Chang",
""
],
[
"Zhang",
"Ruixiang",
""
],
[
"Yang",
"Wen",
""
]
] | Fine-Grained Object Detection (FGOD) is a critical task in high-resolution aerial image analysis. This letter introduces Orthogonal Mapping (OM), a simple yet effective method aimed at addressing the challenge of semantic confusion inherent in FGOD. OM introduces orthogonal constraints in the feature space by decoupling features from the last layer of the classification branch with a class-wise orthogonal vector basis. This effectively mitigates semantic confusion and enhances classification accuracy. Moreover, OM can be seamlessly integrated into mainstream object detectors. Extensive experiments conducted on three FGOD datasets (FAIR1M, ShipRSImageNet, and MAR20) demonstrate the effectiveness and superiority of the proposed approach. Notably, with just one line of code, OM achieves a 4.08% improvement in mean Average Precision (mAP) over FCOS on the ShipRSImageNet dataset. Codes are released at https://github.com/ZhuHaoranEIS/Orthogonal-FGOD. |
1312.0718 | Yong Zeng | Yong Zeng, Rui Zhang, and Zhi Ning Chen | Electromagnetic Lens-focusing Antenna Enabled Massive MIMO: Performance
Improvement and Cost Reduction | 30 pages, 9 figures | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Massive multiple-input multiple-output (MIMO) techniques have been recently
advanced to tremendously improve the performance of wireless communication
networks. However, the use of very large antenna arrays at the base stations
(BSs) brings new issues, such as the significantly increased hardware and
signal processing costs. In order to reap the enormous gain of massive MIMO and
yet reduce its cost to an affordable level, this paper proposes a novel system
design by integrating an electromagnetic (EM) lens with the large antenna
array, termed the EM-lens enabled MIMO. The EM lens has the capability of
focusing the power of an incident wave to a small area of the antenna array,
while the location of the focal area varies with the angle of arrival (AoA) of
the wave. Therefore, in practical scenarios where the arriving signals from
geographically separated users have different AoAs, the EM-lens enabled system
provides two new benefits, namely energy focusing and spatial interference
rejection. By taking into account the effects of imperfect channel estimation
via pilot-assisted training, in this paper we analytically show that the
average received signal-to-noise ratio (SNR) in both the single-user and
multiuser uplink transmissions can be strictly improved by the EM-lens enabled
system. Furthermore, we demonstrate that the proposed design makes it possible
to considerably reduce the hardware and signal processing costs with only
slight degradations in performance. To this end, two complexity/cost reduction
schemes are proposed, which are small-MIMO processing with parallel receiver
filtering applied over subgroups of antennas to reduce the computational
complexity, and channel covariance based antenna selection to reduce the
required number of radio frequency (RF) chains. Numerical results are provided
to corroborate our analysis.
| [
{
"created": "Tue, 3 Dec 2013 07:14:23 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Mar 2014 14:15:44 GMT",
"version": "v2"
}
] | 2014-03-27 | [
[
"Zeng",
"Yong",
""
],
[
"Zhang",
"Rui",
""
],
[
"Chen",
"Zhi Ning",
""
]
] | Massive multiple-input multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless communication networks. However, the use of very large antenna arrays at the base stations (BSs) brings new issues, such as the significantly increased hardware and signal processing costs. In order to reap the enormous gain of massive MIMO and yet reduce its cost to an affordable level, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed the EM-lens enabled MIMO. The EM lens has the capability of focusing the power of an incident wave to a small area of the antenna array, while the location of the focal area varies with the angle of arrival (AoA) of the wave. Therefore, in practical scenarios where the arriving signals from geographically separated users have different AoAs, the EM-lens enabled system provides two new benefits, namely energy focusing and spatial interference rejection. By taking into account the effects of imperfect channel estimation via pilot-assisted training, in this paper we analytically show that the average received signal-to-noise ratio (SNR) in both the single-user and multiuser uplink transmissions can be strictly improved by the EM-lens enabled system. Furthermore, we demonstrate that the proposed design makes it possible to considerably reduce the hardware and signal processing costs with only slight degradations in performance. To this end, two complexity/cost reduction schemes are proposed, which are small-MIMO processing with parallel receiver filtering applied over subgroups of antennas to reduce the computational complexity, and channel covariance based antenna selection to reduce the required number of radio frequency (RF) chains. Numerical results are provided to corroborate our analysis. |
1008.1438 | Ji King | Ji King | Harmonic Analysis and Qualitative Uncertainty Principle | 108 pages,no figures | null | null | null | cs.IT math-ph math.CA math.IT math.MP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the mathematical nature of qualitative uncertainty
principle (QUP), which plays an important role in mathematics, physics and
engineering fields. Consider a 3-tuple (K, H1, H2) that K: H1 -> H2 is an
integral operator. Suppose a signal f in H1, {\Omega}1 and {\Omega}2 are
domains on which f, Kf define respectively. Does this signal f vanish if
|{\Sigma}(f)|<|{\Omega}1|and|{\Sigma}(Kf)|<|{\Omega}2|? The excesses and
deficiencies of integral kernel K({\omega}, t) are found to be greatly related
to this general formulation of QUP. The complete point theory of integral
kernel is so established to deal with the QUP. This theory addresses the
density and linear independence of integral kernels. Some algebraic and
geometric properties of complete points are presented. It is shown that the
satisfaction of QUP depends on the existence of some complete points. By
recognizing complete points of their corresponding integral kernels, the QUP
with Fourier transform, Wigner-Ville distribution, Gabor transform and wavelet
are studied. It is shown the QUP only holds for good behaved integral
operators. An investigation of full violation of QUP shows that L2 space is
large for high resolution harmonic analysis. And the invertible linear integral
transforms whose kernels are complete in L2 probably lead to the satisfaction
of QUP. It indicates the performance limitation of linear integral transforms
in harmonic analysis. Two possible ways bypassing uncertainty principle,
nonlinear method and sparse representation, are thus suggested. The notion of
operator family is developed and is applied to understand remarkable
performances of recent sparse representation.
| [
{
"created": "Mon, 9 Aug 2010 01:59:49 GMT",
"version": "v1"
}
] | 2010-08-10 | [
[
"King",
"Ji",
""
]
] | This paper investigates the mathematical nature of qualitative uncertainty principle (QUP), which plays an important role in mathematics, physics and engineering fields. Consider a 3-tuple (K, H1, H2) that K: H1 -> H2 is an integral operator. Suppose a signal f in H1, {\Omega}1 and {\Omega}2 are domains on which f, Kf define respectively. Does this signal f vanish if |{\Sigma}(f)|<|{\Omega}1|and|{\Sigma}(Kf)|<|{\Omega}2|? The excesses and deficiencies of integral kernel K({\omega}, t) are found to be greatly related to this general formulation of QUP. The complete point theory of integral kernel is so established to deal with the QUP. This theory addresses the density and linear independence of integral kernels. Some algebraic and geometric properties of complete points are presented. It is shown that the satisfaction of QUP depends on the existence of some complete points. By recognizing complete points of their corresponding integral kernels, the QUP with Fourier transform, Wigner-Ville distribution, Gabor transform and wavelet are studied. It is shown the QUP only holds for good behaved integral operators. An investigation of full violation of QUP shows that L2 space is large for high resolution harmonic analysis. And the invertible linear integral transforms whose kernels are complete in L2 probably lead to the satisfaction of QUP. It indicates the performance limitation of linear integral transforms in harmonic analysis. Two possible ways bypassing uncertainty principle, nonlinear method and sparse representation, are thus suggested. The notion of operator family is developed and is applied to understand remarkable performances of recent sparse representation. |
2308.10959 | Sijin Wu | Sijin Wu, Dan Zhang, Teng Hu, Shikun Feng | DocPrompt: Large-scale continue pretrain for zero-shot and few-shot
document question answering | null | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose Docprompt for document question answering tasks
with powerful zero-shot and few-shot performance. We proposed a novel weakly
supervised data generation method, a novel multl-stage training method and a
novel understanding model \& generation model ensemble method. We achieved
state-of-the-art performance on 4 document question answering tasks. This
method greatly improves the delivery efficiency and model performance of
document question answering customer projects, reducing annotation costs and
labor costs. Our demo can be found at
https://huggingface.co/spaces/PaddlePaddle/ERNIE-Layout.
| [
{
"created": "Mon, 21 Aug 2023 18:14:00 GMT",
"version": "v1"
},
{
"created": "Thu, 31 Aug 2023 09:14:17 GMT",
"version": "v2"
}
] | 2023-09-01 | [
[
"Wu",
"Sijin",
""
],
[
"Zhang",
"Dan",
""
],
[
"Hu",
"Teng",
""
],
[
"Feng",
"Shikun",
""
]
] | In this paper, we propose Docprompt for document question answering tasks with powerful zero-shot and few-shot performance. We proposed a novel weakly supervised data generation method, a novel multl-stage training method and a novel understanding model \& generation model ensemble method. We achieved state-of-the-art performance on 4 document question answering tasks. This method greatly improves the delivery efficiency and model performance of document question answering customer projects, reducing annotation costs and labor costs. Our demo can be found at https://huggingface.co/spaces/PaddlePaddle/ERNIE-Layout. |
2402.02055 | Yiping Wang | Yiping Wang, Yifang Chen, Wendan Yan, Kevin Jamieson, Simon Shaolei Du | Variance Alignment Score: A Simple But Tough-to-Beat Data Selection
Method for Multimodal Contrastive Learning | 17 pages, 4 figures | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, data selection has emerged as a core issue for large-scale
visual-language model pretraining, especially on noisy web-curated datasets.
One widely adopted strategy assigns quality scores such as CLIP similarity for
each sample and retains the data pairs with the highest scores. However, these
approaches are agnostic of data distribution and always fail to select the most
informative samples. To solve this problem, we propose a simple yet
theoretically principled metric named Variance Alignment Score (VAS), which has
the form $\langle \Sigma_{\text{test}}, \Sigma_i\rangle$. Here,
$\Sigma_{\text{test}}$ represents the target (cross-)covariance matrix we aim
to align, potentially based on prior knowledge, while $\Sigma_i$ denotes the
tensor product of single or multi-modal representations for the $i$-th sample.
We further design a new data selection method that maximizes the total VAS. We
provide theoretical analysis in a simplified setting to demonstrate the
theoretical advantage of VAS over random or other existing data selection.
Experimentally, applying VAS and CLIP scores together can outperform baselines
by a margin of $1.3\%$ average on 38 evaluation sets for noisy dataset DataComp
and $2.5\%$ on VTAB for high-quality dataset CC12M. Additionally, our ablation
study also shows visual features are better than text for calculating VAS, and
the related classical experimental design methods may fail under this context.
| [
{
"created": "Sat, 3 Feb 2024 06:29:04 GMT",
"version": "v1"
}
] | 2024-02-06 | [
[
"Wang",
"Yiping",
""
],
[
"Chen",
"Yifang",
""
],
[
"Yan",
"Wendan",
""
],
[
"Jamieson",
"Kevin",
""
],
[
"Du",
"Simon Shaolei",
""
]
] | In recent years, data selection has emerged as a core issue for large-scale visual-language model pretraining, especially on noisy web-curated datasets. One widely adopted strategy assigns quality scores such as CLIP similarity for each sample and retains the data pairs with the highest scores. However, these approaches are agnostic of data distribution and always fail to select the most informative samples. To solve this problem, we propose a simple yet theoretically principled metric named Variance Alignment Score (VAS), which has the form $\langle \Sigma_{\text{test}}, \Sigma_i\rangle$. Here, $\Sigma_{\text{test}}$ represents the target (cross-)covariance matrix we aim to align, potentially based on prior knowledge, while $\Sigma_i$ denotes the tensor product of single or multi-modal representations for the $i$-th sample. We further design a new data selection method that maximizes the total VAS. We provide theoretical analysis in a simplified setting to demonstrate the theoretical advantage of VAS over random or other existing data selection. Experimentally, applying VAS and CLIP scores together can outperform baselines by a margin of $1.3\%$ average on 38 evaluation sets for noisy dataset DataComp and $2.5\%$ on VTAB for high-quality dataset CC12M. Additionally, our ablation study also shows visual features are better than text for calculating VAS, and the related classical experimental design methods may fail under this context. |
2006.13202 | Oleh Rybkin | Oleh Rybkin, Kostas Daniilidis, Sergey Levine | Simple and Effective VAE Training with Calibrated Decoders | International Conference on Machine Learning (ICML), 2021. Project
website is at https://orybkin.github.io/sigma-vae/ | null | null | null | cs.LG cs.CV eess.IV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Variational autoencoders (VAEs) provide an effective and simple method for
modeling complex distributions. However, training VAEs often requires
considerable hyperparameter tuning to determine the optimal amount of
information retained by the latent variable. We study the impact of calibrated
decoders, which learn the uncertainty of the decoding distribution and can
determine this amount of information automatically, on the VAE performance.
While many methods for learning calibrated decoders have been proposed, many of
the recent papers that employ VAEs rely on heuristic hyperparameters and ad-hoc
modifications instead. We perform the first comprehensive comparative analysis
of calibrated decoder and provide recommendations for simple and effective VAE
training. Our analysis covers a range of image and video datasets and several
single-image and sequential VAE models. We further propose a simple but novel
modification to the commonly used Gaussian decoder, which computes the
prediction variance analytically. We observe empirically that using heuristic
modifications is not necessary with our method. Project website is at
https://orybkin.github.io/sigma-vae/
| [
{
"created": "Tue, 23 Jun 2020 17:57:47 GMT",
"version": "v1"
},
{
"created": "Sun, 16 Aug 2020 01:09:15 GMT",
"version": "v2"
},
{
"created": "Mon, 12 Jul 2021 04:06:41 GMT",
"version": "v3"
}
] | 2021-07-13 | [
[
"Rybkin",
"Oleh",
""
],
[
"Daniilidis",
"Kostas",
""
],
[
"Levine",
"Sergey",
""
]
] | Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning to determine the optimal amount of information retained by the latent variable. We study the impact of calibrated decoders, which learn the uncertainty of the decoding distribution and can determine this amount of information automatically, on the VAE performance. While many methods for learning calibrated decoders have been proposed, many of the recent papers that employ VAEs rely on heuristic hyperparameters and ad-hoc modifications instead. We perform the first comprehensive comparative analysis of calibrated decoder and provide recommendations for simple and effective VAE training. Our analysis covers a range of image and video datasets and several single-image and sequential VAE models. We further propose a simple but novel modification to the commonly used Gaussian decoder, which computes the prediction variance analytically. We observe empirically that using heuristic modifications is not necessary with our method. Project website is at https://orybkin.github.io/sigma-vae/ |
2007.13639 | Pengcheng Xia | Pengcheng Xia, Haoyu Wang, Xiapu Luo, Lei Wu, Yajin Zhou, Guangdong
Bai, Guoai Xu, Gang Huang, Xuanzhe Liu | Don't Fish in Troubled Waters! Characterizing Coronavirus-themed
Cryptocurrency Scams | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As COVID-19 has been spreading across the world since early 2020, a growing
number of malicious campaigns are capitalizing the topic of COVID-19. COVID-19
themed cryptocurrency scams are increasingly popular during the pandemic.
However, these newly emerging scams are poorly understood by our community. In
this paper, we present the first measurement study of COVID-19 themed
cryptocurrency scams. We first create a comprehensive taxonomy of COVID-19
scams by manually analyzing the existing scams reported by users from online
resources. Then, we propose a hybrid approach to perform the investigation by:
1) collecting reported scams in the wild; and 2) detecting undisclosed ones
based on information collected from suspicious entities (e.g., domains, tweets,
etc). We have collected 195 confirmed COVID-19 cryptocurrency scams in total,
including 91 token scams, 19 giveaway scams, 9 blackmail scams, 14 crypto
malware scams, 9 Ponzi scheme scams, and 53 donation scams. We then identified
over 200 blockchain addresses associated with these scams, which lead to at
least 330K US dollars in losses from 6,329 victims. For each type of scams, we
further investigated the tricks and social engineering techniques they used. To
facilitate future research, we have released all the well-labelled scams to the
research community.
| [
{
"created": "Mon, 27 Jul 2020 15:40:05 GMT",
"version": "v1"
},
{
"created": "Sun, 1 Nov 2020 12:43:09 GMT",
"version": "v2"
}
] | 2020-11-03 | [
[
"Xia",
"Pengcheng",
""
],
[
"Wang",
"Haoyu",
""
],
[
"Luo",
"Xiapu",
""
],
[
"Wu",
"Lei",
""
],
[
"Zhou",
"Yajin",
""
],
[
"Bai",
"Guangdong",
""
],
[
"Xu",
"Guoai",
""
],
[
"Huang",
"Gang",
""
],
[
"Liu",
"Xuanzhe",
""
]
] | As COVID-19 has been spreading across the world since early 2020, a growing number of malicious campaigns are capitalizing the topic of COVID-19. COVID-19 themed cryptocurrency scams are increasingly popular during the pandemic. However, these newly emerging scams are poorly understood by our community. In this paper, we present the first measurement study of COVID-19 themed cryptocurrency scams. We first create a comprehensive taxonomy of COVID-19 scams by manually analyzing the existing scams reported by users from online resources. Then, we propose a hybrid approach to perform the investigation by: 1) collecting reported scams in the wild; and 2) detecting undisclosed ones based on information collected from suspicious entities (e.g., domains, tweets, etc). We have collected 195 confirmed COVID-19 cryptocurrency scams in total, including 91 token scams, 19 giveaway scams, 9 blackmail scams, 14 crypto malware scams, 9 Ponzi scheme scams, and 53 donation scams. We then identified over 200 blockchain addresses associated with these scams, which lead to at least 330K US dollars in losses from 6,329 victims. For each type of scams, we further investigated the tricks and social engineering techniques they used. To facilitate future research, we have released all the well-labelled scams to the research community. |
2207.08256 | Fajrian Yunus | Fajrian Yunus, Chlo\'e Clavel, Catherine Pelachaud | Representation Learning of Image Schema | null | null | null | null | cs.HC cs.AI cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Image schema is a recurrent pattern of reasoning where one entity is mapped
into another. Image schema is similar to conceptual metaphor and is also
related to metaphoric gesture. Our main goal is to generate metaphoric gestures
for an Embodied Conversational Agent.
We propose a technique to learn the vector representation of image schemas.
As far as we are aware of, this is the first work which addresses that problem.
Our technique uses Ravenet et al's algorithm which we use to compute the image
schemas from the text input and also BERT and SenseBERT which we use as the
base word embedding technique to calculate the final vector representation of
the image schema. Our representation learning technique works by clustering:
word embedding vectors which belong to the same image schema should be
relatively closer to each other, and thus form a cluster.
With the image schemas representable as vectors, it also becomes possible to
have a notion that some image schemas are closer or more similar to each other
than to the others because the distance between the vectors is a proxy of the
dissimilarity between the corresponding image schemas. Therefore, after
obtaining the vector representation of the image schemas, we calculate the
distances between those vectors. Based on these, we create visualizations to
illustrate the relative distances between the different image schemas.
| [
{
"created": "Sun, 17 Jul 2022 18:42:37 GMT",
"version": "v1"
}
] | 2022-07-19 | [
[
"Yunus",
"Fajrian",
""
],
[
"Clavel",
"Chloé",
""
],
[
"Pelachaud",
"Catherine",
""
]
] | Image schema is a recurrent pattern of reasoning where one entity is mapped into another. Image schema is similar to conceptual metaphor and is also related to metaphoric gesture. Our main goal is to generate metaphoric gestures for an Embodied Conversational Agent. We propose a technique to learn the vector representation of image schemas. As far as we are aware of, this is the first work which addresses that problem. Our technique uses Ravenet et al's algorithm which we use to compute the image schemas from the text input and also BERT and SenseBERT which we use as the base word embedding technique to calculate the final vector representation of the image schema. Our representation learning technique works by clustering: word embedding vectors which belong to the same image schema should be relatively closer to each other, and thus form a cluster. With the image schemas representable as vectors, it also becomes possible to have a notion that some image schemas are closer or more similar to each other than to the others because the distance between the vectors is a proxy of the dissimilarity between the corresponding image schemas. Therefore, after obtaining the vector representation of the image schemas, we calculate the distances between those vectors. Based on these, we create visualizations to illustrate the relative distances between the different image schemas. |
1404.0408 | Emil Bj\"ornson | Emil Bj\"ornson, Mats Bengtsson, and Bj\"orn Ottersten | Optimal Multiuser Transmit Beamforming: A Difficult Problem with a
Simple Solution Structure | Accepted for publication as lecture note in IEEE Signal Processing
Magazine, 11 pages, 3 figures. The results can be reproduced using the
following Matlab code: https://github.com/emilbjornson/optimal-beamforming | IEEE Signal Processing Magazine, vol. 31, no. 4, pp. 142-148, July
2014 | 10.1109/MSP.2014.2312183 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transmit beamforming is a versatile technique for signal transmission from an
array of $N$ antennas to one or multiple users [1]. In wireless communications,
the goal is to increase the signal power at the intended user and reduce
interference to non-intended users. A high signal power is achieved by
transmitting the same data signal from all antennas, but with different
amplitudes and phases, such that the signal components add coherently at the
user. Low interference is accomplished by making the signal components add
destructively at non-intended users. This corresponds mathematically to
designing beamforming vectors (that describe the amplitudes and phases) to have
large inner products with the vectors describing the intended channels and
small inner products with non-intended user channels.
While it is fairly easy to design a beamforming vector that maximizes the
signal power at the intended user, it is difficult to strike a perfect balance
between maximizing the signal power and minimizing the interference leakage. In
fact, the optimization of multiuser transmit beamforming is generally a
nondeterministic polynomial-time (NP) hard problem [2]. Nevertheless, this
lecture shows that the optimal transmit beamforming has a simple structure with
very intuitive properties and interpretations. This structure provides a
theoretical foundation for practical low-complexity beamforming schemes.
(See this lecture note for the complete abstract/introduction)
| [
{
"created": "Tue, 1 Apr 2014 22:01:02 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Apr 2014 09:54:41 GMT",
"version": "v2"
}
] | 2014-07-22 | [
[
"Björnson",
"Emil",
""
],
[
"Bengtsson",
"Mats",
""
],
[
"Ottersten",
"Björn",
""
]
] | Transmit beamforming is a versatile technique for signal transmission from an array of $N$ antennas to one or multiple users [1]. In wireless communications, the goal is to increase the signal power at the intended user and reduce interference to non-intended users. A high signal power is achieved by transmitting the same data signal from all antennas, but with different amplitudes and phases, such that the signal components add coherently at the user. Low interference is accomplished by making the signal components add destructively at non-intended users. This corresponds mathematically to designing beamforming vectors (that describe the amplitudes and phases) to have large inner products with the vectors describing the intended channels and small inner products with non-intended user channels. While it is fairly easy to design a beamforming vector that maximizes the signal power at the intended user, it is difficult to strike a perfect balance between maximizing the signal power and minimizing the interference leakage. In fact, the optimization of multiuser transmit beamforming is generally a nondeterministic polynomial-time (NP) hard problem [2]. Nevertheless, this lecture shows that the optimal transmit beamforming has a simple structure with very intuitive properties and interpretations. This structure provides a theoretical foundation for practical low-complexity beamforming schemes. (See this lecture note for the complete abstract/introduction) |
1411.6741 | Chaitanya Ahuja | Chaitanya Ahuja, Karan Nathwani and Rajesh M. Hegde | A Complex Matrix Factorization approach to Joint Modeling of Magnitude
and Phase for Source Separation | 5 pages, 3 figures | null | null | null | cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conventional NMF methods for source separation factorize the matrix of
spectral magnitudes. Spectral Phase is not included in the decomposition
process of these methods. However, phase of the speech mixture is generally
used in reconstructing the target speech signal. This results in undesired
traces of interfering sources in the target signal. In this paper the spectral
phase is incorporated in the decomposition process itself. Additionally, the
complex matrix factorization problem is reduced to an NMF problem using simple
transformations. This results in effective separation of speech mixtures since
both magnitude and phase are utilized jointly in the separation process.
Improvement in source separation results are demonstrated using objective
quality evaluations on the GRID corpus.
| [
{
"created": "Tue, 25 Nov 2014 06:18:45 GMT",
"version": "v1"
}
] | 2014-11-26 | [
[
"Ahuja",
"Chaitanya",
""
],
[
"Nathwani",
"Karan",
""
],
[
"Hegde",
"Rajesh M.",
""
]
] | Conventional NMF methods for source separation factorize the matrix of spectral magnitudes. Spectral Phase is not included in the decomposition process of these methods. However, phase of the speech mixture is generally used in reconstructing the target speech signal. This results in undesired traces of interfering sources in the target signal. In this paper the spectral phase is incorporated in the decomposition process itself. Additionally, the complex matrix factorization problem is reduced to an NMF problem using simple transformations. This results in effective separation of speech mixtures since both magnitude and phase are utilized jointly in the separation process. Improvement in source separation results are demonstrated using objective quality evaluations on the GRID corpus. |
1902.06007 | Andrew Silva | Andrew Silva, Matthew Gombolay | Neural-encoding Human Experts' Domain Knowledge to Warm Start
Reinforcement Learning | null | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep reinforcement learning has been successful in a variety of tasks, such
as game playing and robotic manipulation. However, attempting to learn
\textit{tabula rasa} disregards the logical structure of many domains as well
as the wealth of readily available knowledge from domain experts that could
help "warm start" the learning process. We present a novel reinforcement
learning technique that allows for intelligent initialization of a neural
network weights and architecture. Our approach permits the encoding domain
knowledge directly into a neural decision tree, and improves upon that
knowledge with policy gradient updates. We empirically validate our approach on
two OpenAI Gym tasks and two modified StarCraft 2 tasks, showing that our novel
architecture outperforms multilayer-perceptron and recurrent architectures. Our
knowledge-based framework finds superior policies compared to imitation
learning-based and prior knowledge-based approaches. Importantly, we
demonstrate that our approach can be used by untrained humans to initially
provide >80% increase in expected reward relative to baselines prior to
training (p < 0.001), which results in a >60% increase in expected reward after
policy optimization (p = 0.011).
| [
{
"created": "Fri, 15 Feb 2019 23:28:59 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Jul 2019 14:23:30 GMT",
"version": "v2"
},
{
"created": "Mon, 2 Dec 2019 17:47:06 GMT",
"version": "v3"
},
{
"created": "Wed, 23 Sep 2020 22:17:29 GMT",
"version": "v4"
}
] | 2020-09-25 | [
[
"Silva",
"Andrew",
""
],
[
"Gombolay",
"Matthew",
""
]
] | Deep reinforcement learning has been successful in a variety of tasks, such as game playing and robotic manipulation. However, attempting to learn \textit{tabula rasa} disregards the logical structure of many domains as well as the wealth of readily available knowledge from domain experts that could help "warm start" the learning process. We present a novel reinforcement learning technique that allows for intelligent initialization of a neural network weights and architecture. Our approach permits the encoding domain knowledge directly into a neural decision tree, and improves upon that knowledge with policy gradient updates. We empirically validate our approach on two OpenAI Gym tasks and two modified StarCraft 2 tasks, showing that our novel architecture outperforms multilayer-perceptron and recurrent architectures. Our knowledge-based framework finds superior policies compared to imitation learning-based and prior knowledge-based approaches. Importantly, we demonstrate that our approach can be used by untrained humans to initially provide >80% increase in expected reward relative to baselines prior to training (p < 0.001), which results in a >60% increase in expected reward after policy optimization (p = 0.011). |
2312.03567 | Joel Stremmel | Joel Stremmel, Ardavan Saeedi, Hamid Hassanzadeh, Sanjit Batra,
Jeffrey Hertzberg, Jaime Murillo, Eran Halperin | XAIQA: Explainer-Based Data Augmentation for Extractive Question
Answering | Extended Abstract presented at Machine Learning for Health (ML4H)
symposium 2023, December 10th, 2023, New Orleans, United States, 8 pages | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Extractive question answering (QA) systems can enable physicians and
researchers to query medical records, a foundational capability for designing
clinical studies and understanding patient medical history. However, building
these systems typically requires expert-annotated QA pairs. Large language
models (LLMs), which can perform extractive QA, depend on high quality data in
their prompts, specialized for the application domain. We introduce a novel
approach, XAIQA, for generating synthetic QA pairs at scale from data naturally
available in electronic health records. Our method uses the idea of a
classification model explainer to generate questions and answers about medical
concepts corresponding to medical codes. In an expert evaluation with two
physicians, our method identifies $2.2\times$ more semantic matches and
$3.8\times$ more clinical abbreviations than two popular approaches that use
sentence transformers to create QA pairs. In an ML evaluation, adding our QA
pairs improves performance of GPT-4 as an extractive QA model, including on
difficult questions. In both the expert and ML evaluations, we examine
trade-offs between our method and sentence transformers for QA pair generation
depending on question difficulty.
| [
{
"created": "Wed, 6 Dec 2023 15:59:06 GMT",
"version": "v1"
}
] | 2023-12-07 | [
[
"Stremmel",
"Joel",
""
],
[
"Saeedi",
"Ardavan",
""
],
[
"Hassanzadeh",
"Hamid",
""
],
[
"Batra",
"Sanjit",
""
],
[
"Hertzberg",
"Jeffrey",
""
],
[
"Murillo",
"Jaime",
""
],
[
"Halperin",
"Eran",
""
]
] | Extractive question answering (QA) systems can enable physicians and researchers to query medical records, a foundational capability for designing clinical studies and understanding patient medical history. However, building these systems typically requires expert-annotated QA pairs. Large language models (LLMs), which can perform extractive QA, depend on high quality data in their prompts, specialized for the application domain. We introduce a novel approach, XAIQA, for generating synthetic QA pairs at scale from data naturally available in electronic health records. Our method uses the idea of a classification model explainer to generate questions and answers about medical concepts corresponding to medical codes. In an expert evaluation with two physicians, our method identifies $2.2\times$ more semantic matches and $3.8\times$ more clinical abbreviations than two popular approaches that use sentence transformers to create QA pairs. In an ML evaluation, adding our QA pairs improves performance of GPT-4 as an extractive QA model, including on difficult questions. In both the expert and ML evaluations, we examine trade-offs between our method and sentence transformers for QA pair generation depending on question difficulty. |
2205.13280 | Chengyu Qiao | Chengyu Qiao, Zhiyu Xiang and Xinglu Wang | Objects Matter: Learning Object Relation Graph for Robust Camera
Relocalization | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual relocalization aims to estimate the pose of a camera from one or more
images. In recent years deep learning based pose regression methods have
attracted many attentions. They feature predicting the absolute poses without
relying on any prior built maps or stored images, making the relocalization
very efficient. However, robust relocalization under environments with complex
appearance changes and real dynamics remains very challenging. In this paper,
we propose to enhance the distinctiveness of the image features by extracting
the deep relationship among objects. In particular, we extract objects in the
image and construct a deep object relation graph (ORG) to incorporate the
semantic connections and relative spatial clues of the objects. We integrate
our ORG module into several popular pose regression models. Extensive
experiments on various public indoor and outdoor datasets demonstrate that our
method improves the performance significantly and outperforms the previous
approaches.
| [
{
"created": "Thu, 26 May 2022 11:37:11 GMT",
"version": "v1"
}
] | 2022-05-27 | [
[
"Qiao",
"Chengyu",
""
],
[
"Xiang",
"Zhiyu",
""
],
[
"Wang",
"Xinglu",
""
]
] | Visual relocalization aims to estimate the pose of a camera from one or more images. In recent years deep learning based pose regression methods have attracted many attentions. They feature predicting the absolute poses without relying on any prior built maps or stored images, making the relocalization very efficient. However, robust relocalization under environments with complex appearance changes and real dynamics remains very challenging. In this paper, we propose to enhance the distinctiveness of the image features by extracting the deep relationship among objects. In particular, we extract objects in the image and construct a deep object relation graph (ORG) to incorporate the semantic connections and relative spatial clues of the objects. We integrate our ORG module into several popular pose regression models. Extensive experiments on various public indoor and outdoor datasets demonstrate that our method improves the performance significantly and outperforms the previous approaches. |
2004.11568 | Ryan Mann | Ryan L. Mann, Tyler Helmuth | Efficient Algorithms for Approximating Quantum Partition Functions | 7 pages, 0 figures, published version | Journal of Mathematical Physics 62, 022201 (2021) | 10.1063/5.0013689 | null | cs.DS cs.CC math.CO quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We establish a polynomial-time approximation algorithm for partition
functions of quantum spin models at high temperature. Our algorithm is based on
the quantum cluster expansion of Neto\v{c}n\'y and Redig and the cluster
expansion approach to designing algorithms due to Helmuth, Perkins, and Regts.
Similar results have previously been obtained by related methods, and our main
contribution is a simple and slightly sharper analysis for the case of pairwise
interactions on bounded-degree graphs.
| [
{
"created": "Fri, 24 Apr 2020 07:21:43 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Feb 2021 13:59:44 GMT",
"version": "v2"
}
] | 2021-02-02 | [
[
"Mann",
"Ryan L.",
""
],
[
"Helmuth",
"Tyler",
""
]
] | We establish a polynomial-time approximation algorithm for partition functions of quantum spin models at high temperature. Our algorithm is based on the quantum cluster expansion of Neto\v{c}n\'y and Redig and the cluster expansion approach to designing algorithms due to Helmuth, Perkins, and Regts. Similar results have previously been obtained by related methods, and our main contribution is a simple and slightly sharper analysis for the case of pairwise interactions on bounded-degree graphs. |
1601.07932 | Keehwan Park | Keehwan Park and Jean Honorio | Information-Theoretic Lower Bounds for Recovery of Diffusion Network
Structures | ISIT'16 | International Symposium on Information Theory (ISIT) 2016 | null | null | cs.LG cs.IT math.IT stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the information-theoretic lower bound of the sample complexity of
the correct recovery of diffusion network structures. We introduce a
discrete-time diffusion model based on the Independent Cascade model for which
we obtain a lower bound of order $\Omega(k \log p)$, for directed graphs of $p$
nodes, and at most $k$ parents per node. Next, we introduce a continuous-time
diffusion model, for which a similar lower bound of order $\Omega(k \log p)$ is
obtained. Our results show that the algorithm of Pouget-Abadie et al. is
statistically optimal for the discrete-time regime. Our work also opens the
question of whether it is possible to devise an optimal algorithm for the
continuous-time regime.
| [
{
"created": "Thu, 28 Jan 2016 22:12:06 GMT",
"version": "v1"
},
{
"created": "Mon, 23 May 2016 23:29:19 GMT",
"version": "v2"
}
] | 2019-05-28 | [
[
"Park",
"Keehwan",
""
],
[
"Honorio",
"Jean",
""
]
] | We study the information-theoretic lower bound of the sample complexity of the correct recovery of diffusion network structures. We introduce a discrete-time diffusion model based on the Independent Cascade model for which we obtain a lower bound of order $\Omega(k \log p)$, for directed graphs of $p$ nodes, and at most $k$ parents per node. Next, we introduce a continuous-time diffusion model, for which a similar lower bound of order $\Omega(k \log p)$ is obtained. Our results show that the algorithm of Pouget-Abadie et al. is statistically optimal for the discrete-time regime. Our work also opens the question of whether it is possible to devise an optimal algorithm for the continuous-time regime. |
1902.07762 | Ondrej Skopek | Lukas Jendele, Ondrej Skopek, Anton S. Becker, Ender Konukoglu | Adversarial Augmentation for Enhancing Classification of Mammography
Images | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Supervised deep learning relies on the assumption that enough training data
is available, which presents a problem for its application to several fields,
like medical imaging. On the example of a binary image classification task
(breast cancer recognition), we show that pretraining a generative model for
meaningful image augmentation helps enhance the performance of the resulting
classifier. By augmenting the data, performance on downstream classification
tasks could be improved even with a relatively small training set. We show that
this "adversarial augmentation" yields promising results compared to classical
image augmentation on the example of breast cancer classification.
| [
{
"created": "Wed, 20 Feb 2019 20:13:24 GMT",
"version": "v1"
}
] | 2019-02-22 | [
[
"Jendele",
"Lukas",
""
],
[
"Skopek",
"Ondrej",
""
],
[
"Becker",
"Anton S.",
""
],
[
"Konukoglu",
"Ender",
""
]
] | Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging. On the example of a binary image classification task (breast cancer recognition), we show that pretraining a generative model for meaningful image augmentation helps enhance the performance of the resulting classifier. By augmenting the data, performance on downstream classification tasks could be improved even with a relatively small training set. We show that this "adversarial augmentation" yields promising results compared to classical image augmentation on the example of breast cancer classification. |
2301.05466 | Liwang Zhu | Liwang Zhu and Zhongzhi Zhang | A Nearly-Linear Time Algorithm for Minimizing Risk of Conflict in Social
Networks | null | Proceedings of the ACM SIGKDD Conference on Knowledge Discovery
and Data Mining 2022, pp.2648-2656 | 10.1145/3534678.3539469 | null | cs.SI cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Concomitant with the tremendous prevalence of online social media platforms,
the interactions among individuals are unprecedentedly enhanced. People are
free to interact with acquaintances, express and exchange their own opinions
through commenting, liking, retweeting on online social media, leading to
resistance, controversy and other important phenomena over controversial social
issues, which have been the subject of many recent works. In this paper, we
study the problem of minimizing risk of conflict in social networks by
modifying the initial opinions of a small number of nodes. We show that the
objective function of the combinatorial optimization problem is monotone and
supermodular. We then propose a na\"{\i}ve greedy algorithm with a $(1-1/e)$
approximation ratio that solves the problem in cubic time. To overcome the
computation challenge for large networks, we further integrate several
effective approximation strategies to provide a nearly linear time algorithm
with a $(1-1/e-\epsilon)$ approximation ratio for any error parameter
$\epsilon>0$. Extensive experiments on various real-world datasets demonstrate
both the efficiency and effectiveness of our algorithms. In particular, the
fast one scales to large networks with more than two million nodes, and
achieves up to $20\times$ speed-up over the state-of-the-art algorithm.
| [
{
"created": "Fri, 13 Jan 2023 10:32:12 GMT",
"version": "v1"
}
] | 2023-01-16 | [
[
"Zhu",
"Liwang",
""
],
[
"Zhang",
"Zhongzhi",
""
]
] | Concomitant with the tremendous prevalence of online social media platforms, the interactions among individuals are unprecedentedly enhanced. People are free to interact with acquaintances, express and exchange their own opinions through commenting, liking, retweeting on online social media, leading to resistance, controversy and other important phenomena over controversial social issues, which have been the subject of many recent works. In this paper, we study the problem of minimizing risk of conflict in social networks by modifying the initial opinions of a small number of nodes. We show that the objective function of the combinatorial optimization problem is monotone and supermodular. We then propose a na\"{\i}ve greedy algorithm with a $(1-1/e)$ approximation ratio that solves the problem in cubic time. To overcome the computation challenge for large networks, we further integrate several effective approximation strategies to provide a nearly linear time algorithm with a $(1-1/e-\epsilon)$ approximation ratio for any error parameter $\epsilon>0$. Extensive experiments on various real-world datasets demonstrate both the efficiency and effectiveness of our algorithms. In particular, the fast one scales to large networks with more than two million nodes, and achieves up to $20\times$ speed-up over the state-of-the-art algorithm. |
2403.07593 | Juan Jos\'e Cabrera Mora | J.J. Cabrera, A. Santo, A. Gil, C. Viegas and L. Pay\'a | MinkUNeXt: Point Cloud-based Large-scale Place Recognition using 3D
Sparse Convolutions | This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents MinkUNeXt, an effective and efficient architecture for
place-recognition from point clouds entirely based on the new 3D MinkNeXt
Block, a residual block composed of 3D sparse convolutions that follows the
philosophy established by recent Transformers but purely using simple 3D
convolutions. Feature extraction is performed at different scales by a U-Net
encoder-decoder network and the feature aggregation of those features into a
single descriptor is carried out by a Generalized Mean Pooling (GeM). The
proposed architecture demonstrates that it is possible to surpass the current
state-of-the-art by only relying on conventional 3D sparse convolutions without
making use of more complex and sophisticated proposals such as Transformers,
Attention-Layers or Deformable Convolutions. A thorough assessment of the
proposal has been carried out using the Oxford RobotCar and the In-house
datasets. As a result, MinkUNeXt proves to outperform other methods in the
state-of-the-art.
| [
{
"created": "Tue, 12 Mar 2024 12:25:54 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Mar 2024 09:39:14 GMT",
"version": "v2"
}
] | 2024-03-14 | [
[
"Cabrera",
"J. J.",
""
],
[
"Santo",
"A.",
""
],
[
"Gil",
"A.",
""
],
[
"Viegas",
"C.",
""
],
[
"Payá",
"L.",
""
]
] | This paper presents MinkUNeXt, an effective and efficient architecture for place-recognition from point clouds entirely based on the new 3D MinkNeXt Block, a residual block composed of 3D sparse convolutions that follows the philosophy established by recent Transformers but purely using simple 3D convolutions. Feature extraction is performed at different scales by a U-Net encoder-decoder network and the feature aggregation of those features into a single descriptor is carried out by a Generalized Mean Pooling (GeM). The proposed architecture demonstrates that it is possible to surpass the current state-of-the-art by only relying on conventional 3D sparse convolutions without making use of more complex and sophisticated proposals such as Transformers, Attention-Layers or Deformable Convolutions. A thorough assessment of the proposal has been carried out using the Oxford RobotCar and the In-house datasets. As a result, MinkUNeXt proves to outperform other methods in the state-of-the-art. |
1603.08978 | William Waites | William Waites, James Sweet, Roger Baig, Peter Buneman, Marwan Fayed,
Gordon Hughes, Michael Fourman, Richard Simmons | RemIX: A Distributed Internet Exchange for Remote and Rural Networks | null | null | 10.1145/2940157.2940162 | null | cs.NI | http://creativecommons.org/licenses/by-sa/4.0/ | The concept of the IXP, an Ethernet fabric central to the structure of the
global Internet, is largely absent from the development of community-driven
collaborative network infrastructure. The reasons for this are two-fold. IXPs
exist in central, typically urban, environments where strong network
infrastructure ensures high levels of connectivity. Between rural and remote
regions, where networks are separated by distance and terrain, no such
infrastructure exists. In this paper we present RemIX a distributed IXPs
architecture designed for the community network environment. We examine this
praxis using an implementation in Scotland, with suggestions for future
development and research.
| [
{
"created": "Tue, 29 Mar 2016 21:51:02 GMT",
"version": "v1"
}
] | 2020-06-24 | [
[
"Waites",
"William",
""
],
[
"Sweet",
"James",
""
],
[
"Baig",
"Roger",
""
],
[
"Buneman",
"Peter",
""
],
[
"Fayed",
"Marwan",
""
],
[
"Hughes",
"Gordon",
""
],
[
"Fourman",
"Michael",
""
],
[
"Simmons",
"Richard",
""
]
] | The concept of the IXP, an Ethernet fabric central to the structure of the global Internet, is largely absent from the development of community-driven collaborative network infrastructure. The reasons for this are two-fold. IXPs exist in central, typically urban, environments where strong network infrastructure ensures high levels of connectivity. Between rural and remote regions, where networks are separated by distance and terrain, no such infrastructure exists. In this paper we present RemIX a distributed IXPs architecture designed for the community network environment. We examine this praxis using an implementation in Scotland, with suggestions for future development and research. |
1802.07021 | Yuehong Huang | Yuehong Huang, Yu-Chee Tseng | Fusing Video and Inertial Sensor Data for Walking Person Identification | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | An autonomous computer system (such as a robot) typically needs to identify,
locate, and track persons appearing in its sight. However, most solutions have
their limitations regarding efficiency, practicability, or environmental
constraints. In this paper, we propose an effective and practical system which
combines video and inertial sensors for person identification (PID). Persons
who do different activities are easy to identify. To show the robustness and
potential of our system, we propose a walking person identification (WPID)
method to identify persons walking at the same time. By comparing features
derived from both video and inertial sensor data, we can associate sensors in
smartphones with human objects in videos. Results show that the correctly
identified rate of our WPID method can up to 76% in 2 seconds.
| [
{
"created": "Tue, 20 Feb 2018 09:16:21 GMT",
"version": "v1"
}
] | 2018-02-21 | [
[
"Huang",
"Yuehong",
""
],
[
"Tseng",
"Yu-Chee",
""
]
] | An autonomous computer system (such as a robot) typically needs to identify, locate, and track persons appearing in its sight. However, most solutions have their limitations regarding efficiency, practicability, or environmental constraints. In this paper, we propose an effective and practical system which combines video and inertial sensors for person identification (PID). Persons who do different activities are easy to identify. To show the robustness and potential of our system, we propose a walking person identification (WPID) method to identify persons walking at the same time. By comparing features derived from both video and inertial sensor data, we can associate sensors in smartphones with human objects in videos. Results show that the correctly identified rate of our WPID method can up to 76% in 2 seconds. |
2401.11694 | Patrick Cook | Patrick Cook, Danny Jammooa, Morten Hjorth-Jensen, Daniel D. Lee, Dean
Lee | Parametric Matrix Models | Exact same content as previous version (v4); corrected author email | null | null | null | cs.LG cond-mat.dis-nn nucl-th physics.comp-ph quant-ph | http://creativecommons.org/licenses/by-sa/4.0/ | We present a general class of machine learning algorithms called parametric
matrix models. In contrast with most existing machine learning models that
imitate the biology of neurons, parametric matrix models use matrix equations
that emulate the physics of quantum systems. Similar to how physics problems
are usually solved, parametric matrix models learn the governing equations that
lead to the desired outputs. Parametric matrix models can be efficiently
trained from empirical data, and the equations may use algebraic, differential,
or integral relations. While originally designed for scientific computing, we
prove that parametric matrix models are universal function approximators that
can be applied to general machine learning problems. After introducing the
underlying theory, we apply parametric matrix models to a series of different
challenges that show their performance for a wide range of problems. For all
the challenges tested here, parametric matrix models produce accurate results
within an efficient and interpretable computational framework that allows for
input feature extrapolation.
| [
{
"created": "Mon, 22 Jan 2024 05:26:18 GMT",
"version": "v1"
},
{
"created": "Tue, 23 Jan 2024 20:06:38 GMT",
"version": "v2"
},
{
"created": "Mon, 8 Jul 2024 19:55:41 GMT",
"version": "v3"
},
{
"created": "Fri, 12 Jul 2024 20:08:17 GMT",
"version": "v4"
},
{
"created": "Tue, 30 Jul 2024 21:43:28 GMT",
"version": "v5"
}
] | 2024-08-01 | [
[
"Cook",
"Patrick",
""
],
[
"Jammooa",
"Danny",
""
],
[
"Hjorth-Jensen",
"Morten",
""
],
[
"Lee",
"Daniel D.",
""
],
[
"Lee",
"Dean",
""
]
] | We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neurons, parametric matrix models use matrix equations that emulate the physics of quantum systems. Similar to how physics problems are usually solved, parametric matrix models learn the governing equations that lead to the desired outputs. Parametric matrix models can be efficiently trained from empirical data, and the equations may use algebraic, differential, or integral relations. While originally designed for scientific computing, we prove that parametric matrix models are universal function approximators that can be applied to general machine learning problems. After introducing the underlying theory, we apply parametric matrix models to a series of different challenges that show their performance for a wide range of problems. For all the challenges tested here, parametric matrix models produce accurate results within an efficient and interpretable computational framework that allows for input feature extrapolation. |
2210.07795 | Tiannan Wang | Tiannan Wang, Wangchunshu Zhou, Yan Zeng, Xinsong Zhang | EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge
Distillation and Modal-adaptive Pruning | work in progress | null | null | null | cs.CL cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pre-trained vision-language models (VLMs) have achieved impressive results in
a range of vision-language tasks. However, popular VLMs usually consist of
hundreds of millions of parameters which brings challenges for fine-tuning and
deployment in real-world applications due to space, memory, and latency
constraints. In this work, we introduce a distilling then pruning framework to
compress large vision-language models into smaller, faster, and more accurate
ones. We first shrink the size of a pre-trained large VLM and apply knowledge
distillation in the vision-language pre-training stage to obtain a
task-agnostic compact VLM. Then we propose a modal-adaptive pruning algorithm
to automatically infer the importance of vision and language modalities for
different downstream tasks and adaptively remove redundant structures and
neurons in different encoders with controllable target sparsity. We apply our
framework to train EfficientVLM, a fast and accurate vision-language model
consisting of 6 vision layers, 3 text layers, and 3 cross-modal fusion layers,
accounting for only 93 million parameters in total, which is 44.3% of the
teacher model. EfficientVLM retains 98.4% performance of the teacher model and
accelerates its inference speed by 2.2x. EfficientVLM achieves a large absolute
improvement over previous SoTA efficient VLMs of similar sizes by a large
margin on various vision-language tasks, including VQAv2 (+4.9%), NLVR2
(+5.6%), ITR (R@1 on TR +17.2%, on IR + 15.6% ) and COCO caption generation
(CIDEr +6.5), demonstrating a large potential on training lightweight VLMs.
| [
{
"created": "Fri, 14 Oct 2022 13:26:41 GMT",
"version": "v1"
}
] | 2022-10-17 | [
[
"Wang",
"Tiannan",
""
],
[
"Zhou",
"Wangchunshu",
""
],
[
"Zeng",
"Yan",
""
],
[
"Zhang",
"Xinsong",
""
]
] | Pre-trained vision-language models (VLMs) have achieved impressive results in a range of vision-language tasks. However, popular VLMs usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and deployment in real-world applications due to space, memory, and latency constraints. In this work, we introduce a distilling then pruning framework to compress large vision-language models into smaller, faster, and more accurate ones. We first shrink the size of a pre-trained large VLM and apply knowledge distillation in the vision-language pre-training stage to obtain a task-agnostic compact VLM. Then we propose a modal-adaptive pruning algorithm to automatically infer the importance of vision and language modalities for different downstream tasks and adaptively remove redundant structures and neurons in different encoders with controllable target sparsity. We apply our framework to train EfficientVLM, a fast and accurate vision-language model consisting of 6 vision layers, 3 text layers, and 3 cross-modal fusion layers, accounting for only 93 million parameters in total, which is 44.3% of the teacher model. EfficientVLM retains 98.4% performance of the teacher model and accelerates its inference speed by 2.2x. EfficientVLM achieves a large absolute improvement over previous SoTA efficient VLMs of similar sizes by a large margin on various vision-language tasks, including VQAv2 (+4.9%), NLVR2 (+5.6%), ITR (R@1 on TR +17.2%, on IR + 15.6% ) and COCO caption generation (CIDEr +6.5), demonstrating a large potential on training lightweight VLMs. |
1303.6017 | Zhouyun Wu | Zhouyun Wu, Aiping Huang, and Hsiao-Hwa Chen | Scrambling Code Planning in TD-SCDMA Systems | This paper has been withdrawn | null | null | null | cs.IT cs.NI math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper has been withdrawn by the author due to a crucial sign error in
equation 2.
| [
{
"created": "Mon, 25 Mar 2013 02:31:32 GMT",
"version": "v1"
},
{
"created": "Sat, 30 Mar 2013 10:05:47 GMT",
"version": "v2"
}
] | 2013-04-02 | [
[
"Wu",
"Zhouyun",
""
],
[
"Huang",
"Aiping",
""
],
[
"Chen",
"Hsiao-Hwa",
""
]
] | This paper has been withdrawn by the author due to a crucial sign error in equation 2. |
2005.06645 | Michael Vaughn | Michael Vaughn and Thomas Reps | A Generating-Extension-Generator for Machine Code | 21 pages, 8 Figures Fixed inclusion of LaTeX macro in plaintext
abstract | null | null | null | cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of "debloating" programs for security and performance purposes
has begun to see increased attention. Of particular interest in many
environments is debloating commodity off-the-shelf (COTS) software, which is
most commonly made available to end users as stripped binaries (i.e., neither
source code nor symbol-table/debugging information is available). Toward this
end, we created a system, called GenXGen[MC], that specializes stripped
binaries.
Many aspects of the debloating problem can be addressed via techniques from
the literature on partial evaluation. However, applying such techniques to
real-world programs, particularly stripped binaries, involves non-trivial
state-management manipulations that have never been addressed in a completely
satisfactory manner in previous systems. In particular, a partial evaluator
needs to be able to (i) save and restore arbitrary program states, and (ii)
determine whether a program state is equal to one that arose earlier. Moreover,
to specialize stripped binaries, the system must also be able to handle program
states consisting of memory that is undifferentiated beyond the standard coarse
division into regions for the stack, the heap, and global data.
This paper presents a new approach to state management in a program
specializer. The technique has been incorporated into GenXGen[MC], a novel tool
for producing machine-code generating extensions. Our experiments show that our
solution to issue (i) significantly decreases the space required to represent
program states, and our solution to issue (ii) drastically improves the time
for producing a specialized program (as much as 13,000x speedup).
| [
{
"created": "Wed, 13 May 2020 22:19:04 GMT",
"version": "v1"
},
{
"created": "Fri, 15 May 2020 00:53:30 GMT",
"version": "v2"
}
] | 2020-05-18 | [
[
"Vaughn",
"Michael",
""
],
[
"Reps",
"Thomas",
""
]
] | The problem of "debloating" programs for security and performance purposes has begun to see increased attention. Of particular interest in many environments is debloating commodity off-the-shelf (COTS) software, which is most commonly made available to end users as stripped binaries (i.e., neither source code nor symbol-table/debugging information is available). Toward this end, we created a system, called GenXGen[MC], that specializes stripped binaries. Many aspects of the debloating problem can be addressed via techniques from the literature on partial evaluation. However, applying such techniques to real-world programs, particularly stripped binaries, involves non-trivial state-management manipulations that have never been addressed in a completely satisfactory manner in previous systems. In particular, a partial evaluator needs to be able to (i) save and restore arbitrary program states, and (ii) determine whether a program state is equal to one that arose earlier. Moreover, to specialize stripped binaries, the system must also be able to handle program states consisting of memory that is undifferentiated beyond the standard coarse division into regions for the stack, the heap, and global data. This paper presents a new approach to state management in a program specializer. The technique has been incorporated into GenXGen[MC], a novel tool for producing machine-code generating extensions. Our experiments show that our solution to issue (i) significantly decreases the space required to represent program states, and our solution to issue (ii) drastically improves the time for producing a specialized program (as much as 13,000x speedup). |
2303.04249 | Brandon Clark | Brandon Clark, Alec Kerrigan, Parth Parag Kulkarni, Vicente Vivanco
Cepeda, Mubarak Shah | Where We Are and What We're Looking At: Query Based Worldwide Image
Geo-localization Using Hierarchies and Scenes | CVPR 2023 | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Determining the exact latitude and longitude that a photo was taken is a
useful and widely applicable task, yet it remains exceptionally difficult
despite the accelerated progress of other computer vision tasks. Most previous
approaches have opted to learn a single representation of query images, which
are then classified at different levels of geographic granularity. These
approaches fail to exploit the different visual cues that give context to
different hierarchies, such as the country, state, and city level. To this end,
we introduce an end-to-end transformer-based architecture that exploits the
relationship between different geographic levels (which we refer to as
hierarchies) and the corresponding visual scene information in an image through
hierarchical cross-attention. We achieve this by learning a query for each
geographic hierarchy and scene type. Furthermore, we learn a separate
representation for different environmental scenes, as different scenes in the
same location are often defined by completely different visual features. We
achieve state of the art street level accuracy on 4 standard geo-localization
datasets : Im2GPS, Im2GPS3k, YFCC4k, and YFCC26k, as well as qualitatively
demonstrate how our method learns different representations for different
visual hierarchies and scenes, which has not been demonstrated in the previous
methods. These previous testing datasets mostly consist of iconic landmarks or
images taken from social media, which makes them either a memorization task, or
biased towards certain places. To address this issue we introduce a much harder
testing dataset, Google-World-Streets-15k, comprised of images taken from
Google Streetview covering the whole planet and present state of the art
results. Our code will be made available in the camera-ready version.
| [
{
"created": "Tue, 7 Mar 2023 21:47:58 GMT",
"version": "v1"
}
] | 2023-03-09 | [
[
"Clark",
"Brandon",
""
],
[
"Kerrigan",
"Alec",
""
],
[
"Kulkarni",
"Parth Parag",
""
],
[
"Cepeda",
"Vicente Vivanco",
""
],
[
"Shah",
"Mubarak",
""
]
] | Determining the exact latitude and longitude that a photo was taken is a useful and widely applicable task, yet it remains exceptionally difficult despite the accelerated progress of other computer vision tasks. Most previous approaches have opted to learn a single representation of query images, which are then classified at different levels of geographic granularity. These approaches fail to exploit the different visual cues that give context to different hierarchies, such as the country, state, and city level. To this end, we introduce an end-to-end transformer-based architecture that exploits the relationship between different geographic levels (which we refer to as hierarchies) and the corresponding visual scene information in an image through hierarchical cross-attention. We achieve this by learning a query for each geographic hierarchy and scene type. Furthermore, we learn a separate representation for different environmental scenes, as different scenes in the same location are often defined by completely different visual features. We achieve state of the art street level accuracy on 4 standard geo-localization datasets : Im2GPS, Im2GPS3k, YFCC4k, and YFCC26k, as well as qualitatively demonstrate how our method learns different representations for different visual hierarchies and scenes, which has not been demonstrated in the previous methods. These previous testing datasets mostly consist of iconic landmarks or images taken from social media, which makes them either a memorization task, or biased towards certain places. To address this issue we introduce a much harder testing dataset, Google-World-Streets-15k, comprised of images taken from Google Streetview covering the whole planet and present state of the art results. Our code will be made available in the camera-ready version. |
2310.20703 | Noam Razin | Noam Razin, Hattie Zhou, Omid Saremi, Vimal Thilak, Arwen Bradley,
Preetum Nakkiran, Joshua Susskind, Etai Littwin | Vanishing Gradients in Reinforcement Finetuning of Language Models | Accepted to ICLR 2024 | null | null | null | cs.LG cs.AI cs.CL stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pretrained language models are commonly aligned with human preferences and
downstream tasks via reinforcement finetuning (RFT), which refers to maximizing
a (possibly learned) reward function using policy gradient algorithms. This
work identifies a fundamental optimization obstacle in RFT: we prove that the
expected gradient for an input vanishes when its reward standard deviation
under the model is small, even if the expected reward is far from optimal.
Through experiments on an RFT benchmark and controlled environments, as well as
a theoretical analysis, we then demonstrate that vanishing gradients due to
small reward standard deviation are prevalent and detrimental, leading to
extremely slow reward maximization. Lastly, we explore ways to overcome
vanishing gradients in RFT. We find the common practice of an initial
supervised finetuning (SFT) phase to be the most promising candidate, which
sheds light on its importance in an RFT pipeline. Moreover, we show that a
relatively small number of SFT optimization steps on as few as 1% of the input
samples can suffice, indicating that the initial SFT phase need not be
expensive in terms of compute and data labeling efforts. Overall, our results
emphasize that being mindful for inputs whose expected gradient vanishes, as
measured by the reward standard deviation, is crucial for successful execution
of RFT.
| [
{
"created": "Tue, 31 Oct 2023 17:59:05 GMT",
"version": "v1"
},
{
"created": "Wed, 31 Jan 2024 12:39:06 GMT",
"version": "v2"
},
{
"created": "Thu, 14 Mar 2024 08:05:18 GMT",
"version": "v3"
}
] | 2024-03-15 | [
[
"Razin",
"Noam",
""
],
[
"Zhou",
"Hattie",
""
],
[
"Saremi",
"Omid",
""
],
[
"Thilak",
"Vimal",
""
],
[
"Bradley",
"Arwen",
""
],
[
"Nakkiran",
"Preetum",
""
],
[
"Susskind",
"Joshua",
""
],
[
"Littwin",
"Etai",
""
]
] | Pretrained language models are commonly aligned with human preferences and downstream tasks via reinforcement finetuning (RFT), which refers to maximizing a (possibly learned) reward function using policy gradient algorithms. This work identifies a fundamental optimization obstacle in RFT: we prove that the expected gradient for an input vanishes when its reward standard deviation under the model is small, even if the expected reward is far from optimal. Through experiments on an RFT benchmark and controlled environments, as well as a theoretical analysis, we then demonstrate that vanishing gradients due to small reward standard deviation are prevalent and detrimental, leading to extremely slow reward maximization. Lastly, we explore ways to overcome vanishing gradients in RFT. We find the common practice of an initial supervised finetuning (SFT) phase to be the most promising candidate, which sheds light on its importance in an RFT pipeline. Moreover, we show that a relatively small number of SFT optimization steps on as few as 1% of the input samples can suffice, indicating that the initial SFT phase need not be expensive in terms of compute and data labeling efforts. Overall, our results emphasize that being mindful for inputs whose expected gradient vanishes, as measured by the reward standard deviation, is crucial for successful execution of RFT. |
2305.16943 | Hayeon Lee | Sohyun An, Hayeon Lee, Jaehyeong Jo, Seanie Lee, Sung Ju Hwang | DiffusionNAG: Predictor-guided Neural Architecture Generation with
Diffusion Models | Accepted to ICLR 2024 | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Existing NAS methods suffer from either an excessive amount of time for
repetitive sampling and training of many task-irrelevant architectures. To
tackle such limitations of existing NAS methods, we propose a paradigm shift
from NAS to a novel conditional Neural Architecture Generation (NAG) framework
based on diffusion models, dubbed DiffusionNAG. Specifically, we consider the
neural architectures as directed graphs and propose a graph diffusion model for
generating them. Moreover, with the guidance of parameterized predictors,
DiffusionNAG can flexibly generate task-optimal architectures with the desired
properties for diverse tasks, by sampling from a region that is more likely to
satisfy the properties. This conditional NAG scheme is significantly more
efficient than previous NAS schemes which sample the architectures and filter
them using the property predictors. We validate the effectiveness of
DiffusionNAG through extensive experiments in two predictor-based NAS
scenarios: Transferable NAS and Bayesian Optimization (BO)-based NAS.
DiffusionNAG achieves superior performance with speedups of up to 35 times when
compared to the baselines on Transferable NAS benchmarks. Furthermore, when
integrated into a BO-based algorithm, DiffusionNAG outperforms existing
BO-based NAS approaches, particularly in the large MobileNetV3 search space on
the ImageNet 1K dataset. Code is available at
https://github.com/CownowAn/DiffusionNAG.
| [
{
"created": "Fri, 26 May 2023 13:58:18 GMT",
"version": "v1"
},
{
"created": "Sun, 31 Dec 2023 00:30:53 GMT",
"version": "v2"
},
{
"created": "Fri, 19 Jan 2024 21:38:42 GMT",
"version": "v3"
},
{
"created": "Sun, 24 Mar 2024 22:00:04 GMT",
"version": "v4"
}
] | 2024-03-26 | [
[
"An",
"Sohyun",
""
],
[
"Lee",
"Hayeon",
""
],
[
"Jo",
"Jaehyeong",
""
],
[
"Lee",
"Seanie",
""
],
[
"Hwang",
"Sung Ju",
""
]
] | Existing NAS methods suffer from either an excessive amount of time for repetitive sampling and training of many task-irrelevant architectures. To tackle such limitations of existing NAS methods, we propose a paradigm shift from NAS to a novel conditional Neural Architecture Generation (NAG) framework based on diffusion models, dubbed DiffusionNAG. Specifically, we consider the neural architectures as directed graphs and propose a graph diffusion model for generating them. Moreover, with the guidance of parameterized predictors, DiffusionNAG can flexibly generate task-optimal architectures with the desired properties for diverse tasks, by sampling from a region that is more likely to satisfy the properties. This conditional NAG scheme is significantly more efficient than previous NAS schemes which sample the architectures and filter them using the property predictors. We validate the effectiveness of DiffusionNAG through extensive experiments in two predictor-based NAS scenarios: Transferable NAS and Bayesian Optimization (BO)-based NAS. DiffusionNAG achieves superior performance with speedups of up to 35 times when compared to the baselines on Transferable NAS benchmarks. Furthermore, when integrated into a BO-based algorithm, DiffusionNAG outperforms existing BO-based NAS approaches, particularly in the large MobileNetV3 search space on the ImageNet 1K dataset. Code is available at https://github.com/CownowAn/DiffusionNAG. |
1702.05939 | Andr\'e Gr\"uning | Joseph Chrol-Cannon and Yaochu Jin and Andr\'e Gr\"uning | An Efficient Method for online Detection of Polychronous Patterns in
Spiking Neural Network | 17 pages, 8 figures | null | 10.1016/j.neucom.2017.06.025 | null | cs.NE q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Polychronous neural groups are effective structures for the recognition of
precise spike-timing patterns but the detection method is an inefficient
multi-stage brute force process that works off-line on pre-recorded simulation
data. This work presents a new model of polychronous patterns that can capture
precise sequences of spikes directly in the neural simulation. In this scheme,
each neuron is assigned a randomized code that is used to tag the post-synaptic
neurons whenever a spike is transmitted. This creates a polychronous code that
preserves the order of pre-synaptic activity and can be registered in a hash
table when the post-synaptic neuron spikes. A polychronous code is a
sub-component of a polychronous group that will occur, along with others, when
the group is active. We demonstrate the representational and pattern
recognition ability of polychronous codes on a direction selective visual task
involving moving bars that is typical of a computation performed by simple
cells in the cortex. The computational efficiency of the proposed algorithm far
exceeds existing polychronous group detection methods and is well suited for
online detection.
| [
{
"created": "Mon, 20 Feb 2017 12:02:50 GMT",
"version": "v1"
}
] | 2017-07-12 | [
[
"Chrol-Cannon",
"Joseph",
""
],
[
"Jin",
"Yaochu",
""
],
[
"Grüning",
"André",
""
]
] | Polychronous neural groups are effective structures for the recognition of precise spike-timing patterns but the detection method is an inefficient multi-stage brute force process that works off-line on pre-recorded simulation data. This work presents a new model of polychronous patterns that can capture precise sequences of spikes directly in the neural simulation. In this scheme, each neuron is assigned a randomized code that is used to tag the post-synaptic neurons whenever a spike is transmitted. This creates a polychronous code that preserves the order of pre-synaptic activity and can be registered in a hash table when the post-synaptic neuron spikes. A polychronous code is a sub-component of a polychronous group that will occur, along with others, when the group is active. We demonstrate the representational and pattern recognition ability of polychronous codes on a direction selective visual task involving moving bars that is typical of a computation performed by simple cells in the cortex. The computational efficiency of the proposed algorithm far exceeds existing polychronous group detection methods and is well suited for online detection. |
2005.10460 | Md. Redowan Mahmud | Redowan Mahmud, Kotagiri Ramamohanarao and Rajkumar Buyya | Application Management in Fog Computing Environments: A Taxonomy, Review
and Future Directions | null | ACM Computing Surveys, 2020 | 10.1145/3403955 | Volume: 53, Issue: 4 | cs.DC eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Internet of Things (IoT) paradigm is being rapidly adopted for the
creation of smart environments in various domains. The IoT-enabled
Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry
4.0 and Agtech handle a huge volume of data and require data processing
services from different types of applications in real-time. The Cloud-centric
execution of IoT applications barely meets such requirements as the Cloud
datacentres reside at a multi-hop distance from the IoT devices. \textit{Fog
computing}, an extension of Cloud at the edge network, can execute these
applications closer to data sources. Thus, Fog computing can improve
application service delivery time and resist network congestion. However, the
Fog nodes are highly distributed, heterogeneous and most of them are
constrained in resources and spatial sharing. Therefore, efficient management
of applications is necessary to fully exploit the capabilities of Fog nodes. In
this work, we investigate the existing application management strategies in Fog
computing and review them in terms of architecture, placement and maintenance.
Additionally, we propose a comprehensive taxonomy and highlight the research
gaps in Fog-based application management. We also discuss a perspective model
and provide future research directions for further improvement of application
management in Fog computing.
| [
{
"created": "Thu, 21 May 2020 04:43:44 GMT",
"version": "v1"
}
] | 2020-07-28 | [
[
"Mahmud",
"Redowan",
""
],
[
"Ramamohanarao",
"Kotagiri",
""
],
[
"Buyya",
"Rajkumar",
""
]
] | The Internet of Things (IoT) paradigm is being rapidly adopted for the creation of smart environments in various domains. The IoT-enabled Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry 4.0 and Agtech handle a huge volume of data and require data processing services from different types of applications in real-time. The Cloud-centric execution of IoT applications barely meets such requirements as the Cloud datacentres reside at a multi-hop distance from the IoT devices. \textit{Fog computing}, an extension of Cloud at the edge network, can execute these applications closer to data sources. Thus, Fog computing can improve application service delivery time and resist network congestion. However, the Fog nodes are highly distributed, heterogeneous and most of them are constrained in resources and spatial sharing. Therefore, efficient management of applications is necessary to fully exploit the capabilities of Fog nodes. In this work, we investigate the existing application management strategies in Fog computing and review them in terms of architecture, placement and maintenance. Additionally, we propose a comprehensive taxonomy and highlight the research gaps in Fog-based application management. We also discuss a perspective model and provide future research directions for further improvement of application management in Fog computing. |
2308.04526 | Jord\~ao Bragantini | Jord\~ao Bragantini, Merlin Lange, Lo\"ic Royer | Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours
Maps | 13 pages, 7 figures, 4 tables | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | In this work, we describe a method for large-scale 3D cell-tracking through a
segmentation selection approach. The proposed method is effective at tracking
cells across large microscopy datasets on two fronts: (i) It can solve problems
containing millions of segmentation instances in terabyte-scale 3D+t datasets;
(ii) It achieves competitive results with or without deep learning, which
requires 3D annotated data, that is scarce in the fluorescence microscopy
field. The proposed method computes cell tracks and segments using a hierarchy
of segmentation hypotheses and selects disjoint segments by maximizing the
overlap between adjacent frames. We show that this method achieves
state-of-the-art results in 3D images from the cell tracking challenge and has
a faster integer linear programming formulation. Moreover, our framework is
flexible and supports segmentations from off-the-shelf cell segmentation models
and can combine them into an ensemble that improves tracking. The code is
available https://github.com/royerlab/ultrack.
| [
{
"created": "Tue, 8 Aug 2023 18:41:38 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Apr 2024 23:50:32 GMT",
"version": "v2"
}
] | 2024-04-15 | [
[
"Bragantini",
"Jordão",
""
],
[
"Lange",
"Merlin",
""
],
[
"Royer",
"Loïc",
""
]
] | In this work, we describe a method for large-scale 3D cell-tracking through a segmentation selection approach. The proposed method is effective at tracking cells across large microscopy datasets on two fronts: (i) It can solve problems containing millions of segmentation instances in terabyte-scale 3D+t datasets; (ii) It achieves competitive results with or without deep learning, which requires 3D annotated data, that is scarce in the fluorescence microscopy field. The proposed method computes cell tracks and segments using a hierarchy of segmentation hypotheses and selects disjoint segments by maximizing the overlap between adjacent frames. We show that this method achieves state-of-the-art results in 3D images from the cell tracking challenge and has a faster integer linear programming formulation. Moreover, our framework is flexible and supports segmentations from off-the-shelf cell segmentation models and can combine them into an ensemble that improves tracking. The code is available https://github.com/royerlab/ultrack. |
1412.6141 | Song-Ju Kim Dr. | Song-Ju Kim, Masashi Aono, and Etsushi Nameda | Efficient Decision-Making by Volume-Conserving Physical Object | 5 pages, 3 figures | null | 10.1088/1367-2630/17/8/083023 | null | cs.AI cs.LG nlin.AO physics.data-an | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We demonstrate that any physical object, as long as its volume is conserved
when coupled with suitable operations, provides a sophisticated decision-making
capability. We consider the problem of finding, as accurately and quickly as
possible, the most profitable option from a set of options that gives
stochastic rewards. These decisions are made as dictated by a physical object,
which is moved in a manner similar to the fluctuations of a rigid body in a
tug-of-war game. Our analytical calculations validate statistical reasons why
our method exhibits higher efficiency than conventional algorithms.
| [
{
"created": "Thu, 30 Oct 2014 08:23:13 GMT",
"version": "v1"
}
] | 2015-09-02 | [
[
"Kim",
"Song-Ju",
""
],
[
"Aono",
"Masashi",
""
],
[
"Nameda",
"Etsushi",
""
]
] | We demonstrate that any physical object, as long as its volume is conserved when coupled with suitable operations, provides a sophisticated decision-making capability. We consider the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards. These decisions are made as dictated by a physical object, which is moved in a manner similar to the fluctuations of a rigid body in a tug-of-war game. Our analytical calculations validate statistical reasons why our method exhibits higher efficiency than conventional algorithms. |
2112.01998 | Hariprasad Kodamana | Dibyendu Ghosh, Srija Chakraborty, Hariprasad Kodamana, Supriya
Chakraborty | Application of Machine Learning in understanding plant virus
pathogenesis: Trends and perspectives on emergence, diagnosis, host-virus
interplay and management | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Inclusion of high throughput technologies in the field of biology has
generated massive amounts of biological data in the recent years. Now,
transforming these huge volumes of data into knowledge is the primary challenge
in computational biology. The traditional methods of data analysis have failed
to carry out the task. Hence, researchers are turning to machine learning based
approaches for the analysis of high-dimensional big data. In machine learning,
once a model is trained with a training dataset, it can be applied on a testing
dataset which is independent. In current times, deep learning algorithms
further promote the application of machine learning in several field of biology
including plant virology. Considering a significant progress in the application
of machine learning in understanding plant virology, this review highlights an
introductory note on machine learning and comprehensively discusses the trends
and prospects of machine learning in diagnosis of viral diseases, understanding
host-virus interplay and emergence of plant viruses.
| [
{
"created": "Fri, 3 Dec 2021 16:25:26 GMT",
"version": "v1"
}
] | 2021-12-06 | [
[
"Ghosh",
"Dibyendu",
""
],
[
"Chakraborty",
"Srija",
""
],
[
"Kodamana",
"Hariprasad",
""
],
[
"Chakraborty",
"Supriya",
""
]
] | Inclusion of high throughput technologies in the field of biology has generated massive amounts of biological data in the recent years. Now, transforming these huge volumes of data into knowledge is the primary challenge in computational biology. The traditional methods of data analysis have failed to carry out the task. Hence, researchers are turning to machine learning based approaches for the analysis of high-dimensional big data. In machine learning, once a model is trained with a training dataset, it can be applied on a testing dataset which is independent. In current times, deep learning algorithms further promote the application of machine learning in several field of biology including plant virology. Considering a significant progress in the application of machine learning in understanding plant virology, this review highlights an introductory note on machine learning and comprehensively discusses the trends and prospects of machine learning in diagnosis of viral diseases, understanding host-virus interplay and emergence of plant viruses. |
2208.14966 | Andrew Bai | Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar, Neil Y. C. Lin, Cho-Jui
Hsieh | Concept Gradient: Concept-based Interpretation Without Linear Assumption | 21 pages, 7 figures, published in ICLR 2023 | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Concept-based interpretations of black-box models are often more intuitive
for humans to understand. The most widely adopted approach for concept-based
interpretation is Concept Activation Vector (CAV). CAV relies on learning a
linear relation between some latent representation of a given model and
concepts. The linear separability is usually implicitly assumed but does not
hold true in general. In this work, we started from the original intent of
concept-based interpretation and proposed Concept Gradient (CG), extending
concept-based interpretation beyond linear concept functions. We showed that
for a general (potentially non-linear) concept, we can mathematically evaluate
how a small change of concept affecting the model's prediction, which leads to
an extension of gradient-based interpretation to the concept space. We
demonstrated empirically that CG outperforms CAV in both toy examples and real
world datasets.
| [
{
"created": "Wed, 31 Aug 2022 17:06:46 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Feb 2024 21:27:45 GMT",
"version": "v2"
}
] | 2024-02-07 | [
[
"Bai",
"Andrew",
""
],
[
"Yeh",
"Chih-Kuan",
""
],
[
"Ravikumar",
"Pradeep",
""
],
[
"Lin",
"Neil Y. C.",
""
],
[
"Hsieh",
"Cho-Jui",
""
]
] | Concept-based interpretations of black-box models are often more intuitive for humans to understand. The most widely adopted approach for concept-based interpretation is Concept Activation Vector (CAV). CAV relies on learning a linear relation between some latent representation of a given model and concepts. The linear separability is usually implicitly assumed but does not hold true in general. In this work, we started from the original intent of concept-based interpretation and proposed Concept Gradient (CG), extending concept-based interpretation beyond linear concept functions. We showed that for a general (potentially non-linear) concept, we can mathematically evaluate how a small change of concept affecting the model's prediction, which leads to an extension of gradient-based interpretation to the concept space. We demonstrated empirically that CG outperforms CAV in both toy examples and real world datasets. |
2405.09409 | Markus Ralf Bujotzek | Markus R. Bujotzek, \"Unal Ak\"unal, Stefan Denner, Peter Neher,
Maximilian Zenk, Eric Frodl, Astha Jaiswal, Moon Kim, Nicolai R. Krekiehn,
Manuel Nickel, Richard Ruppel, Marcus Both, Felix D\"ollinger, Marcel Opitz,
Thorsten Persigehl, Jens Kleesiek, Tobias Penzkofer, Klaus Maier-Hein,
Rickmer Braren, Andreas Bucher | Real-World Federated Learning in Radiology: Hurdles to overcome and
Benefits to gain | null | null | null | null | cs.CV cs.DC | http://creativecommons.org/licenses/by/4.0/ | Objective: Federated Learning (FL) enables collaborative model training while
keeping data locally. Currently, most FL studies in radiology are conducted in
simulated environments due to numerous hurdles impeding its translation into
practice. The few existing real-world FL initiatives rarely communicate
specific measures taken to overcome these hurdles, leaving behind a significant
knowledge gap. Minding efforts to implement real-world FL, there is a notable
lack of comprehensive assessment comparing FL to less complex alternatives.
Materials & Methods: We extensively reviewed FL literature, categorizing
insights along with our findings according to their nature and phase while
establishing a FL initiative, summarized to a comprehensive guide. We developed
our own FL infrastructure within the German Radiological Cooperative Network
(RACOON) and demonstrated its functionality by training FL models on lung
pathology segmentation tasks across six university hospitals. We extensively
evaluated FL against less complex alternatives in three distinct evaluation
scenarios. Results: The proposed guide outlines essential steps, identified
hurdles, and proposed solutions for establishing successful FL initiatives
conducting real-world experiments. Our experimental results show that FL
outperforms less complex alternatives in all evaluation scenarios, justifying
the effort required to translate FL into real-world applications. Discussion &
Conclusion: Our proposed guide aims to aid future FL researchers in
circumventing pitfalls and accelerating translation of FL into radiological
applications. Our results underscore the value of efforts needed to translate
FL into real-world applications by demonstrating advantageous performance over
alternatives, and emphasize the importance of strategic organization, robust
management of distributed data and infrastructure in real-world settings.
| [
{
"created": "Wed, 15 May 2024 15:04:27 GMT",
"version": "v1"
}
] | 2024-05-16 | [
[
"Bujotzek",
"Markus R.",
""
],
[
"Akünal",
"Ünal",
""
],
[
"Denner",
"Stefan",
""
],
[
"Neher",
"Peter",
""
],
[
"Zenk",
"Maximilian",
""
],
[
"Frodl",
"Eric",
""
],
[
"Jaiswal",
"Astha",
""
],
[
"Kim",
"Moon",
""
],
[
"Krekiehn",
"Nicolai R.",
""
],
[
"Nickel",
"Manuel",
""
],
[
"Ruppel",
"Richard",
""
],
[
"Both",
"Marcus",
""
],
[
"Döllinger",
"Felix",
""
],
[
"Opitz",
"Marcel",
""
],
[
"Persigehl",
"Thorsten",
""
],
[
"Kleesiek",
"Jens",
""
],
[
"Penzkofer",
"Tobias",
""
],
[
"Maier-Hein",
"Klaus",
""
],
[
"Braren",
"Rickmer",
""
],
[
"Bucher",
"Andreas",
""
]
] | Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles, leaving behind a significant knowledge gap. Minding efforts to implement real-world FL, there is a notable lack of comprehensive assessment comparing FL to less complex alternatives. Materials & Methods: We extensively reviewed FL literature, categorizing insights along with our findings according to their nature and phase while establishing a FL initiative, summarized to a comprehensive guide. We developed our own FL infrastructure within the German Radiological Cooperative Network (RACOON) and demonstrated its functionality by training FL models on lung pathology segmentation tasks across six university hospitals. We extensively evaluated FL against less complex alternatives in three distinct evaluation scenarios. Results: The proposed guide outlines essential steps, identified hurdles, and proposed solutions for establishing successful FL initiatives conducting real-world experiments. Our experimental results show that FL outperforms less complex alternatives in all evaluation scenarios, justifying the effort required to translate FL into real-world applications. Discussion & Conclusion: Our proposed guide aims to aid future FL researchers in circumventing pitfalls and accelerating translation of FL into radiological applications. Our results underscore the value of efforts needed to translate FL into real-world applications by demonstrating advantageous performance over alternatives, and emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings. |
2311.07453 | Mubashara Akhtar | Mubashara Akhtar, Nikesh Subedi, Vivek Gupta, Sahar Tahmasebi, Oana
Cocarascu, Elena Simperl | ChartCheck: Explainable Fact-Checking over Real-World Chart Images | null | null | null | null | cs.CL cs.CV | http://creativecommons.org/licenses/by/4.0/ | Whilst fact verification has attracted substantial interest in the natural
language processing community, verifying misinforming statements against data
visualizations such as charts has so far been overlooked. Charts are commonly
used in the real-world to summarize and communicate key information, but they
can also be easily misused to spread misinformation and promote certain
agendas. In this paper, we introduce ChartCheck, a novel, large-scale dataset
for explainable fact-checking against real-world charts, consisting of 1.7k
charts and 10.5k human-written claims and explanations. We systematically
evaluate ChartCheck using vision-language and chart-to-table models, and
propose a baseline to the community. Finally, we study chart reasoning types
and visual attributes that pose a challenge to these models
| [
{
"created": "Mon, 13 Nov 2023 16:35:29 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Feb 2024 12:14:05 GMT",
"version": "v2"
}
] | 2024-02-19 | [
[
"Akhtar",
"Mubashara",
""
],
[
"Subedi",
"Nikesh",
""
],
[
"Gupta",
"Vivek",
""
],
[
"Tahmasebi",
"Sahar",
""
],
[
"Cocarascu",
"Oana",
""
],
[
"Simperl",
"Elena",
""
]
] | Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked. Charts are commonly used in the real-world to summarize and communicate key information, but they can also be easily misused to spread misinformation and promote certain agendas. In this paper, we introduce ChartCheck, a novel, large-scale dataset for explainable fact-checking against real-world charts, consisting of 1.7k charts and 10.5k human-written claims and explanations. We systematically evaluate ChartCheck using vision-language and chart-to-table models, and propose a baseline to the community. Finally, we study chart reasoning types and visual attributes that pose a challenge to these models |
1711.02484 | Elie Ngomseu Mambou | Ebenezer Esenogho and Elie Ngomseu Mambou | Evaluation of Handover Exchange Schemes Between Two Cognitive Radio Base
Stations with and without Buffers | 5 pages, 7 figures, conference | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article investigates and evaluate a handover exchange scheme between two
secondary users (SUs) moving in different directions across the handover region
of neighbouring cell in a cognitive radio network. More specifically, this
investigation compares the performance of SUs in a cellular cognitive radio
network with and without channel exchange systems. The investigation shows
reduced handover failure, blocking, forced and access probabilities
respectively, for handover exchange scheme with buffer as compared to the other
scenario.
| [
{
"created": "Tue, 7 Nov 2017 14:27:16 GMT",
"version": "v1"
}
] | 2017-11-08 | [
[
"Esenogho",
"Ebenezer",
""
],
[
"Mambou",
"Elie Ngomseu",
""
]
] | This article investigates and evaluate a handover exchange scheme between two secondary users (SUs) moving in different directions across the handover region of neighbouring cell in a cognitive radio network. More specifically, this investigation compares the performance of SUs in a cellular cognitive radio network with and without channel exchange systems. The investigation shows reduced handover failure, blocking, forced and access probabilities respectively, for handover exchange scheme with buffer as compared to the other scenario. |
2305.14329 | Fivos Kalogiannis | Fivos Kalogiannis, Ioannis Panageas | Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient
Computation of Nash Equilibria | Added missing proofs for the infinite-horizon | null | null | null | cs.GT cs.MA cs.SI | http://creativecommons.org/licenses/by/4.0/ | The works of (Daskalakis et al., 2009, 2022; Jin et al., 2022; Deng et al.,
2023) indicate that computing Nash equilibria in multi-player Markov games is a
computationally hard task. This fact raises the question of whether or not
computational intractability can be circumvented if one focuses on specific
classes of Markov games. One such example is two-player zero-sum Markov games,
in which efficient ways to compute a Nash equilibrium are known. Inspired by
zero-sum polymatrix normal-form games (Cai et al., 2016), we define a class of
zero-sum multi-agent Markov games in which there are only pairwise interactions
described by a graph that changes per state. For this class of Markov games, we
show that an $\epsilon$-approximate Nash equilibrium can be found efficiently.
To do so, we generalize the techniques of (Cai et al., 2016), by showing that
the set of coarse-correlated equilibria collapses to the set of Nash
equilibria. Afterwards, it is possible to use any algorithm in the literature
that computes approximate coarse-correlated equilibria Markovian policies to
get an approximate Nash equilibrium.
| [
{
"created": "Tue, 23 May 2023 17:56:45 GMT",
"version": "v1"
},
{
"created": "Mon, 29 May 2023 17:57:58 GMT",
"version": "v2"
}
] | 2023-05-30 | [
[
"Kalogiannis",
"Fivos",
""
],
[
"Panageas",
"Ioannis",
""
]
] | The works of (Daskalakis et al., 2009, 2022; Jin et al., 2022; Deng et al., 2023) indicate that computing Nash equilibria in multi-player Markov games is a computationally hard task. This fact raises the question of whether or not computational intractability can be circumvented if one focuses on specific classes of Markov games. One such example is two-player zero-sum Markov games, in which efficient ways to compute a Nash equilibrium are known. Inspired by zero-sum polymatrix normal-form games (Cai et al., 2016), we define a class of zero-sum multi-agent Markov games in which there are only pairwise interactions described by a graph that changes per state. For this class of Markov games, we show that an $\epsilon$-approximate Nash equilibrium can be found efficiently. To do so, we generalize the techniques of (Cai et al., 2016), by showing that the set of coarse-correlated equilibria collapses to the set of Nash equilibria. Afterwards, it is possible to use any algorithm in the literature that computes approximate coarse-correlated equilibria Markovian policies to get an approximate Nash equilibrium. |
2303.10288 | Jun Zhao | Terence Jie Chua, Wenhan Yu, Jun Zhao | Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse
with Deep Reinforcement Learning | This paper appears in IEEE International Conference on
Communications, 2023 | null | null | null | cs.NI cs.AI | http://creativecommons.org/licenses/by/4.0/ | Real-time Digital Twinning of physical world scenes onto the Metaverse is
necessary for a myriad of applications such as augmented-reality (AR) assisted
driving. In AR assisted driving, physical environment scenes are first captured
by Internet of Vehicles (IoVs) and are uploaded to the Metaverse. A central
Metaverse Map Service Provider (MMSP) will aggregate information from all IoVs
to develop a central Metaverse Map. Information from the Metaverse Map can then
be downloaded into individual IoVs on demand and be delivered as AR scenes to
the driver. However, the growing interest in developing AR assisted driving
applications which relies on digital twinning invites adversaries. These
adversaries may place physical adversarial patches on physical world objects
such as cars, signboards, or on roads, seeking to contort the virtual world
digital twin. Hence, there is a need to detect these physical world adversarial
patches. Nevertheless, as real-time, accurate detection of adversarial patches
is compute-intensive, these physical world scenes have to be offloaded to the
Metaverse Map Base Stations (MMBS) for computation. Hence in our work, we
considered an environment with moving Internet of Vehicles (IoV), uploading
real-time physical world scenes to the MMBSs. We formulated a realistic joint
variable optimization problem where the MMSPs' objective is to maximize
adversarial patch detection mean average precision (mAP), while minimizing the
computed AR scene up-link transmission latency and IoVs' up-link transmission
idle count, through optimizing the IoV-MMBS allocation and IoV up-link scene
resolution selection. We proposed a Heterogeneous Action Proximal Policy
Optimization (HAPPO) (discrete-continuous) algorithm to tackle the proposed
problem. Extensive experiments shows HAPPO outperforms baseline models when
compared against key metrics.
| [
{
"created": "Sat, 18 Mar 2023 00:03:50 GMT",
"version": "v1"
}
] | 2023-03-21 | [
[
"Chua",
"Terence Jie",
""
],
[
"Yu",
"Wenhan",
""
],
[
"Zhao",
"Jun",
""
]
] | Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted driving, physical environment scenes are first captured by Internet of Vehicles (IoVs) and are uploaded to the Metaverse. A central Metaverse Map Service Provider (MMSP) will aggregate information from all IoVs to develop a central Metaverse Map. Information from the Metaverse Map can then be downloaded into individual IoVs on demand and be delivered as AR scenes to the driver. However, the growing interest in developing AR assisted driving applications which relies on digital twinning invites adversaries. These adversaries may place physical adversarial patches on physical world objects such as cars, signboards, or on roads, seeking to contort the virtual world digital twin. Hence, there is a need to detect these physical world adversarial patches. Nevertheless, as real-time, accurate detection of adversarial patches is compute-intensive, these physical world scenes have to be offloaded to the Metaverse Map Base Stations (MMBS) for computation. Hence in our work, we considered an environment with moving Internet of Vehicles (IoV), uploading real-time physical world scenes to the MMBSs. We formulated a realistic joint variable optimization problem where the MMSPs' objective is to maximize adversarial patch detection mean average precision (mAP), while minimizing the computed AR scene up-link transmission latency and IoVs' up-link transmission idle count, through optimizing the IoV-MMBS allocation and IoV up-link scene resolution selection. We proposed a Heterogeneous Action Proximal Policy Optimization (HAPPO) (discrete-continuous) algorithm to tackle the proposed problem. Extensive experiments shows HAPPO outperforms baseline models when compared against key metrics. |
2306.07512 | Wang Ruijie | Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan,
Shengzhong Liu, Hanghang Tong, Tarek F. Abdelzaher | Noisy Positive-Unlabeled Learning with Self-Training for Speculative
Knowledge Graph Reasoning | This paper is accepted by ACL-Findings 2023 | null | null | null | cs.LG cs.AI cs.CL cs.SI | http://creativecommons.org/licenses/by/4.0/ | This paper studies speculative reasoning task on real-world knowledge graphs
(KG) that contain both \textit{false negative issue} (i.e., potential true
facts being excluded) and \textit{false positive issue} (i.e., unreliable or
outdated facts being included). State-of-the-art methods fall short in the
speculative reasoning ability, as they assume the correctness of a fact is
solely determined by its presence in KG, making them vulnerable to false
negative/positive issues. The new reasoning task is formulated as a noisy
Positive-Unlabeled learning problem. We propose a variational framework, namely
nPUGraph, that jointly estimates the correctness of both collected and
uncollected facts (which we call \textit{label posterior}) and updates model
parameters during training. The label posterior estimation facilitates
speculative reasoning from two perspectives. First, it improves the robustness
of a label posterior-aware graph encoder against false positive links. Second,
it identifies missing facts to provide high-quality grounds of reasoning. They
are unified in a simple yet effective self-training procedure. Empirically,
extensive experiments on three benchmark KG and one Twitter dataset with
various degrees of false negative/positive cases demonstrate the effectiveness
of nPUGraph.
| [
{
"created": "Tue, 13 Jun 2023 02:43:21 GMT",
"version": "v1"
}
] | 2023-06-14 | [
[
"Wang",
"Ruijie",
""
],
[
"Li",
"Baoyu",
""
],
[
"Lu",
"Yichen",
""
],
[
"Sun",
"Dachun",
""
],
[
"Li",
"Jinning",
""
],
[
"Yan",
"Yuchen",
""
],
[
"Liu",
"Shengzhong",
""
],
[
"Tong",
"Hanghang",
""
],
[
"Abdelzaher",
"Tarek F.",
""
]
] | This paper studies speculative reasoning task on real-world knowledge graphs (KG) that contain both \textit{false negative issue} (i.e., potential true facts being excluded) and \textit{false positive issue} (i.e., unreliable or outdated facts being included). State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues. The new reasoning task is formulated as a noisy Positive-Unlabeled learning problem. We propose a variational framework, namely nPUGraph, that jointly estimates the correctness of both collected and uncollected facts (which we call \textit{label posterior}) and updates model parameters during training. The label posterior estimation facilitates speculative reasoning from two perspectives. First, it improves the robustness of a label posterior-aware graph encoder against false positive links. Second, it identifies missing facts to provide high-quality grounds of reasoning. They are unified in a simple yet effective self-training procedure. Empirically, extensive experiments on three benchmark KG and one Twitter dataset with various degrees of false negative/positive cases demonstrate the effectiveness of nPUGraph. |
2301.07868 | Bowen Zhang | Xiaojie Jin, Bowen Zhang, Weibo Gong, Kai Xu, XueQing Deng, Peng Wang,
Zhao Zhang, Xiaohui Shen, Jiashi Feng | MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State-of-the-art video-text retrieval (VTR) methods typically involve fully
fine-tuning a pre-trained model (e.g. CLIP) on specific datasets. However, this
can result in significant storage costs in practical applications as a separate
model per task must be stored. To address this issue, we present our pioneering
work that enables parameter-efficient VTR using a pre-trained model, with only
a small number of tunable parameters during training. Towards this goal, we
propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for
efficiently transferring the knowledge in the pre-trained CLIP from image-text
to video-text. Specifically, MV-Adapter utilizes bottleneck structures in both
video and text branches, along with two novel components. The first is a
Temporal Adaptation Module that is incorporated in the video branch to
introduce global and local temporal contexts. We also train weights
calibrations to adjust to dynamic variations across frames. The second is Cross
Modality Tying that generates weights for video/text branches through sharing
cross modality factors, for better aligning between modalities. Thanks to above
innovations, MV-Adapter can achieve comparable or better performance than
standard full fine-tuning with negligible parameters overhead. Notably,
MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks
with large margins on five widely used VTR benchmarks (MSR-VTT, MSVD, LSMDC,
DiDemo, and ActivityNet).
| [
{
"created": "Thu, 19 Jan 2023 03:42:56 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Apr 2024 06:21:29 GMT",
"version": "v2"
}
] | 2024-04-12 | [
[
"Jin",
"Xiaojie",
""
],
[
"Zhang",
"Bowen",
""
],
[
"Gong",
"Weibo",
""
],
[
"Xu",
"Kai",
""
],
[
"Deng",
"XueQing",
""
],
[
"Wang",
"Peng",
""
],
[
"Zhang",
"Zhao",
""
],
[
"Shen",
"Xiaohui",
""
],
[
"Feng",
"Jiashi",
""
]
] | State-of-the-art video-text retrieval (VTR) methods typically involve fully fine-tuning a pre-trained model (e.g. CLIP) on specific datasets. However, this can result in significant storage costs in practical applications as a separate model per task must be stored. To address this issue, we present our pioneering work that enables parameter-efficient VTR using a pre-trained model, with only a small number of tunable parameters during training. Towards this goal, we propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for efficiently transferring the knowledge in the pre-trained CLIP from image-text to video-text. Specifically, MV-Adapter utilizes bottleneck structures in both video and text branches, along with two novel components. The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts. We also train weights calibrations to adjust to dynamic variations across frames. The second is Cross Modality Tying that generates weights for video/text branches through sharing cross modality factors, for better aligning between modalities. Thanks to above innovations, MV-Adapter can achieve comparable or better performance than standard full fine-tuning with negligible parameters overhead. Notably, MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks with large margins on five widely used VTR benchmarks (MSR-VTT, MSVD, LSMDC, DiDemo, and ActivityNet). |
2202.03575 | Peiying Zhang | Peiying Zhang, Chao Wang, Chunxiao Jiang, and Zhu Han | Deep Reinforcement Learning Assisted Federated Learning Algorithm for
Data Management of IIoT | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The continuous expanded scale of the industrial Internet of Things (IIoT)
leads to IIoT equipments generating massive amounts of user data every moment.
According to the different requirement of end users, these data usually have
high heterogeneity and privacy, while most of users are reluctant to expose
them to the public view. How to manage these time series data in an efficient
and safe way in the field of IIoT is still an open issue, such that it has
attracted extensive attention from academia and industry. As a new machine
learning (ML) paradigm, federated learning (FL) has great advantages in
training heterogeneous and private data. This paper studies the FL technology
applications to manage IIoT equipment data in wireless network environments. In
order to increase the model aggregation rate and reduce communication costs, we
apply deep reinforcement learning (DRL) to IIoT equipment selection process,
specifically to select those IIoT equipment nodes with accurate models.
Therefore, we propose a FL algorithm assisted by DRL, which can take into
account the privacy and efficiency of data training of IIoT equipment. By
analyzing the data characteristics of IIoT equipments, we use MNIST, fashion
MNIST and CIFAR-10 data sets to represent the data generated by IIoT. During
the experiment, we employ the deep neural network (DNN) model to train the
data, and experimental results show that the accuracy can reach more than 97\%,
which corroborates the effectiveness of the proposed algorithm.
| [
{
"created": "Thu, 3 Feb 2022 07:12:36 GMT",
"version": "v1"
}
] | 2022-02-09 | [
[
"Zhang",
"Peiying",
""
],
[
"Wang",
"Chao",
""
],
[
"Jiang",
"Chunxiao",
""
],
[
"Han",
"Zhu",
""
]
] | The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning (ML) paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST and CIFAR-10 data sets to represent the data generated by IIoT. During the experiment, we employ the deep neural network (DNN) model to train the data, and experimental results show that the accuracy can reach more than 97\%, which corroborates the effectiveness of the proposed algorithm. |
2103.06742 | Qianhao Wang | Qianhao Wang, Yuman Gao, Jialin Ji, Chao Xu, and Fei Gao | Visibility-aware Trajectory Optimization with Application to Aerial
Tracking | null | null | null | null | cs.RO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The visibility of targets determines performance and even success rate of
various applications, such as active slam, exploration, and target tracking.
Therefore, it is crucial to take the visibility of targets into explicit
account in trajectory planning. In this paper, we propose a general metric for
target visibility, considering observation distance and angle as well as
occlusion effect. We formulate this metric into a differentiable visibility
cost function, with which spatial trajectory and yaw can be jointly optimized.
Furthermore, this visibility-aware trajectory optimization handles dynamic
feasibility of position and yaw simultaneously. To validate that our method is
practical and generic, we integrate it into a customized quadrotor tracking
system. The experimental results show that our visibility-aware planner
performs more robustly and observes targets better. In order to benefit related
researches, we release our code to the public.
| [
{
"created": "Thu, 11 Mar 2021 15:43:13 GMT",
"version": "v1"
}
] | 2021-03-12 | [
[
"Wang",
"Qianhao",
""
],
[
"Gao",
"Yuman",
""
],
[
"Ji",
"Jialin",
""
],
[
"Xu",
"Chao",
""
],
[
"Gao",
"Fei",
""
]
] | The visibility of targets determines performance and even success rate of various applications, such as active slam, exploration, and target tracking. Therefore, it is crucial to take the visibility of targets into explicit account in trajectory planning. In this paper, we propose a general metric for target visibility, considering observation distance and angle as well as occlusion effect. We formulate this metric into a differentiable visibility cost function, with which spatial trajectory and yaw can be jointly optimized. Furthermore, this visibility-aware trajectory optimization handles dynamic feasibility of position and yaw simultaneously. To validate that our method is practical and generic, we integrate it into a customized quadrotor tracking system. The experimental results show that our visibility-aware planner performs more robustly and observes targets better. In order to benefit related researches, we release our code to the public. |
2403.05066 | Hongjoon Ahn | Hongjoon Ahn, Jinu Hyeon, Youngmin Oh, Bosun Hwang, and Taesup Moon | Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual
Reinforcement Learning | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | We argue that the negative transfer problem occurring when the new task to
learn arrives is an important problem that needs not be overlooked when
developing effective Continual Reinforcement Learning (CRL) algorithms. Through
comprehensive experimental validation, we demonstrate that such issue
frequently exists in CRL and cannot be effectively addressed by several recent
work on mitigating plasticity loss of RL agents. To that end, we develop Reset
& Distill (R&D), a simple yet highly effective method, to overcome the negative
transfer problem in CRL. R&D combines a strategy of resetting the agent's
online actor and critic networks to learn a new task and an offline learning
step for distilling the knowledge from the online actor and previous expert's
action probabilities. We carried out extensive experiments on long sequence of
Meta World tasks and show that our method consistently outperforms recent
baselines, achieving significantly higher success rates across a range of
tasks. Our findings highlight the importance of considering negative transfer
in CRL and emphasize the need for robust strategies like R&D to mitigate its
detrimental effects.
| [
{
"created": "Fri, 8 Mar 2024 05:37:59 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Aug 2024 06:32:11 GMT",
"version": "v2"
}
] | 2024-08-15 | [
[
"Ahn",
"Hongjoon",
""
],
[
"Hyeon",
"Jinu",
""
],
[
"Oh",
"Youngmin",
""
],
[
"Hwang",
"Bosun",
""
],
[
"Moon",
"Taesup",
""
]
] | We argue that the negative transfer problem occurring when the new task to learn arrives is an important problem that needs not be overlooked when developing effective Continual Reinforcement Learning (CRL) algorithms. Through comprehensive experimental validation, we demonstrate that such issue frequently exists in CRL and cannot be effectively addressed by several recent work on mitigating plasticity loss of RL agents. To that end, we develop Reset & Distill (R&D), a simple yet highly effective method, to overcome the negative transfer problem in CRL. R&D combines a strategy of resetting the agent's online actor and critic networks to learn a new task and an offline learning step for distilling the knowledge from the online actor and previous expert's action probabilities. We carried out extensive experiments on long sequence of Meta World tasks and show that our method consistently outperforms recent baselines, achieving significantly higher success rates across a range of tasks. Our findings highlight the importance of considering negative transfer in CRL and emphasize the need for robust strategies like R&D to mitigate its detrimental effects. |
1311.2677 | Raman Singh Mr. | Raman Singh, Harish Kumar and R.K. Singla | Sampling Based Approaches to Handle Imbalances in Network Traffic
Dataset for Machine Learning Techniques | 12 pages | null | 10.5121/csit.2013.3704 | null | cs.NI cs.CR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network traffic data is huge, varying and imbalanced because various classes
are not equally distributed. Machine learning (ML) algorithms for traffic
analysis uses the samples from this data to recommend the actions to be taken
by the network administrators as well as training. Due to imbalances in
dataset, it is difficult to train machine learning algorithms for traffic
analysis and these may give biased or false results leading to serious
degradation in performance of these algorithms. Various techniques can be
applied during sampling to minimize the effect of imbalanced instances. In this
paper various sampling techniques have been analysed in order to compare the
decrease in variation in imbalances of network traffic datasets sampled for
these algorithms. Various parameters like missing classes in samples,
probability of sampling of the different instances have been considered for
comparison.
| [
{
"created": "Tue, 12 Nov 2013 05:32:48 GMT",
"version": "v1"
}
] | 2013-11-13 | [
[
"Singh",
"Raman",
""
],
[
"Kumar",
"Harish",
""
],
[
"Singla",
"R. K.",
""
]
] | Network traffic data is huge, varying and imbalanced because various classes are not equally distributed. Machine learning (ML) algorithms for traffic analysis uses the samples from this data to recommend the actions to be taken by the network administrators as well as training. Due to imbalances in dataset, it is difficult to train machine learning algorithms for traffic analysis and these may give biased or false results leading to serious degradation in performance of these algorithms. Various techniques can be applied during sampling to minimize the effect of imbalanced instances. In this paper various sampling techniques have been analysed in order to compare the decrease in variation in imbalances of network traffic datasets sampled for these algorithms. Various parameters like missing classes in samples, probability of sampling of the different instances have been considered for comparison. |
1709.04579 | Behzad Ghazanfari | Behzad Ghazanfari and Matthew E. Taylor | Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement
Learning and Multi-task Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement learning (RL), while often powerful, can suffer from slow
learning speeds, particularly in high dimensional spaces. The autonomous
decomposition of tasks and use of hierarchical methods hold the potential to
significantly speed up learning in such domains. This paper proposes a novel
practical method that can autonomously decompose tasks, by leveraging
association rule mining, which discovers hidden relationship among entities in
data mining. We introduce a novel method called ARM-HSTRL (Association Rule
Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning).
It extracts temporal and structural relationships of sub-goals in RL, and
multi-task RL. In particular,it finds sub-goals and relationship among them. It
is shown the significant efficiency and performance of the proposed method in
two main topics of RL.
| [
{
"created": "Thu, 14 Sep 2017 01:43:13 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Sep 2017 16:21:03 GMT",
"version": "v2"
}
] | 2017-09-18 | [
[
"Ghazanfari",
"Behzad",
""
],
[
"Taylor",
"Matthew E.",
""
]
] | Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learning in such domains. This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, which discovers hidden relationship among entities in data mining. We introduce a novel method called ARM-HSTRL (Association Rule Mining to extract Hierarchical Structure of Tasks in Reinforcement Learning). It extracts temporal and structural relationships of sub-goals in RL, and multi-task RL. In particular,it finds sub-goals and relationship among them. It is shown the significant efficiency and performance of the proposed method in two main topics of RL. |
2203.16428 | Stuart Millar Mr | Stuart Millar | Vulnerability Detection in Open Source Software: An Introduction | This version dated March 26th 2017 | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | This paper is an introductory discussion on the cause of open source software
vulnerabilities, their importance in the cybersecurity ecosystem, and a
selection of detection methods. A recent application security report showed 44%
of applications contain critical vulnerabilities in an open source component, a
concerning proportion. Most companies do not have a reliable way of being
directly and promptly notified when zero-day vulnerabilities are found and then
when patches are made available. This means attack vectors in open source exist
longer than necessary. Conventional approaches to vulnerability detection are
outlined alongside some newer research trends. A conclusion is made that it may
not be possible to entirely replace expert human inspection of open source
software, although it can be effectively augmented with techniques such as
machine learning, IDE plug-ins and repository linking to make implementation
and review less time intensive. Underpinning any technological advances should
be better knowledge at the human level. Development teams need trained, coached
and improved so they can implement open source more securely, know what
vulnerabilities to look for and how to handle them. It is the use of this
blended approach to detection which is key.
| [
{
"created": "Sun, 6 Mar 2022 16:46:58 GMT",
"version": "v1"
}
] | 2022-03-31 | [
[
"Millar",
"Stuart",
""
]
] | This paper is an introductory discussion on the cause of open source software vulnerabilities, their importance in the cybersecurity ecosystem, and a selection of detection methods. A recent application security report showed 44% of applications contain critical vulnerabilities in an open source component, a concerning proportion. Most companies do not have a reliable way of being directly and promptly notified when zero-day vulnerabilities are found and then when patches are made available. This means attack vectors in open source exist longer than necessary. Conventional approaches to vulnerability detection are outlined alongside some newer research trends. A conclusion is made that it may not be possible to entirely replace expert human inspection of open source software, although it can be effectively augmented with techniques such as machine learning, IDE plug-ins and repository linking to make implementation and review less time intensive. Underpinning any technological advances should be better knowledge at the human level. Development teams need trained, coached and improved so they can implement open source more securely, know what vulnerabilities to look for and how to handle them. It is the use of this blended approach to detection which is key. |
2103.13447 | Seunghun Lee | Seunghun Lee, Sunghyun Cho, Sunghoon Im | DRANet: Disentangling Representation and Adaptation Networks for
Unsupervised Cross-Domain Adaptation | Accepted to CVPR 2021 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present DRANet, a network architecture that disentangles
image representations and transfers the visual attributes in a latent space for
unsupervised cross-domain adaptation. Unlike the existing domain adaptation
methods that learn associated features sharing a domain, DRANet preserves the
distinctiveness of each domain's characteristics. Our model encodes individual
representations of content (scene structure) and style (artistic appearance)
from both source and target images. Then, it adapts the domain by incorporating
the transferred style factor into the content factor along with learnable
weights specified for each domain. This learning framework allows
bi-/multi-directional domain adaptation with a single encoder-decoder network
and aligns their domain shift. Additionally, we propose a content-adaptive
domain transfer module that helps retain scene structure while transferring
style. Extensive experiments show our model successfully separates
content-style factors and synthesizes visually pleasing domain-transferred
images. The proposed method demonstrates state-of-the-art performance on
standard digit classification tasks as well as semantic segmentation tasks.
| [
{
"created": "Wed, 24 Mar 2021 18:54:23 GMT",
"version": "v1"
},
{
"created": "Sun, 28 Mar 2021 07:14:37 GMT",
"version": "v2"
}
] | 2021-03-30 | [
[
"Lee",
"Seunghun",
""
],
[
"Cho",
"Sunghyun",
""
],
[
"Im",
"Sunghoon",
""
]
] | In this paper, we present DRANet, a network architecture that disentangles image representations and transfers the visual attributes in a latent space for unsupervised cross-domain adaptation. Unlike the existing domain adaptation methods that learn associated features sharing a domain, DRANet preserves the distinctiveness of each domain's characteristics. Our model encodes individual representations of content (scene structure) and style (artistic appearance) from both source and target images. Then, it adapts the domain by incorporating the transferred style factor into the content factor along with learnable weights specified for each domain. This learning framework allows bi-/multi-directional domain adaptation with a single encoder-decoder network and aligns their domain shift. Additionally, we propose a content-adaptive domain transfer module that helps retain scene structure while transferring style. Extensive experiments show our model successfully separates content-style factors and synthesizes visually pleasing domain-transferred images. The proposed method demonstrates state-of-the-art performance on standard digit classification tasks as well as semantic segmentation tasks. |
1502.06732 | Zhiwen Zeng | Zhiwen Zeng, Xiangke Wang, Zhiqiang Zheng | Convergence Analysis using the Edge Laplacian: Robust Consensus of
Nonlinear Multi-agent Systems via ISS Method | 22 pages, 10 figures; Submitted to International Journal of Robust
and Nonlinear Control | null | null | null | cs.SY cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study develops an original and innovative matrix representation with
respect to the information flow for networked multi-agent system. To begin
with, the general concepts of the edge Laplacian of digraph are proposed with
its algebraic properties. Benefit from this novel graph-theoretic tool, we can
build a bridge between the consensus problem and the edge agreement problem; we
also show that the edge Laplacian sheds a new light on solving the leaderless
consensus problem. Based on the edge agreement framework, the technical
challenges caused by unknown but bounded disturbances and inherently nonlinear
dynamics can be well handled. In particular, we design an integrated procedure
for a new robust consensus protocol that is based on a blend of algebraic graph
theory and the newly developed cyclic-small-gain theorem. Besides, to highlight
the intricate relationship between the original graph and cyclic-small-gain
theorem, the concept of edge-interconnection graph is introduced for the first
time. Finally, simulation results are provided to verify the theoretical
analysis.
| [
{
"created": "Tue, 24 Feb 2015 09:52:52 GMT",
"version": "v1"
}
] | 2015-02-25 | [
[
"Zeng",
"Zhiwen",
""
],
[
"Wang",
"Xiangke",
""
],
[
"Zheng",
"Zhiqiang",
""
]
] | This study develops an original and innovative matrix representation with respect to the information flow for networked multi-agent system. To begin with, the general concepts of the edge Laplacian of digraph are proposed with its algebraic properties. Benefit from this novel graph-theoretic tool, we can build a bridge between the consensus problem and the edge agreement problem; we also show that the edge Laplacian sheds a new light on solving the leaderless consensus problem. Based on the edge agreement framework, the technical challenges caused by unknown but bounded disturbances and inherently nonlinear dynamics can be well handled. In particular, we design an integrated procedure for a new robust consensus protocol that is based on a blend of algebraic graph theory and the newly developed cyclic-small-gain theorem. Besides, to highlight the intricate relationship between the original graph and cyclic-small-gain theorem, the concept of edge-interconnection graph is introduced for the first time. Finally, simulation results are provided to verify the theoretical analysis. |
2103.08698 | Zden\v{e}k Dvo\v{r}\'ak | Zden\v{e}k Dvo\v{r}\'ak | Approximation metatheorems for classes with bounded expansion | 35 pages, no figures; revised the presentation | null | null | null | cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We give a number of approximation metatheorems for monotone maximization
problems expressible in the first-order logic, in substantially more general
settings than the previously known. We obtain * constant-factor approximation
algorithm in any class of graphs with bounded expansion, * a QPTAS in any class
with strongly sublinear separators, and * a PTAS in any fractionally
treewidth-fragile class (which includes all common classes with strongly
sublinear separators. Moreover, our tools also give an exact
subexponential-time algorithm in any class with strongly sublinear separators.
| [
{
"created": "Mon, 15 Mar 2021 20:26:05 GMT",
"version": "v1"
},
{
"created": "Sat, 11 Sep 2021 16:12:58 GMT",
"version": "v2"
},
{
"created": "Sat, 9 Oct 2021 23:06:38 GMT",
"version": "v3"
}
] | 2021-10-12 | [
[
"Dvořák",
"Zdeněk",
""
]
] | We give a number of approximation metatheorems for monotone maximization problems expressible in the first-order logic, in substantially more general settings than the previously known. We obtain * constant-factor approximation algorithm in any class of graphs with bounded expansion, * a QPTAS in any class with strongly sublinear separators, and * a PTAS in any fractionally treewidth-fragile class (which includes all common classes with strongly sublinear separators. Moreover, our tools also give an exact subexponential-time algorithm in any class with strongly sublinear separators. |
2302.13399 | Lingjie Kong | Lingjie Kong and Yun Liao | Path Integral Based Convolution and Pooling for Heterogeneous Graph
Neural Networks | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph neural networks (GNN) extends deep learning to graph-structure dataset.
Similar to Convolutional Neural Networks (CNN) using on image prediction,
convolutional and pooling layers are the foundation to success for GNN on graph
prediction tasks. In the initial PAN paper, it uses a path integral based graph
neural networks for graph prediction. Specifically, it uses a convolution
operation that involves every path linking the message sender and receiver with
learnable weights depending on the path length, which corresponds to the
maximal entropy random walk. It further generalizes such convolution operation
to a new transition matrix called maximal entropy transition (MET). Because the
diagonal entries of the MET matrix is directly related to the subgraph
centrality, it provide a trial mechanism for pooling based on centrality score.
While the initial PAN paper only considers node features. We further extends
its capability to handle complex heterogeneous graph including both node and
edge features.
| [
{
"created": "Sun, 26 Feb 2023 20:05:23 GMT",
"version": "v1"
}
] | 2023-02-28 | [
[
"Kong",
"Lingjie",
""
],
[
"Liao",
"Yun",
""
]
] | Graph neural networks (GNN) extends deep learning to graph-structure dataset. Similar to Convolutional Neural Networks (CNN) using on image prediction, convolutional and pooling layers are the foundation to success for GNN on graph prediction tasks. In the initial PAN paper, it uses a path integral based graph neural networks for graph prediction. Specifically, it uses a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It further generalizes such convolution operation to a new transition matrix called maximal entropy transition (MET). Because the diagonal entries of the MET matrix is directly related to the subgraph centrality, it provide a trial mechanism for pooling based on centrality score. While the initial PAN paper only considers node features. We further extends its capability to handle complex heterogeneous graph including both node and edge features. |
2310.12274 | Chen Jin | Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare | An Image is Worth Multiple Words: Discovering Object Level Concepts
using Multi-Concept Prompt Learning | ICML 2024; project page: https://astrazeneca.github.io/mcpl.github.io | null | null | null | cs.CV cs.AI cs.CL cs.GR cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Textural Inversion, a prompt learning method, learns a singular text
embedding for a new "word" to represent image style and appearance, allowing it
to be integrated into natural language sentences to generate novel synthesised
images. However, identifying multiple unknown object-level concepts within one
scene remains a complex challenge. While recent methods have resorted to
cropping or masking individual images to learn multiple concepts, these
techniques often require prior knowledge of new concepts and are
labour-intensive. To address this challenge, we introduce Multi-Concept Prompt
Learning (MCPL), where multiple unknown "words" are simultaneously learned from
a single sentence-image pair, without any imagery annotations. To enhance the
accuracy of word-concept correlation and refine attention mask boundaries, we
propose three regularisation techniques: Attention Masking, Prompts Contrastive
Loss, and Bind Adjective. Extensive quantitative comparisons with both
real-world categories and biomedical images demonstrate that our method can
learn new semantically disentangled concepts. Our approach emphasises learning
solely from textual embeddings, using less than 10% of the storage space
compared to others. The project page, code, and data are available at
https://astrazeneca.github.io/mcpl.github.io.
| [
{
"created": "Wed, 18 Oct 2023 19:18:19 GMT",
"version": "v1"
},
{
"created": "Sat, 25 May 2024 00:01:46 GMT",
"version": "v2"
}
] | 2024-05-28 | [
[
"Jin",
"Chen",
""
],
[
"Tanno",
"Ryutaro",
""
],
[
"Saseendran",
"Amrutha",
""
],
[
"Diethe",
"Tom",
""
],
[
"Teare",
"Philip",
""
]
] | Textural Inversion, a prompt learning method, learns a singular text embedding for a new "word" to represent image style and appearance, allowing it to be integrated into natural language sentences to generate novel synthesised images. However, identifying multiple unknown object-level concepts within one scene remains a complex challenge. While recent methods have resorted to cropping or masking individual images to learn multiple concepts, these techniques often require prior knowledge of new concepts and are labour-intensive. To address this challenge, we introduce Multi-Concept Prompt Learning (MCPL), where multiple unknown "words" are simultaneously learned from a single sentence-image pair, without any imagery annotations. To enhance the accuracy of word-concept correlation and refine attention mask boundaries, we propose three regularisation techniques: Attention Masking, Prompts Contrastive Loss, and Bind Adjective. Extensive quantitative comparisons with both real-world categories and biomedical images demonstrate that our method can learn new semantically disentangled concepts. Our approach emphasises learning solely from textual embeddings, using less than 10% of the storage space compared to others. The project page, code, and data are available at https://astrazeneca.github.io/mcpl.github.io. |
1304.6501 | Evmorfia Argyriou N. | Evmorfia N. Argyriou and Aikaterini A. Sotiraki and Antonios Symvonis | Occupational Fraud Detection Through Visualization | null | null | null | null | cs.CY cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Occupational fraud affects many companies worldwide causing them economic
loss and liability issues towards their customers and other involved entities.
Detecting internal fraud in a company requires significant effort and,
unfortunately cannot be entirely prevented. The internal auditors have to
process a huge amount of data produced by diverse systems, which are in most
cases in textual form, with little automated support. In this paper, we exploit
the advantages of information visualization and present a system that aims to
detect occupational fraud in systems which involve a pair of entities (e.g., an
employee and a client) and periodic activity. The main visualization is based
on a spiral system on which the events are drawn appropriately according to
their time-stamp. Suspicious events are considered those which appear along the
same radius or on close radii of the spiral. Before producing the
visualization, the system ranks both involved entities according to the
specifications of the internal auditor and generates a video file of the
activity such that events with strong evidence of fraud appear first in the
video. The system is also equipped with several different visualizations and
mechanisms in order to meet the requirements of an internal fraud detection
system.
| [
{
"created": "Wed, 24 Apr 2013 07:57:53 GMT",
"version": "v1"
}
] | 2013-04-25 | [
[
"Argyriou",
"Evmorfia N.",
""
],
[
"Sotiraki",
"Aikaterini A.",
""
],
[
"Symvonis",
"Antonios",
""
]
] | Occupational fraud affects many companies worldwide causing them economic loss and liability issues towards their customers and other involved entities. Detecting internal fraud in a company requires significant effort and, unfortunately cannot be entirely prevented. The internal auditors have to process a huge amount of data produced by diverse systems, which are in most cases in textual form, with little automated support. In this paper, we exploit the advantages of information visualization and present a system that aims to detect occupational fraud in systems which involve a pair of entities (e.g., an employee and a client) and periodic activity. The main visualization is based on a spiral system on which the events are drawn appropriately according to their time-stamp. Suspicious events are considered those which appear along the same radius or on close radii of the spiral. Before producing the visualization, the system ranks both involved entities according to the specifications of the internal auditor and generates a video file of the activity such that events with strong evidence of fraud appear first in the video. The system is also equipped with several different visualizations and mechanisms in order to meet the requirements of an internal fraud detection system. |
2404.08850 | Amit Sharma | Amit Sharma, Teodor-Dumitru Ene, Kishor Kunal, Mingjie Liu, Zafar
Hasan and Haoxing Ren | Assessing Economic Viability: A Comparative Analysis of Total Cost of
Ownership for Domain-Adapted Large Language Models versus State-of-the-art
Counterparts in Chip Design Coding Assistance | Paper accepted in IEEE-ACM conference: 2024 IEEE LLM-Aided Design
Workshop (LAD) | null | null | null | cs.AI cs.CE cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a comparative analysis of total cost of ownership (TCO)
and performance between domain-adapted large language models (LLM) and
state-of-the-art (SoTA) LLMs , with a particular emphasis on tasks related to
coding assistance for chip design. We examine the TCO and performance metrics
of a domain-adaptive LLM, ChipNeMo, against two leading LLMs, Claude 3 Opus and
ChatGPT-4 Turbo, to assess their efficacy in chip design coding generation.
Through a detailed evaluation of the accuracy of the model, training
methodologies, and operational expenditures, this study aims to provide
stakeholders with critical information to select the most economically viable
and performance-efficient solutions for their specific needs. Our results
underscore the benefits of employing domain-adapted models, such as ChipNeMo,
that demonstrate improved performance at significantly reduced costs compared
to their general-purpose counterparts. In particular, we reveal the potential
of domain-adapted LLMs to decrease TCO by approximately 90%-95%, with the cost
advantages becoming increasingly evident as the deployment scale expands. With
expansion of deployment, the cost benefits of ChipNeMo become more pronounced,
making domain-adaptive LLMs an attractive option for organizations with
substantial coding needs supported by LLMs
| [
{
"created": "Fri, 12 Apr 2024 23:37:56 GMT",
"version": "v1"
},
{
"created": "Tue, 28 May 2024 17:11:44 GMT",
"version": "v2"
}
] | 2024-05-29 | [
[
"Sharma",
"Amit",
""
],
[
"Ene",
"Teodor-Dumitru",
""
],
[
"Kunal",
"Kishor",
""
],
[
"Liu",
"Mingjie",
""
],
[
"Hasan",
"Zafar",
""
],
[
"Ren",
"Haoxing",
""
]
] | This paper presents a comparative analysis of total cost of ownership (TCO) and performance between domain-adapted large language models (LLM) and state-of-the-art (SoTA) LLMs , with a particular emphasis on tasks related to coding assistance for chip design. We examine the TCO and performance metrics of a domain-adaptive LLM, ChipNeMo, against two leading LLMs, Claude 3 Opus and ChatGPT-4 Turbo, to assess their efficacy in chip design coding generation. Through a detailed evaluation of the accuracy of the model, training methodologies, and operational expenditures, this study aims to provide stakeholders with critical information to select the most economically viable and performance-efficient solutions for their specific needs. Our results underscore the benefits of employing domain-adapted models, such as ChipNeMo, that demonstrate improved performance at significantly reduced costs compared to their general-purpose counterparts. In particular, we reveal the potential of domain-adapted LLMs to decrease TCO by approximately 90%-95%, with the cost advantages becoming increasingly evident as the deployment scale expands. With expansion of deployment, the cost benefits of ChipNeMo become more pronounced, making domain-adaptive LLMs an attractive option for organizations with substantial coding needs supported by LLMs |
0908.1077 | Ali Tajer | Ali Tajer, Narayan Prasad, and Xiaodong Wang | Beamforming and Rate Allocation in MISO Cognitive Radio Networks | 32 pages, 6 figures | null | 10.1109/TSP.2009.2031280 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider decentralized multi-antenna cognitive radio networks where
secondary (cognitive) users are granted simultaneous spectrum access along with
license-holding (primary) users. We treat the problem of distributed
beamforming and rate allocation for the secondary users such that the minimum
weighted secondary rate is maximized. Such an optimization is subject to (1) a
limited weighted sum-power budget for the secondary users and (2) guaranteed
protection for the primary users in the sense that the interference level
imposed on each primary receiver does not exceed a specified level. Based on
the decoding method deployed by the secondary receivers, we consider three
scenarios for solving this problem. In the first scenario each secondary
receiver decodes only its designated transmitter while suppressing the rest as
Gaussian interferers (single-user decoding). In the second case each secondary
receiver employs the maximum likelihood decoder (MLD) to jointly decode all
secondary transmissions, and in the third one each secondary receiver uses the
unconstrained group decoder (UGD). By deploying the UGD, each secondary user is
allowed to decode any arbitrary subset of users (which contains its designated
user) after suppressing or canceling the remaining users.
| [
{
"created": "Fri, 7 Aug 2009 15:46:00 GMT",
"version": "v1"
}
] | 2015-05-13 | [
[
"Tajer",
"Ali",
""
],
[
"Prasad",
"Narayan",
""
],
[
"Wang",
"Xiaodong",
""
]
] | We consider decentralized multi-antenna cognitive radio networks where secondary (cognitive) users are granted simultaneous spectrum access along with license-holding (primary) users. We treat the problem of distributed beamforming and rate allocation for the secondary users such that the minimum weighted secondary rate is maximized. Such an optimization is subject to (1) a limited weighted sum-power budget for the secondary users and (2) guaranteed protection for the primary users in the sense that the interference level imposed on each primary receiver does not exceed a specified level. Based on the decoding method deployed by the secondary receivers, we consider three scenarios for solving this problem. In the first scenario each secondary receiver decodes only its designated transmitter while suppressing the rest as Gaussian interferers (single-user decoding). In the second case each secondary receiver employs the maximum likelihood decoder (MLD) to jointly decode all secondary transmissions, and in the third one each secondary receiver uses the unconstrained group decoder (UGD). By deploying the UGD, each secondary user is allowed to decode any arbitrary subset of users (which contains its designated user) after suppressing or canceling the remaining users. |
1612.00414 | Farzad Salehisadaghiani | Farzad Salehisadaghiani and Lacra Pavel | Distributed Nash Equilibrium Seeking via the Alternating Direction
Method of Multipliers | null | null | null | null | cs.SY cs.GT math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, the problem of finding a Nash equilibrium of a multi-player
game is considered. The players are only aware of their own cost functions as
well as the action space of all players. We develop a relatively fast algorithm
within the framework of inexact-ADMM. It requires a communication graph for the
information exchange between the players as well as a few mild assumptions on
cost functions. The convergence proof of the algorithm to a Nash equilibrium of
the game is then provided. Moreover, the convergence rate is investigated via
simulations.
| [
{
"created": "Thu, 1 Dec 2016 20:23:48 GMT",
"version": "v1"
}
] | 2017-05-09 | [
[
"Salehisadaghiani",
"Farzad",
""
],
[
"Pavel",
"Lacra",
""
]
] | In this paper, the problem of finding a Nash equilibrium of a multi-player game is considered. The players are only aware of their own cost functions as well as the action space of all players. We develop a relatively fast algorithm within the framework of inexact-ADMM. It requires a communication graph for the information exchange between the players as well as a few mild assumptions on cost functions. The convergence proof of the algorithm to a Nash equilibrium of the game is then provided. Moreover, the convergence rate is investigated via simulations. |
1810.11112 | Ammar Ahmad Awan | Ammar Ahmad Awan, Jeroen Bedorf, Ching-Hsiang Chu, Hari Subramoni, and
Dhabaleswar K. Panda | Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI:
Characterization, Designs, and Performance Evaluation | 10 pages, 9 figures, submitted to IEEE IPDPS 2019 for peer-review | IEEE CCGrid, 2019 | 10.1109/CCGRID.2019.00064 | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | TensorFlow has been the most widely adopted Machine/Deep Learning framework.
However, little exists in the literature that provides a thorough understanding
of the capabilities which TensorFlow offers for the distributed training of
large ML/DL models that need computation and communication at scale. Most
commonly used distributed training approaches for TF can be categorized as
follows: 1) Google Remote Procedure Call (gRPC), 2) gRPC+X: X=(InfiniBand
Verbs, Message Passing Interface, and GPUDirect RDMA), and 3) No-gRPC: Baidu
Allreduce with MPI, Horovod with MPI, and Horovod with NVIDIA NCCL. In this
paper, we provide an in-depth performance characterization and analysis of
these distributed training approaches on various GPU clusters including the Piz
Daint system (6 on Top500). We perform experiments to gain novel insights along
the following vectors: 1) Application-level scalability of DNN training, 2)
Effect of Batch Size on scaling efficiency, 3) Impact of the MPI library used
for no-gRPC approaches, and 4) Type and size of DNN architectures. Based on
these experiments, we present two key insights: 1) Overall, No-gRPC designs
achieve better performance compared to gRPC-based approaches for most
configurations, and 2) The performance of No-gRPC is heavily influenced by the
gradient aggregation using Allreduce. Finally, we propose a truly CUDA-Aware
MPI Allreduce design that exploits CUDA kernels and pointer caching to perform
large reductions efficiently. Our proposed designs offer 5-17X better
performance than NCCL2 for small and medium messages, and reduces latency by
29% for large messages. The proposed optimizations help Horovod-MPI to achieve
approximately 90% scaling efficiency for ResNet-50 training on 64 GPUs.
Further, Horovod-MPI achieves 1.8X and 3.2X higher throughput than the native
gRPC method for ResNet-50 and MobileNet, respectively, on the Piz Daint
cluster.
| [
{
"created": "Thu, 25 Oct 2018 21:25:26 GMT",
"version": "v1"
}
] | 2019-11-14 | [
[
"Awan",
"Ammar Ahmad",
""
],
[
"Bedorf",
"Jeroen",
""
],
[
"Chu",
"Ching-Hsiang",
""
],
[
"Subramoni",
"Hari",
""
],
[
"Panda",
"Dhabaleswar K.",
""
]
] | TensorFlow has been the most widely adopted Machine/Deep Learning framework. However, little exists in the literature that provides a thorough understanding of the capabilities which TensorFlow offers for the distributed training of large ML/DL models that need computation and communication at scale. Most commonly used distributed training approaches for TF can be categorized as follows: 1) Google Remote Procedure Call (gRPC), 2) gRPC+X: X=(InfiniBand Verbs, Message Passing Interface, and GPUDirect RDMA), and 3) No-gRPC: Baidu Allreduce with MPI, Horovod with MPI, and Horovod with NVIDIA NCCL. In this paper, we provide an in-depth performance characterization and analysis of these distributed training approaches on various GPU clusters including the Piz Daint system (6 on Top500). We perform experiments to gain novel insights along the following vectors: 1) Application-level scalability of DNN training, 2) Effect of Batch Size on scaling efficiency, 3) Impact of the MPI library used for no-gRPC approaches, and 4) Type and size of DNN architectures. Based on these experiments, we present two key insights: 1) Overall, No-gRPC designs achieve better performance compared to gRPC-based approaches for most configurations, and 2) The performance of No-gRPC is heavily influenced by the gradient aggregation using Allreduce. Finally, we propose a truly CUDA-Aware MPI Allreduce design that exploits CUDA kernels and pointer caching to perform large reductions efficiently. Our proposed designs offer 5-17X better performance than NCCL2 for small and medium messages, and reduces latency by 29% for large messages. The proposed optimizations help Horovod-MPI to achieve approximately 90% scaling efficiency for ResNet-50 training on 64 GPUs. Further, Horovod-MPI achieves 1.8X and 3.2X higher throughput than the native gRPC method for ResNet-50 and MobileNet, respectively, on the Piz Daint cluster. |
2311.03078 | Md. Shahad Mahmud Chowdhury | Sadia Afrin, Md. Shahad Mahmud Chowdhury, Md. Ekramul Islam, Faisal
Ahamed Khan, Labib Imam Chowdhury, MD. Motahar Mahtab, Nazifa Nuha Chowdhury,
Massud Forkan, Neelima Kundu, Hakim Arif, Mohammad Mamun Or Rashid, Mohammad
Ruhul Amin, Nabeel Mohammed | BanLemma: A Word Formation Dependent Rule and Dictionary Based Bangla
Lemmatizer | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Lemmatization holds significance in both natural language processing (NLP)
and linguistics, as it effectively decreases data density and aids in
comprehending contextual meaning. However, due to the highly inflected nature
and morphological richness, lemmatization in Bangla text poses a complex
challenge. In this study, we propose linguistic rules for lemmatization and
utilize a dictionary along with the rules to design a lemmatizer specifically
for Bangla. Our system aims to lemmatize words based on their parts of speech
class within a given sentence. Unlike previous rule-based approaches, we
analyzed the suffix marker occurrence according to the morpho-syntactic values
and then utilized sequences of suffix markers instead of entire suffixes. To
develop our rules, we analyze a large corpus of Bangla text from various
domains, sources, and time periods to observe the word formation of inflected
words. The lemmatizer achieves an accuracy of 96.36% when tested against a
manually annotated test dataset by trained linguists and demonstrates
competitive performance on three previously published Bangla lemmatization
datasets. We are making the code and datasets publicly available at
https://github.com/eblict-gigatech/BanLemma in order to contribute to the
further advancement of Bangla NLP.
| [
{
"created": "Mon, 6 Nov 2023 13:02:07 GMT",
"version": "v1"
}
] | 2023-11-07 | [
[
"Afrin",
"Sadia",
""
],
[
"Chowdhury",
"Md. Shahad Mahmud",
""
],
[
"Islam",
"Md. Ekramul",
""
],
[
"Khan",
"Faisal Ahamed",
""
],
[
"Chowdhury",
"Labib Imam",
""
],
[
"Mahtab",
"MD. Motahar",
""
],
[
"Chowdhury",
"Nazifa Nuha",
""
],
[
"Forkan",
"Massud",
""
],
[
"Kundu",
"Neelima",
""
],
[
"Arif",
"Hakim",
""
],
[
"Rashid",
"Mohammad Mamun Or",
""
],
[
"Amin",
"Mohammad Ruhul",
""
],
[
"Mohammed",
"Nabeel",
""
]
] | Lemmatization holds significance in both natural language processing (NLP) and linguistics, as it effectively decreases data density and aids in comprehending contextual meaning. However, due to the highly inflected nature and morphological richness, lemmatization in Bangla text poses a complex challenge. In this study, we propose linguistic rules for lemmatization and utilize a dictionary along with the rules to design a lemmatizer specifically for Bangla. Our system aims to lemmatize words based on their parts of speech class within a given sentence. Unlike previous rule-based approaches, we analyzed the suffix marker occurrence according to the morpho-syntactic values and then utilized sequences of suffix markers instead of entire suffixes. To develop our rules, we analyze a large corpus of Bangla text from various domains, sources, and time periods to observe the word formation of inflected words. The lemmatizer achieves an accuracy of 96.36% when tested against a manually annotated test dataset by trained linguists and demonstrates competitive performance on three previously published Bangla lemmatization datasets. We are making the code and datasets publicly available at https://github.com/eblict-gigatech/BanLemma in order to contribute to the further advancement of Bangla NLP. |
2405.09942 | Siliang Ma | Siliang Ma, Yong Xu | FPDIoU Loss: A Loss Function for Efficient Bounding Box Regression of
Rotated Object Detection | arXiv admin note: text overlap with arXiv:2307.07662, text overlap
with arXiv:1902.09630 by other authors | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bounding box regression is one of the important steps of object detection.
However, rotation detectors often involve a more complicated loss based on
SkewIoU which is unfriendly to gradient-based training. Most of the existing
loss functions for rotated object detection calculate the difference between
two bounding boxes only focus on the deviation of area or each points distance
(e.g., $\mathcal{L}_{Smooth-\ell 1}$, $\mathcal{L}_{RotatedIoU}$ and
$\mathcal{L}_{PIoU}$). The calculation process of some loss functions is
extremely complex (e.g. $\mathcal{L}_{KFIoU}$). In order to improve the
efficiency and accuracy of bounding box regression for rotated object
detection, we proposed a novel metric for arbitrary shapes comparison based on
minimum points distance, which takes most of the factors from existing loss
functions for rotated object detection into account, i.e., the overlap or
nonoverlapping area, the central points distance and the rotation angle. We
also proposed a loss function called $\mathcal{L}_{FPDIoU}$ based on four
points distance for accurate bounding box regression focusing on faster and
high quality anchor boxes. In the experiments, $FPDIoU$ loss has been applied
to state-of-the-art rotated object detection (e.g., RTMDET, H2RBox) models
training with three popular benchmarks of rotated object detection including
DOTA, DIOR, HRSC2016 and two benchmarks of arbitrary orientation scene text
detection including ICDAR 2017 RRC-MLT and ICDAR 2019 RRC-MLT, which achieves
better performance than existing loss functions.
| [
{
"created": "Thu, 16 May 2024 09:44:00 GMT",
"version": "v1"
},
{
"created": "Sun, 19 May 2024 04:32:53 GMT",
"version": "v2"
}
] | 2024-05-21 | [
[
"Ma",
"Siliang",
""
],
[
"Xu",
"Yong",
""
]
] | Bounding box regression is one of the important steps of object detection. However, rotation detectors often involve a more complicated loss based on SkewIoU which is unfriendly to gradient-based training. Most of the existing loss functions for rotated object detection calculate the difference between two bounding boxes only focus on the deviation of area or each points distance (e.g., $\mathcal{L}_{Smooth-\ell 1}$, $\mathcal{L}_{RotatedIoU}$ and $\mathcal{L}_{PIoU}$). The calculation process of some loss functions is extremely complex (e.g. $\mathcal{L}_{KFIoU}$). In order to improve the efficiency and accuracy of bounding box regression for rotated object detection, we proposed a novel metric for arbitrary shapes comparison based on minimum points distance, which takes most of the factors from existing loss functions for rotated object detection into account, i.e., the overlap or nonoverlapping area, the central points distance and the rotation angle. We also proposed a loss function called $\mathcal{L}_{FPDIoU}$ based on four points distance for accurate bounding box regression focusing on faster and high quality anchor boxes. In the experiments, $FPDIoU$ loss has been applied to state-of-the-art rotated object detection (e.g., RTMDET, H2RBox) models training with three popular benchmarks of rotated object detection including DOTA, DIOR, HRSC2016 and two benchmarks of arbitrary orientation scene text detection including ICDAR 2017 RRC-MLT and ICDAR 2019 RRC-MLT, which achieves better performance than existing loss functions. |
2007.15951 | Brian Kenji Iwana | Brian Kenji Iwana, Seiichi Uchida | An Empirical Survey of Data Augmentation for Time Series Classification
with Neural Networks | null | null | 10.1371/journal.pone.0254841 | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent times, deep artificial neural networks have achieved many successes
in pattern recognition. Part of this success can be attributed to the reliance
on big data to increase generalization. However, in the field of time series
recognition, many datasets are often very small. One method of addressing this
problem is through the use of data augmentation. In this paper, we survey data
augmentation techniques for time series and their application to time series
classification with neural networks. We propose a taxonomy and outline the four
families in time series data augmentation, including transformation-based
methods, pattern mixing, generative models, and decomposition methods.
Furthermore, we empirically evaluate 12 time series data augmentation methods
on 128 time series classification datasets with six different types of neural
networks. Through the results, we are able to analyze the characteristics,
advantages and disadvantages, and recommendations of each data augmentation
method. This survey aims to help in the selection of time series data
augmentation for neural network applications.
| [
{
"created": "Fri, 31 Jul 2020 10:33:54 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Feb 2021 09:58:48 GMT",
"version": "v2"
},
{
"created": "Mon, 24 May 2021 07:40:30 GMT",
"version": "v3"
},
{
"created": "Fri, 2 Jul 2021 09:15:08 GMT",
"version": "v4"
}
] | 2021-09-15 | [
[
"Iwana",
"Brian Kenji",
""
],
[
"Uchida",
"Seiichi",
""
]
] | In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications. |
2203.01927 | Jannis Vamvas | Jannis Vamvas and Rico Sennrich | As Little as Possible, as Much as Necessary: Detecting Over- and
Undertranslations with Contrastive Conditioning | ACL 2022 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Omission and addition of content is a typical issue in neural machine
translation. We propose a method for detecting such phenomena with
off-the-shelf translation models. Using contrastive conditioning, we compare
the likelihood of a full sequence under a translation model to the likelihood
of its parts, given the corresponding source or target sequence. This allows to
pinpoint superfluous words in the translation and untranslated words in the
source even in the absence of a reference translation. The accuracy of our
method is comparable to a supervised method that requires a custom quality
estimation model.
| [
{
"created": "Thu, 3 Mar 2022 18:59:02 GMT",
"version": "v1"
}
] | 2022-03-04 | [
[
"Vamvas",
"Jannis",
""
],
[
"Sennrich",
"Rico",
""
]
] | Omission and addition of content is a typical issue in neural machine translation. We propose a method for detecting such phenomena with off-the-shelf translation models. Using contrastive conditioning, we compare the likelihood of a full sequence under a translation model to the likelihood of its parts, given the corresponding source or target sequence. This allows to pinpoint superfluous words in the translation and untranslated words in the source even in the absence of a reference translation. The accuracy of our method is comparable to a supervised method that requires a custom quality estimation model. |
2206.14846 | Kaixuan Huang | Kaixuan Huang, Yu Wu, Xuezhou Zhang, Shenyinying Tu, Qingyun Wu,
Mengdi Wang, Huazheng Wang | Provably Efficient Reinforcement Learning for Online Adaptive Influence
Maximization | null | null | null | null | cs.LG cs.SI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online influence maximization aims to maximize the influence spread of a
content in a social network with unknown network model by selecting a few seed
nodes. Recent studies followed a non-adaptive setting, where the seed nodes are
selected before the start of the diffusion process and network parameters are
updated when the diffusion stops. We consider an adaptive version of
content-dependent online influence maximization problem where the seed nodes
are sequentially activated based on real-time feedback. In this paper, we
formulate the problem as an infinite-horizon discounted MDP under a linear
diffusion process and present a model-based reinforcement learning solution.
Our algorithm maintains a network model estimate and selects seed users
adaptively, exploring the social network while improving the optimal policy
optimistically. We establish $\widetilde O(\sqrt{T})$ regret bound for our
algorithm. Empirical evaluations on synthetic network demonstrate the
efficiency of our algorithm.
| [
{
"created": "Wed, 29 Jun 2022 18:17:28 GMT",
"version": "v1"
}
] | 2022-07-01 | [
[
"Huang",
"Kaixuan",
""
],
[
"Wu",
"Yu",
""
],
[
"Zhang",
"Xuezhou",
""
],
[
"Tu",
"Shenyinying",
""
],
[
"Wu",
"Qingyun",
""
],
[
"Wang",
"Mengdi",
""
],
[
"Wang",
"Huazheng",
""
]
] | Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies followed a non-adaptive setting, where the seed nodes are selected before the start of the diffusion process and network parameters are updated when the diffusion stops. We consider an adaptive version of content-dependent online influence maximization problem where the seed nodes are sequentially activated based on real-time feedback. In this paper, we formulate the problem as an infinite-horizon discounted MDP under a linear diffusion process and present a model-based reinforcement learning solution. Our algorithm maintains a network model estimate and selects seed users adaptively, exploring the social network while improving the optimal policy optimistically. We establish $\widetilde O(\sqrt{T})$ regret bound for our algorithm. Empirical evaluations on synthetic network demonstrate the efficiency of our algorithm. |
1907.09029 | Bestoun Ahmed Dr. | Bestoun S. Ahmed and Angelo Gargantini and Kamal Z. Zamli and Cemal
Yilmaz and Miroslav Bures and Marek Szeles | Code-Aware Combinatorial Interaction Testing | 28 pages | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Combinatorial interaction testing (CIT) is a useful testing technique to
address the interaction of input parameters in software systems. In many
applications, the technique has been used as a systematic sampling technique to
sample the enormous possibilities of test cases. In the last decade, most of
the research activities focused on the generation of CIT test suites as it is a
computationally complex problem. Although promising, less effort has been paid
for the application of CIT. In general, to apply the CIT, practitioners must
identify the input parameters for the Software-under-test (SUT), feed these
parameters to the CIT tool to generate the test suite, and then run those tests
on the application with some pass and fail criteria for verification. Using
this approach, CIT is used as a black-box testing technique without knowing the
effect of the internal code. Although useful, practically, not all the
parameters having the same impact on the SUT. This paper introduces a different
approach to use the CIT as a gray-box testing technique by considering the
internal code structure of the SUT to know the impact of each input parameter
and thus use this impact in the test generation stage. We applied our approach
to five reliable case studies. The results showed that this approach would help
to detect new faults as compared to the equal impact parameter approach.
| [
{
"created": "Sun, 21 Jul 2019 20:27:28 GMT",
"version": "v1"
}
] | 2019-07-23 | [
[
"Ahmed",
"Bestoun S.",
""
],
[
"Gargantini",
"Angelo",
""
],
[
"Zamli",
"Kamal Z.",
""
],
[
"Yilmaz",
"Cemal",
""
],
[
"Bures",
"Miroslav",
""
],
[
"Szeles",
"Marek",
""
]
] | Combinatorial interaction testing (CIT) is a useful testing technique to address the interaction of input parameters in software systems. In many applications, the technique has been used as a systematic sampling technique to sample the enormous possibilities of test cases. In the last decade, most of the research activities focused on the generation of CIT test suites as it is a computationally complex problem. Although promising, less effort has been paid for the application of CIT. In general, to apply the CIT, practitioners must identify the input parameters for the Software-under-test (SUT), feed these parameters to the CIT tool to generate the test suite, and then run those tests on the application with some pass and fail criteria for verification. Using this approach, CIT is used as a black-box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper introduces a different approach to use the CIT as a gray-box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. We applied our approach to five reliable case studies. The results showed that this approach would help to detect new faults as compared to the equal impact parameter approach. |
2104.01772 | Haimin Luo | Haimin Luo, Anpei Chen, Qixuan Zhang, Bai Pang, Minye Wu, Lan Xu, and
Jingyi Yu | Convolutional Neural Opacity Radiance Fields | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Photo-realistic modeling and rendering of fuzzy objects with complex opacity
are critical for numerous immersive VR/AR applications, but it suffers from
strong view-dependent brightness, color. In this paper, we propose a novel
scheme to generate opacity radiance fields with a convolutional neural renderer
for fuzzy objects, which is the first to combine both explicit opacity
supervision and convolutional mechanism into the neural radiance field
framework so as to enable high-quality appearance and global consistent alpha
mattes generation in arbitrary novel views. More specifically, we propose an
efficient sampling strategy along with both the camera rays and image plane,
which enables efficient radiance field sampling and learning in a patch-wise
manner, as well as a novel volumetric feature integration scheme that generates
per-patch hybrid feature embeddings to reconstruct the view-consistent
fine-detailed appearance and opacity output. We further adopt a patch-wise
adversarial training scheme to preserve both high-frequency appearance and
opacity details in a self-supervised framework. We also introduce an effective
multi-view image capture system to capture high-quality color and alpha maps
for challenging fuzzy objects. Extensive experiments on existing and our new
challenging fuzzy object dataset demonstrate that our method achieves
photo-realistic, globally consistent, and fined detailed appearance and opacity
free-viewpoint rendering for various fuzzy objects.
| [
{
"created": "Mon, 5 Apr 2021 04:46:46 GMT",
"version": "v1"
}
] | 2021-04-06 | [
[
"Luo",
"Haimin",
""
],
[
"Chen",
"Anpei",
""
],
[
"Zhang",
"Qixuan",
""
],
[
"Pang",
"Bai",
""
],
[
"Wu",
"Minye",
""
],
[
"Xu",
"Lan",
""
],
[
"Yu",
"Jingyi",
""
]
] | Photo-realistic modeling and rendering of fuzzy objects with complex opacity are critical for numerous immersive VR/AR applications, but it suffers from strong view-dependent brightness, color. In this paper, we propose a novel scheme to generate opacity radiance fields with a convolutional neural renderer for fuzzy objects, which is the first to combine both explicit opacity supervision and convolutional mechanism into the neural radiance field framework so as to enable high-quality appearance and global consistent alpha mattes generation in arbitrary novel views. More specifically, we propose an efficient sampling strategy along with both the camera rays and image plane, which enables efficient radiance field sampling and learning in a patch-wise manner, as well as a novel volumetric feature integration scheme that generates per-patch hybrid feature embeddings to reconstruct the view-consistent fine-detailed appearance and opacity output. We further adopt a patch-wise adversarial training scheme to preserve both high-frequency appearance and opacity details in a self-supervised framework. We also introduce an effective multi-view image capture system to capture high-quality color and alpha maps for challenging fuzzy objects. Extensive experiments on existing and our new challenging fuzzy object dataset demonstrate that our method achieves photo-realistic, globally consistent, and fined detailed appearance and opacity free-viewpoint rendering for various fuzzy objects. |
1705.06457 | Augustin Speyer | Augustin Speyer, Robin Lemke | Information Density as a Factor for Variation in the Embedding of
Relative Clauses | 10 pages. To be submitted in a German version to 'Sprachwissenschaft' | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In German, relative clauses can be positioned in-situ or extraposed. A
potential factor for the variation might be information density. In this study,
this hypothesis is tested with a corpus of 17th century German funeral sermons.
For each referent in the relative clauses and their matrix clauses, the
attention state was determined (first calculation). In a second calculation,
for each word the surprisal values were determined, using a bi-gram language
model. In a third calculation, the surprisal values were accommodated as to
whether it is the first occurrence of the word in question or not. All three
calculations pointed in the same direction: With in-situ relative clauses, the
rate of new referents was lower and the average surprisal values were lower,
especially the accommodated surprisal values, than with extraposed relative
clauses. This indicated that in-formation density is a factor governing the
choice between in-situ and extraposed relative clauses. The study also sheds
light on the intrinsic relation-ship between the information theoretic concept
of information density and in-formation structural concepts such as givenness
which are used under a more linguistic perspective.
| [
{
"created": "Thu, 18 May 2017 08:16:20 GMT",
"version": "v1"
}
] | 2017-05-19 | [
[
"Speyer",
"Augustin",
""
],
[
"Lemke",
"Robin",
""
]
] | In German, relative clauses can be positioned in-situ or extraposed. A potential factor for the variation might be information density. In this study, this hypothesis is tested with a corpus of 17th century German funeral sermons. For each referent in the relative clauses and their matrix clauses, the attention state was determined (first calculation). In a second calculation, for each word the surprisal values were determined, using a bi-gram language model. In a third calculation, the surprisal values were accommodated as to whether it is the first occurrence of the word in question or not. All three calculations pointed in the same direction: With in-situ relative clauses, the rate of new referents was lower and the average surprisal values were lower, especially the accommodated surprisal values, than with extraposed relative clauses. This indicated that in-formation density is a factor governing the choice between in-situ and extraposed relative clauses. The study also sheds light on the intrinsic relation-ship between the information theoretic concept of information density and in-formation structural concepts such as givenness which are used under a more linguistic perspective. |
1507.06199 | Rani Izsak | Moran Feldman and Rani Izsak | Building a Good Team: Secretary Problems and the Supermodular Degree | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the Secretary Problem, one has to hire the best among n candidates. The
candidates are interviewed, one at a time, at a random order, and one has to
decide on the spot, whether to hire a candidate or continue interviewing. It is
well known that the best candidate can be hired with a probability of 1/e
(Dynkin, 1963). Recent works extend this problem to settings in which multiple
candidates can be hired, subject to some constraint. Here, one wishes to hire a
set of candidates maximizing a given set function.
Almost all extensions considered in the literature assume the objective set
function is either linear or submodular. Unfortunately, real world functions
might not have either of these properties. Consider, for example, a scenario
where one hires researchers for a project. Indeed, it can be that some
researchers can substitute others for that matter. However, it can also be that
some combinations of researchers result in synergy (see, e.g, Woolley et al.,
Science 2010, for a research about collective intelligence). The first
phenomenon can be modeled by a submoudlar set function, while the latter
cannot.
In this work, we study the secretary problem with an arbitrary non-negative
monotone function, subject to a general matroid constraint. It is not difficult
to prove that, generally, only very poor results can be obtained for this class
of objective functions. We tackle this hardness by combining the following:
1.Parametrizing our algorithms by the supermodular degree of the objective
function (defined by Feige and Izsak, ITCS 2013), which, roughly speaking,
measures the distance of a function from being submodular. 2.Suggesting an
(arguably) natural model that permits approximation guarantees that are
polynomial in the supermodular degree (as opposed to the standard model which
allows only exponential guarantees).
| [
{
"created": "Wed, 22 Jul 2015 14:15:10 GMT",
"version": "v1"
}
] | 2015-07-23 | [
[
"Feldman",
"Moran",
""
],
[
"Izsak",
"Rani",
""
]
] | In the Secretary Problem, one has to hire the best among n candidates. The candidates are interviewed, one at a time, at a random order, and one has to decide on the spot, whether to hire a candidate or continue interviewing. It is well known that the best candidate can be hired with a probability of 1/e (Dynkin, 1963). Recent works extend this problem to settings in which multiple candidates can be hired, subject to some constraint. Here, one wishes to hire a set of candidates maximizing a given set function. Almost all extensions considered in the literature assume the objective set function is either linear or submodular. Unfortunately, real world functions might not have either of these properties. Consider, for example, a scenario where one hires researchers for a project. Indeed, it can be that some researchers can substitute others for that matter. However, it can also be that some combinations of researchers result in synergy (see, e.g, Woolley et al., Science 2010, for a research about collective intelligence). The first phenomenon can be modeled by a submoudlar set function, while the latter cannot. In this work, we study the secretary problem with an arbitrary non-negative monotone function, subject to a general matroid constraint. It is not difficult to prove that, generally, only very poor results can be obtained for this class of objective functions. We tackle this hardness by combining the following: 1.Parametrizing our algorithms by the supermodular degree of the objective function (defined by Feige and Izsak, ITCS 2013), which, roughly speaking, measures the distance of a function from being submodular. 2.Suggesting an (arguably) natural model that permits approximation guarantees that are polynomial in the supermodular degree (as opposed to the standard model which allows only exponential guarantees). |
2012.09790 | Yilun Du | Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B. Tenenbaum, Jiajun Wu | Neural Radiance Flow for 4D View Synthesis and Video Processing | ICCV 2021. Website: https://yilundu.github.io/nerflow/ | null | null | null | cs.CV cs.LG cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method, Neural Radiance Flow (NeRFlow),to learn a 4D
spatial-temporal representation of a dynamic scene from a set of RGB images.
Key to our approach is the use of a neural implicit representation that learns
to capture the 3D occupancy, radiance, and dynamics of the scene. By enforcing
consistency across different modalities, our representation enables multi-view
rendering in diverse dynamic scenes, including water pouring, robotic
interaction, and real images, outperforming state-of-the-art methods for
spatial-temporal view synthesis. Our approach works even when inputs images are
captured with only one camera. We further demonstrate that the learned
representation can serve as an implicit scene prior, enabling video processing
tasks such as image super-resolution and de-noising without any additional
supervision.
| [
{
"created": "Thu, 17 Dec 2020 17:54:32 GMT",
"version": "v1"
},
{
"created": "Sun, 5 Sep 2021 16:39:21 GMT",
"version": "v2"
}
] | 2021-09-07 | [
[
"Du",
"Yilun",
""
],
[
"Zhang",
"Yinan",
""
],
[
"Yu",
"Hong-Xing",
""
],
[
"Tenenbaum",
"Joshua B.",
""
],
[
"Wu",
"Jiajun",
""
]
] | We present a method, Neural Radiance Flow (NeRFlow),to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images. Key to our approach is the use of a neural implicit representation that learns to capture the 3D occupancy, radiance, and dynamics of the scene. By enforcing consistency across different modalities, our representation enables multi-view rendering in diverse dynamic scenes, including water pouring, robotic interaction, and real images, outperforming state-of-the-art methods for spatial-temporal view synthesis. Our approach works even when inputs images are captured with only one camera. We further demonstrate that the learned representation can serve as an implicit scene prior, enabling video processing tasks such as image super-resolution and de-noising without any additional supervision. |
2204.02822 | Ritesh Kumar | Ritesh Kumar, Bornini Lahiri | Language Resources and Technologies for Non-Scheduled and Endangered
Indian Languages | To appear in Proceedings of Conference on Sanskrit and Indian
Languages: Technology | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the present paper, we will present a survey of the language resources and
technologies available for the non-scheduled and endangered languages of India.
While there have been different estimates from different sources about the
number of languages in India, it could be assumed that there are more than
1,000 languages currently being spoken in India. However barring some of the 22
languages included in the 8th Schedule of the Indian Constitution (called the
scheduled languages), there is hardly any substantial resource or technology
available for the rest of the languages. Nonetheless there have been some
individual attempts at developing resources and technologies for the different
languages across the country. Of late, some financial support has also become
available for the endangered languages. In this paper, we give a summary of the
resources and technologies for those Indian languages which are not included in
the 8th schedule of the Indian Constitution and/or which are endangered.
| [
{
"created": "Wed, 6 Apr 2022 13:33:24 GMT",
"version": "v1"
}
] | 2022-04-07 | [
[
"Kumar",
"Ritesh",
""
],
[
"Lahiri",
"Bornini",
""
]
] | In the present paper, we will present a survey of the language resources and technologies available for the non-scheduled and endangered languages of India. While there have been different estimates from different sources about the number of languages in India, it could be assumed that there are more than 1,000 languages currently being spoken in India. However barring some of the 22 languages included in the 8th Schedule of the Indian Constitution (called the scheduled languages), there is hardly any substantial resource or technology available for the rest of the languages. Nonetheless there have been some individual attempts at developing resources and technologies for the different languages across the country. Of late, some financial support has also become available for the endangered languages. In this paper, we give a summary of the resources and technologies for those Indian languages which are not included in the 8th schedule of the Indian Constitution and/or which are endangered. |
2211.03509 | Gang Cao | Zijie Lou, Gang Cao, Man Lin | Black-Box Attack against GAN-Generated Image Detector with Contrastive
Perturbation | null | null | null | null | cs.CV cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visually realistic GAN-generated facial images raise obvious concerns on
potential misuse. Many effective forensic algorithms have been developed to
detect such synthetic images in recent years. It is significant to assess the
vulnerability of such forensic detectors against adversarial attacks. In this
paper, we propose a new black-box attack method against GAN-generated image
detectors. A novel contrastive learning strategy is adopted to train the
encoder-decoder network based anti-forensic model under a contrastive loss
function. GAN images and their simulated real counterparts are constructed as
positive and negative samples, respectively. Leveraging on the trained attack
model, imperceptible contrastive perturbation could be applied to input
synthetic images for removing GAN fingerprint to some extent. As such, existing
GAN-generated image detectors are expected to be deceived. Extensive
experimental results verify that the proposed attack effectively reduces the
accuracy of three state-of-the-art detectors on six popular GANs. High visual
quality of the attacked images is also achieved. The source code will be
available at https://github.com/ZXMMD/BAttGAND.
| [
{
"created": "Mon, 7 Nov 2022 12:56:14 GMT",
"version": "v1"
}
] | 2022-11-08 | [
[
"Lou",
"Zijie",
""
],
[
"Cao",
"Gang",
""
],
[
"Lin",
"Man",
""
]
] | Visually realistic GAN-generated facial images raise obvious concerns on potential misuse. Many effective forensic algorithms have been developed to detect such synthetic images in recent years. It is significant to assess the vulnerability of such forensic detectors against adversarial attacks. In this paper, we propose a new black-box attack method against GAN-generated image detectors. A novel contrastive learning strategy is adopted to train the encoder-decoder network based anti-forensic model under a contrastive loss function. GAN images and their simulated real counterparts are constructed as positive and negative samples, respectively. Leveraging on the trained attack model, imperceptible contrastive perturbation could be applied to input synthetic images for removing GAN fingerprint to some extent. As such, existing GAN-generated image detectors are expected to be deceived. Extensive experimental results verify that the proposed attack effectively reduces the accuracy of three state-of-the-art detectors on six popular GANs. High visual quality of the attacked images is also achieved. The source code will be available at https://github.com/ZXMMD/BAttGAND. |
1409.1045 | Uwe Aickelin | Josie C. McCullochy, Chris J. Hinde, Christian Wagner and Uwe Aickelin | A Fuzzy Directional Distance Measure | Proceedings of the 2014 World Congress on Computational Intelligence
(WCCI 2014), pp. 141-148, 2014 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The measure of distance between two fuzzy sets is a fundamental tool within
fuzzy set theory, however, distance measures currently within the literature
use a crisp value to represent the distance between fuzzy sets. A real valued
distance measure is developed into a fuzzy distance measure which better
reflects the uncertainty inherent in fuzzy sets and a fuzzy directional
distance measure is presented, which accounts for the direction of change
between fuzzy sets. A multiplicative version is explored as a full maximal
assignment is computationally intractable so an intermediate solution is
offered.
| [
{
"created": "Wed, 3 Sep 2014 11:48:23 GMT",
"version": "v1"
}
] | 2014-09-04 | [
[
"McCullochy",
"Josie C.",
""
],
[
"Hinde",
"Chris J.",
""
],
[
"Wagner",
"Christian",
""
],
[
"Aickelin",
"Uwe",
""
]
] | The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory, however, distance measures currently within the literature use a crisp value to represent the distance between fuzzy sets. A real valued distance measure is developed into a fuzzy distance measure which better reflects the uncertainty inherent in fuzzy sets and a fuzzy directional distance measure is presented, which accounts for the direction of change between fuzzy sets. A multiplicative version is explored as a full maximal assignment is computationally intractable so an intermediate solution is offered. |
1712.01329 | Dana Kianfar | Mircea Mironenco, Dana Kianfar, Ke Tran, Evangelos Kanoulas,
Efstratios Gavves | Examining Cooperation in Visual Dialog Models | 9 pages, 5 figures, 2 tables, code at
http://github.com/danakianfar/Examining-Cooperation-in-VDM/ | null | null | null | cs.CV cs.AI cs.CL cs.IR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work we propose a blackbox intervention method for visual dialog
models, with the aim of assessing the contribution of individual linguistic or
visual components. Concretely, we conduct structured or randomized
interventions that aim to impair an individual component of the model, and
observe changes in task performance. We reproduce a state-of-the-art visual
dialog model and demonstrate that our methodology yields surprising insights,
namely that both dialog and image information have minimal contributions to
task performance. The intervention method presented here can be applied as a
sanity check for the strength and robustness of each component in visual dialog
systems.
| [
{
"created": "Mon, 4 Dec 2017 20:16:52 GMT",
"version": "v1"
}
] | 2017-12-06 | [
[
"Mironenco",
"Mircea",
""
],
[
"Kianfar",
"Dana",
""
],
[
"Tran",
"Ke",
""
],
[
"Kanoulas",
"Evangelos",
""
],
[
"Gavves",
"Efstratios",
""
]
] | In this work we propose a blackbox intervention method for visual dialog models, with the aim of assessing the contribution of individual linguistic or visual components. Concretely, we conduct structured or randomized interventions that aim to impair an individual component of the model, and observe changes in task performance. We reproduce a state-of-the-art visual dialog model and demonstrate that our methodology yields surprising insights, namely that both dialog and image information have minimal contributions to task performance. The intervention method presented here can be applied as a sanity check for the strength and robustness of each component in visual dialog systems. |
1909.05569 | Michal Kleinbort | Michal Kleinbort, Edgar Granados, Kiril Solovey, Riccardo Bonalli,
Kostas E. Bekris, Dan Halperin | Refined Analysis of Asymptotically-Optimal Kinodynamic Planning in the
State-Cost Space | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel analysis of AO-RRT: a tree-based planner for motion
planning with kinodynamic constraints, originally described by Hauser and Zhou
(AO-X, 2016). AO-RRT explores the state-cost space and has been shown to
efficiently obtain high-quality solutions in practice without relying on the
availability of a computationally-intensive two-point boundary-value solver.
Our main contribution is an optimality proof for the single-tree version of the
algorithm---a variant that was not analyzed before. Our proof only requires a
mild and easily-verifiable set of assumptions on the problem and system:
Lipschitz-continuity of the cost function and the dynamics. In particular, we
prove that for any system satisfying these assumptions, any trajectory having a
piecewise-constant control function and positive clearance from the obstacles
can be approximated arbitrarily well by a trajectory found by AO-RRT. We also
discuss practical aspects of AO-RRT and present experimental comparisons of
variants of the algorithm.
| [
{
"created": "Thu, 12 Sep 2019 11:18:55 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Sep 2019 04:58:54 GMT",
"version": "v2"
},
{
"created": "Mon, 9 Mar 2020 14:43:35 GMT",
"version": "v3"
}
] | 2020-03-10 | [
[
"Kleinbort",
"Michal",
""
],
[
"Granados",
"Edgar",
""
],
[
"Solovey",
"Kiril",
""
],
[
"Bonalli",
"Riccardo",
""
],
[
"Bekris",
"Kostas E.",
""
],
[
"Halperin",
"Dan",
""
]
] | We present a novel analysis of AO-RRT: a tree-based planner for motion planning with kinodynamic constraints, originally described by Hauser and Zhou (AO-X, 2016). AO-RRT explores the state-cost space and has been shown to efficiently obtain high-quality solutions in practice without relying on the availability of a computationally-intensive two-point boundary-value solver. Our main contribution is an optimality proof for the single-tree version of the algorithm---a variant that was not analyzed before. Our proof only requires a mild and easily-verifiable set of assumptions on the problem and system: Lipschitz-continuity of the cost function and the dynamics. In particular, we prove that for any system satisfying these assumptions, any trajectory having a piecewise-constant control function and positive clearance from the obstacles can be approximated arbitrarily well by a trajectory found by AO-RRT. We also discuss practical aspects of AO-RRT and present experimental comparisons of variants of the algorithm. |
1511.08987 | Napas Udomsak | Napas Udomsak | How do the naive Bayes classifier and the Support Vector Machine compare
in their ability to forecast the Stock Exchange of Thailand? | 16 pages | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | This essay investigates the question of how the naive Bayes classifier and
the support vector machine compare in their ability to forecast the Stock
Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier
is explored. The algorithms are trained using data from the month of January
2010, extracted from the MarketWatch.com website. Input features are selected
based on previous studies of the SET100 Index. The Weka 3 software is used to
create models from the labeled training data. Mean squared error and proportion
of correctly classified instances, and a number of other error measurements are
the used to compare the two algorithms. This essay shows that these two
algorithms are currently not advanced enough to accurately model the stock
exchange. Nevertheless, the naive Bayes is better than the support vector
machine at predicting the Stock Exchange of Thailand.
| [
{
"created": "Sun, 29 Nov 2015 09:57:42 GMT",
"version": "v1"
}
] | 2015-12-01 | [
[
"Udomsak",
"Napas",
""
]
] | This essay investigates the question of how the naive Bayes classifier and the support vector machine compare in their ability to forecast the Stock Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier is explored. The algorithms are trained using data from the month of January 2010, extracted from the MarketWatch.com website. Input features are selected based on previous studies of the SET100 Index. The Weka 3 software is used to create models from the labeled training data. Mean squared error and proportion of correctly classified instances, and a number of other error measurements are the used to compare the two algorithms. This essay shows that these two algorithms are currently not advanced enough to accurately model the stock exchange. Nevertheless, the naive Bayes is better than the support vector machine at predicting the Stock Exchange of Thailand. |
2401.15688 | Zhenyu Wang | Zhenyu Wang, Enze Xie, Aoxue Li, Zhongdao Wang, Xihui Liu, Zhenguo Li | Divide and Conquer: Language Models can Plan and Self-Correct for
Compositional Text-to-Image Generation | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Despite significant advancements in text-to-image models for generating
high-quality images, these methods still struggle to ensure the controllability
of text prompts over images in the context of complex text prompts, especially
when it comes to retaining object attributes and relationships. In this paper,
we propose CompAgent, a training-free approach for compositional text-to-image
generation, with a large language model (LLM) agent as its core. The
fundamental idea underlying CompAgent is premised on a divide-and-conquer
methodology. Given a complex text prompt containing multiple concepts including
objects, attributes, and relationships, the LLM agent initially decomposes it,
which entails the extraction of individual objects, their associated
attributes, and the prediction of a coherent scene layout. These individual
objects can then be independently conquered. Subsequently, the agent performs
reasoning by analyzing the text, plans and employs the tools to compose these
isolated objects. The verification and human feedback mechanism is finally
incorporated into our agent to further correct the potential attribute errors
and refine the generated images. Guided by the LLM agent, we propose a
tuning-free multi-concept customization model and a layout-to-image generation
model as the tools for concept composition, and a local image editing method as
the tool to interact with the agent for verification. The scene layout controls
the image generation process among these tools to prevent confusion among
multiple objects. Extensive experiments demonstrate the superiority of our
approach for compositional text-to-image generation: CompAgent achieves more
than 10\% improvement on T2I-CompBench, a comprehensive benchmark for
open-world compositional T2I generation. The extension to various related tasks
also illustrates the flexibility of our CompAgent for potential applications.
| [
{
"created": "Sun, 28 Jan 2024 16:18:39 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jan 2024 13:05:13 GMT",
"version": "v2"
}
] | 2024-01-31 | [
[
"Wang",
"Zhenyu",
""
],
[
"Xie",
"Enze",
""
],
[
"Li",
"Aoxue",
""
],
[
"Wang",
"Zhongdao",
""
],
[
"Liu",
"Xihui",
""
],
[
"Li",
"Zhenguo",
""
]
] | Despite significant advancements in text-to-image models for generating high-quality images, these methods still struggle to ensure the controllability of text prompts over images in the context of complex text prompts, especially when it comes to retaining object attributes and relationships. In this paper, we propose CompAgent, a training-free approach for compositional text-to-image generation, with a large language model (LLM) agent as its core. The fundamental idea underlying CompAgent is premised on a divide-and-conquer methodology. Given a complex text prompt containing multiple concepts including objects, attributes, and relationships, the LLM agent initially decomposes it, which entails the extraction of individual objects, their associated attributes, and the prediction of a coherent scene layout. These individual objects can then be independently conquered. Subsequently, the agent performs reasoning by analyzing the text, plans and employs the tools to compose these isolated objects. The verification and human feedback mechanism is finally incorporated into our agent to further correct the potential attribute errors and refine the generated images. Guided by the LLM agent, we propose a tuning-free multi-concept customization model and a layout-to-image generation model as the tools for concept composition, and a local image editing method as the tool to interact with the agent for verification. The scene layout controls the image generation process among these tools to prevent confusion among multiple objects. Extensive experiments demonstrate the superiority of our approach for compositional text-to-image generation: CompAgent achieves more than 10\% improvement on T2I-CompBench, a comprehensive benchmark for open-world compositional T2I generation. The extension to various related tasks also illustrates the flexibility of our CompAgent for potential applications. |
1601.06043 | Junaid Qadir | Sana Habib, Junaid Qadir, Anwaar Ali, Durdana Habib, Ming Li, Arjuna
Sathiaseelan | The Past, Present, and Future of Transport-Layer Multipath | null | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multipathing in communication networks is gaining momentum due to its
attractive features of increased reliability, throughput, fault tolerance, and
load balancing capabilities. In particular, wireless environments and
datacenters are envisioned to become largely dependent on the power of
multipathing for seamless handovers, virtual machine (VM) migration and in
general, pooling less proficient resources together for achieving overall high
proficiency. The transport layer, with its knowledge about end-to-end path
characteristics, is well placed to enhance performance through better
utilization of multiple paths. Realizing the importance of transport-layer
multipath, this paper investigates the modernization of traditional connection
establishment, flow control, sequence number splitting, acknowledgement, and
flow scheduling mechanisms for use with multiple paths. Since congestion
control defines a fundamental feature of the transport layer, we study the
working of multipath rate control and analyze its stability and convergence. We
also discuss how various multipath congestion control algorithms differ in
their window increase and decrease functions, their TCP-friendliness, and
responsiveness. To the best of our knowledge, this is the first in-depth survey
paper that has chronicled the evolution of the transport layer of the Internet
from the traditional single-path TCP to the recent development of the modern
multipath TCP (MPTCP) protocol. Along with describing the history of this
evolution, we also highlight in this paper the remaining challenges and
research issues.
| [
{
"created": "Fri, 22 Jan 2016 15:38:11 GMT",
"version": "v1"
}
] | 2016-01-25 | [
[
"Habib",
"Sana",
""
],
[
"Qadir",
"Junaid",
""
],
[
"Ali",
"Anwaar",
""
],
[
"Habib",
"Durdana",
""
],
[
"Li",
"Ming",
""
],
[
"Sathiaseelan",
"Arjuna",
""
]
] | Multipathing in communication networks is gaining momentum due to its attractive features of increased reliability, throughput, fault tolerance, and load balancing capabilities. In particular, wireless environments and datacenters are envisioned to become largely dependent on the power of multipathing for seamless handovers, virtual machine (VM) migration and in general, pooling less proficient resources together for achieving overall high proficiency. The transport layer, with its knowledge about end-to-end path characteristics, is well placed to enhance performance through better utilization of multiple paths. Realizing the importance of transport-layer multipath, this paper investigates the modernization of traditional connection establishment, flow control, sequence number splitting, acknowledgement, and flow scheduling mechanisms for use with multiple paths. Since congestion control defines a fundamental feature of the transport layer, we study the working of multipath rate control and analyze its stability and convergence. We also discuss how various multipath congestion control algorithms differ in their window increase and decrease functions, their TCP-friendliness, and responsiveness. To the best of our knowledge, this is the first in-depth survey paper that has chronicled the evolution of the transport layer of the Internet from the traditional single-path TCP to the recent development of the modern multipath TCP (MPTCP) protocol. Along with describing the history of this evolution, we also highlight in this paper the remaining challenges and research issues. |
1010.4603 | Aravind Iyengar | Aravind R. Iyengar, Paul H. Siegel, Jack K. Wolf | Write Channel Model for Bit-Patterned Media Recording | 11 pages, 12 figures, journal | null | 10.1109/TMAG.2010.2080667 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new write channel model for bit-patterned media recording that
reflects the data dependence of write synchronization errors. It is shown that
this model accommodates both substitution-like errors and insertion-deletion
errors whose statistics are determined by an underlying channel state process.
We study information theoretic properties of the write channel model, including
the capacity, symmetric information rate, Markov-1 rate and the zero-error
capacity.
| [
{
"created": "Fri, 22 Oct 2010 02:28:04 GMT",
"version": "v1"
}
] | 2011-06-02 | [
[
"Iyengar",
"Aravind R.",
""
],
[
"Siegel",
"Paul H.",
""
],
[
"Wolf",
"Jack K.",
""
]
] | We propose a new write channel model for bit-patterned media recording that reflects the data dependence of write synchronization errors. It is shown that this model accommodates both substitution-like errors and insertion-deletion errors whose statistics are determined by an underlying channel state process. We study information theoretic properties of the write channel model, including the capacity, symmetric information rate, Markov-1 rate and the zero-error capacity. |
2202.04620 | Md Morshed Alam | Md Morshed Alam, Md Sajidul Islam Sajid, Weichao Wang, Jinpeng Wei
(Department of Software and Information Systems, University of North Carolina
at Charlotte, Charlotte, USA) | IoTMonitor: A Hidden Markov Model-based Security System to Identify
Crucial Attack Nodes in Trigger-action IoT Platforms | This paper appears in the 2022 IEEE Wireless Communications and
Networking Conference (WCNC 2022). Personal use of this material is
permitted. Permission from IEEE must be obtained for all other uses | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | With the emergence and fast development of trigger-action platforms in IoT
settings, security vulnerabilities caused by the interactions among IoT devices
become more prevalent. The event occurrence at one device triggers an action in
another device, which may eventually contribute to the creation of a chain of
events in a network. Adversaries exploit the chain effect to compromise IoT
devices and trigger actions of interest remotely just by injecting malicious
events into the chain. To address security vulnerabilities caused by
trigger-action scenarios, existing research efforts focus on the validation of
the security properties of devices or verification of the occurrence of certain
events based on their physical fingerprints on a device. We propose IoTMonitor,
a security analysis system that discerns the underlying chain of event
occurrences with the highest probability by observing a chain of physical
evidence collected by sensors. We use the Baum-Welch algorithm to estimate
transition and emission probabilities and the Viterbi algorithm to discern the
event sequence. We can then identify the crucial nodes in the trigger-action
sequence whose compromise allows attackers to reach their final goals. The
experiment results of our designed system upon the PEEVES datasets show that we
can rebuild the event occurrence sequence with high accuracy from the
observations and identify the crucial nodes on the attack paths.
| [
{
"created": "Wed, 9 Feb 2022 18:36:42 GMT",
"version": "v1"
}
] | 2022-02-10 | [
[
"Alam",
"Md Morshed",
"",
"Department of Software and Information Systems, University of North Carolina\n at Charlotte, Charlotte, USA"
],
[
"Sajid",
"Md Sajidul Islam",
"",
"Department of Software and Information Systems, University of North Carolina\n at Charlotte, Charlotte, USA"
],
[
"Wang",
"Weichao",
"",
"Department of Software and Information Systems, University of North Carolina\n at Charlotte, Charlotte, USA"
],
[
"Wei",
"Jinpeng",
"",
"Department of Software and Information Systems, University of North Carolina\n at Charlotte, Charlotte, USA"
]
] | With the emergence and fast development of trigger-action platforms in IoT settings, security vulnerabilities caused by the interactions among IoT devices become more prevalent. The event occurrence at one device triggers an action in another device, which may eventually contribute to the creation of a chain of events in a network. Adversaries exploit the chain effect to compromise IoT devices and trigger actions of interest remotely just by injecting malicious events into the chain. To address security vulnerabilities caused by trigger-action scenarios, existing research efforts focus on the validation of the security properties of devices or verification of the occurrence of certain events based on their physical fingerprints on a device. We propose IoTMonitor, a security analysis system that discerns the underlying chain of event occurrences with the highest probability by observing a chain of physical evidence collected by sensors. We use the Baum-Welch algorithm to estimate transition and emission probabilities and the Viterbi algorithm to discern the event sequence. We can then identify the crucial nodes in the trigger-action sequence whose compromise allows attackers to reach their final goals. The experiment results of our designed system upon the PEEVES datasets show that we can rebuild the event occurrence sequence with high accuracy from the observations and identify the crucial nodes on the attack paths. |
2406.07497 | Judith Dineley Dr | Nicholas Cummins, Lauren L. White, Zahia Rahman, Catriona Lucas, Tian
Pan, Ewan Carr, Faith Matcham, Johnny Downs, Richard J. Dobson and Judith
Dineley | A pilot protocol and cohort for the investigation of non-pathological
variability in speech | 29 pages. Pre peer review | null | null | null | cs.SD eess.AS | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Background Speech-based biomarkers have potential as a means for regular,
objective assessment of symptom severity, remotely and in-clinic in combination
with advanced analytical models. However, the complex nature of speech and the
often subtle changes associated with health mean that findings are highly
dependent on methodological and cohort choices. These are often not reported
adequately in studies investigating speech-based health assessment Objective To
develop and apply an exemplar protocol to generate a pilot dataset of healthy
speech with detailed metadata for the assessment of factors in the speech
recording-analysis pipeline, including device choice, speech elicitation task
and non-pathological variability. Methods We developed our collection protocol
and choice of exemplar speech features based on a thematic literature review.
Our protocol includes the elicitation of three different speech types. With a
focus towards remote applications, we also choose to collect speech with three
different microphone types. We developed a pipeline to extract a set of 14
exemplar speech features. Results We collected speech from 28 individuals three
times in one day, repeated at the same times 8-11 weeks later, and from 25
healthy individuals three times in one week. Participant characteristics
collected included sex, age, native language status and voice use habits of the
participant. A preliminary set of 14 speech features covering timing, prosody,
voice quality, articulation and spectral moment characteristics were extracted
that provide a resource of normative values. Conclusions There are multiple
methodological factors involved in the collection, processing and analysis of
speech recordings. Consistent reporting and greater harmonisation of study
protocols are urgently required to aid the translation of speech processing
into clinical research and practice.
| [
{
"created": "Tue, 11 Jun 2024 17:32:28 GMT",
"version": "v1"
}
] | 2024-06-12 | [
[
"Cummins",
"Nicholas",
""
],
[
"White",
"Lauren L.",
""
],
[
"Rahman",
"Zahia",
""
],
[
"Lucas",
"Catriona",
""
],
[
"Pan",
"Tian",
""
],
[
"Carr",
"Ewan",
""
],
[
"Matcham",
"Faith",
""
],
[
"Downs",
"Johnny",
""
],
[
"Dobson",
"Richard J.",
""
],
[
"Dineley",
"Judith",
""
]
] | Background Speech-based biomarkers have potential as a means for regular, objective assessment of symptom severity, remotely and in-clinic in combination with advanced analytical models. However, the complex nature of speech and the often subtle changes associated with health mean that findings are highly dependent on methodological and cohort choices. These are often not reported adequately in studies investigating speech-based health assessment Objective To develop and apply an exemplar protocol to generate a pilot dataset of healthy speech with detailed metadata for the assessment of factors in the speech recording-analysis pipeline, including device choice, speech elicitation task and non-pathological variability. Methods We developed our collection protocol and choice of exemplar speech features based on a thematic literature review. Our protocol includes the elicitation of three different speech types. With a focus towards remote applications, we also choose to collect speech with three different microphone types. We developed a pipeline to extract a set of 14 exemplar speech features. Results We collected speech from 28 individuals three times in one day, repeated at the same times 8-11 weeks later, and from 25 healthy individuals three times in one week. Participant characteristics collected included sex, age, native language status and voice use habits of the participant. A preliminary set of 14 speech features covering timing, prosody, voice quality, articulation and spectral moment characteristics were extracted that provide a resource of normative values. Conclusions There are multiple methodological factors involved in the collection, processing and analysis of speech recordings. Consistent reporting and greater harmonisation of study protocols are urgently required to aid the translation of speech processing into clinical research and practice. |
2005.07427 | Chen Luo | Lei Cai, Zhengzhang Chen, Chen Luo, Jiaping Gui, Jingchao Ni, Ding Li,
Haifeng Chen | Structural Temporal Graph Neural Networks for Anomaly Detection in
Dynamic Graphs | null | null | null | null | cs.LG cs.SI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Detecting anomalies in dynamic graphs is a vital task, with numerous
practical applications in areas such as security, finance, and social media.
Previous network embedding based methods have been mostly focusing on learning
good node representations, whereas largely ignoring the subgraph structural
changes related to the target nodes in dynamic graphs. In this paper, we
propose StrGNN, an end-to-end structural temporal Graph Neural Network model
for detecting anomalous edges in dynamic graphs. In particular, we first
extract the $h$-hop enclosing subgraph centered on the target edge and propose
the node labeling function to identify the role of each node in the subgraph.
Then, we leverage graph convolution operation and Sortpooling layer to extract
the fixed-size feature from each snapshot/timestamp. Based on the extracted
features, we utilize Gated recurrent units (GRUs) to capture the temporal
information for anomaly detection. Extensive experiments on six benchmark
datasets and a real enterprise security system demonstrate the effectiveness of
StrGNN.
| [
{
"created": "Fri, 15 May 2020 09:17:08 GMT",
"version": "v1"
},
{
"created": "Mon, 25 May 2020 08:38:54 GMT",
"version": "v2"
}
] | 2020-05-26 | [
[
"Cai",
"Lei",
""
],
[
"Chen",
"Zhengzhang",
""
],
[
"Luo",
"Chen",
""
],
[
"Gui",
"Jiaping",
""
],
[
"Ni",
"Jingchao",
""
],
[
"Li",
"Ding",
""
],
[
"Chen",
"Haifeng",
""
]
] | Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Previous network embedding based methods have been mostly focusing on learning good node representations, whereas largely ignoring the subgraph structural changes related to the target nodes in dynamic graphs. In this paper, we propose StrGNN, an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Then, we leverage graph convolution operation and Sortpooling layer to extract the fixed-size feature from each snapshot/timestamp. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection. Extensive experiments on six benchmark datasets and a real enterprise security system demonstrate the effectiveness of StrGNN. |
2005.02470 | Zein Shaheen | Zein Shaheen, Gerhard Wohlgenannt, Bassel Zaity, Dmitry Mouromtsev,
Vadim Pak | Russian Natural Language Generation: Creation of a Language Modelling
Dataset and Evaluation with Modern Neural Architectures | null | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generating coherent, grammatically correct, and meaningful text is very
challenging, however, it is crucial to many modern NLP systems. So far,
research has mostly focused on English language, for other languages both
standardized datasets, as well as experiments with state-of-the-art models, are
rare. In this work, we i) provide a novel reference dataset for Russian
language modeling, ii) experiment with popular modern methods for text
generation, namely variational autoencoders, and generative adversarial
networks, which we trained on the new dataset. We evaluate the generated text
regarding metrics such as perplexity, grammatical correctness and lexical
diversity.
| [
{
"created": "Tue, 5 May 2020 20:20:25 GMT",
"version": "v1"
}
] | 2020-05-07 | [
[
"Shaheen",
"Zein",
""
],
[
"Wohlgenannt",
"Gerhard",
""
],
[
"Zaity",
"Bassel",
""
],
[
"Mouromtsev",
"Dmitry",
""
],
[
"Pak",
"Vadim",
""
]
] | Generating coherent, grammatically correct, and meaningful text is very challenging, however, it is crucial to many modern NLP systems. So far, research has mostly focused on English language, for other languages both standardized datasets, as well as experiments with state-of-the-art models, are rare. In this work, we i) provide a novel reference dataset for Russian language modeling, ii) experiment with popular modern methods for text generation, namely variational autoencoders, and generative adversarial networks, which we trained on the new dataset. We evaluate the generated text regarding metrics such as perplexity, grammatical correctness and lexical diversity. |
1906.01069 | Deepan Muthirayan | Deepan Muthirayan, Dileep Kalathil, Sen Li, Kameshwar Poolla and
Pravin Varaiya | Selling Demand Response Using Options | null | null | null | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wholesale electricity markets in many jurisdictions use a two-settlement
structure: a day-ahead market for bulk power transactions and a real-time
market for fine-grain supply-demand balancing. This paper explores trading
demand response assets within this two-settlement market structure. We consider
two approaches for trading demand response assets: (a) an intermediate spot
market with contingent pricing, and (b) an over-the-counter options contract.
In the first case, we characterize the competitive equilibrium of the spot
market, and show that it is socially optimal. Economic orthodoxy advocates spot
markets, but these require expensive infrastructure and regulatory blessing. In
the second case, we characterize competitive equilibria and compare its
efficiency with the idealized spot market. Options contract are private
bilateral over-the-counter transactions and do not require regulatory approval.
We show that the optimal social welfare is, in general, not supported. We then
design optimal option prices that minimize the social welfare gap. This optimal
design serves to approximate the ideal spot market for demand response using
options with modest loss of efficiency. Our results are validated through
numerical simulations.
| [
{
"created": "Mon, 3 Jun 2019 20:34:13 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Mar 2020 04:33:52 GMT",
"version": "v2"
},
{
"created": "Fri, 13 Mar 2020 00:35:07 GMT",
"version": "v3"
},
{
"created": "Mon, 20 Jul 2020 23:13:45 GMT",
"version": "v4"
},
{
"created": "Mon, 3 Aug 2020 01:16:47 GMT",
"version": "v5"
}
] | 2020-08-04 | [
[
"Muthirayan",
"Deepan",
""
],
[
"Kalathil",
"Dileep",
""
],
[
"Li",
"Sen",
""
],
[
"Poolla",
"Kameshwar",
""
],
[
"Varaiya",
"Pravin",
""
]
] | Wholesale electricity markets in many jurisdictions use a two-settlement structure: a day-ahead market for bulk power transactions and a real-time market for fine-grain supply-demand balancing. This paper explores trading demand response assets within this two-settlement market structure. We consider two approaches for trading demand response assets: (a) an intermediate spot market with contingent pricing, and (b) an over-the-counter options contract. In the first case, we characterize the competitive equilibrium of the spot market, and show that it is socially optimal. Economic orthodoxy advocates spot markets, but these require expensive infrastructure and regulatory blessing. In the second case, we characterize competitive equilibria and compare its efficiency with the idealized spot market. Options contract are private bilateral over-the-counter transactions and do not require regulatory approval. We show that the optimal social welfare is, in general, not supported. We then design optimal option prices that minimize the social welfare gap. This optimal design serves to approximate the ideal spot market for demand response using options with modest loss of efficiency. Our results are validated through numerical simulations. |
2008.01365 | Zehao Huang | Zehao Huang, Zehui Chen, Qiaofei Li, Hongkai Zhang, Naiyan Wang | 1st Place Solutions of Waymo Open Dataset Challenge 2020 -- 2D Object
Detection Track | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this technical report, we present our solutions of Waymo Open Dataset
(WOD) Challenge 2020 - 2D Object Track. We adopt FPN as our basic framework.
Cascade RCNN, stacked PAFPN Neck and Double-Head are used for performance
improvements. In order to handle the small object detection problem in WOD, we
use very large image scales for both training and testing. Using our methods,
our team RW-TSDet achieved the 1st place in the 2D Object Detection Track.
| [
{
"created": "Tue, 4 Aug 2020 06:46:28 GMT",
"version": "v1"
}
] | 2020-08-05 | [
[
"Huang",
"Zehao",
""
],
[
"Chen",
"Zehui",
""
],
[
"Li",
"Qiaofei",
""
],
[
"Zhang",
"Hongkai",
""
],
[
"Wang",
"Naiyan",
""
]
] | In this technical report, we present our solutions of Waymo Open Dataset (WOD) Challenge 2020 - 2D Object Track. We adopt FPN as our basic framework. Cascade RCNN, stacked PAFPN Neck and Double-Head are used for performance improvements. In order to handle the small object detection problem in WOD, we use very large image scales for both training and testing. Using our methods, our team RW-TSDet achieved the 1st place in the 2D Object Detection Track. |
2111.08919 | Yaya Shi | Yaya Shi, Xu Yang, Haiyang Xu, Chunfeng Yuan, Bing Li, Weiming Hu,
Zheng-Jun Zha | EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained
Embedding Matching | cvpr2022 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Current metrics for video captioning are mostly based on the text-level
comparison between reference and candidate captions. However, they have some
insuperable drawbacks, e.g., they cannot handle videos without references, and
they may result in biased evaluation due to the one-to-many nature of
video-to-text and the neglect of visual relevance. From the human evaluator's
viewpoint, a high-quality caption should be consistent with the provided video,
but not necessarily be similar to the reference in literal or semantics.
Inspired by human evaluation, we propose EMScore (Embedding Matching-based
score), a novel reference-free metric for video captioning, which directly
measures similarity between video and candidate captions. Benefit from the
recent development of large-scale pre-training models, we exploit a well
pre-trained vision-language model to extract visual and linguistic embeddings
for computing EMScore. Specifically, EMScore combines matching scores of both
coarse-grained (video and caption) and fine-grained (frames and words) levels,
which takes the overall understanding and detailed characteristics of the video
into account. Furthermore, considering the potential information gain, EMScore
can be flexibly extended to the conditions where human-labeled references are
available. Last but not least, we collect VATEX-EVAL and ActivityNet-FOIl
datasets to systematically evaluate the existing metrics. VATEX-EVAL
experiments demonstrate that EMScore has higher human correlation and lower
reference dependency. ActivityNet-FOIL experiment verifies that EMScore can
effectively identify "hallucinating" captions. The datasets will be released to
facilitate the development of video captioning metrics. The code is available
at: https://github.com/ShiYaya/emscore.
| [
{
"created": "Wed, 17 Nov 2021 06:02:43 GMT",
"version": "v1"
},
{
"created": "Sun, 17 Jul 2022 04:35:18 GMT",
"version": "v2"
}
] | 2022-07-19 | [
[
"Shi",
"Yaya",
""
],
[
"Yang",
"Xu",
""
],
[
"Xu",
"Haiyang",
""
],
[
"Yuan",
"Chunfeng",
""
],
[
"Li",
"Bing",
""
],
[
"Hu",
"Weiming",
""
],
[
"Zha",
"Zheng-Jun",
""
]
] | Current metrics for video captioning are mostly based on the text-level comparison between reference and candidate captions. However, they have some insuperable drawbacks, e.g., they cannot handle videos without references, and they may result in biased evaluation due to the one-to-many nature of video-to-text and the neglect of visual relevance. From the human evaluator's viewpoint, a high-quality caption should be consistent with the provided video, but not necessarily be similar to the reference in literal or semantics. Inspired by human evaluation, we propose EMScore (Embedding Matching-based score), a novel reference-free metric for video captioning, which directly measures similarity between video and candidate captions. Benefit from the recent development of large-scale pre-training models, we exploit a well pre-trained vision-language model to extract visual and linguistic embeddings for computing EMScore. Specifically, EMScore combines matching scores of both coarse-grained (video and caption) and fine-grained (frames and words) levels, which takes the overall understanding and detailed characteristics of the video into account. Furthermore, considering the potential information gain, EMScore can be flexibly extended to the conditions where human-labeled references are available. Last but not least, we collect VATEX-EVAL and ActivityNet-FOIl datasets to systematically evaluate the existing metrics. VATEX-EVAL experiments demonstrate that EMScore has higher human correlation and lower reference dependency. ActivityNet-FOIL experiment verifies that EMScore can effectively identify "hallucinating" captions. The datasets will be released to facilitate the development of video captioning metrics. The code is available at: https://github.com/ShiYaya/emscore. |
1709.08521 | Omar Al-Harbi | Omar Al-Harbi | Using objective words in the reviews to improve the colloquial arabic
sentiment analysis | 14 pages, 1 figure, International Journal on Natural Language
Computing (IJNLC) | International Journal on Natural Language Computing (IJNLC) Vol.
6, No.3, June 2017 | 10.5121/ijnlc | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the main difficulties in sentiment analysis of the Arabic language is
the presence of the colloquialism. In this paper, we examine the effect of
using objective words in conjunction with sentimental words on sentiment
classification for the colloquial Arabic reviews, specifically Jordanian
colloquial reviews. The reviews often include both sentimental and objective
words, however, the most existing sentiment analysis models ignore the
objective words as they are considered useless. In this work, we created two
lexicons: the first includes the colloquial sentimental words and compound
phrases, while the other contains the objective words associated with values of
sentiment tendency based on a particular estimation method. We used these
lexicons to extract sentiment features that would be training input to the
Support Vector Machines (SVM) to classify the sentiment polarity of the
reviews. The reviews dataset have been collected manually from JEERAN website.
The results of the experiments show that the proposed approach improves the
polarity classification in comparison to two baseline models, with accuracy
95.6%.
| [
{
"created": "Mon, 25 Sep 2017 14:40:28 GMT",
"version": "v1"
}
] | 2017-09-26 | [
[
"Al-Harbi",
"Omar",
""
]
] | One of the main difficulties in sentiment analysis of the Arabic language is the presence of the colloquialism. In this paper, we examine the effect of using objective words in conjunction with sentimental words on sentiment classification for the colloquial Arabic reviews, specifically Jordanian colloquial reviews. The reviews often include both sentimental and objective words, however, the most existing sentiment analysis models ignore the objective words as they are considered useless. In this work, we created two lexicons: the first includes the colloquial sentimental words and compound phrases, while the other contains the objective words associated with values of sentiment tendency based on a particular estimation method. We used these lexicons to extract sentiment features that would be training input to the Support Vector Machines (SVM) to classify the sentiment polarity of the reviews. The reviews dataset have been collected manually from JEERAN website. The results of the experiments show that the proposed approach improves the polarity classification in comparison to two baseline models, with accuracy 95.6%. |
2002.01078 | Mordechai Guri | Mordechai Guri, Dima Bykhovsky, Yuval Elovici | BRIGHTNESS: Leaking Sensitive Data from Air-Gapped Workstations via
Screen Brightness | 2019 12th CMI Conference on Cybersecurity and Privacy (CMI) | null | 10.1109/CMI48017.2019.8962137 | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Air-gapped computers are systems that are kept isolated from the Internet
since they store or process sensitive information.
In this paper, we introduce an optical covert channel in which an attacker
can leak (or, exfiltlrate) sensitive information from air-gapped computers
through manipulations on the screen brightness. This covert channel is
invisible and it works even while the user is working on the computer. Malware
on a compromised computer can obtain sensitive data (e.g., files, images,
encryption keys and passwords), and modulate it within the screen brightness,
invisible to users. The small changes in the brightness are invisible to humans
but can be recovered from video streams taken by cameras such as a local
security camera, smartphone camera or a webcam. We present related work and
discuss the technical and scientific background of this covert channel. We
examined the channel's boundaries under various parameters, with different
types of computer and TV screens, and at several distances. We also tested
different types of camera receivers to demonstrate the covert channel. Lastly,
we present relevant countermeasures to this type of attack. Lastly, we present
relevant countermeasures to this type of attack.
| [
{
"created": "Tue, 4 Feb 2020 01:25:44 GMT",
"version": "v1"
}
] | 2020-02-05 | [
[
"Guri",
"Mordechai",
""
],
[
"Bykhovsky",
"Dima",
""
],
[
"Elovici",
"Yuval",
""
]
] | Air-gapped computers are systems that are kept isolated from the Internet since they store or process sensitive information. In this paper, we introduce an optical covert channel in which an attacker can leak (or, exfiltlrate) sensitive information from air-gapped computers through manipulations on the screen brightness. This covert channel is invisible and it works even while the user is working on the computer. Malware on a compromised computer can obtain sensitive data (e.g., files, images, encryption keys and passwords), and modulate it within the screen brightness, invisible to users. The small changes in the brightness are invisible to humans but can be recovered from video streams taken by cameras such as a local security camera, smartphone camera or a webcam. We present related work and discuss the technical and scientific background of this covert channel. We examined the channel's boundaries under various parameters, with different types of computer and TV screens, and at several distances. We also tested different types of camera receivers to demonstrate the covert channel. Lastly, we present relevant countermeasures to this type of attack. Lastly, we present relevant countermeasures to this type of attack. |
2312.10504 | Chi Zhang | Chi Zhang (1), Wenkai Xiang (1), Xingzhi Guo (2), Baojian Zhou (1),
Deqing Yang (1) ((1) Fudan University, Shanghai Key Laboratory of Data
Science, (2) Stony Brook University) | SubAnom: Efficient Subgraph Anomaly Detection Framework over Dynamic
Graphs | null | null | null | null | cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given a dynamic graph, the efficient tracking of anomalous subgraphs via
their node embeddings poses a significant challenge. Addressing this issue
necessitates an effective scoring mechanism and an innovative anomalous
subgraph strategy. Existing methods predominantly focus on designing scoring
strategies or employing graph structures that consider nodes in isolation,
resulting in ineffective capture of the anomalous subgraph structure
information.
In this paper, we introduce SUBANOM, a novel framework for subgraph anomaly
detection that is adept at identifying anomalous subgraphs. SUBANOM has three
key components: 1) We implement current state-of-the-art dynamic embedding
methods to efficiently calculate node embeddings, thereby capturing all
node-level anomalies successfully; 2) We devise novel subgraph identification
strategies, which include k-hop and triadic-closure. These strategies form the
crucial component that can proficiently differentiate between strong and weak
neighbors, thus effectively capturing the anomaly structure information; 3) For
qualifying the anomaly subgraphs, we propose using Lp-norm-based score
aggregation functions. These iterative steps enable us to process large-scale
dynamic graphs effectively.
Experiments conducted on a real-world dynamic graph underscore the efficacy
of our framework in detecting anomalous subgraphs, outperforming
state-of-the-art methods. Experimental results further signify that our
framework is a potent tool for identifying anomalous subgraphs in real-world
scenarios. For instance, the F1 score under the optimal subgraph identification
strategy, can peak at 0.6679, while the highest achievable score using the
corresponding baseline method is 0.5677.
| [
{
"created": "Sat, 16 Dec 2023 17:18:30 GMT",
"version": "v1"
}
] | 2023-12-19 | [
[
"Zhang",
"Chi",
""
],
[
"Xiang",
"Wenkai",
""
],
[
"Guo",
"Xingzhi",
""
],
[
"Zhou",
"Baojian",
""
],
[
"Yang",
"Deqing",
""
]
] | Given a dynamic graph, the efficient tracking of anomalous subgraphs via their node embeddings poses a significant challenge. Addressing this issue necessitates an effective scoring mechanism and an innovative anomalous subgraph strategy. Existing methods predominantly focus on designing scoring strategies or employing graph structures that consider nodes in isolation, resulting in ineffective capture of the anomalous subgraph structure information. In this paper, we introduce SUBANOM, a novel framework for subgraph anomaly detection that is adept at identifying anomalous subgraphs. SUBANOM has three key components: 1) We implement current state-of-the-art dynamic embedding methods to efficiently calculate node embeddings, thereby capturing all node-level anomalies successfully; 2) We devise novel subgraph identification strategies, which include k-hop and triadic-closure. These strategies form the crucial component that can proficiently differentiate between strong and weak neighbors, thus effectively capturing the anomaly structure information; 3) For qualifying the anomaly subgraphs, we propose using Lp-norm-based score aggregation functions. These iterative steps enable us to process large-scale dynamic graphs effectively. Experiments conducted on a real-world dynamic graph underscore the efficacy of our framework in detecting anomalous subgraphs, outperforming state-of-the-art methods. Experimental results further signify that our framework is a potent tool for identifying anomalous subgraphs in real-world scenarios. For instance, the F1 score under the optimal subgraph identification strategy, can peak at 0.6679, while the highest achievable score using the corresponding baseline method is 0.5677. |
1810.09294 | Michael Taynnan Barros | Michael Taynnan Barros and Walisson Silva and Carlos Danilo Miranda
Regis | The Multi-Scale Impact of the Alzheimer's Disease in the Topology
Diversity of Astrocytes Molecular Communications Nanonetworks | Submitted to journal publication | null | null | null | cs.ET q-bio.MN | http://creativecommons.org/licenses/by/4.0/ | The Internet of Bio-Nano-Things is a new paradigm that can bring novel
remotely controlled actuation and sensing techniques inside the human body.
Towards precise bionano sensing techniques in the brain, we investigate the
challenges of modelling spatial distribution of astrocyte networks in
developing a mathematical framework that lay the groundwork for future
early-detection techniques of neurodegenerative disease. In this paper, we
investigate the effect of the $\beta$-amyloid plaques in astrocytes with the
Alzheimer's disease. We developed a computation model of healthy and
Alzheimer's diseases astrocytes networks from the state of the art models and
results that account for the intracellular pathways, IP$_3$ dynamics, gap
junctions, voltage-gated calcium channels and astrocytes volumes. We also
implemented different types of astrocytes network topologies including shortcut
networks, regular degree networks, Erd\"os R\'enyi networks and link radius
networks. A proposed multi-scale stochastic computational model captures the
relationship between the intracellular and intercellular scales. Lastly, we
designed and evaluated a single-hop communication system with frequency
modulation using metrics such as propagation extend, molecular delay and
channel gain. The results show that the more unstable but at the same time
lower level oscillations of Alzheimer's astrocyte networks can create a
multi-scale effect on communication between astrocytes with increased molecular
delay and lower channel gain compared to healthy astrocytes, with an elevated
impact on Erd\"os R\'enyi networks and link radius networks topologies.
| [
{
"created": "Mon, 22 Oct 2018 13:54:43 GMT",
"version": "v1"
}
] | 2018-10-23 | [
[
"Barros",
"Michael Taynnan",
""
],
[
"Silva",
"Walisson",
""
],
[
"Regis",
"Carlos Danilo Miranda",
""
]
] | The Internet of Bio-Nano-Things is a new paradigm that can bring novel remotely controlled actuation and sensing techniques inside the human body. Towards precise bionano sensing techniques in the brain, we investigate the challenges of modelling spatial distribution of astrocyte networks in developing a mathematical framework that lay the groundwork for future early-detection techniques of neurodegenerative disease. In this paper, we investigate the effect of the $\beta$-amyloid plaques in astrocytes with the Alzheimer's disease. We developed a computation model of healthy and Alzheimer's diseases astrocytes networks from the state of the art models and results that account for the intracellular pathways, IP$_3$ dynamics, gap junctions, voltage-gated calcium channels and astrocytes volumes. We also implemented different types of astrocytes network topologies including shortcut networks, regular degree networks, Erd\"os R\'enyi networks and link radius networks. A proposed multi-scale stochastic computational model captures the relationship between the intracellular and intercellular scales. Lastly, we designed and evaluated a single-hop communication system with frequency modulation using metrics such as propagation extend, molecular delay and channel gain. The results show that the more unstable but at the same time lower level oscillations of Alzheimer's astrocyte networks can create a multi-scale effect on communication between astrocytes with increased molecular delay and lower channel gain compared to healthy astrocytes, with an elevated impact on Erd\"os R\'enyi networks and link radius networks topologies. |
1704.02738 | Xin Tao | Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia | Detail-revealing Deep Video Super-resolution | 9 pages, submitted to conference | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Previous CNN-based video super-resolution approaches need to align multiple
frames to the reference. In this paper, we show that proper frame alignment and
motion compensation is crucial for achieving high quality results. We
accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN
framework. Analysis and experiments show the suitability of this layer in video
SR. The final end-to-end, scalable CNN framework effectively incorporates the
SPMC layer and fuses multiple frames to reveal image details. Our
implementation can generate visually and quantitatively high-quality results,
superior to current state-of-the-arts, without the need of parameter tuning.
| [
{
"created": "Mon, 10 Apr 2017 07:28:27 GMT",
"version": "v1"
}
] | 2017-04-11 | [
[
"Tao",
"Xin",
""
],
[
"Gao",
"Hongyun",
""
],
[
"Liao",
"Renjie",
""
],
[
"Wang",
"Jue",
""
],
[
"Jia",
"Jiaya",
""
]
] | Previous CNN-based video super-resolution approaches need to align multiple frames to the reference. In this paper, we show that proper frame alignment and motion compensation is crucial for achieving high quality results. We accordingly propose a `sub-pixel motion compensation' (SPMC) layer in a CNN framework. Analysis and experiments show the suitability of this layer in video SR. The final end-to-end, scalable CNN framework effectively incorporates the SPMC layer and fuses multiple frames to reveal image details. Our implementation can generate visually and quantitatively high-quality results, superior to current state-of-the-arts, without the need of parameter tuning. |
2306.00356 | Hyunsu Kim | Hyunsu Kim, Hyungi Lee, Hongseok Yang, and Juho Lee | Regularizing Towards Soft Equivariance Under Mixed Symmetries | Proceedings of the International Conference on Machine Learning
(ICML), 2023 | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Datasets often have their intrinsic symmetries, and particular deep-learning
models called equivariant or invariant models have been developed to exploit
these symmetries. However, if some or all of these symmetries are only
approximate, which frequently happens in practice, these models may be
suboptimal due to the architectural restrictions imposed on them. We tackle
this issue of approximate symmetries in a setup where symmetries are mixed,
i.e., they are symmetries of not single but multiple different types and the
degree of approximation varies across these types. Instead of proposing a new
architectural restriction as in most of the previous approaches, we present a
regularizer-based method for building a model for a dataset with mixed
approximate symmetries. The key component of our method is what we call
equivariance regularizer for a given type of symmetries, which measures how
much a model is equivariant with respect to the symmetries of the type. Our
method is trained with these regularizers, one per each symmetry type, and the
strength of the regularizers is automatically tuned during training, leading to
the discovery of the approximation levels of some candidate symmetry types
without explicit supervision. Using synthetic function approximation and motion
forecasting tasks, we demonstrate that our method achieves better accuracy than
prior approaches while discovering the approximate symmetry levels correctly.
| [
{
"created": "Thu, 1 Jun 2023 05:33:41 GMT",
"version": "v1"
}
] | 2023-06-02 | [
[
"Kim",
"Hyunsu",
""
],
[
"Lee",
"Hyungi",
""
],
[
"Yang",
"Hongseok",
""
],
[
"Lee",
"Juho",
""
]
] | Datasets often have their intrinsic symmetries, and particular deep-learning models called equivariant or invariant models have been developed to exploit these symmetries. However, if some or all of these symmetries are only approximate, which frequently happens in practice, these models may be suboptimal due to the architectural restrictions imposed on them. We tackle this issue of approximate symmetries in a setup where symmetries are mixed, i.e., they are symmetries of not single but multiple different types and the degree of approximation varies across these types. Instead of proposing a new architectural restriction as in most of the previous approaches, we present a regularizer-based method for building a model for a dataset with mixed approximate symmetries. The key component of our method is what we call equivariance regularizer for a given type of symmetries, which measures how much a model is equivariant with respect to the symmetries of the type. Our method is trained with these regularizers, one per each symmetry type, and the strength of the regularizers is automatically tuned during training, leading to the discovery of the approximation levels of some candidate symmetry types without explicit supervision. Using synthetic function approximation and motion forecasting tasks, we demonstrate that our method achieves better accuracy than prior approaches while discovering the approximate symmetry levels correctly. |
1707.00825 | Juan Colmenares | Juan A. Colmenares, Reza Dorrigiv and Daniel G. Waddington | Ingestion, Indexing and Retrieval of High-Velocity Multidimensional
Sensor Data on a Single Node | null | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multidimensional data are becoming more prevalent, partly due to the rise of
the Internet of Things (IoT), and with that the need to ingest and analyze data
streams at rates higher than before. Some industrial IoT applications require
ingesting millions of records per second, while processing queries on recently
ingested and historical data. Unfortunately, existing database systems suited
to multidimensional data exhibit low per-node ingestion performance, and even
if they can scale horizontally in distributed settings, they require large
number of nodes to meet such ingest demands. For this reason, in this paper we
evaluate a single-node multidimensional data store for high-velocity sensor
data. Its design centers around a two-level indexing structure, wherein the
global index is an in-memory R*-tree and the local indices are serialized
kd-trees. This study is confined to records with numerical indexing fields and
range queries, and covers ingest throughput, query response time, and storage
footprint. We show that the adopted design streamlines data ingestion and
offers ingress rates two orders of magnitude higher than those of Percona
Server, SQLite, and Druid. Our prototype also reports query response times
comparable to or better than those of Percona Server and Druid, and compares
favorably in terms of storage footprint. In addition, we evaluate a kd-tree
partitioning based scheme for grouping incoming streamed data records. Compared
to a random scheme, this scheme produces less overlap between groups of
streamed records, but contrary to what we expected, such reduced overlap does
not translate into better query performance. By contrast, the local indices
prove much more beneficial to query performance. We believe the experience
reported in this paper is valuable to practitioners and researchers alike
interested in building database systems for high-velocity multidimensional
data.
| [
{
"created": "Tue, 4 Jul 2017 06:30:37 GMT",
"version": "v1"
}
] | 2017-07-05 | [
[
"Colmenares",
"Juan A.",
""
],
[
"Dorrigiv",
"Reza",
""
],
[
"Waddington",
"Daniel G.",
""
]
] | Multidimensional data are becoming more prevalent, partly due to the rise of the Internet of Things (IoT), and with that the need to ingest and analyze data streams at rates higher than before. Some industrial IoT applications require ingesting millions of records per second, while processing queries on recently ingested and historical data. Unfortunately, existing database systems suited to multidimensional data exhibit low per-node ingestion performance, and even if they can scale horizontally in distributed settings, they require large number of nodes to meet such ingest demands. For this reason, in this paper we evaluate a single-node multidimensional data store for high-velocity sensor data. Its design centers around a two-level indexing structure, wherein the global index is an in-memory R*-tree and the local indices are serialized kd-trees. This study is confined to records with numerical indexing fields and range queries, and covers ingest throughput, query response time, and storage footprint. We show that the adopted design streamlines data ingestion and offers ingress rates two orders of magnitude higher than those of Percona Server, SQLite, and Druid. Our prototype also reports query response times comparable to or better than those of Percona Server and Druid, and compares favorably in terms of storage footprint. In addition, we evaluate a kd-tree partitioning based scheme for grouping incoming streamed data records. Compared to a random scheme, this scheme produces less overlap between groups of streamed records, but contrary to what we expected, such reduced overlap does not translate into better query performance. By contrast, the local indices prove much more beneficial to query performance. We believe the experience reported in this paper is valuable to practitioners and researchers alike interested in building database systems for high-velocity multidimensional data. |
1910.06437 | Sebastiano Vigna | Sebastiano Vigna | It is high time we let go of the Mersenne Twister | null | null | null | null | cs.DS cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When the Mersenne Twister made his first appearance in 1997 it was a powerful
example of how linear maps on $\mathbf F_2$ could be used to generate
pseudorandom numbers. In particular, the easiness with which generators with
long periods could be defined gave the Mersenne Twister a large following, in
spite of the fact that such long periods are not a measure of quality, and they
require a large amount of memory. Even at the time of its publication, several
defects of the Mersenne Twister were predictable, but they were somewhat
obscured by other interesting properties. Today the Mersenne Twister is the
default generator in C compilers, the Python language, the Maple mathematical
computation system, and in many other environments. Nonetheless, knowledge
accumulated in the last $20$ years suggests that the Mersenne Twister has, in
fact, severe defects, and should never be used as a general-purpose
pseudorandom number generator. Many of these results are folklore, or are
scattered through very specialized literature. This paper surveys these results
for the non-specialist, providing new, simple, understandable examples, and it
is intended as a guide for the final user, or for language implementors, so
that they can take an informed decision about whether to use the Mersenne
Twister or not.
| [
{
"created": "Mon, 14 Oct 2019 21:44:14 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Nov 2019 14:50:14 GMT",
"version": "v2"
}
] | 2019-11-15 | [
[
"Vigna",
"Sebastiano",
""
]
] | When the Mersenne Twister made his first appearance in 1997 it was a powerful example of how linear maps on $\mathbf F_2$ could be used to generate pseudorandom numbers. In particular, the easiness with which generators with long periods could be defined gave the Mersenne Twister a large following, in spite of the fact that such long periods are not a measure of quality, and they require a large amount of memory. Even at the time of its publication, several defects of the Mersenne Twister were predictable, but they were somewhat obscured by other interesting properties. Today the Mersenne Twister is the default generator in C compilers, the Python language, the Maple mathematical computation system, and in many other environments. Nonetheless, knowledge accumulated in the last $20$ years suggests that the Mersenne Twister has, in fact, severe defects, and should never be used as a general-purpose pseudorandom number generator. Many of these results are folklore, or are scattered through very specialized literature. This paper surveys these results for the non-specialist, providing new, simple, understandable examples, and it is intended as a guide for the final user, or for language implementors, so that they can take an informed decision about whether to use the Mersenne Twister or not. |
1510.00225 | Frederick Benaben | Matthieu Lauras, Frederick Benaben, Sebastien Truptil, Aurelie Charles
(UL2) | Event-Cloud Platform to Support Decision- Making in Emergency Management | null | Information Systems Frontiers, Springer Verlag (Germany), 2015, 17
(4), pp.857-869 | 10.1007/s10796-013-9475-0 | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The challenge of this paper is to underline the capability of an Event-Cloud
Platform to support efficiently an emergency situation. We chose to focus on a
nuclear crisis use case. The proposed approach consists in modeling the
business processes of crisis response on the one hand, and in supporting the
orchestration and execution of these processes by using an Event-Cloud Platform
on the other hand. This paper shows how the use of Event-Cloud techniques can
support crisis management stakeholders by automatizing non-value added tasks
and by directing decision- makers on what really requires their capabilities of
choice. If Event-Cloud technology is a very interesting and topical subject,
very few research works have considered this to improve emergency management.
This paper tries to fill this gap by considering and applying these
technologies on a nuclear crisis use-case.
| [
{
"created": "Thu, 1 Oct 2015 13:34:44 GMT",
"version": "v1"
}
] | 2015-10-02 | [
[
"Lauras",
"Matthieu",
"",
"UL2"
],
[
"Benaben",
"Frederick",
"",
"UL2"
],
[
"Truptil",
"Sebastien",
"",
"UL2"
],
[
"Charles",
"Aurelie",
"",
"UL2"
]
] | The challenge of this paper is to underline the capability of an Event-Cloud Platform to support efficiently an emergency situation. We chose to focus on a nuclear crisis use case. The proposed approach consists in modeling the business processes of crisis response on the one hand, and in supporting the orchestration and execution of these processes by using an Event-Cloud Platform on the other hand. This paper shows how the use of Event-Cloud techniques can support crisis management stakeholders by automatizing non-value added tasks and by directing decision- makers on what really requires their capabilities of choice. If Event-Cloud technology is a very interesting and topical subject, very few research works have considered this to improve emergency management. This paper tries to fill this gap by considering and applying these technologies on a nuclear crisis use-case. |
2209.08353 | Fengxin Li | Zhaoxin Fan, Fengxin Li, Hongyan Liu, Jun He, Xiaoyong Du | Human Pose Driven Object Effects Recommendation | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we research the new topic of object effects recommendation in
micro-video platforms, which is a challenging but important task for many
practical applications such as advertisement insertion. To avoid the problem of
introducing background bias caused by directly learning video content from
image frames, we propose to utilize the meaningful body language hidden in 3D
human pose for recommendation. To this end, in this work, a novel human pose
driven object effects recommendation network termed PoseRec is introduced.
PoseRec leverages the advantages of 3D human pose detection and learns
information from multi-frame 3D human pose for video-item registration,
resulting in high quality object effects recommendation performance. Moreover,
to solve the inherent ambiguity and sparsity issues that exist in object
effects recommendation, we further propose a novel item-aware implicit
prototype learning module and a novel pose-aware transductive hard-negative
mining module to better learn pose-item relationships. What's more, to
benchmark methods for the new research topic, we build a new dataset for object
effects recommendation named Pose-OBE. Extensive experiments on Pose-OBE
demonstrate that our method can achieve superior performance than strong
baselines.
| [
{
"created": "Sat, 17 Sep 2022 15:32:54 GMT",
"version": "v1"
}
] | 2022-09-20 | [
[
"Fan",
"Zhaoxin",
""
],
[
"Li",
"Fengxin",
""
],
[
"Liu",
"Hongyan",
""
],
[
"He",
"Jun",
""
],
[
"Du",
"Xiaoyong",
""
]
] | In this paper, we research the new topic of object effects recommendation in micro-video platforms, which is a challenging but important task for many practical applications such as advertisement insertion. To avoid the problem of introducing background bias caused by directly learning video content from image frames, we propose to utilize the meaningful body language hidden in 3D human pose for recommendation. To this end, in this work, a novel human pose driven object effects recommendation network termed PoseRec is introduced. PoseRec leverages the advantages of 3D human pose detection and learns information from multi-frame 3D human pose for video-item registration, resulting in high quality object effects recommendation performance. Moreover, to solve the inherent ambiguity and sparsity issues that exist in object effects recommendation, we further propose a novel item-aware implicit prototype learning module and a novel pose-aware transductive hard-negative mining module to better learn pose-item relationships. What's more, to benchmark methods for the new research topic, we build a new dataset for object effects recommendation named Pose-OBE. Extensive experiments on Pose-OBE demonstrate that our method can achieve superior performance than strong baselines. |
2010.01243 | Yae Jee Cho | Yae Jee Cho and Jianyu Wang and Gauri Joshi | Client Selection in Federated Learning: Convergence Analysis and
Power-of-Choice Selection Strategies | null | null | null | null | cs.LG cs.DC stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated learning is a distributed optimization paradigm that enables a
large number of resource-limited client nodes to cooperatively train a model
without data sharing. Several works have analyzed the convergence of federated
learning by accounting of data heterogeneity, communication and computation
limitations, and partial client participation. However, they assume unbiased
client participation, where clients are selected at random or in proportion of
their data sizes. In this paper, we present the first convergence analysis of
federated optimization for biased client selection strategies, and quantify how
the selection bias affects convergence speed. We reveal that biasing client
selection towards clients with higher local loss achieves faster error
convergence. Using this insight, we propose Power-of-Choice, a communication-
and computation-efficient client selection framework that can flexibly span the
trade-off between convergence speed and solution bias. Our experiments
demonstrate that Power-of-Choice strategies converge up to 3 $\times$ faster
and give $10$% higher test accuracy than the baseline random selection.
| [
{
"created": "Sat, 3 Oct 2020 01:04:17 GMT",
"version": "v1"
}
] | 2020-10-06 | [
[
"Cho",
"Yae Jee",
""
],
[
"Wang",
"Jianyu",
""
],
[
"Joshi",
"Gauri",
""
]
] | Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Several works have analyzed the convergence of federated learning by accounting of data heterogeneity, communication and computation limitations, and partial client participation. However, they assume unbiased client participation, where clients are selected at random or in proportion of their data sizes. In this paper, we present the first convergence analysis of federated optimization for biased client selection strategies, and quantify how the selection bias affects convergence speed. We reveal that biasing client selection towards clients with higher local loss achieves faster error convergence. Using this insight, we propose Power-of-Choice, a communication- and computation-efficient client selection framework that can flexibly span the trade-off between convergence speed and solution bias. Our experiments demonstrate that Power-of-Choice strategies converge up to 3 $\times$ faster and give $10$% higher test accuracy than the baseline random selection. |
1704.03822 | Wenzhen Yuan | Wenzhen Yuan, Shaoxiong Wang, Siyuan Dong, Edward Adelson | Connecting Look and Feel: Associating the visual and tactile properties
of physical materials | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For machines to interact with the physical world, they must understand the
physical properties of objects and materials they encounter. We use fabrics as
an example of a deformable material with a rich set of mechanical properties. A
thin flexible fabric, when draped, tends to look different from a heavy stiff
fabric. It also feels different when touched. Using a collection of 118 fabric
sample, we captured color and depth images of draped fabrics along with tactile
data from a high resolution touch sensor. We then sought to associate the
information from vision and touch by jointly training CNNs across the three
modalities. Through the CNN, each input, regardless of the modality, generates
an embedding vector that records the fabric's physical property. By comparing
the embeddings, our system is able to look at a fabric image and predict how it
will feel, and vice versa. We also show that a system jointly trained on vision
and touch data can outperform a similar system trained only on visual data when
tested purely with visual inputs.
| [
{
"created": "Wed, 12 Apr 2017 16:28:14 GMT",
"version": "v1"
}
] | 2017-04-13 | [
[
"Yuan",
"Wenzhen",
""
],
[
"Wang",
"Shaoxiong",
""
],
[
"Dong",
"Siyuan",
""
],
[
"Adelson",
"Edward",
""
]
] | For machines to interact with the physical world, they must understand the physical properties of objects and materials they encounter. We use fabrics as an example of a deformable material with a rich set of mechanical properties. A thin flexible fabric, when draped, tends to look different from a heavy stiff fabric. It also feels different when touched. Using a collection of 118 fabric sample, we captured color and depth images of draped fabrics along with tactile data from a high resolution touch sensor. We then sought to associate the information from vision and touch by jointly training CNNs across the three modalities. Through the CNN, each input, regardless of the modality, generates an embedding vector that records the fabric's physical property. By comparing the embeddings, our system is able to look at a fabric image and predict how it will feel, and vice versa. We also show that a system jointly trained on vision and touch data can outperform a similar system trained only on visual data when tested purely with visual inputs. |
2404.18239 | Jinghan Jia | Jinghan Jia, Yihua Zhang, Yimeng Zhang, Jiancheng Liu, Bharat Runwal,
James Diffenderfer, Bhavya Kailkhura, Sijia Liu | SOUL: Unlocking the Power of Second-Order Optimization for LLM
Unlearning | null | null | null | null | cs.LG cs.CL | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have highlighted the necessity of effective
unlearning mechanisms to comply with data regulations and ethical AI practices.
LLM unlearning aims at removing undesired data influences and associated model
capabilities without compromising utility beyond the scope of unlearning. While
interest in studying LLM unlearning is growing, the impact of the optimizer
choice for LLM unlearning remains unexplored. In this work, we shed light on
the significance of optimizer selection in LLM unlearning for the first time,
establishing a clear connection between second-order optimization and influence
unlearning (a classical approach using influence functions to update the model
for data influence removal). This insight propels us to develop a second-order
optimization-based LLM unlearning framework, termed Second-Order UnLearning
(SOUL), which extends the static, one-shot model update using influence
unlearning to a dynamic, iterative unlearning process. Our extensive
experiments show that SOUL consistently outperforms conventional first-order
methods across various unlearning tasks, models, and metrics, indicating that
second-order optimization offers an effective and broadly applicable solution
for LLM unlearning. Codes are available at https://github.com/OPTML-Group/SOUL.
| [
{
"created": "Sun, 28 Apr 2024 16:31:32 GMT",
"version": "v1"
},
{
"created": "Fri, 31 May 2024 17:38:51 GMT",
"version": "v2"
},
{
"created": "Mon, 3 Jun 2024 01:10:53 GMT",
"version": "v3"
},
{
"created": "Mon, 24 Jun 2024 20:24:53 GMT",
"version": "v4"
}
] | 2024-06-26 | [
[
"Jia",
"Jinghan",
""
],
[
"Zhang",
"Yihua",
""
],
[
"Zhang",
"Yimeng",
""
],
[
"Liu",
"Jiancheng",
""
],
[
"Runwal",
"Bharat",
""
],
[
"Diffenderfer",
"James",
""
],
[
"Kailkhura",
"Bhavya",
""
],
[
"Liu",
"Sijia",
""
]
] | Large Language Models (LLMs) have highlighted the necessity of effective unlearning mechanisms to comply with data regulations and ethical AI practices. LLM unlearning aims at removing undesired data influences and associated model capabilities without compromising utility beyond the scope of unlearning. While interest in studying LLM unlearning is growing, the impact of the optimizer choice for LLM unlearning remains unexplored. In this work, we shed light on the significance of optimizer selection in LLM unlearning for the first time, establishing a clear connection between second-order optimization and influence unlearning (a classical approach using influence functions to update the model for data influence removal). This insight propels us to develop a second-order optimization-based LLM unlearning framework, termed Second-Order UnLearning (SOUL), which extends the static, one-shot model update using influence unlearning to a dynamic, iterative unlearning process. Our extensive experiments show that SOUL consistently outperforms conventional first-order methods across various unlearning tasks, models, and metrics, indicating that second-order optimization offers an effective and broadly applicable solution for LLM unlearning. Codes are available at https://github.com/OPTML-Group/SOUL. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.