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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1712.05244 | Hamdi Joudeh | Enrico Piovano, Hamdi Joudeh, Bruno Clerckx | Generalized Degrees of Freedom of the Symmetric Cache-Aided MISO
Broadcast Channel with Partial CSIT | first revision | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the cache-aided MISO broadcast channel (BC) in which a
multi-antenna transmitter serves $K$ single-antenna receivers, each equipped
with a cache memory. The transmitter has access to partial knowledge of the
channel state information. For a symmetric setting, in terms of channel
strength levels, partial channel knowledge levels and cache sizes, we
characterize the generalized degrees of freedom (GDoF) up to a constant
multiplicative factor. The achievability scheme exploits the interplay between
spatial multiplexing gains and coded-multicasting gain. On the other hand, a
cut-set-based argument in conjunction with a GDoF outer bound for a parallel
MISO BC under channel uncertainty are used for the converse. We further show
that the characterized order-optimal GDoF is also attained in a decentralized
setting, where no coordination is required for content placement in the caches.
| [
{
"created": "Thu, 14 Dec 2017 14:24:50 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Dec 2018 16:18:31 GMT",
"version": "v2"
}
] | 2018-12-05 | [
[
"Piovano",
"Enrico",
""
],
[
"Joudeh",
"Hamdi",
""
],
[
"Clerckx",
"Bruno",
""
]
] | We consider the cache-aided MISO broadcast channel (BC) in which a multi-antenna transmitter serves $K$ single-antenna receivers, each equipped with a cache memory. The transmitter has access to partial knowledge of the channel state information. For a symmetric setting, in terms of channel strength levels, partial channel knowledge levels and cache sizes, we characterize the generalized degrees of freedom (GDoF) up to a constant multiplicative factor. The achievability scheme exploits the interplay between spatial multiplexing gains and coded-multicasting gain. On the other hand, a cut-set-based argument in conjunction with a GDoF outer bound for a parallel MISO BC under channel uncertainty are used for the converse. We further show that the characterized order-optimal GDoF is also attained in a decentralized setting, where no coordination is required for content placement in the caches. |
1911.11856 | Jonathan Kuck | Jonathan Kuck and Tri Dao and Hamid Rezatofighi and Ashish Sabharwal
and Stefano Ermon | Approximating the Permanent by Sampling from Adaptive Partitions | 19 pages | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computing the permanent of a non-negative matrix is a core problem with
practical applications ranging from target tracking to statistical
thermodynamics. However, this problem is also #P-complete, which leaves little
hope for finding an exact solution that can be computed efficiently. While the
problem admits a fully polynomial randomized approximation scheme, this method
has seen little use because it is both inefficient in practice and difficult to
implement. We present AdaPart, a simple and efficient method for drawing exact
samples from an unnormalized distribution. Using AdaPart, we show how to
construct tight bounds on the permanent which hold with high probability, with
guaranteed polynomial runtime for dense matrices. We find that AdaPart can
provide empirical speedups exceeding 25x over prior sampling methods on
matrices that are challenging for variational based approaches. Finally, in the
context of multi-target tracking, exact sampling from the distribution defined
by the matrix permanent allows us to use the optimal proposal distribution
during particle filtering. Using AdaPart, we show that this leads to improved
tracking performance using an order of magnitude fewer samples.
| [
{
"created": "Tue, 26 Nov 2019 22:05:28 GMT",
"version": "v1"
}
] | 2019-11-28 | [
[
"Kuck",
"Jonathan",
""
],
[
"Dao",
"Tri",
""
],
[
"Rezatofighi",
"Hamid",
""
],
[
"Sabharwal",
"Ashish",
""
],
[
"Ermon",
"Stefano",
""
]
] | Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is also #P-complete, which leaves little hope for finding an exact solution that can be computed efficiently. While the problem admits a fully polynomial randomized approximation scheme, this method has seen little use because it is both inefficient in practice and difficult to implement. We present AdaPart, a simple and efficient method for drawing exact samples from an unnormalized distribution. Using AdaPart, we show how to construct tight bounds on the permanent which hold with high probability, with guaranteed polynomial runtime for dense matrices. We find that AdaPart can provide empirical speedups exceeding 25x over prior sampling methods on matrices that are challenging for variational based approaches. Finally, in the context of multi-target tracking, exact sampling from the distribution defined by the matrix permanent allows us to use the optimal proposal distribution during particle filtering. Using AdaPart, we show that this leads to improved tracking performance using an order of magnitude fewer samples. |
2408.04205 | Xinwei Chen | Xinwei Chen, Xiaofeng Zhong, Zijian Zhang, Linglong Dai and Shidong
Zhou | High-Efficiency Urban 3D Radio Map Estimation Based on Sparse
Measurements | 5 pages,7 figures | null | null | null | cs.IT math.IT | http://creativecommons.org/publicdomain/zero/1.0/ | Recent widespread applications for unmanned aerial vehicles (UAVs) -- from
infrastructure inspection to urban logistics -- have prompted an urgent need
for high-accuracy three-dimensional (3D) radio maps. However, existing methods
designed for two-dimensional radio maps face challenges of high measurement
costs and limited data availability when extended to 3D scenarios. To tackle
these challenges, we first build a real-world large-scale 3D radio map dataset,
covering over 4.2 million m^3 and over 4 thousand data points in complex urban
environments. We propose a Gaussian Process Regression-based scheme for 3D
radio map estimation, allowing us to realize more accurate map recovery with a
lower RMSE than state-of-the-art schemes by over 2.5 dB. To further enhance
data efficiency, we propose two methods for training point selection, including
an offline clustering-based method and an online maximum a posterior
(MAP)-based method. Extensive experiments demonstrate that the proposed scheme
not only achieves full-map recovery with only 2% of UAV measurements, but also
sheds light on future studies on 3D radio maps.
| [
{
"created": "Thu, 8 Aug 2024 04:05:18 GMT",
"version": "v1"
}
] | 2024-08-09 | [
[
"Chen",
"Xinwei",
""
],
[
"Zhong",
"Xiaofeng",
""
],
[
"Zhang",
"Zijian",
""
],
[
"Dai",
"Linglong",
""
],
[
"Zhou",
"Shidong",
""
]
] | Recent widespread applications for unmanned aerial vehicles (UAVs) -- from infrastructure inspection to urban logistics -- have prompted an urgent need for high-accuracy three-dimensional (3D) radio maps. However, existing methods designed for two-dimensional radio maps face challenges of high measurement costs and limited data availability when extended to 3D scenarios. To tackle these challenges, we first build a real-world large-scale 3D radio map dataset, covering over 4.2 million m^3 and over 4 thousand data points in complex urban environments. We propose a Gaussian Process Regression-based scheme for 3D radio map estimation, allowing us to realize more accurate map recovery with a lower RMSE than state-of-the-art schemes by over 2.5 dB. To further enhance data efficiency, we propose two methods for training point selection, including an offline clustering-based method and an online maximum a posterior (MAP)-based method. Extensive experiments demonstrate that the proposed scheme not only achieves full-map recovery with only 2% of UAV measurements, but also sheds light on future studies on 3D radio maps. |
2407.10011 | Joel Sol | Joel Sol, Jamil Fayyad, Shadi Alijani and Homayoun Najjaran | Sim-to-Real Domain Adaptation for Deformation Classification | 7 pages, 5 figures, submitted to SMC | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Deformation detection is vital for enabling accurate assessment and
prediction of structural changes in materials, ensuring timely and effective
interventions to maintain safety and integrity. Automating deformation
detection through computer vision is crucial for efficient monitoring, but it
faces significant challenges in creating a comprehensive dataset of both
deformed and non-deformed objects, which can be difficult to obtain in many
scenarios. In this paper, we introduce a novel framework for generating
controlled synthetic data that simulates deformed objects. This approach allows
for the realistic modeling of object deformations under various conditions. Our
framework integrates an intelligent adapter network that facilitates
sim-to-real domain adaptation, enhancing classification results without
requiring real data from deformed objects. We conduct experiments on domain
adaptation and classification tasks and demonstrate that our framework improves
sim-to-real classification results compared to simulation baseline.
| [
{
"created": "Sat, 13 Jul 2024 21:35:13 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Sol",
"Joel",
""
],
[
"Fayyad",
"Jamil",
""
],
[
"Alijani",
"Shadi",
""
],
[
"Najjaran",
"Homayoun",
""
]
] | Deformation detection is vital for enabling accurate assessment and prediction of structural changes in materials, ensuring timely and effective interventions to maintain safety and integrity. Automating deformation detection through computer vision is crucial for efficient monitoring, but it faces significant challenges in creating a comprehensive dataset of both deformed and non-deformed objects, which can be difficult to obtain in many scenarios. In this paper, we introduce a novel framework for generating controlled synthetic data that simulates deformed objects. This approach allows for the realistic modeling of object deformations under various conditions. Our framework integrates an intelligent adapter network that facilitates sim-to-real domain adaptation, enhancing classification results without requiring real data from deformed objects. We conduct experiments on domain adaptation and classification tasks and demonstrate that our framework improves sim-to-real classification results compared to simulation baseline. |
2102.08157 | Erixhen Sula | Erixhen Sula and Michael Gastpar | Lower bound on Wyner's Common Information | 6 pages, 3 figures | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | An important notion of common information between two random variables is due
to Wyner. In this paper, we derive a lower bound on Wyner's common information
for continuous random variables. The new bound improves on the only other
general lower bound on Wyner's common information, which is the mutual
information. We also show that the new lower bound is tight for the so-called
"Gaussian channels" case, namely, when the joint distribution of the random
variables can be written as the sum of a single underlying random variable and
Gaussian noises. We motivate this work from the recent variations of Wyner's
common information and applications to network data compression problems such
as the Gray-Wyner network.
| [
{
"created": "Tue, 16 Feb 2021 13:56:45 GMT",
"version": "v1"
}
] | 2021-02-17 | [
[
"Sula",
"Erixhen",
""
],
[
"Gastpar",
"Michael",
""
]
] | An important notion of common information between two random variables is due to Wyner. In this paper, we derive a lower bound on Wyner's common information for continuous random variables. The new bound improves on the only other general lower bound on Wyner's common information, which is the mutual information. We also show that the new lower bound is tight for the so-called "Gaussian channels" case, namely, when the joint distribution of the random variables can be written as the sum of a single underlying random variable and Gaussian noises. We motivate this work from the recent variations of Wyner's common information and applications to network data compression problems such as the Gray-Wyner network. |
2207.10797 | Hidetoshi Kawaguchi | Hidetoshi Kawaguchi, Yuichi Nakatani and Shogo Okada | IDPS Signature Classification with a Reject Option and the Incorporation
of Expert Knowledge | 9 pages, 5 figures, 3 tables | null | null | null | cs.CR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the importance of intrusion detection and prevention systems (IDPSs)
increases, great costs are incurred to manage the signatures that are generated
by malicious communication pattern files. Experts in network security need to
classify signatures by importance for an IDPS to work. We propose and evaluate
a machine learning signature classification model with a reject option (RO) to
reduce the cost of setting up an IDPS. To train the proposed model, it is
essential to design features that are effective for signature classification.
Experts classify signatures with predefined if-then rules. An if-then rule
returns a label of low, medium, high, or unknown importance based on keyword
matching of the elements in the signature. Therefore, we first design two types
of features, symbolic features (SFs) and keyword features (KFs), which are used
in keyword matching for the if-then rules. Next, we design web information and
message features (WMFs) to capture the properties of signatures that do not
match the if-then rules. The WMFs are extracted as term frequency-inverse
document frequency (TF-IDF) features of the message text in the signatures. The
features are obtained by web scraping from the referenced external attack
identification systems described in the signature. Because failure needs to be
minimized in the classification of IDPS signatures, as in the medical field, we
consider introducing a RO in our proposed model. The effectiveness of the
proposed classification model is evaluated in experiments with two real
datasets composed of signatures labeled by experts: a dataset that can be
classified with if-then rules and a dataset with elements that do not match an
if-then rule. In the experiment, the proposed model is evaluated. In both
cases, the combined SFs and WMFs performed better than the combined SFs and
KFs. In addition, we also performed feature analysis.
| [
{
"created": "Tue, 19 Jul 2022 06:09:33 GMT",
"version": "v1"
}
] | 2022-07-25 | [
[
"Kawaguchi",
"Hidetoshi",
""
],
[
"Nakatani",
"Yuichi",
""
],
[
"Okada",
"Shogo",
""
]
] | As the importance of intrusion detection and prevention systems (IDPSs) increases, great costs are incurred to manage the signatures that are generated by malicious communication pattern files. Experts in network security need to classify signatures by importance for an IDPS to work. We propose and evaluate a machine learning signature classification model with a reject option (RO) to reduce the cost of setting up an IDPS. To train the proposed model, it is essential to design features that are effective for signature classification. Experts classify signatures with predefined if-then rules. An if-then rule returns a label of low, medium, high, or unknown importance based on keyword matching of the elements in the signature. Therefore, we first design two types of features, symbolic features (SFs) and keyword features (KFs), which are used in keyword matching for the if-then rules. Next, we design web information and message features (WMFs) to capture the properties of signatures that do not match the if-then rules. The WMFs are extracted as term frequency-inverse document frequency (TF-IDF) features of the message text in the signatures. The features are obtained by web scraping from the referenced external attack identification systems described in the signature. Because failure needs to be minimized in the classification of IDPS signatures, as in the medical field, we consider introducing a RO in our proposed model. The effectiveness of the proposed classification model is evaluated in experiments with two real datasets composed of signatures labeled by experts: a dataset that can be classified with if-then rules and a dataset with elements that do not match an if-then rule. In the experiment, the proposed model is evaluated. In both cases, the combined SFs and WMFs performed better than the combined SFs and KFs. In addition, we also performed feature analysis. |
1804.05772 | Nils Gessert | Kaori V. Laino, Thore Saathoff, Thiusius R. Savarimuthu, Kim Lindberg
Schwaner, Nils Gessert, Alexander Schlaefer | Design and implementation of a wireless instrument adapter | Published at CURAC 2017 | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The evaluation of new methods for control and manipulation in minimally
invasive robotic surgery requires a realistic setup. To decouple the evaluation
of methods from overall clinical systems, we propose an instrument adapter for
the S line EndoWrist\c{opyright} instruments of the da Vinci surgical system.
The adapter is small and lightweight and can be mounted to any robot to mimic
motion. We describe its design and implementation, as well as a setup to
calibrate instruments to study precise motion control. Our results indicate
that each instrument requires individual calibration. The calibration shows
that the system is not fully linear. The repeatability of poses in the same
sense of rotation has an RMSE of 0.27{\deg}/ and a standard deviation below
0.3{\deg} for pitching and 4.7{\deg} for yawing averaged over three
measurements. When comparing the same poses in clockwise and counter-clockwise
direction the RMSE is 12.8{\deg} and 5.7{\deg} for pitching and yawing,
respectively. This is likely due to motor hysteresis.
| [
{
"created": "Mon, 16 Apr 2018 16:22:08 GMT",
"version": "v1"
},
{
"created": "Tue, 17 Apr 2018 10:33:39 GMT",
"version": "v2"
}
] | 2018-04-18 | [
[
"Laino",
"Kaori V.",
""
],
[
"Saathoff",
"Thore",
""
],
[
"Savarimuthu",
"Thiusius R.",
""
],
[
"Schwaner",
"Kim Lindberg",
""
],
[
"Gessert",
"Nils",
""
],
[
"Schlaefer",
"Alexander",
""
]
] | The evaluation of new methods for control and manipulation in minimally invasive robotic surgery requires a realistic setup. To decouple the evaluation of methods from overall clinical systems, we propose an instrument adapter for the S line EndoWrist\c{opyright} instruments of the da Vinci surgical system. The adapter is small and lightweight and can be mounted to any robot to mimic motion. We describe its design and implementation, as well as a setup to calibrate instruments to study precise motion control. Our results indicate that each instrument requires individual calibration. The calibration shows that the system is not fully linear. The repeatability of poses in the same sense of rotation has an RMSE of 0.27{\deg}/ and a standard deviation below 0.3{\deg} for pitching and 4.7{\deg} for yawing averaged over three measurements. When comparing the same poses in clockwise and counter-clockwise direction the RMSE is 12.8{\deg} and 5.7{\deg} for pitching and yawing, respectively. This is likely due to motor hysteresis. |
2211.09790 | James Smith | James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun
Kim, Rameswar Panda, David Cox, Diyi Yang, Zsolt Kira, Rogerio Feris, Leonid
Karlinsky | ConStruct-VL: Data-Free Continual Structured VL Concepts Learning | Accepted by the 2023 IEEE/CVF Conference on Computer Vision and
Pattern Recognition (CVPR 2023) | null | null | null | cs.LG cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, large-scale pre-trained Vision-and-Language (VL) foundation models
have demonstrated remarkable capabilities in many zero-shot downstream tasks,
achieving competitive results for recognizing objects defined by as little as
short text prompts. However, it has also been shown that VL models are still
brittle in Structured VL Concept (SVLC) reasoning, such as the ability to
recognize object attributes, states, and inter-object relations. This leads to
reasoning mistakes, which need to be corrected as they occur by teaching VL
models the missing SVLC skills; often this must be done using private data
where the issue was found, which naturally leads to a data-free continual (no
task-id) VL learning setting. In this work, we introduce the first Continual
Data-Free Structured VL Concepts Learning (ConStruct-VL) benchmark and show it
is challenging for many existing data-free CL strategies. We, therefore,
propose a data-free method comprised of a new approach of Adversarial
Pseudo-Replay (APR) which generates adversarial reminders of past tasks from
past task models. To use this method efficiently, we also propose a continual
parameter-efficient Layered-LoRA (LaLo) neural architecture allowing
no-memory-cost access to all past models at train time. We show this approach
outperforms all data-free methods by as much as ~7% while even matching some
levels of experience-replay (prohibitive for applications where data-privacy
must be preserved). Our code is publicly available at
https://github.com/jamessealesmith/ConStruct-VL
| [
{
"created": "Thu, 17 Nov 2022 18:57:03 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Mar 2023 17:59:16 GMT",
"version": "v2"
}
] | 2023-03-31 | [
[
"Smith",
"James Seale",
""
],
[
"Cascante-Bonilla",
"Paola",
""
],
[
"Arbelle",
"Assaf",
""
],
[
"Kim",
"Donghyun",
""
],
[
"Panda",
"Rameswar",
""
],
[
"Cox",
"David",
""
],
[
"Yang",
"Diyi",
""
],
[
"Kira",
"Zsolt",
""
],
[
"Feris",
"Rogerio",
""
],
[
"Karlinsky",
"Leonid",
""
]
] | Recently, large-scale pre-trained Vision-and-Language (VL) foundation models have demonstrated remarkable capabilities in many zero-shot downstream tasks, achieving competitive results for recognizing objects defined by as little as short text prompts. However, it has also been shown that VL models are still brittle in Structured VL Concept (SVLC) reasoning, such as the ability to recognize object attributes, states, and inter-object relations. This leads to reasoning mistakes, which need to be corrected as they occur by teaching VL models the missing SVLC skills; often this must be done using private data where the issue was found, which naturally leads to a data-free continual (no task-id) VL learning setting. In this work, we introduce the first Continual Data-Free Structured VL Concepts Learning (ConStruct-VL) benchmark and show it is challenging for many existing data-free CL strategies. We, therefore, propose a data-free method comprised of a new approach of Adversarial Pseudo-Replay (APR) which generates adversarial reminders of past tasks from past task models. To use this method efficiently, we also propose a continual parameter-efficient Layered-LoRA (LaLo) neural architecture allowing no-memory-cost access to all past models at train time. We show this approach outperforms all data-free methods by as much as ~7% while even matching some levels of experience-replay (prohibitive for applications where data-privacy must be preserved). Our code is publicly available at https://github.com/jamessealesmith/ConStruct-VL |
1510.06595 | Angela Yao | Bj\"orn Kr\"uger, Anna V\"ogele, Tobias Willig, Angela Yao, Reinhard
Klein, Andreas Weber | Efficient Unsupervised Temporal Segmentation of Motion Data | 15 pages, submitted to TPAMI | null | 10.1109/TMM.2016.2635030 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce a method for automated temporal segmentation of human motion
data into distinct actions and compositing motion primitives based on
self-similar structures in the motion sequence. We use neighbourhood graphs for
the partitioning and the similarity information in the graph is further
exploited to cluster the motion primitives into larger entities of semantic
significance. The method requires no assumptions about the motion sequences at
hand and no user interaction is required for the segmentation or clustering. In
addition, we introduce a feature bundling preprocessing technique to make the
segmentation more robust to noise, as well as a notion of motion symmetry for
more refined primitive detection. We test our method on several sensor
modalities, including markered and markerless motion capture as well as on
electromyograph and accelerometer recordings. The results highlight our
system's capabilities for both segmentation and for analysis of the finer
structures of motion data, all in a completely unsupervised manner.
| [
{
"created": "Thu, 22 Oct 2015 12:20:04 GMT",
"version": "v1"
}
] | 2021-12-07 | [
[
"Krüger",
"Björn",
""
],
[
"Vögele",
"Anna",
""
],
[
"Willig",
"Tobias",
""
],
[
"Yao",
"Angela",
""
],
[
"Klein",
"Reinhard",
""
],
[
"Weber",
"Andreas",
""
]
] | We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence. We use neighbourhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance. The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. In addition, we introduce a feature bundling preprocessing technique to make the segmentation more robust to noise, as well as a notion of motion symmetry for more refined primitive detection. We test our method on several sensor modalities, including markered and markerless motion capture as well as on electromyograph and accelerometer recordings. The results highlight our system's capabilities for both segmentation and for analysis of the finer structures of motion data, all in a completely unsupervised manner. |
1303.5508 | George Chen | George H. Chen, Christian Wachinger, Polina Golland | Sparse Projections of Medical Images onto Manifolds | International Conference on Information Processing in Medical Imaging
(IPMI 2013) | null | null | null | cs.CV cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Manifold learning has been successfully applied to a variety of medical
imaging problems. Its use in real-time applications requires fast projection
onto the low-dimensional space. To this end, out-of-sample extensions are
applied by constructing an interpolation function that maps from the input
space to the low-dimensional manifold. Commonly used approaches such as the
Nystr\"{o}m extension and kernel ridge regression require using all training
points. We propose an interpolation function that only depends on a small
subset of the input training data. Consequently, in the testing phase each new
point only needs to be compared against a small number of input training data
in order to project the point onto the low-dimensional space. We interpret our
method as an out-of-sample extension that approximates kernel ridge regression.
Our method involves solving a simple convex optimization problem and has the
attractive property of guaranteeing an upper bound on the approximation error,
which is crucial for medical applications. Tuning this error bound controls the
sparsity of the resulting interpolation function. We illustrate our method in
two clinical applications that require fast mapping of input images onto a
low-dimensional space.
| [
{
"created": "Fri, 22 Mar 2013 03:24:10 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Mar 2013 19:21:33 GMT",
"version": "v2"
}
] | 2013-03-29 | [
[
"Chen",
"George H.",
""
],
[
"Wachinger",
"Christian",
""
],
[
"Golland",
"Polina",
""
]
] | Manifold learning has been successfully applied to a variety of medical imaging problems. Its use in real-time applications requires fast projection onto the low-dimensional space. To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold. Commonly used approaches such as the Nystr\"{o}m extension and kernel ridge regression require using all training points. We propose an interpolation function that only depends on a small subset of the input training data. Consequently, in the testing phase each new point only needs to be compared against a small number of input training data in order to project the point onto the low-dimensional space. We interpret our method as an out-of-sample extension that approximates kernel ridge regression. Our method involves solving a simple convex optimization problem and has the attractive property of guaranteeing an upper bound on the approximation error, which is crucial for medical applications. Tuning this error bound controls the sparsity of the resulting interpolation function. We illustrate our method in two clinical applications that require fast mapping of input images onto a low-dimensional space. |
2403.01827 | Ankur Singh | Ankur Singh, Sanghyeon Choi, Gunuk Wang, Maryaradhiya Daimari, and
Byung-Geun Lee | Analysis and Fully Memristor-based Reservoir Computing for Temporal Data
Classification | 22 pages, 20 figures, Journal, Typo corrected and updated reference | null | null | null | cs.NE cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Reservoir computing (RC) offers a neuromorphic framework that is particularly
effective for processing spatiotemporal signals. Known for its temporal
processing prowess, RC significantly lowers training costs compared to
conventional recurrent neural networks. A key component in its hardware
deployment is the ability to generate dynamic reservoir states. Our research
introduces a novel dual-memory RC system, integrating a short-term memory via a
WOx-based memristor, capable of achieving 16 distinct states encoded over 4
bits, and a long-term memory component using a TiOx-based memristor within the
readout layer. We thoroughly examine both memristor types and leverage the RC
system to process temporal data sets. The performance of the proposed RC system
is validated through two benchmark tasks: isolated spoken digit recognition
with incomplete inputs and Mackey-Glass time series prediction. The system
delivered an impressive 98.84% accuracy in digit recognition and sustained a
low normalized root mean square error (NRMSE) of 0.036 in the time series
prediction task, underscoring its capability. This study illuminates the
adeptness of memristor-based RC systems in managing intricate temporal
challenges, laying the groundwork for further innovations in neuromorphic
computing.
| [
{
"created": "Mon, 4 Mar 2024 08:22:29 GMT",
"version": "v1"
},
{
"created": "Sat, 16 Mar 2024 15:43:04 GMT",
"version": "v2"
}
] | 2024-03-19 | [
[
"Singh",
"Ankur",
""
],
[
"Choi",
"Sanghyeon",
""
],
[
"Wang",
"Gunuk",
""
],
[
"Daimari",
"Maryaradhiya",
""
],
[
"Lee",
"Byung-Geun",
""
]
] | Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for processing spatiotemporal signals. Known for its temporal processing prowess, RC significantly lowers training costs compared to conventional recurrent neural networks. A key component in its hardware deployment is the ability to generate dynamic reservoir states. Our research introduces a novel dual-memory RC system, integrating a short-term memory via a WOx-based memristor, capable of achieving 16 distinct states encoded over 4 bits, and a long-term memory component using a TiOx-based memristor within the readout layer. We thoroughly examine both memristor types and leverage the RC system to process temporal data sets. The performance of the proposed RC system is validated through two benchmark tasks: isolated spoken digit recognition with incomplete inputs and Mackey-Glass time series prediction. The system delivered an impressive 98.84% accuracy in digit recognition and sustained a low normalized root mean square error (NRMSE) of 0.036 in the time series prediction task, underscoring its capability. This study illuminates the adeptness of memristor-based RC systems in managing intricate temporal challenges, laying the groundwork for further innovations in neuromorphic computing. |
2312.02521 | Haoran Tang | Haoran Tang, Xin Zhou, Jieren Deng, Zhihong Pan, Hao Tian, Pratik
Chaudhari | Retrieving Conditions from Reference Images for Diffusion Models | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Newly developed diffusion-based techniques have showcased phenomenal
abilities in producing a wide range of high-quality images, sparking
considerable interest in various applications. A prevalent scenario is to
generate new images based on a subject from reference images. This subject
could be face identity for styled avatars, body and clothing for virtual try-on
and so on. Satisfying this requirement is evolving into a field called
Subject-Driven Generation. In this paper, we consider Subject-Driven Generation
as a unified retrieval problem with diffusion models. We introduce a novel
diffusion model architecture, named RetriNet, designed to address and solve
these problems by retrieving subject attributes from reference images
precisely, and filter out irrelevant information. RetriNet demonstrates
impressive performance when compared to existing state-of-the-art approaches in
face generation. We further propose a research and iteration friendly dataset,
RetriBooru, to study a more difficult problem, concept composition. Finally, to
better evaluate alignment between similarity and diversity or measure diversity
that have been previously unaccounted for, we introduce a novel class of
metrics named Similarity Weighted Diversity (SWD).
| [
{
"created": "Tue, 5 Dec 2023 06:04:16 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Mar 2024 04:37:32 GMT",
"version": "v2"
}
] | 2024-03-18 | [
[
"Tang",
"Haoran",
""
],
[
"Zhou",
"Xin",
""
],
[
"Deng",
"Jieren",
""
],
[
"Pan",
"Zhihong",
""
],
[
"Tian",
"Hao",
""
],
[
"Chaudhari",
"Pratik",
""
]
] | Newly developed diffusion-based techniques have showcased phenomenal abilities in producing a wide range of high-quality images, sparking considerable interest in various applications. A prevalent scenario is to generate new images based on a subject from reference images. This subject could be face identity for styled avatars, body and clothing for virtual try-on and so on. Satisfying this requirement is evolving into a field called Subject-Driven Generation. In this paper, we consider Subject-Driven Generation as a unified retrieval problem with diffusion models. We introduce a novel diffusion model architecture, named RetriNet, designed to address and solve these problems by retrieving subject attributes from reference images precisely, and filter out irrelevant information. RetriNet demonstrates impressive performance when compared to existing state-of-the-art approaches in face generation. We further propose a research and iteration friendly dataset, RetriBooru, to study a more difficult problem, concept composition. Finally, to better evaluate alignment between similarity and diversity or measure diversity that have been previously unaccounted for, we introduce a novel class of metrics named Similarity Weighted Diversity (SWD). |
2102.10695 | Luis Puche Rondon | Luis Puche Rondon, Leonardo Babun, Ahmet Aris, Kemal Akkaya, and A.
Selcuk Uluagac | Survey on Enterprise Internet-of-Things Systems (E-IoT): A Security
Perspective | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | As technology becomes more widely available, millions of users worldwide have
installed some form of smart device in their homes or workplaces. These devices
are often off-the-shelf commodity systems, such as Google Home or Samsung
SmartThings, that are installed by end-users looking to automate a small
deployment. In contrast to these "plug-and-play" systems, purpose-built
Enterprise Internet-of-Things (E-IoT) systems such as Crestron, Control4, RTI,
Savant offer a smart solution for more sophisticated applications (e.g.,
complete lighting control, A/V management, security). In contrast to commodity
systems, E-IoT systems are usually closed source, costly, require certified
installers, and are overall more robust for their use cases. Due to this, E-IoT
systems are often found in expensive smart homes, government and academic
conference rooms, yachts, and smart private offices. However, while there has
been plenty of research on the topic of commodity systems, no current study
exists that provides a complete picture of E-IoT systems, their components, and
relevant threats. As such, lack of knowledge of E-IoT system threats, coupled
with the cost of E-IoT systems has led many to assume that E-IoT systems are
secure. To address this research gap, raise awareness on E-IoT security, and
motivate further research, this work emphasizes E-IoT system components, E-IoT
vulnerabilities, solutions, and their security implications. In order to
systematically analyze the security of E-IoT systems, we divide E-IoT systems
into four layers: E-IoT Devices Layer, Communications Layer, Monitoring and
Applications Layer, and Business Layer. We survey attacks and defense
mechanisms, considering the E-IoT components at each layer and the associated
threats. In addition, we present key observations in state-of-the-art E-IoT
security and provide a list of open research problems that need further
research.
| [
{
"created": "Sun, 21 Feb 2021 21:51:11 GMT",
"version": "v1"
}
] | 2021-02-23 | [
[
"Rondon",
"Luis Puche",
""
],
[
"Babun",
"Leonardo",
""
],
[
"Aris",
"Ahmet",
""
],
[
"Akkaya",
"Kemal",
""
],
[
"Uluagac",
"A. Selcuk",
""
]
] | As technology becomes more widely available, millions of users worldwide have installed some form of smart device in their homes or workplaces. These devices are often off-the-shelf commodity systems, such as Google Home or Samsung SmartThings, that are installed by end-users looking to automate a small deployment. In contrast to these "plug-and-play" systems, purpose-built Enterprise Internet-of-Things (E-IoT) systems such as Crestron, Control4, RTI, Savant offer a smart solution for more sophisticated applications (e.g., complete lighting control, A/V management, security). In contrast to commodity systems, E-IoT systems are usually closed source, costly, require certified installers, and are overall more robust for their use cases. Due to this, E-IoT systems are often found in expensive smart homes, government and academic conference rooms, yachts, and smart private offices. However, while there has been plenty of research on the topic of commodity systems, no current study exists that provides a complete picture of E-IoT systems, their components, and relevant threats. As such, lack of knowledge of E-IoT system threats, coupled with the cost of E-IoT systems has led many to assume that E-IoT systems are secure. To address this research gap, raise awareness on E-IoT security, and motivate further research, this work emphasizes E-IoT system components, E-IoT vulnerabilities, solutions, and their security implications. In order to systematically analyze the security of E-IoT systems, we divide E-IoT systems into four layers: E-IoT Devices Layer, Communications Layer, Monitoring and Applications Layer, and Business Layer. We survey attacks and defense mechanisms, considering the E-IoT components at each layer and the associated threats. In addition, we present key observations in state-of-the-art E-IoT security and provide a list of open research problems that need further research. |
2007.07990 | Thodoris Lykouris | Shuchi Chawla, Nikhil Devanur, Thodoris Lykouris | Static pricing for multi-unit prophet inequalities | null | null | null | null | cs.GT cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a pricing problem where a seller has $k$ identical copies of a
product, buyers arrive sequentially, and the seller prices the items aiming to
maximize social welfare. When $k=1$, this is the so called "prophet inequality"
problem for which there is a simple pricing scheme achieving a competitive
ratio of $1/2$. On the other end of the spectrum, as $k$ goes to infinity, the
asymptotic performance of both static and adaptive pricing is well understood.
We provide a static pricing scheme for the small-supply regime: where $k$ is
small but larger than $1$. Prior to our work, the best competitive ratio known
for this setting was the $1/2$ that follows from the single-unit prophet
inequality. Our pricing scheme is easy to describe as well as practical -- it
is anonymous, non-adaptive, and order-oblivious. We pick a single price that
equalizes the expected fraction of items sold and the probability that the
supply does not sell out before all customers are served; this price is then
offered to each customer while supply lasts. This extends an approach
introduced by Samuel-Cahn for the case of $k=1$. This pricing scheme achieves a
competitive ratio that increases gradually with the supply. Subsequent work by
Jiang, Ma, and Zhang shows that our pricing scheme is the optimal static
pricing for every value of $k$.
| [
{
"created": "Wed, 15 Jul 2020 20:57:29 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Dec 2021 02:21:18 GMT",
"version": "v2"
},
{
"created": "Wed, 18 Jan 2023 15:53:21 GMT",
"version": "v3"
},
{
"created": "Tue, 20 Jun 2023 11:01:00 GMT",
"version": "v4"
}
] | 2023-06-21 | [
[
"Chawla",
"Shuchi",
""
],
[
"Devanur",
"Nikhil",
""
],
[
"Lykouris",
"Thodoris",
""
]
] | We study a pricing problem where a seller has $k$ identical copies of a product, buyers arrive sequentially, and the seller prices the items aiming to maximize social welfare. When $k=1$, this is the so called "prophet inequality" problem for which there is a simple pricing scheme achieving a competitive ratio of $1/2$. On the other end of the spectrum, as $k$ goes to infinity, the asymptotic performance of both static and adaptive pricing is well understood. We provide a static pricing scheme for the small-supply regime: where $k$ is small but larger than $1$. Prior to our work, the best competitive ratio known for this setting was the $1/2$ that follows from the single-unit prophet inequality. Our pricing scheme is easy to describe as well as practical -- it is anonymous, non-adaptive, and order-oblivious. We pick a single price that equalizes the expected fraction of items sold and the probability that the supply does not sell out before all customers are served; this price is then offered to each customer while supply lasts. This extends an approach introduced by Samuel-Cahn for the case of $k=1$. This pricing scheme achieves a competitive ratio that increases gradually with the supply. Subsequent work by Jiang, Ma, and Zhang shows that our pricing scheme is the optimal static pricing for every value of $k$. |
2406.14446 | Shruthi K. Hiremath | Shruthi K. Hiremath and Thomas Ploetz | Maintenance Required: Updating and Extending Bootstrapped Human Activity
Recognition Systems for Smart Homes | 12 pages, 5 figures, accepted at The 6th International Conference on
Activity and Behavior Computing, under print at IEEE Explore | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Developing human activity recognition (HAR) systems for smart homes is not
straightforward due to varied layouts of the homes and their personalized
settings, as well as idiosyncratic behaviors of residents. As such,
off-the-shelf HAR systems are effective in limited capacity for an individual
home, and HAR systems often need to be derived "from scratch", which comes with
substantial efforts and often is burdensome to the resident. Previous work has
successfully targeted the initial phase. At the end of this initial phase, we
identify seed points. We build on bootstrapped HAR systems and introduce an
effective updating and extension procedure for continuous improvement of HAR
systems with the aim of keeping up with ever changing life circumstances. Our
method makes use of the seed points identified at the end of the initial
bootstrapping phase. A contrastive learning framework is trained using these
seed points and labels obtained for the same. This model is then used to
improve the segmentation accuracy of the identified prominent activities.
Improvements in the activity recognition system through this procedure help
model the majority of the routine activities in the smart home. We demonstrate
the effectiveness of our procedure through experiments on the CASAS datasets
that show the practical value of our approach.
| [
{
"created": "Thu, 20 Jun 2024 16:08:40 GMT",
"version": "v1"
}
] | 2024-06-21 | [
[
"Hiremath",
"Shruthi K.",
""
],
[
"Ploetz",
"Thomas",
""
]
] | Developing human activity recognition (HAR) systems for smart homes is not straightforward due to varied layouts of the homes and their personalized settings, as well as idiosyncratic behaviors of residents. As such, off-the-shelf HAR systems are effective in limited capacity for an individual home, and HAR systems often need to be derived "from scratch", which comes with substantial efforts and often is burdensome to the resident. Previous work has successfully targeted the initial phase. At the end of this initial phase, we identify seed points. We build on bootstrapped HAR systems and introduce an effective updating and extension procedure for continuous improvement of HAR systems with the aim of keeping up with ever changing life circumstances. Our method makes use of the seed points identified at the end of the initial bootstrapping phase. A contrastive learning framework is trained using these seed points and labels obtained for the same. This model is then used to improve the segmentation accuracy of the identified prominent activities. Improvements in the activity recognition system through this procedure help model the majority of the routine activities in the smart home. We demonstrate the effectiveness of our procedure through experiments on the CASAS datasets that show the practical value of our approach. |
2203.15589 | Xingyu Zhou | Xingyu Zhou and Bo Ji | On Kernelized Multi-Armed Bandits with Constraints | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | We study a stochastic bandit problem with a general unknown reward function
and a general unknown constraint function. Both functions can be non-linear
(even non-convex) and are assumed to lie in a reproducing kernel Hilbert space
(RKHS) with a bounded norm. This kernelized bandit setup strictly generalizes
standard multi-armed bandits and linear bandits. In contrast to safety-type
hard constraints studied in prior works, we consider soft constraints that may
be violated in any round as long as the cumulative violations are small, which
is motivated by various practical applications. Our ultimate goal is to study
how to utilize the nature of soft constraints to attain a finer
complexity-regret-constraint trade-off in the kernelized bandit setting. To
this end, leveraging primal-dual optimization, we propose a general framework
for both algorithm design and performance analysis. This framework builds upon
a novel sufficient condition, which not only is satisfied under general
exploration strategies, including \emph{upper confidence bound} (UCB),
\emph{Thompson sampling} (TS), and new ones based on \emph{random exploration},
but also enables a unified analysis for showing both sublinear regret and
sublinear or even zero constraint violation. We demonstrate the superior
performance of our proposed algorithms via numerical experiments based on both
synthetic and real-world datasets. Along the way, we also make the first
detailed comparison between two popular methods for analyzing constrained
bandits and Markov decision processes (MDPs) by discussing the key difference
and some subtleties in the analysis, which could be of independent interest to
the communities.
| [
{
"created": "Tue, 29 Mar 2022 14:02:03 GMT",
"version": "v1"
}
] | 2022-03-30 | [
[
"Zhou",
"Xingyu",
""
],
[
"Ji",
"Bo",
""
]
] | We study a stochastic bandit problem with a general unknown reward function and a general unknown constraint function. Both functions can be non-linear (even non-convex) and are assumed to lie in a reproducing kernel Hilbert space (RKHS) with a bounded norm. This kernelized bandit setup strictly generalizes standard multi-armed bandits and linear bandits. In contrast to safety-type hard constraints studied in prior works, we consider soft constraints that may be violated in any round as long as the cumulative violations are small, which is motivated by various practical applications. Our ultimate goal is to study how to utilize the nature of soft constraints to attain a finer complexity-regret-constraint trade-off in the kernelized bandit setting. To this end, leveraging primal-dual optimization, we propose a general framework for both algorithm design and performance analysis. This framework builds upon a novel sufficient condition, which not only is satisfied under general exploration strategies, including \emph{upper confidence bound} (UCB), \emph{Thompson sampling} (TS), and new ones based on \emph{random exploration}, but also enables a unified analysis for showing both sublinear regret and sublinear or even zero constraint violation. We demonstrate the superior performance of our proposed algorithms via numerical experiments based on both synthetic and real-world datasets. Along the way, we also make the first detailed comparison between two popular methods for analyzing constrained bandits and Markov decision processes (MDPs) by discussing the key difference and some subtleties in the analysis, which could be of independent interest to the communities. |
2310.04778 | Edgar Martinez-Moro | Yang Li, Shixin Zhu and Edgar Mart\'inez-Moro | On $\ell$-MDS codes and a conjecture on infinite families of $1$-MDS
codes | null | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The class of $\ell$-maximum distance separable ($\ell$-MDS) codes {is a}
generalization of maximum distance separable (MDS) codes {that} has attracted a
lot of attention due to its applications in several areas such as secret
sharing schemes, index coding problems, informed source coding problems, and
combinatorial $t$-designs. In this paper, for $\ell=1$, we completely solve a
conjecture recently proposed by Heng $et~al.$ (Discrete Mathematics, 346(10):
113538, 2023) and obtain infinite families of $1$-MDS codes with general
dimensions holding $2$-designs. These later codes are also been proven to be
optimal locally recoverable codes. For general {positive integers} $\ell$ and
$\ell'$, we construct new $\ell$-MDS codes from known $\ell'$-MDS codes via
some classical propagation rules involving the extended, expurgated, and
$(u,u+v)$ constructions. Finally, we study some general results including
characterization, weight distributions, and bounds on maximum lengths of
$\ell$-MDS codes, which generalize, simplify, or improve some known results in
the literature.
| [
{
"created": "Sat, 7 Oct 2023 11:19:51 GMT",
"version": "v1"
}
] | 2023-10-10 | [
[
"Li",
"Yang",
""
],
[
"Zhu",
"Shixin",
""
],
[
"Martínez-Moro",
"Edgar",
""
]
] | The class of $\ell$-maximum distance separable ($\ell$-MDS) codes {is a} generalization of maximum distance separable (MDS) codes {that} has attracted a lot of attention due to its applications in several areas such as secret sharing schemes, index coding problems, informed source coding problems, and combinatorial $t$-designs. In this paper, for $\ell=1$, we completely solve a conjecture recently proposed by Heng $et~al.$ (Discrete Mathematics, 346(10): 113538, 2023) and obtain infinite families of $1$-MDS codes with general dimensions holding $2$-designs. These later codes are also been proven to be optimal locally recoverable codes. For general {positive integers} $\ell$ and $\ell'$, we construct new $\ell$-MDS codes from known $\ell'$-MDS codes via some classical propagation rules involving the extended, expurgated, and $(u,u+v)$ constructions. Finally, we study some general results including characterization, weight distributions, and bounds on maximum lengths of $\ell$-MDS codes, which generalize, simplify, or improve some known results in the literature. |
1705.02257 | Sascha Witt | Michael Axtmann, Sascha Witt, Daniel Ferizovic, Peter Sanders | In-place Parallel Super Scalar Samplesort (IPS$^4$o) | null | null | null | null | cs.DC | http://creativecommons.org/licenses/by/4.0/ | We present a sorting algorithm that works in-place, executes in parallel, is
cache-efficient, avoids branch-mispredictions, and performs work O(n log n) for
arbitrary inputs with high probability. The main algorithmic contributions are
new ways to make distribution-based algorithms in-place: On the practical side,
by using coarse-grained block-based permutations, and on the theoretical side,
we show how to eliminate the recursion stack. Extensive experiments show that
our algorithm IPS$^4$o scales well on a variety of multi-core machines. We
outperform our closest in-place competitor by a factor of up to 3. Even as a
sequential algorithm, we are up to 1.5 times faster than the closest sequential
competitor, BlockQuicksort.
| [
{
"created": "Fri, 5 May 2017 15:18:08 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Jun 2017 18:27:34 GMT",
"version": "v2"
}
] | 2017-07-03 | [
[
"Axtmann",
"Michael",
""
],
[
"Witt",
"Sascha",
""
],
[
"Ferizovic",
"Daniel",
""
],
[
"Sanders",
"Peter",
""
]
] | We present a sorting algorithm that works in-place, executes in parallel, is cache-efficient, avoids branch-mispredictions, and performs work O(n log n) for arbitrary inputs with high probability. The main algorithmic contributions are new ways to make distribution-based algorithms in-place: On the practical side, by using coarse-grained block-based permutations, and on the theoretical side, we show how to eliminate the recursion stack. Extensive experiments show that our algorithm IPS$^4$o scales well on a variety of multi-core machines. We outperform our closest in-place competitor by a factor of up to 3. Even as a sequential algorithm, we are up to 1.5 times faster than the closest sequential competitor, BlockQuicksort. |
1309.1780 | Anshu Dubey | A. Dubey, S. Brandt, R. Brower, M. Giles, P. Hovland, D.Q. Lamb, F.
Loffler, B. Norris, B. OShea, C. Rebbi, M. Snir, R. Thakur | Software Abstractions and Methodologies for HPC Simulation Codes on
Future Architectures | Position Paper | null | 10.5334/jors.aw | null | cs.CE cs.MS cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large, complex, multi-scale, multi-physics simulation codes, running on high
performance com-puting (HPC) platforms, have become essential to advancing
science and engineering. These codes simulate multi-scale, multi-physics
phenomena with unprecedented fidelity on petascale platforms, and are used by
large communities. Continued ability of these codes to run on future platforms
is as crucial to their communities as continued improvements in instruments and
facilities are to experimental scientists. However, the ability of code
developers to do these things faces a serious challenge with the paradigm shift
underway in platform architecture. The complexity and uncertainty of the future
platforms makes it essential to approach this challenge cooperatively as a
community. We need to develop common abstractions, frameworks, programming
models and software development methodologies that can be applied across a
broad range of complex simulation codes, and common software infrastructure to
support them. In this position paper we express and discuss our belief that
such an infrastructure is critical to the deployment of existing and new large,
multi-scale, multi-physics codes on future HPC platforms.
| [
{
"created": "Fri, 6 Sep 2013 21:41:20 GMT",
"version": "v1"
}
] | 2014-10-24 | [
[
"Dubey",
"A.",
""
],
[
"Brandt",
"S.",
""
],
[
"Brower",
"R.",
""
],
[
"Giles",
"M.",
""
],
[
"Hovland",
"P.",
""
],
[
"Lamb",
"D. Q.",
""
],
[
"Loffler",
"F.",
""
],
[
"Norris",
"B.",
""
],
[
"OShea",
"B.",
""
],
[
"Rebbi",
"C.",
""
],
[
"Snir",
"M.",
""
],
[
"Thakur",
"R.",
""
]
] | Large, complex, multi-scale, multi-physics simulation codes, running on high performance com-puting (HPC) platforms, have become essential to advancing science and engineering. These codes simulate multi-scale, multi-physics phenomena with unprecedented fidelity on petascale platforms, and are used by large communities. Continued ability of these codes to run on future platforms is as crucial to their communities as continued improvements in instruments and facilities are to experimental scientists. However, the ability of code developers to do these things faces a serious challenge with the paradigm shift underway in platform architecture. The complexity and uncertainty of the future platforms makes it essential to approach this challenge cooperatively as a community. We need to develop common abstractions, frameworks, programming models and software development methodologies that can be applied across a broad range of complex simulation codes, and common software infrastructure to support them. In this position paper we express and discuss our belief that such an infrastructure is critical to the deployment of existing and new large, multi-scale, multi-physics codes on future HPC platforms. |
2102.05456 | Leo Laugier | Leo Laugier, John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon | Civil Rephrases Of Toxic Texts With Self-Supervised Transformers | null | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Platforms that support online commentary, from social networks to news sites,
are increasingly leveraging machine learning to assist their moderation
efforts. But this process does not typically provide feedback to the author
that would help them contribute according to the community guidelines. This is
prohibitively time-consuming for human moderators to do, and computational
approaches are still nascent. This work focuses on models that can help suggest
rephrasings of toxic comments in a more civil manner. Inspired by recent
progress in unpaired sequence-to-sequence tasks, a self-supervised learning
model is introduced, called CAE-T5. CAE-T5 employs a pre-trained text-to-text
transformer, which is fine tuned with a denoising and cyclic auto-encoder loss.
Experimenting with the largest toxicity detection dataset to date (Civil
Comments) our model generates sentences that are more fluent and better at
preserving the initial content compared to earlier text style transfer systems
which we compare with using several scoring systems and human evaluation.
| [
{
"created": "Mon, 1 Feb 2021 15:27:52 GMT",
"version": "v1"
},
{
"created": "Thu, 11 Feb 2021 14:11:35 GMT",
"version": "v2"
}
] | 2021-02-12 | [
[
"Laugier",
"Leo",
""
],
[
"Pavlopoulos",
"John",
""
],
[
"Sorensen",
"Jeffrey",
""
],
[
"Dixon",
"Lucas",
""
]
] | Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does not typically provide feedback to the author that would help them contribute according to the community guidelines. This is prohibitively time-consuming for human moderators to do, and computational approaches are still nascent. This work focuses on models that can help suggest rephrasings of toxic comments in a more civil manner. Inspired by recent progress in unpaired sequence-to-sequence tasks, a self-supervised learning model is introduced, called CAE-T5. CAE-T5 employs a pre-trained text-to-text transformer, which is fine tuned with a denoising and cyclic auto-encoder loss. Experimenting with the largest toxicity detection dataset to date (Civil Comments) our model generates sentences that are more fluent and better at preserving the initial content compared to earlier text style transfer systems which we compare with using several scoring systems and human evaluation. |
1204.6563 | Prabhu Kaliamoorthi Mr | Prabhu Kaliamoorthi and Ramakrishna Kakarala | Parametric annealing: a stochastic search method for human pose tracking | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Model based methods to marker-free motion capture have a very high
computational overhead that make them unattractive. In this paper we describe a
method that improves on existing global optimization techniques to tracking
articulated objects. Our method improves on the state-of-the-art Annealed
Particle Filter (APF) by reusing samples across annealing layers and by using
an adaptive parametric density for diffusion. We compare the proposed method
with APF on a scalable problem and study how the two methods scale with the
dimensionality, multi-modality and the range of search. Then we perform
sensitivity analysis on the parameters of our algorithm and show that it
tolerates a wide range of parameter settings. We also show results on tracking
human pose from the widely-used Human Eva I dataset. Our results show that the
proposed method reduces the tracking error despite using less than 50% of the
computational resources as APF. The tracked output also shows a significant
qualitative improvement over APF as demonstrated through image and video
results.
| [
{
"created": "Mon, 30 Apr 2012 07:04:08 GMT",
"version": "v1"
},
{
"created": "Wed, 2 May 2012 04:37:03 GMT",
"version": "v2"
}
] | 2012-05-03 | [
[
"Kaliamoorthi",
"Prabhu",
""
],
[
"Kakarala",
"Ramakrishna",
""
]
] | Model based methods to marker-free motion capture have a very high computational overhead that make them unattractive. In this paper we describe a method that improves on existing global optimization techniques to tracking articulated objects. Our method improves on the state-of-the-art Annealed Particle Filter (APF) by reusing samples across annealing layers and by using an adaptive parametric density for diffusion. We compare the proposed method with APF on a scalable problem and study how the two methods scale with the dimensionality, multi-modality and the range of search. Then we perform sensitivity analysis on the parameters of our algorithm and show that it tolerates a wide range of parameter settings. We also show results on tracking human pose from the widely-used Human Eva I dataset. Our results show that the proposed method reduces the tracking error despite using less than 50% of the computational resources as APF. The tracked output also shows a significant qualitative improvement over APF as demonstrated through image and video results. |
2403.01792 | Kuan-Hsun Ho | Kuan-Hsun Ho, Jeih-weih Hung, and Berlin Chen | ConSep: a Noise- and Reverberation-Robust Speech Separation Framework by
Magnitude Conditioning | null | null | null | null | cs.SD eess.AS | http://creativecommons.org/licenses/by-sa/4.0/ | Speech separation has recently made significant progress thanks to the
fine-grained vision used in time-domain methods. However, several studies have
shown that adopting Short-Time Fourier Transform (STFT) for feature extraction
could be beneficial when encountering harsher conditions, such as noise or
reverberation. Therefore, we propose a magnitude-conditioned time-domain
framework, ConSep, to inherit the beneficial characteristics. The experiment
shows that ConSep promotes performance in anechoic, noisy, and reverberant
settings compared to two celebrated methods, SepFormer and Bi-Sep. Furthermore,
we visualize the components of ConSep to strengthen the advantages and cohere
with the actualities we have found in preliminary studies.
| [
{
"created": "Mon, 4 Mar 2024 07:34:24 GMT",
"version": "v1"
}
] | 2024-03-05 | [
[
"Ho",
"Kuan-Hsun",
""
],
[
"Hung",
"Jeih-weih",
""
],
[
"Chen",
"Berlin",
""
]
] | Speech separation has recently made significant progress thanks to the fine-grained vision used in time-domain methods. However, several studies have shown that adopting Short-Time Fourier Transform (STFT) for feature extraction could be beneficial when encountering harsher conditions, such as noise or reverberation. Therefore, we propose a magnitude-conditioned time-domain framework, ConSep, to inherit the beneficial characteristics. The experiment shows that ConSep promotes performance in anechoic, noisy, and reverberant settings compared to two celebrated methods, SepFormer and Bi-Sep. Furthermore, we visualize the components of ConSep to strengthen the advantages and cohere with the actualities we have found in preliminary studies. |
2010.05675 | Patrick Lambein-Monette | Bernadette Charron-Bost and Patrick Lambein-Monette | Average Consensus: A Little Learning Goes A Long Way | null | null | null | null | cs.DC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | When networked systems of autonomous agents carry out complex tasks, the
control and coordination sought after generally depend on a few fundamental
control primitives. Chief among these primitives is consensus, where agents are
to converge to a common estimate within the range of initial values, which
becomes average consensus when the joint limit should be the average of the
initial values. To provide reliable services that are easy to deploy, these
primitives should operate even when the network is subject to frequent and
unpredictable changes. Moreover, they should mobilize few computational
resources so that low powered, deterministic, and anonymous agents can partake
in the network. In this stringent adversarial context, we investigate the
distributed implementation of these primitives over networks with
bidirectional, but potentially short-lived, communication links. Inspired by
the classic EqualNeighbor and Metropolis agreement rules for multi-agent
systems, we design distributed algorithms for consensus and average consensus,
which we show to operate in polynomial time in a synchronous temporal model.
These algorithms are fully distributed, requiring neither symmetry-breaking
devices such as unique identifiers, nor global control or knowledge of the
network. Our strategy consists in making agents learn simple structural
parameters of the network -- namely, their largest degrees -- which constitutes
enough information to build simple update rules, implementable locally with
little computational and memory overhead.
| [
{
"created": "Mon, 12 Oct 2020 13:15:33 GMT",
"version": "v1"
}
] | 2020-10-13 | [
[
"Charron-Bost",
"Bernadette",
""
],
[
"Lambein-Monette",
"Patrick",
""
]
] | When networked systems of autonomous agents carry out complex tasks, the control and coordination sought after generally depend on a few fundamental control primitives. Chief among these primitives is consensus, where agents are to converge to a common estimate within the range of initial values, which becomes average consensus when the joint limit should be the average of the initial values. To provide reliable services that are easy to deploy, these primitives should operate even when the network is subject to frequent and unpredictable changes. Moreover, they should mobilize few computational resources so that low powered, deterministic, and anonymous agents can partake in the network. In this stringent adversarial context, we investigate the distributed implementation of these primitives over networks with bidirectional, but potentially short-lived, communication links. Inspired by the classic EqualNeighbor and Metropolis agreement rules for multi-agent systems, we design distributed algorithms for consensus and average consensus, which we show to operate in polynomial time in a synchronous temporal model. These algorithms are fully distributed, requiring neither symmetry-breaking devices such as unique identifiers, nor global control or knowledge of the network. Our strategy consists in making agents learn simple structural parameters of the network -- namely, their largest degrees -- which constitutes enough information to build simple update rules, implementable locally with little computational and memory overhead. |
2304.06335 | Chien-Pin Liu | Chien-Pin Liu, Ju-Hsuan Li, En-Ping Chu, Chia-Yeh Hsieh, Kai-Chun Liu,
Chia-Tai Chan, Yu Tsao | Deep Learning-based Fall Detection Algorithm Using Ensemble Model of
Coarse-fine CNN and GRU Networks | null | null | null | null | cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Falls are the public health issue for the elderly all over the world since
the fall-induced injuries are associated with a large amount of healthcare
cost. Falls can cause serious injuries, even leading to death if the elderly
suffers a "long-lie". Hence, a reliable fall detection (FD) system is required
to provide an emergency alarm for first aid. Due to the advances in wearable
device technology and artificial intelligence, some fall detection systems have
been developed using machine learning and deep learning methods to analyze the
signal collected from accelerometer and gyroscopes. In order to achieve better
fall detection performance, an ensemble model that combines a coarse-fine
convolutional neural network and gated recurrent unit is proposed in this
study. The parallel structure design used in this model restores the different
grains of spatial characteristics and capture temporal dependencies for feature
representation. This study applies the FallAllD public dataset to validate the
reliability of the proposed model, which achieves a recall, precision, and
F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate
the reliability of the proposed ensemble model in discriminating falls from
daily living activities and its superior performance compared to the
state-of-the-art convolutional neural network long short-term memory (CNN-LSTM)
for FD.
| [
{
"created": "Thu, 13 Apr 2023 08:30:46 GMT",
"version": "v1"
}
] | 2023-04-14 | [
[
"Liu",
"Chien-Pin",
""
],
[
"Li",
"Ju-Hsuan",
""
],
[
"Chu",
"En-Ping",
""
],
[
"Hsieh",
"Chia-Yeh",
""
],
[
"Liu",
"Kai-Chun",
""
],
[
"Chan",
"Chia-Tai",
""
],
[
"Tsao",
"Yu",
""
]
] | Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD. |
1812.09298 | Shibashis Guha | Benjamin Bordais, Shibashis Guha, Jean-Fran\c{c}ois Raskin | Expected Window Mean-Payoff | Replaced PP-hardness of direct fixed window objective with
PSPACE-hardness, added alternative definition of window mean-payoff | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the window mean-payoff objective, given an infinite path, instead of
considering a long run average, we consider the minimum payoff that can be
ensured at every position of the path over a finite window that slides over the
entire path. Chatterjee et al. studied the problem to decide if in a two-player
game, Player 1 has a strategy to ensure a window mean-payoff of at least 0.
In this work, we consider a function that given a path returns the supremum
value of the window mean-payoff that can be ensured over the path and we show
how to compute its expected value in Markov chains and Markov decision
processes. We consider two variants of the function: Fixed window mean-payoff
in which a fixed window length $l_{max}$ is provided; and Bounded window
mean-payoff in which we compute the maximum possible value of the window
mean-payoff over all possible window lengths. Further, for both variants, we
consider (i) a direct version of the problem where for each path, the payoff
that can be ensured from its very beginning and (ii) a non-direct version that
is the prefix independent counterpart of the direct version of the problem.
| [
{
"created": "Fri, 21 Dec 2018 18:19:00 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Dec 2019 20:24:26 GMT",
"version": "v2"
}
] | 2019-12-09 | [
[
"Bordais",
"Benjamin",
""
],
[
"Guha",
"Shibashis",
""
],
[
"Raskin",
"Jean-François",
""
]
] | In the window mean-payoff objective, given an infinite path, instead of considering a long run average, we consider the minimum payoff that can be ensured at every position of the path over a finite window that slides over the entire path. Chatterjee et al. studied the problem to decide if in a two-player game, Player 1 has a strategy to ensure a window mean-payoff of at least 0. In this work, we consider a function that given a path returns the supremum value of the window mean-payoff that can be ensured over the path and we show how to compute its expected value in Markov chains and Markov decision processes. We consider two variants of the function: Fixed window mean-payoff in which a fixed window length $l_{max}$ is provided; and Bounded window mean-payoff in which we compute the maximum possible value of the window mean-payoff over all possible window lengths. Further, for both variants, we consider (i) a direct version of the problem where for each path, the payoff that can be ensured from its very beginning and (ii) a non-direct version that is the prefix independent counterpart of the direct version of the problem. |
2404.03263 | Sean Farhat | Sean Farhat, Deming Chen | On the Surprising Efficacy of Distillation as an Alternative to
Pre-Training Small Models | ICLR 2024. 5th Workshop on Practical ML for Low Resource Settings
(PML4LRS). Code can be found at https://github.com/sfarhat/dapt | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we propose that small models may not need to absorb the cost
of pre-training to reap its benefits. Instead, they can capitalize on the
astonishing results achieved by modern, enormous models to a surprising degree.
We observe that, when distilled on a task from a pre-trained teacher model, a
small model can achieve or surpass the performance it would achieve if it was
pre-trained then finetuned on that task. To allow this phenomenon to be easily
leveraged, we establish a connection reducing knowledge distillation to modern
contrastive learning, opening two doors: (1) vastly different model
architecture pairings can work for the distillation, and (2) most contrastive
learning algorithms rooted in the theory of Noise Contrastive Estimation can be
easily applied and used. We demonstrate this paradigm using pre-trained teacher
models from open-source model hubs, Transformer and convolution based model
combinations, and a novel distillation algorithm that massages the
Alignment/Uniformity perspective of contrastive learning by Wang & Isola (2020)
into a distillation objective. We choose this flavor of contrastive learning
due to its low computational cost, an overarching theme of this work. We also
observe that this phenomenon tends not to occur if the task is data-limited.
However, this can be alleviated by leveraging yet another scale-inspired
development: large, pre-trained generative models for dataset augmentation.
Again, we use an open-source model, and our rudimentary prompts are sufficient
to boost the small model`s performance. Thus, we highlight a training method
for small models that is up to 94% faster than the standard pre-training
paradigm without sacrificing performance. For practitioners discouraged from
fully utilizing modern foundation datasets for their small models due to the
prohibitive scale, we believe our work keeps that door open.
| [
{
"created": "Thu, 4 Apr 2024 07:38:11 GMT",
"version": "v1"
},
{
"created": "Fri, 3 May 2024 06:08:30 GMT",
"version": "v2"
}
] | 2024-05-06 | [
[
"Farhat",
"Sean",
""
],
[
"Chen",
"Deming",
""
]
] | In this paper, we propose that small models may not need to absorb the cost of pre-training to reap its benefits. Instead, they can capitalize on the astonishing results achieved by modern, enormous models to a surprising degree. We observe that, when distilled on a task from a pre-trained teacher model, a small model can achieve or surpass the performance it would achieve if it was pre-trained then finetuned on that task. To allow this phenomenon to be easily leveraged, we establish a connection reducing knowledge distillation to modern contrastive learning, opening two doors: (1) vastly different model architecture pairings can work for the distillation, and (2) most contrastive learning algorithms rooted in the theory of Noise Contrastive Estimation can be easily applied and used. We demonstrate this paradigm using pre-trained teacher models from open-source model hubs, Transformer and convolution based model combinations, and a novel distillation algorithm that massages the Alignment/Uniformity perspective of contrastive learning by Wang & Isola (2020) into a distillation objective. We choose this flavor of contrastive learning due to its low computational cost, an overarching theme of this work. We also observe that this phenomenon tends not to occur if the task is data-limited. However, this can be alleviated by leveraging yet another scale-inspired development: large, pre-trained generative models for dataset augmentation. Again, we use an open-source model, and our rudimentary prompts are sufficient to boost the small model`s performance. Thus, we highlight a training method for small models that is up to 94% faster than the standard pre-training paradigm without sacrificing performance. For practitioners discouraged from fully utilizing modern foundation datasets for their small models due to the prohibitive scale, we believe our work keeps that door open. |
1710.07991 | Mrinal Haloi | Mrinal Haloi | Rethinking Convolutional Semantic Segmentation Learning | null | null | null | null | cs.LG cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep convolutional semantic segmentation (DCSS) learning doesn't converge to
an optimal local minimum with random parameters initializations; a pre-trained
model on the same domain becomes necessary to achieve convergence.In this work,
we propose a joint cooperative end-to-end learning method for DCSS. It
addresses many drawbacks with existing deep semantic segmentation learning; the
proposed approach simultaneously learn both segmentation and classification;
taking away the essential need of the pre-trained model for learning
convergence. We present an improved inception based architecture with partial
attention gating (PAG) over encoder information. The PAG also adds to achieve
faster convergence and better accuracy for segmentation task. We will show the
effectiveness of this learning on a diabetic retinopathy classification and
segmentation dataset.
| [
{
"created": "Sun, 22 Oct 2017 18:13:24 GMT",
"version": "v1"
}
] | 2017-10-24 | [
[
"Haloi",
"Mrinal",
""
]
] | Deep convolutional semantic segmentation (DCSS) learning doesn't converge to an optimal local minimum with random parameters initializations; a pre-trained model on the same domain becomes necessary to achieve convergence.In this work, we propose a joint cooperative end-to-end learning method for DCSS. It addresses many drawbacks with existing deep semantic segmentation learning; the proposed approach simultaneously learn both segmentation and classification; taking away the essential need of the pre-trained model for learning convergence. We present an improved inception based architecture with partial attention gating (PAG) over encoder information. The PAG also adds to achieve faster convergence and better accuracy for segmentation task. We will show the effectiveness of this learning on a diabetic retinopathy classification and segmentation dataset. |
1207.3437 | Massimiliano Vasile | Massimiliano Vasile | Robust Mission Design Through Evidence Theory and Multi-Agent
Collaborative Search | null | Annals of the New York Academy of Science, Volume 1065, New Trends
in Astrodynamics and Applications pages 152-173, December 2005 | 10.1196/annals.1370.024 | null | cs.CE cs.NE cs.SY math.OC math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, the preliminary design of a space mission is approached
introducing uncertainties on the design parameters and formulating the
resulting reliable design problem as a multiobjective optimization problem.
Uncertainties are modelled through evidence theory and the belief, or
credibility, in the successful achievement of mission goals is maximised along
with the reliability of constraint satisfaction. The multiobjective
optimisation problem is solved through a novel algorithm based on the
collaboration of a population of agents in search for the set of highly
reliable solutions. Two typical problems in mission analysis are used to
illustrate the proposed methodology.
| [
{
"created": "Sat, 14 Jul 2012 16:17:52 GMT",
"version": "v1"
}
] | 2015-06-05 | [
[
"Vasile",
"Massimiliano",
""
]
] | In this paper, the preliminary design of a space mission is approached introducing uncertainties on the design parameters and formulating the resulting reliable design problem as a multiobjective optimization problem. Uncertainties are modelled through evidence theory and the belief, or credibility, in the successful achievement of mission goals is maximised along with the reliability of constraint satisfaction. The multiobjective optimisation problem is solved through a novel algorithm based on the collaboration of a population of agents in search for the set of highly reliable solutions. Two typical problems in mission analysis are used to illustrate the proposed methodology. |
2403.11631 | Kun Ding | Kun Ding and Xiaohui Li and Qiang Yu and Ying Wang and Haojian Zhang
and Shiming Xiang | Compositional Kronecker Context Optimization for Vision-Language Models | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Context Optimization (CoOp) has emerged as a simple yet effective technique
for adapting CLIP-like vision-language models to downstream image recognition
tasks. Nevertheless, learning compact context with satisfactory base-to-new,
domain and cross-task generalization ability while adapting to new tasks is
still a challenge. To tackle such a challenge, we propose a lightweight yet
generalizable approach termed Compositional Kronecker Context Optimization
(CK-CoOp). Technically, the prompt's context words in CK-CoOp are learnable
vectors, which are crafted by linearly combining base vectors sourced from a
dictionary. These base vectors consist of a non-learnable component obtained by
quantizing the weights in the token embedding layer, and a learnable component
constructed by applying Kronecker product on several learnable tiny matrices.
Intuitively, the compositional structure mitigates the risk of overfitting on
training data by remembering more pre-trained knowledge. Meantime, the
Kronecker product breaks the non-learnable restrictions of the dictionary,
thereby enhancing representation ability with minimal additional parameters.
Extensive experiments confirm that CK-CoOp achieves state-of-the-art
performance under base-to-new, domain and cross-task generalization evaluation,
but also has the metrics of fewer learnable parameters and efficient training
and inference speed.
| [
{
"created": "Mon, 18 Mar 2024 10:09:28 GMT",
"version": "v1"
}
] | 2024-03-19 | [
[
"Ding",
"Kun",
""
],
[
"Li",
"Xiaohui",
""
],
[
"Yu",
"Qiang",
""
],
[
"Wang",
"Ying",
""
],
[
"Zhang",
"Haojian",
""
],
[
"Xiang",
"Shiming",
""
]
] | Context Optimization (CoOp) has emerged as a simple yet effective technique for adapting CLIP-like vision-language models to downstream image recognition tasks. Nevertheless, learning compact context with satisfactory base-to-new, domain and cross-task generalization ability while adapting to new tasks is still a challenge. To tackle such a challenge, we propose a lightweight yet generalizable approach termed Compositional Kronecker Context Optimization (CK-CoOp). Technically, the prompt's context words in CK-CoOp are learnable vectors, which are crafted by linearly combining base vectors sourced from a dictionary. These base vectors consist of a non-learnable component obtained by quantizing the weights in the token embedding layer, and a learnable component constructed by applying Kronecker product on several learnable tiny matrices. Intuitively, the compositional structure mitigates the risk of overfitting on training data by remembering more pre-trained knowledge. Meantime, the Kronecker product breaks the non-learnable restrictions of the dictionary, thereby enhancing representation ability with minimal additional parameters. Extensive experiments confirm that CK-CoOp achieves state-of-the-art performance under base-to-new, domain and cross-task generalization evaluation, but also has the metrics of fewer learnable parameters and efficient training and inference speed. |
2010.07804 | Xiao Luo | Xiao Luo, Daqing Wu, Zeyu Ma, Chong Chen, Minghua Deng, Jinwen Ma,
Zhongming Jin, Jianqiang Huang and Xian-Sheng Hua | CIMON: Towards High-quality Hash Codes | Accepted by IJCAI 21 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, hashing is widely used in approximate nearest neighbor search for
its storage and computational efficiency. Most of the unsupervised hashing
methods learn to map images into semantic similarity-preserving hash codes by
constructing local semantic similarity structure from the pre-trained model as
the guiding information, i.e., treating each point pair similar if their
distance is small in feature space. However, due to the inefficient
representation ability of the pre-trained model, many false positives and
negatives in local semantic similarity will be introduced and lead to error
propagation during the hash code learning. Moreover, few of the methods
consider the robustness of models, which will cause instability of hash codes
to disturbance. In this paper, we propose a new method named
{\textbf{C}}omprehensive s{\textbf{I}}milarity {\textbf{M}}ining and
c{\textbf{O}}nsistency lear{\textbf{N}}ing (CIMON). First, we use global
refinement and similarity statistical distribution to obtain reliable and
smooth guidance. Second, both semantic and contrastive consistency learning are
introduced to derive both disturb-invariant and discriminative hash codes.
Extensive experiments on several benchmark datasets show that the proposed
method outperforms a wide range of state-of-the-art methods in both retrieval
performance and robustness.
| [
{
"created": "Thu, 15 Oct 2020 14:47:14 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Oct 2020 09:18:50 GMT",
"version": "v2"
},
{
"created": "Thu, 5 Nov 2020 08:44:26 GMT",
"version": "v3"
},
{
"created": "Sat, 21 Aug 2021 04:13:07 GMT",
"version": "v4"
}
] | 2021-08-24 | [
[
"Luo",
"Xiao",
""
],
[
"Wu",
"Daqing",
""
],
[
"Ma",
"Zeyu",
""
],
[
"Chen",
"Chong",
""
],
[
"Deng",
"Minghua",
""
],
[
"Ma",
"Jinwen",
""
],
[
"Jin",
"Zhongming",
""
],
[
"Huang",
"Jianqiang",
""
],
[
"Hua",
"Xian-Sheng",
""
]
] | Recently, hashing is widely used in approximate nearest neighbor search for its storage and computational efficiency. Most of the unsupervised hashing methods learn to map images into semantic similarity-preserving hash codes by constructing local semantic similarity structure from the pre-trained model as the guiding information, i.e., treating each point pair similar if their distance is small in feature space. However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning. Moreover, few of the methods consider the robustness of models, which will cause instability of hash codes to disturbance. In this paper, we propose a new method named {\textbf{C}}omprehensive s{\textbf{I}}milarity {\textbf{M}}ining and c{\textbf{O}}nsistency lear{\textbf{N}}ing (CIMON). First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods in both retrieval performance and robustness. |
1211.4264 | Kunal Narayan Chaudhury | Kunal N. Chaudhury, Amit Singer | Non-Local Patch Regression: Robust Image Denoising in Patch Space | Submitted | IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP), 2013 | 10.1109/ICASSP.2013.6637870 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It was recently demonstrated in [Chaudhury et al.,Non-Local Euclidean
Medians,2012] that the denoising performance of Non-Local Means (NLM) can be
improved at large noise levels by replacing the mean by the robust Euclidean
median. Numerical experiments on synthetic and natural images showed that the
latter consistently performed better than NLM beyond a certain noise level, and
significantly so for images with sharp edges. The Euclidean mean and median can
be put into a common regression (on the patch space) framework, in which the
l_2 norm of the residuals is considered in the former, while the l_1 norm is
considered in the latter. The natural question then is what happens if we
consider l_p (0<p<1) regression? We investigate this possibility in this paper.
| [
{
"created": "Sun, 18 Nov 2012 22:36:43 GMT",
"version": "v1"
}
] | 2016-11-18 | [
[
"Chaudhury",
"Kunal N.",
""
],
[
"Singer",
"Amit",
""
]
] | It was recently demonstrated in [Chaudhury et al.,Non-Local Euclidean Medians,2012] that the denoising performance of Non-Local Means (NLM) can be improved at large noise levels by replacing the mean by the robust Euclidean median. Numerical experiments on synthetic and natural images showed that the latter consistently performed better than NLM beyond a certain noise level, and significantly so for images with sharp edges. The Euclidean mean and median can be put into a common regression (on the patch space) framework, in which the l_2 norm of the residuals is considered in the former, while the l_1 norm is considered in the latter. The natural question then is what happens if we consider l_p (0<p<1) regression? We investigate this possibility in this paper. |
2406.08607 | Jiabao Ji | Jiabao Ji, Yujian Liu, Yang Zhang, Gaowen Liu, Ramana Rao Kompella,
Sijia Liu, Shiyu Chang | Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning
Framework from Logit Difference | 21 pages, 11 figures | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | As Large Language Models (LLMs) demonstrate extensive capability in learning
from documents, LLM unlearning becomes an increasingly important research area
to address concerns of LLMs in terms of privacy, copyright, etc. A conventional
LLM unlearning task typically involves two goals: (1) The target LLM should
forget the knowledge in the specified forget documents, and (2) it should
retain the other knowledge that the LLM possesses, for which we assume access
to a small number of retain documents. To achieve both goals, a mainstream
class of LLM unlearning methods introduces an optimization framework with a
combination of two objectives - maximizing the prediction loss on the forget
documents while minimizing that on the retain documents, which suffers from two
challenges, degenerated output and catastrophic forgetting. In this paper, we
propose a novel unlearning framework called Unlearning from Logit Difference
(ULD), which introduces an assistant LLM that aims to achieve the opposite of
the unlearning goals: remembering the forget documents and forgetting the
retain knowledge. ULD then derives the unlearned LLM by computing the logit
difference between the target and the assistant LLMs. We show that such
reversed objectives would naturally resolve both aforementioned challenges
while significantly improving the training efficiency. Extensive experiments
demonstrate that our method efficiently achieves the intended forgetting while
preserving the LLM's overall capabilities, reducing training time by more than
threefold. Notably, our method loses 0% of model utility on the ToFU benchmark,
whereas baseline methods may sacrifice 17% of utility on average to achieve
comparable forget quality. Our code will be publicly available at
https://github.com/UCSB-NLP-Chang/ULD.
| [
{
"created": "Wed, 12 Jun 2024 19:26:35 GMT",
"version": "v1"
}
] | 2024-06-14 | [
[
"Ji",
"Jiabao",
""
],
[
"Liu",
"Yujian",
""
],
[
"Zhang",
"Yang",
""
],
[
"Liu",
"Gaowen",
""
],
[
"Kompella",
"Ramana Rao",
""
],
[
"Liu",
"Sijia",
""
],
[
"Chang",
"Shiyu",
""
]
] | As Large Language Models (LLMs) demonstrate extensive capability in learning from documents, LLM unlearning becomes an increasingly important research area to address concerns of LLMs in terms of privacy, copyright, etc. A conventional LLM unlearning task typically involves two goals: (1) The target LLM should forget the knowledge in the specified forget documents, and (2) it should retain the other knowledge that the LLM possesses, for which we assume access to a small number of retain documents. To achieve both goals, a mainstream class of LLM unlearning methods introduces an optimization framework with a combination of two objectives - maximizing the prediction loss on the forget documents while minimizing that on the retain documents, which suffers from two challenges, degenerated output and catastrophic forgetting. In this paper, we propose a novel unlearning framework called Unlearning from Logit Difference (ULD), which introduces an assistant LLM that aims to achieve the opposite of the unlearning goals: remembering the forget documents and forgetting the retain knowledge. ULD then derives the unlearned LLM by computing the logit difference between the target and the assistant LLMs. We show that such reversed objectives would naturally resolve both aforementioned challenges while significantly improving the training efficiency. Extensive experiments demonstrate that our method efficiently achieves the intended forgetting while preserving the LLM's overall capabilities, reducing training time by more than threefold. Notably, our method loses 0% of model utility on the ToFU benchmark, whereas baseline methods may sacrifice 17% of utility on average to achieve comparable forget quality. Our code will be publicly available at https://github.com/UCSB-NLP-Chang/ULD. |
0910.0317 | Rdv Ijcsis | Abbas Karimi, Faraneh Zarafshan, Adznan.b. Jantan, A.R Ramli, M.Iqbal
b.Saripan | A New Fuzzy Approach for Dynamic Load Balancing Algorithm | 5 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423,
http://sites.google.com/site/ijcsis/ | International Journal of Computer Science and Information
Security, IJCSIS, Vol. 6, No. 1, pp. 01-05, October 2009, USA | null | ISSN 1947 5500 | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Load balancing is the process of improving the Performance of a parallel and
distributed system through is distribution of load among the processors [1-2].
Most of the previous work in load balancing and distributed decision making in
general, do not effectively take into account the uncertainty and inconsistency
in state information but in fuzzy logic, we have advantage of using crisps
inputs. In this paper, we present a new approach for implementing dynamic load
balancing algorithm with fuzzy logic, which can face to uncertainty and
inconsistency of previous algorithms, further more our algorithm shows better
response time than round robin and randomize algorithm respectively 30.84
percent and 45.45 percent.
| [
{
"created": "Fri, 2 Oct 2009 03:32:09 GMT",
"version": "v1"
}
] | 2009-10-05 | [
[
"Karimi",
"Abbas",
""
],
[
"Zarafshan",
"Faraneh",
""
],
[
"Jantan",
"Adznan. b.",
""
],
[
"Ramli",
"A. R",
""
],
[
"Saripan",
"M. Iqbal b.",
""
]
] | Load balancing is the process of improving the Performance of a parallel and distributed system through is distribution of load among the processors [1-2]. Most of the previous work in load balancing and distributed decision making in general, do not effectively take into account the uncertainty and inconsistency in state information but in fuzzy logic, we have advantage of using crisps inputs. In this paper, we present a new approach for implementing dynamic load balancing algorithm with fuzzy logic, which can face to uncertainty and inconsistency of previous algorithms, further more our algorithm shows better response time than round robin and randomize algorithm respectively 30.84 percent and 45.45 percent. |
1109.0597 | Prateek Mittal | Prateek Mittal, Ahmed Khurshid, Joshua Juen, Matthew Caesar, Nikita
Borisov | Stealthy Traffic Analysis of Low-Latency Anonymous Communication Using
Throughput Fingerprinting | Accepted for publication in ACM CCS 2011 | null | null | null | cs.CR cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anonymity systems such as Tor aim to enable users to communicate in a manner
that is untraceable by adversaries that control a small number of machines. To
provide efficient service to users, these anonymity systems make full use of
forwarding capacity when sending traffic between intermediate relays. In this
paper, we show that doing this leaks information about the set of Tor relays in
a circuit (path). We present attacks that, with high confidence and based
solely on throughput information, can (a) reduce the attacker's uncertainty
about the bottleneck relay of any Tor circuit whose throughput can be observed,
(b) exactly identify the guard relay(s) of a Tor user when circuit throughput
can be observed over multiple connections, and (c) identify whether two
concurrent TCP connections belong to the same Tor user, breaking unlinkability.
Our attacks are stealthy, and cannot be readily detected by a user or by Tor
relays. We validate our attacks using experiments over the live Tor network. We
find that the attacker can substantially reduce the entropy of a bottleneck
relay distribution of a Tor circuit whose throughput can be observed-the
entropy gets reduced by a factor of 2 in the median case. Such information
leaks from a single Tor circuit can be combined over multiple connections to
exactly identify a user's guard relay(s). Finally, we are also able to link two
connections from the same initiator with a crossover error rate of less than
1.5% in under 5 minutes. Our attacks are also more accurate and require fewer
resources than previous attacks on Tor.
| [
{
"created": "Sat, 3 Sep 2011 06:43:53 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Nov 2011 21:05:49 GMT",
"version": "v2"
}
] | 2015-03-19 | [
[
"Mittal",
"Prateek",
""
],
[
"Khurshid",
"Ahmed",
""
],
[
"Juen",
"Joshua",
""
],
[
"Caesar",
"Matthew",
""
],
[
"Borisov",
"Nikita",
""
]
] | Anonymity systems such as Tor aim to enable users to communicate in a manner that is untraceable by adversaries that control a small number of machines. To provide efficient service to users, these anonymity systems make full use of forwarding capacity when sending traffic between intermediate relays. In this paper, we show that doing this leaks information about the set of Tor relays in a circuit (path). We present attacks that, with high confidence and based solely on throughput information, can (a) reduce the attacker's uncertainty about the bottleneck relay of any Tor circuit whose throughput can be observed, (b) exactly identify the guard relay(s) of a Tor user when circuit throughput can be observed over multiple connections, and (c) identify whether two concurrent TCP connections belong to the same Tor user, breaking unlinkability. Our attacks are stealthy, and cannot be readily detected by a user or by Tor relays. We validate our attacks using experiments over the live Tor network. We find that the attacker can substantially reduce the entropy of a bottleneck relay distribution of a Tor circuit whose throughput can be observed-the entropy gets reduced by a factor of 2 in the median case. Such information leaks from a single Tor circuit can be combined over multiple connections to exactly identify a user's guard relay(s). Finally, we are also able to link two connections from the same initiator with a crossover error rate of less than 1.5% in under 5 minutes. Our attacks are also more accurate and require fewer resources than previous attacks on Tor. |
2105.08758 | David Krackhardt | Vineet Kumar, David Krackhardt, Scott Feld | Interventions with Inversity in Unknown Networks Can Help Regulate
Contagion | 32 pages including supplemental materials | null | null | null | cs.SI physics.soc-ph | http://creativecommons.org/licenses/by/4.0/ | Network intervention problems often benefit from selecting a highly-connected
node to perform interventions using these nodes, e.g. immunization. However, in
many network contexts, the structure of network connections is unknown, leading
to a challenge. We develop and examine the mathematical properties of two
distinct informationally light strategies, a novel global strategy and local
strategy, that yield higher degree nodes in virtually any network structure. We
further identify a novel network property called Inversity, whose sign
determines which of the two strategies, local or global, will be most effective
for a network. We demonstrate that local and global strategies obtain a
several-fold improvement in node degree relative to a random selection
benchmark for generated and real networks (including contact, affiliation and
online networks). In some networks, they achieve a 100-fold improvement. We
show how these new strategies can be used to control contagion of an epidemic
spreading across a set of village networks, finding that the strategies
developed here require far fewer ($<50\%$) nodes to be immunized, relative to
the random strategy baseline. Prior research has typically used the complete
network structure to choose nodes for optimal seeding. The relevant network is
often costly to collect, and is privacy-invasive, requiring knowing each
person's network neighbors, and might not be possible to obtain for
time-sensitive interventions. Our interventions are less invasive of individual
privacy, since each selected node only needs to nominate some network neighbors
for intervention, while mathematically guaranteed to provide better connected
nodes.
| [
{
"created": "Tue, 18 May 2021 18:14:11 GMT",
"version": "v1"
}
] | 2021-05-20 | [
[
"Kumar",
"Vineet",
""
],
[
"Krackhardt",
"David",
""
],
[
"Feld",
"Scott",
""
]
] | Network intervention problems often benefit from selecting a highly-connected node to perform interventions using these nodes, e.g. immunization. However, in many network contexts, the structure of network connections is unknown, leading to a challenge. We develop and examine the mathematical properties of two distinct informationally light strategies, a novel global strategy and local strategy, that yield higher degree nodes in virtually any network structure. We further identify a novel network property called Inversity, whose sign determines which of the two strategies, local or global, will be most effective for a network. We demonstrate that local and global strategies obtain a several-fold improvement in node degree relative to a random selection benchmark for generated and real networks (including contact, affiliation and online networks). In some networks, they achieve a 100-fold improvement. We show how these new strategies can be used to control contagion of an epidemic spreading across a set of village networks, finding that the strategies developed here require far fewer ($<50\%$) nodes to be immunized, relative to the random strategy baseline. Prior research has typically used the complete network structure to choose nodes for optimal seeding. The relevant network is often costly to collect, and is privacy-invasive, requiring knowing each person's network neighbors, and might not be possible to obtain for time-sensitive interventions. Our interventions are less invasive of individual privacy, since each selected node only needs to nominate some network neighbors for intervention, while mathematically guaranteed to provide better connected nodes. |
2210.08316 | Animesh Chaturvedi Dr. | Animesh Chaturvedi | Call Graph Evolution Analytics over a Version Series of an Evolving
Software System | null | null | 10.1145/3551349.3559573 | null | cs.SE cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Call Graph evolution analytics can aid a software engineer when maintaining
or evolving a software system. This paper proposes Call Graph Evolution
Analytics to extract information from an evolving call graph ECG = CG_1,
CG_2,... CG_N for their version series VS = V_1, V_2, ... V_N of an evolving
software system. This is done using Call Graph Evolution Rules (CGERs) and Call
Graph Evolution Subgraphs (CGESs). Similar to association rule mining, the
CGERs are used to capture co-occurrences of dependencies in the system. Like
subgraph patterns in a call graph, the CGESs are used to capture evolution of
dependency patterns in evolving call graphs. Call graph analytics on the
evolution in these patterns can identify potentially affected dependencies (or
procedure calls) that need attention. The experiments are done on the evolving
call graphs of 10 large evolving systems to support dependency evolution
management. We also consider results from a detailed study for evolving call
graphs of Maven-Core's version series.
| [
{
"created": "Sat, 15 Oct 2022 15:12:20 GMT",
"version": "v1"
}
] | 2023-05-31 | [
[
"Chaturvedi",
"Animesh",
""
]
] | Call Graph evolution analytics can aid a software engineer when maintaining or evolving a software system. This paper proposes Call Graph Evolution Analytics to extract information from an evolving call graph ECG = CG_1, CG_2,... CG_N for their version series VS = V_1, V_2, ... V_N of an evolving software system. This is done using Call Graph Evolution Rules (CGERs) and Call Graph Evolution Subgraphs (CGESs). Similar to association rule mining, the CGERs are used to capture co-occurrences of dependencies in the system. Like subgraph patterns in a call graph, the CGESs are used to capture evolution of dependency patterns in evolving call graphs. Call graph analytics on the evolution in these patterns can identify potentially affected dependencies (or procedure calls) that need attention. The experiments are done on the evolving call graphs of 10 large evolving systems to support dependency evolution management. We also consider results from a detailed study for evolving call graphs of Maven-Core's version series. |
2201.03702 | John Wahlig | John Wahlig | Learning Logic Programs From Noisy Failures | Thesis for MSc in Computer Science | null | null | null | cs.AI cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inductive Logic Programming (ILP) is a form of machine learning (ML) which in
contrast to many other state of the art ML methods typically produces highly
interpretable and reusable models. However, many ILP systems lack the ability
to naturally learn from any noisy or partially misclassified training data. We
introduce the relaxed learning from failures approach to ILP, a noise handling
modification of the previously introduced learning from failures (LFF) approach
which is incapable of handling noise. We additionally introduce the novel Noisy
Popper ILP system which implements this relaxed approach and is a modification
of the existing Popper system. Like Popper, Noisy Popper takes a
generate-test-constrain loop to search its hypothesis space wherein failed
hypotheses are used to construct hypothesis constraints. These constraints are
used to prune the hypothesis space, making the hypothesis search more
efficient. However, in the relaxed setting, constraints are generated in a more
lax fashion as to avoid allowing noisy training data to lead to hypothesis
constraints which prune optimal hypotheses. Constraints unique to the relaxed
setting are generated via hypothesis comparison. Additional constraints are
generated by weighing the accuracy of hypotheses against their sizes to avoid
overfitting through an application of the minimum description length. We
support this new setting through theoretical proofs as well as experimental
results which suggest that Noisy Popper improves the noise handling
capabilities of Popper but at the cost of overall runtime efficiency.
| [
{
"created": "Tue, 28 Dec 2021 16:48:00 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Jan 2022 01:05:32 GMT",
"version": "v2"
}
] | 2022-01-26 | [
[
"Wahlig",
"John",
""
]
] | Inductive Logic Programming (ILP) is a form of machine learning (ML) which in contrast to many other state of the art ML methods typically produces highly interpretable and reusable models. However, many ILP systems lack the ability to naturally learn from any noisy or partially misclassified training data. We introduce the relaxed learning from failures approach to ILP, a noise handling modification of the previously introduced learning from failures (LFF) approach which is incapable of handling noise. We additionally introduce the novel Noisy Popper ILP system which implements this relaxed approach and is a modification of the existing Popper system. Like Popper, Noisy Popper takes a generate-test-constrain loop to search its hypothesis space wherein failed hypotheses are used to construct hypothesis constraints. These constraints are used to prune the hypothesis space, making the hypothesis search more efficient. However, in the relaxed setting, constraints are generated in a more lax fashion as to avoid allowing noisy training data to lead to hypothesis constraints which prune optimal hypotheses. Constraints unique to the relaxed setting are generated via hypothesis comparison. Additional constraints are generated by weighing the accuracy of hypotheses against their sizes to avoid overfitting through an application of the minimum description length. We support this new setting through theoretical proofs as well as experimental results which suggest that Noisy Popper improves the noise handling capabilities of Popper but at the cost of overall runtime efficiency. |
2305.07366 | Maha Riad | Maha Riad, Vinicius Renan de Carvalho and Fatemeh Golpayegani | Multi-Value Alignment in Normative Multi-Agent System: Evolutionary
Optimisation Approach | null | null | null | null | cs.MA cs.AI cs.NE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Value-alignment in normative multi-agent systems is used to promote a certain
value and to ensure the consistent behavior of agents in autonomous intelligent
systems with human values. However, the current literature is limited to
incorporation of effective norms for single value alignment with no
consideration of agents' heterogeneity and the requirement of simultaneous
promotion and alignment of multiple values. This research proposes a
multi-value promotion model that uses multi-objective evolutionary algorithms
to produce the optimum parametric set of norms that is aligned with multiple
simultaneous values of heterogeneous agents and the system. To understand
various aspects of this complex problem, several evolutionary algorithms were
used to find a set of optimised norm parameters considering two toy tax
scenarios with two and five values are considered. The results are analysed
from different perspectives to show the impact of a selected evolutionary
algorithm on the solution, and the importance of understanding the relation
between values when prioritising them.
| [
{
"created": "Fri, 12 May 2023 10:30:20 GMT",
"version": "v1"
}
] | 2023-05-15 | [
[
"Riad",
"Maha",
""
],
[
"de Carvalho",
"Vinicius Renan",
""
],
[
"Golpayegani",
"Fatemeh",
""
]
] | Value-alignment in normative multi-agent systems is used to promote a certain value and to ensure the consistent behavior of agents in autonomous intelligent systems with human values. However, the current literature is limited to incorporation of effective norms for single value alignment with no consideration of agents' heterogeneity and the requirement of simultaneous promotion and alignment of multiple values. This research proposes a multi-value promotion model that uses multi-objective evolutionary algorithms to produce the optimum parametric set of norms that is aligned with multiple simultaneous values of heterogeneous agents and the system. To understand various aspects of this complex problem, several evolutionary algorithms were used to find a set of optimised norm parameters considering two toy tax scenarios with two and five values are considered. The results are analysed from different perspectives to show the impact of a selected evolutionary algorithm on the solution, and the importance of understanding the relation between values when prioritising them. |
1508.07343 | Sepideh Pourazarm | Sepideh Pourazarm, Christos G. Cassandras | Lifetime Maximization of Wireless Sensor Networks with a Mobile Source
Node | A shorter version of this work will be published in Proceedings of
2016 IEEE Conference on Decision and Control | null | 10.1109/CDC.2015.7403388 | null | cs.NI math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of routing in sensor networks where the goal is to
maximize the network's lifetime. Previous work has considered this problem for
fixed-topology networks. Here, we add mobility to the source node, which
requires a new definition of the network lifetime. In particular, we redefine
lifetime to be the time until the source node depletes its energy. When the
mobile node's trajectory is unknown in advance, we formulate three versions of
an optimal control problem aiming at this lifetime maximization. We show that
in all cases, the solution can be reduced to a sequence of Non- Linear
Programming (NLP) problems solved on line as the source node trajectory
evolves.
| [
{
"created": "Fri, 28 Aug 2015 20:50:43 GMT",
"version": "v1"
}
] | 2016-11-18 | [
[
"Pourazarm",
"Sepideh",
""
],
[
"Cassandras",
"Christos G.",
""
]
] | We study the problem of routing in sensor networks where the goal is to maximize the network's lifetime. Previous work has considered this problem for fixed-topology networks. Here, we add mobility to the source node, which requires a new definition of the network lifetime. In particular, we redefine lifetime to be the time until the source node depletes its energy. When the mobile node's trajectory is unknown in advance, we formulate three versions of an optimal control problem aiming at this lifetime maximization. We show that in all cases, the solution can be reduced to a sequence of Non- Linear Programming (NLP) problems solved on line as the source node trajectory evolves. |
1810.08951 | Ta-Chung Chi | Ta-Chung Chi, Ching-Yen Shih, Yun-Nung Chen | BCWS: Bilingual Contextual Word Similarity | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces the first dataset for evaluating English-Chinese
Bilingual Contextual Word Similarity, namely BCWS
(https://github.com/MiuLab/BCWS). The dataset consists of 2,091 English-Chinese
word pairs with the corresponding sentential contexts and their similarity
scores annotated by the human. Our annotated dataset has higher consistency
compared to other similar datasets. We establish several baselines for the
bilingual embedding task to benchmark the experiments. Modeling cross-lingual
sense representations as provided in this dataset has the potential of moving
artificial intelligence from monolingual understanding towards multilingual
understanding.
| [
{
"created": "Sun, 21 Oct 2018 13:57:17 GMT",
"version": "v1"
}
] | 2018-10-23 | [
[
"Chi",
"Ta-Chung",
""
],
[
"Shih",
"Ching-Yen",
""
],
[
"Chen",
"Yun-Nung",
""
]
] | This paper introduces the first dataset for evaluating English-Chinese Bilingual Contextual Word Similarity, namely BCWS (https://github.com/MiuLab/BCWS). The dataset consists of 2,091 English-Chinese word pairs with the corresponding sentential contexts and their similarity scores annotated by the human. Our annotated dataset has higher consistency compared to other similar datasets. We establish several baselines for the bilingual embedding task to benchmark the experiments. Modeling cross-lingual sense representations as provided in this dataset has the potential of moving artificial intelligence from monolingual understanding towards multilingual understanding. |
2211.05809 | Caner Hazirbas | Caner Hazirbas, Yejin Bang, Tiezheng Yu, Parisa Assar, Bilal Porgali,
V\'itor Albiero, Stefan Hermanek, Jacqueline Pan, Emily McReynolds, Miranda
Bogen, Pascale Fung, Cristian Canton Ferrer | Casual Conversations v2: Designing a large consent-driven dataset to
measure algorithmic bias and robustness | null | null | null | null | cs.CV cs.AI cs.CL cs.CY | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Developing robust and fair AI systems require datasets with comprehensive set
of labels that can help ensure the validity and legitimacy of relevant
measurements. Recent efforts, therefore, focus on collecting person-related
datasets that have carefully selected labels, including sensitive
characteristics, and consent forms in place to use those attributes for model
testing and development. Responsible data collection involves several stages,
including but not limited to determining use-case scenarios, selecting
categories (annotations) such that the data are fit for the purpose of
measuring algorithmic bias for subgroups and most importantly ensure that the
selected categories/subcategories are robust to regional diversities and
inclusive of as many subgroups as possible.
Meta, in a continuation of our efforts to measure AI algorithmic bias and
robustness
(https://ai.facebook.com/blog/shedding-light-on-fairness-in-ai-with-a-new-data-set),
is working on collecting a large consent-driven dataset with a comprehensive
list of categories. This paper describes our proposed design of such categories
and subcategories for Casual Conversations v2.
| [
{
"created": "Thu, 10 Nov 2022 19:06:21 GMT",
"version": "v1"
}
] | 2022-11-14 | [
[
"Hazirbas",
"Caner",
""
],
[
"Bang",
"Yejin",
""
],
[
"Yu",
"Tiezheng",
""
],
[
"Assar",
"Parisa",
""
],
[
"Porgali",
"Bilal",
""
],
[
"Albiero",
"Vítor",
""
],
[
"Hermanek",
"Stefan",
""
],
[
"Pan",
"Jacqueline",
""
],
[
"McReynolds",
"Emily",
""
],
[
"Bogen",
"Miranda",
""
],
[
"Fung",
"Pascale",
""
],
[
"Ferrer",
"Cristian Canton",
""
]
] | Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, therefore, focus on collecting person-related datasets that have carefully selected labels, including sensitive characteristics, and consent forms in place to use those attributes for model testing and development. Responsible data collection involves several stages, including but not limited to determining use-case scenarios, selecting categories (annotations) such that the data are fit for the purpose of measuring algorithmic bias for subgroups and most importantly ensure that the selected categories/subcategories are robust to regional diversities and inclusive of as many subgroups as possible. Meta, in a continuation of our efforts to measure AI algorithmic bias and robustness (https://ai.facebook.com/blog/shedding-light-on-fairness-in-ai-with-a-new-data-set), is working on collecting a large consent-driven dataset with a comprehensive list of categories. This paper describes our proposed design of such categories and subcategories for Casual Conversations v2. |
1502.06470 | Eric Tramel | Eric W. Tramel and Ang\'elique Dr\'emeau and Florent Krzakala | Approximate Message Passing with Restricted Boltzmann Machine Priors | null | J. Stat. Mech. (2016) 073401 | 10.1088/1742-5468/2016/07/073401 | null | cs.IT cond-mat.dis-nn math.IT physics.data-an stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Approximate Message Passing (AMP) has been shown to be an excellent
statistical approach to signal inference and compressed sensing problem. The
AMP framework provides modularity in the choice of signal prior; here we
propose a hierarchical form of the Gauss-Bernouilli prior which utilizes a
Restricted Boltzmann Machine (RBM) trained on the signal support to push
reconstruction performance beyond that of simple iid priors for signals whose
support can be well represented by a trained binary RBM. We present and analyze
two methods of RBM factorization and demonstrate how these affect signal
reconstruction performance within our proposed algorithm. Finally, using the
MNIST handwritten digit dataset, we show experimentally that using an RBM
allows AMP to approach oracle-support performance.
| [
{
"created": "Mon, 23 Feb 2015 15:51:07 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Jun 2015 14:05:45 GMT",
"version": "v2"
},
{
"created": "Thu, 10 Dec 2015 03:45:32 GMT",
"version": "v3"
}
] | 2016-07-11 | [
[
"Tramel",
"Eric W.",
""
],
[
"Drémeau",
"Angélique",
""
],
[
"Krzakala",
"Florent",
""
]
] | Approximate Message Passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem. The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernouilli prior which utilizes a Restricted Boltzmann Machine (RBM) trained on the signal support to push reconstruction performance beyond that of simple iid priors for signals whose support can be well represented by a trained binary RBM. We present and analyze two methods of RBM factorization and demonstrate how these affect signal reconstruction performance within our proposed algorithm. Finally, using the MNIST handwritten digit dataset, we show experimentally that using an RBM allows AMP to approach oracle-support performance. |
2403.16880 | Tianshuai Hu | Tianshuai Hu, Jianhao Jiao, Yucheng Xu, Hongji Liu, Sheng Wang, Ming
Liu | DHP-Mapping: A Dense Panoptic Mapping System with Hierarchical World
Representation and Label Optimization Techniques | Submit to IROS 2024. Project website
https://github.com/hutslib/DHP-Mapping | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Maps provide robots with crucial environmental knowledge, thereby enabling
them to perform interactive tasks effectively. Easily accessing accurate
abstract-to-detailed geometric and semantic concepts from maps is crucial for
robots to make informed and efficient decisions. To comprehensively model the
environment and effectively manage the map data structure, we propose
DHP-Mapping, a dense mapping system that utilizes multiple Truncated Signed
Distance Field (TSDF) submaps and panoptic labels to hierarchically model the
environment. The output map is able to maintain both voxel- and submap-level
metric and semantic information. Two modules are presented to enhance the
mapping efficiency and label consistency: (1) an inter-submaps label fusion
strategy to eliminate duplicate points across submaps and (2) a conditional
random field (CRF) based approach to enhance panoptic labels through object
label comprehension and contextual information. We conducted experiments with
two public datasets including indoor and outdoor scenarios. Our system performs
comparably to state-of-the-art (SOTA) methods across geometry and label
accuracy evaluation metrics. The experiment results highlight the effectiveness
and scalability of our system, as it is capable of constructing precise
geometry and maintaining consistent panoptic labels. Our code is publicly
available at https://github.com/hutslib/DHP-Mapping.
| [
{
"created": "Mon, 25 Mar 2024 15:47:06 GMT",
"version": "v1"
}
] | 2024-03-26 | [
[
"Hu",
"Tianshuai",
""
],
[
"Jiao",
"Jianhao",
""
],
[
"Xu",
"Yucheng",
""
],
[
"Liu",
"Hongji",
""
],
[
"Wang",
"Sheng",
""
],
[
"Liu",
"Ming",
""
]
] | Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structure, we propose DHP-Mapping, a dense mapping system that utilizes multiple Truncated Signed Distance Field (TSDF) submaps and panoptic labels to hierarchically model the environment. The output map is able to maintain both voxel- and submap-level metric and semantic information. Two modules are presented to enhance the mapping efficiency and label consistency: (1) an inter-submaps label fusion strategy to eliminate duplicate points across submaps and (2) a conditional random field (CRF) based approach to enhance panoptic labels through object label comprehension and contextual information. We conducted experiments with two public datasets including indoor and outdoor scenarios. Our system performs comparably to state-of-the-art (SOTA) methods across geometry and label accuracy evaluation metrics. The experiment results highlight the effectiveness and scalability of our system, as it is capable of constructing precise geometry and maintaining consistent panoptic labels. Our code is publicly available at https://github.com/hutslib/DHP-Mapping. |
2311.00579 | Hansika Weerasena | Hansika Weerasena and Prabhat Mishra | Revealing CNN Architectures via Side-Channel Analysis in Dataflow-based
Inference Accelerators | null | null | null | null | cs.CR cs.AR cs.LG | http://creativecommons.org/licenses/by/4.0/ | Convolution Neural Networks (CNNs) are widely used in various domains. Recent
advances in dataflow-based CNN accelerators have enabled CNN inference in
resource-constrained edge devices. These dataflow accelerators utilize inherent
data reuse of convolution layers to process CNN models efficiently. Concealing
the architecture of CNN models is critical for privacy and security. This paper
evaluates memory-based side-channel information to recover CNN architectures
from dataflow-based CNN inference accelerators. The proposed attack exploits
spatial and temporal data reuse of the dataflow mapping on CNN accelerators and
architectural hints to recover the structure of CNN models. Experimental
results demonstrate that our proposed side-channel attack can recover the
structures of popular CNN models, namely Lenet, Alexnet, and VGGnet16.
| [
{
"created": "Wed, 1 Nov 2023 15:23:04 GMT",
"version": "v1"
}
] | 2023-11-02 | [
[
"Weerasena",
"Hansika",
""
],
[
"Mishra",
"Prabhat",
""
]
] | Convolution Neural Networks (CNNs) are widely used in various domains. Recent advances in dataflow-based CNN accelerators have enabled CNN inference in resource-constrained edge devices. These dataflow accelerators utilize inherent data reuse of convolution layers to process CNN models efficiently. Concealing the architecture of CNN models is critical for privacy and security. This paper evaluates memory-based side-channel information to recover CNN architectures from dataflow-based CNN inference accelerators. The proposed attack exploits spatial and temporal data reuse of the dataflow mapping on CNN accelerators and architectural hints to recover the structure of CNN models. Experimental results demonstrate that our proposed side-channel attack can recover the structures of popular CNN models, namely Lenet, Alexnet, and VGGnet16. |
2103.13997 | Yonatan Alon | Yonatan Alon | Real-time low-resource phoneme recognition on edge devices | The model and code described in this paper are publicly available at
https://github.com/yonatankarimish/YonaVox | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | While speech recognition has seen a surge in interest and research over the
last decade, most machine learning models for speech recognition either require
large training datasets or lots of storage and memory. Combined with the
prominence of English as the number one language in which audio data is
available, this means most other languages currently lack good speech
recognition models.
The method presented in this paper shows how to create and train models for
speech recognition in any language which are not only highly accurate, but also
require very little storage, memory and training data when compared with
traditional models. This allows training models to recognize any language and
deploying them on edge devices such as mobile phones or car displays for fast
real-time speech recognition.
| [
{
"created": "Thu, 25 Mar 2021 17:34:59 GMT",
"version": "v1"
}
] | 2021-03-26 | [
[
"Alon",
"Yonatan",
""
]
] | While speech recognition has seen a surge in interest and research over the last decade, most machine learning models for speech recognition either require large training datasets or lots of storage and memory. Combined with the prominence of English as the number one language in which audio data is available, this means most other languages currently lack good speech recognition models. The method presented in this paper shows how to create and train models for speech recognition in any language which are not only highly accurate, but also require very little storage, memory and training data when compared with traditional models. This allows training models to recognize any language and deploying them on edge devices such as mobile phones or car displays for fast real-time speech recognition. |
2204.02525 | Vamsi Addanki | Vamsi Addanki, Chen Avin, Stefan Schmid | Mars: Near-Optimal Throughput with Shallow Buffers in Reconfigurable
Datacenter Networks | null | null | null | null | cs.NI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The performance of large-scale computing systems often critically depends on
high-performance communication networks. Dynamically reconfigurable topologies,
e.g., based on optical circuit switches, are emerging as an innovative new
technology to deal with the explosive growth of datacenter traffic.
Specifically, \emph{periodic} reconfigurable datacenter networks (RDCNs) such
as RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have
been shown to provide high throughput, by emulating a \emph{complete graph}
through fast periodic circuit switch scheduling.
However, to achieve such a high throughput, existing reconfigurable network
designs pay a high price: in terms of potentially high delays, but also, as we
show as a first contribution in this paper, in terms of the high buffer
requirements. In particular, we show that under buffer constraints, emulating
the high-throughput complete graph is infeasible at scale, and we uncover a
spectrum of unvisited and attractive alternative RDCNs, which emulate regular
graphs, but with lower node degree than the complete graph.
We present Mars, a periodic reconfigurable topology which emulates a
$d$-regular graph with near-optimal throughput. In particular, we
systematically analyze how the degree~$d$ can be optimized for throughput given
the available buffer and delay tolerance of the datacenter. We further show
empirically that Mars achieves higher throughput compared to existing systems
when buffer sizes are bounded.
| [
{
"created": "Wed, 6 Apr 2022 00:32:58 GMT",
"version": "v1"
},
{
"created": "Thu, 7 Apr 2022 22:39:11 GMT",
"version": "v2"
},
{
"created": "Wed, 28 Dec 2022 15:32:24 GMT",
"version": "v3"
}
] | 2022-12-29 | [
[
"Addanki",
"Vamsi",
""
],
[
"Avin",
"Chen",
""
],
[
"Schmid",
"Stefan",
""
]
] | The performance of large-scale computing systems often critically depends on high-performance communication networks. Dynamically reconfigurable topologies, e.g., based on optical circuit switches, are emerging as an innovative new technology to deal with the explosive growth of datacenter traffic. Specifically, \emph{periodic} reconfigurable datacenter networks (RDCNs) such as RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have been shown to provide high throughput, by emulating a \emph{complete graph} through fast periodic circuit switch scheduling. However, to achieve such a high throughput, existing reconfigurable network designs pay a high price: in terms of potentially high delays, but also, as we show as a first contribution in this paper, in terms of the high buffer requirements. In particular, we show that under buffer constraints, emulating the high-throughput complete graph is infeasible at scale, and we uncover a spectrum of unvisited and attractive alternative RDCNs, which emulate regular graphs, but with lower node degree than the complete graph. We present Mars, a periodic reconfigurable topology which emulates a $d$-regular graph with near-optimal throughput. In particular, we systematically analyze how the degree~$d$ can be optimized for throughput given the available buffer and delay tolerance of the datacenter. We further show empirically that Mars achieves higher throughput compared to existing systems when buffer sizes are bounded. |
2310.11867 | Junaid Ali | Junaid Ali, Matthaeus Kleindessner, Florian Wenzel, Kailash
Budhathoki, Volkan Cevher and Chris Russell | Evaluating the Fairness of Discriminative Foundation Models in Computer
Vision | Accepted at AIES'23 | null | 10.1145/3600211.3604720 | null | cs.CV cs.CY cs.LG | http://creativecommons.org/licenses/by/4.0/ | We propose a novel taxonomy for bias evaluation of discriminative foundation
models, such as Contrastive Language-Pretraining (CLIP), that are used for
labeling tasks. We then systematically evaluate existing methods for mitigating
bias in these models with respect to our taxonomy. Specifically, we evaluate
OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot
classification, image retrieval and image captioning. We categorize desired
behaviors based around three axes: (i) if the task concerns humans; (ii) how
subjective the task is (i.e., how likely it is that people from a diverse range
of backgrounds would agree on a labeling); and (iii) the intended purpose of
the task and if fairness is better served by impartiality (i.e., making
decisions independent of the protected attributes) or representation (i.e.,
making decisions to maximize diversity). Finally, we provide quantitative
fairness evaluations for both binary-valued and multi-valued protected
attributes over ten diverse datasets. We find that fair PCA, a post-processing
method for fair representations, works very well for debiasing in most of the
aforementioned tasks while incurring only minor loss of performance. However,
different debiasing approaches vary in their effectiveness depending on the
task. Hence, one should choose the debiasing approach depending on the specific
use case.
| [
{
"created": "Wed, 18 Oct 2023 10:32:39 GMT",
"version": "v1"
}
] | 2023-10-19 | [
[
"Ali",
"Junaid",
""
],
[
"Kleindessner",
"Matthaeus",
""
],
[
"Wenzel",
"Florian",
""
],
[
"Budhathoki",
"Kailash",
""
],
[
"Cevher",
"Volkan",
""
],
[
"Russell",
"Chris",
""
]
] | We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks. We then systematically evaluate existing methods for mitigating bias in these models with respect to our taxonomy. Specifically, we evaluate OpenAI's CLIP and OpenCLIP models for key applications, such as zero-shot classification, image retrieval and image captioning. We categorize desired behaviors based around three axes: (i) if the task concerns humans; (ii) how subjective the task is (i.e., how likely it is that people from a diverse range of backgrounds would agree on a labeling); and (iii) the intended purpose of the task and if fairness is better served by impartiality (i.e., making decisions independent of the protected attributes) or representation (i.e., making decisions to maximize diversity). Finally, we provide quantitative fairness evaluations for both binary-valued and multi-valued protected attributes over ten diverse datasets. We find that fair PCA, a post-processing method for fair representations, works very well for debiasing in most of the aforementioned tasks while incurring only minor loss of performance. However, different debiasing approaches vary in their effectiveness depending on the task. Hence, one should choose the debiasing approach depending on the specific use case. |
2202.09806 | Andrew Cropper | Andrew Cropper and C\'eline Hocquette | Learning logic programs by discovering where not to search | Preprint for AAAI23 | null | null | null | cs.LG cs.AI cs.LO | http://creativecommons.org/licenses/by/4.0/ | The goal of inductive logic programming (ILP) is to search for a hypothesis
that generalises training examples and background knowledge (BK). To improve
performance, we introduce an approach that, before searching for a hypothesis,
first discovers where not to search. We use given BK to discover constraints on
hypotheses, such as that a number cannot be both even and odd. We use the
constraints to bootstrap a constraint-driven ILP system. Our experiments on
multiple domains (including program synthesis and game playing) show that our
approach can (i) substantially reduce learning times by up to 97%, and (ii)
scale to domains with millions of facts.
| [
{
"created": "Sun, 20 Feb 2022 12:32:03 GMT",
"version": "v1"
},
{
"created": "Mon, 5 Dec 2022 09:42:29 GMT",
"version": "v2"
}
] | 2022-12-06 | [
[
"Cropper",
"Andrew",
""
],
[
"Hocquette",
"Céline",
""
]
] | The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers where not to search. We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and game playing) show that our approach can (i) substantially reduce learning times by up to 97%, and (ii) scale to domains with millions of facts. |
1401.3421 | Rourab Paul | Sruti Agarwal, Sangeet Saha, Rourab Paul, Amlan Chakrabarti | Performance Evaluation of ECC in Single and Multi Processor
Architectures on FPGA Based Embedded System | Published Book Title: Elsevier Science and Technology, ICCN 2013,
Bangalore, Page(s): 140 - 147, Volume 3, 03.elsevierst.2013.3.ICCN16, ISBN
:9789351071044, Paper
link:-http://searchdl.org/index.php/book_series/view/917 | null | null | null | cs.AR cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cryptographic algorithms are computationally costly and the challenge is more
if we need to execute them in resource constrained embedded systems. Field
Programmable Gate Arrays (FPGAs) having programmable logic de- vices and
processing cores, have proven to be highly feasible implementation platforms
for embedded systems providing lesser design time and reconfig- urability.
Design parameters like throughput, resource utilization and power requirements
are the key issues. The popular Elliptic Curve Cryptography (ECC), which is
superior over other public-key crypto-systems like RSA in many ways, such as
providing greater security for a smaller key size, is cho- sen in this work and
the possibilities of its implementation in FPGA based embedded systems for both
single and dual processor core architectures in- volving task parallelization
have been explored. This exploration, which is first of its kind considering
the other existing works, is a needed activity for evaluating the best possible
architectural environment for ECC implementa- tion on FPGA (Virtex4 XC4VFX12,
FF668, -10) based embedded platform.
| [
{
"created": "Wed, 15 Jan 2014 03:25:41 GMT",
"version": "v1"
}
] | 2014-02-20 | [
[
"Agarwal",
"Sruti",
""
],
[
"Saha",
"Sangeet",
""
],
[
"Paul",
"Rourab",
""
],
[
"Chakrabarti",
"Amlan",
""
]
] | Cryptographic algorithms are computationally costly and the challenge is more if we need to execute them in resource constrained embedded systems. Field Programmable Gate Arrays (FPGAs) having programmable logic de- vices and processing cores, have proven to be highly feasible implementation platforms for embedded systems providing lesser design time and reconfig- urability. Design parameters like throughput, resource utilization and power requirements are the key issues. The popular Elliptic Curve Cryptography (ECC), which is superior over other public-key crypto-systems like RSA in many ways, such as providing greater security for a smaller key size, is cho- sen in this work and the possibilities of its implementation in FPGA based embedded systems for both single and dual processor core architectures in- volving task parallelization have been explored. This exploration, which is first of its kind considering the other existing works, is a needed activity for evaluating the best possible architectural environment for ECC implementa- tion on FPGA (Virtex4 XC4VFX12, FF668, -10) based embedded platform. |
2403.03173 | Beiming Yuan | Ruizhuo Song, Beiming Yuan | Solving the Clustering Reasoning Problems by Modeling a
Deep-Learning-Based Probabilistic Model | 14 pages, 17 figures, 4 tables | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual abstract reasoning problems pose significant challenges to the
perception and cognition abilities of artificial intelligence algorithms,
demanding deeper pattern recognition and inductive reasoning beyond mere
identification of explicit image features. Research advancements in this field
often provide insights and technical support for other similar domains. In this
study, we introduce PMoC, a deep-learning-based probabilistic model, achieving
high reasoning accuracy in the Bongard-Logo, which stands as one of the most
challenging clustering reasoning tasks. PMoC is a novel approach for
constructing probabilistic models based on deep learning, which is distinctly
different from previous techniques. PMoC revitalizes the probabilistic
approach, which has been relatively weak in visual abstract reasoning. As a
bonus, we also designed Pose-Transformer for complex visual abstract reasoning
tasks. Inspired by capsule networks, it focuses on positional relationships in
image data, boosting accuracy when combined with PMoC. Our Pose-Transformer
effectively addresses reasoning difficulties associated with changes in the
position of entities, outperforming previous models on RAVEN dataset, and the
PGM dataset. RAVEN and PGM represent two significant progressive pattern
reasoning problems. Finally, considering the deployment difficulties of
Pose-Transformer, we introduced Straw-Pose-Transformer, a lightweight version.
This study contributes to enhancing the capabilities of artificial intelligence
in abstract reasoning, cognitive pattern, and probabilistic modeling of complex
systems.
| [
{
"created": "Tue, 5 Mar 2024 18:08:29 GMT",
"version": "v1"
},
{
"created": "Sat, 9 Mar 2024 18:53:21 GMT",
"version": "v2"
},
{
"created": "Mon, 25 Mar 2024 04:42:22 GMT",
"version": "v3"
},
{
"created": "Tue, 7 May 2024 14:34:34 GMT",
"version": "v4"
},
{
"created": "Mon, 20 May 2024 01:54:21 GMT",
"version": "v5"
},
{
"created": "Sat, 25 May 2024 16:01:07 GMT",
"version": "v6"
},
{
"created": "Sun, 2 Jun 2024 16:35:23 GMT",
"version": "v7"
},
{
"created": "Thu, 13 Jun 2024 09:41:55 GMT",
"version": "v8"
}
] | 2024-06-14 | [
[
"Song",
"Ruizhuo",
""
],
[
"Yuan",
"Beiming",
""
]
] | Visual abstract reasoning problems pose significant challenges to the perception and cognition abilities of artificial intelligence algorithms, demanding deeper pattern recognition and inductive reasoning beyond mere identification of explicit image features. Research advancements in this field often provide insights and technical support for other similar domains. In this study, we introduce PMoC, a deep-learning-based probabilistic model, achieving high reasoning accuracy in the Bongard-Logo, which stands as one of the most challenging clustering reasoning tasks. PMoC is a novel approach for constructing probabilistic models based on deep learning, which is distinctly different from previous techniques. PMoC revitalizes the probabilistic approach, which has been relatively weak in visual abstract reasoning. As a bonus, we also designed Pose-Transformer for complex visual abstract reasoning tasks. Inspired by capsule networks, it focuses on positional relationships in image data, boosting accuracy when combined with PMoC. Our Pose-Transformer effectively addresses reasoning difficulties associated with changes in the position of entities, outperforming previous models on RAVEN dataset, and the PGM dataset. RAVEN and PGM represent two significant progressive pattern reasoning problems. Finally, considering the deployment difficulties of Pose-Transformer, we introduced Straw-Pose-Transformer, a lightweight version. This study contributes to enhancing the capabilities of artificial intelligence in abstract reasoning, cognitive pattern, and probabilistic modeling of complex systems. |
1006.2955 | Dinesh Dash | Dinesh Dash and Arijit Bishnu and Arobinda Gupta and Subhas C. Nandy | Approximation Algorithm for Line Segment Coverage for Wireless Sensor
Network | 16 pages, 5 figures, | Wireless Networks 19(5): 857-870 (2013) | 10.1007/s11276-012-0506-4 | null | cs.CG cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The coverage problem in wireless sensor networks deals with the problem of
covering a region or parts of it with sensors. In this paper, we address the
problem of covering a set of line segments in sensor networks. A line segment `
is said to be covered if it intersects the sensing regions of at least one
sensor distributed in that region. We show that the problem of finding the
minimum number of sensors needed to cover each member in a given set of line
segments in a rectangular area is NP-hard. Next, we propose a constant factor
approximation algorithm for the problem of covering a set of axis-parallel line
segments. We also show that a PTAS exists for this problem.
| [
{
"created": "Tue, 15 Jun 2010 10:51:44 GMT",
"version": "v1"
}
] | 2017-11-16 | [
[
"Dash",
"Dinesh",
""
],
[
"Bishnu",
"Arijit",
""
],
[
"Gupta",
"Arobinda",
""
],
[
"Nandy",
"Subhas C.",
""
]
] | The coverage problem in wireless sensor networks deals with the problem of covering a region or parts of it with sensors. In this paper, we address the problem of covering a set of line segments in sensor networks. A line segment ` is said to be covered if it intersects the sensing regions of at least one sensor distributed in that region. We show that the problem of finding the minimum number of sensors needed to cover each member in a given set of line segments in a rectangular area is NP-hard. Next, we propose a constant factor approximation algorithm for the problem of covering a set of axis-parallel line segments. We also show that a PTAS exists for this problem. |
2308.02400 | Felix Staudigl | Felix Staudigl, Mohammed Hossein, Tobias Ziegler, Hazem Al Indari,
Rebecca Pelke, Sebastian Siegel, Dirk J. Wouters, Dominik Sisejkovic, Jan
Moritz Joseph, and Rainer Leupers | Work-in-Progress: A Universal Instrumentation Platform for Non-Volatile
Memories | null | null | null | null | cs.AR cs.CY | http://creativecommons.org/licenses/by/4.0/ | Emerging non-volatile memories (NVMs) represent a disruptive technology that
allows a paradigm shift from the conventional von Neumann architecture towards
more efficient computing-in-memory (CIM) architectures. Several instrumentation
platforms have been proposed to interface NVMs allowing the characterization of
single cells and crossbar structures. However, these platforms suffer from low
flexibility and are not capable of performing CIM operations on NVMs.
Therefore, we recently designed and built the NeuroBreakoutBoard, a highly
versatile instrumentation platform capable of executing CIM on NVMs. We present
our preliminary results demonstrating a relative error < 5% in the range of 1
k$\Omega$ to 1 M$\Omega$ and showcase the switching behavior of a
HfO$_2$/Ti-based memristive cell.
| [
{
"created": "Thu, 3 Aug 2023 14:24:57 GMT",
"version": "v1"
}
] | 2023-08-07 | [
[
"Staudigl",
"Felix",
""
],
[
"Hossein",
"Mohammed",
""
],
[
"Ziegler",
"Tobias",
""
],
[
"Indari",
"Hazem Al",
""
],
[
"Pelke",
"Rebecca",
""
],
[
"Siegel",
"Sebastian",
""
],
[
"Wouters",
"Dirk J.",
""
],
[
"Sisejkovic",
"Dominik",
""
],
[
"Joseph",
"Jan Moritz",
""
],
[
"Leupers",
"Rainer",
""
]
] | Emerging non-volatile memories (NVMs) represent a disruptive technology that allows a paradigm shift from the conventional von Neumann architecture towards more efficient computing-in-memory (CIM) architectures. Several instrumentation platforms have been proposed to interface NVMs allowing the characterization of single cells and crossbar structures. However, these platforms suffer from low flexibility and are not capable of performing CIM operations on NVMs. Therefore, we recently designed and built the NeuroBreakoutBoard, a highly versatile instrumentation platform capable of executing CIM on NVMs. We present our preliminary results demonstrating a relative error < 5% in the range of 1 k$\Omega$ to 1 M$\Omega$ and showcase the switching behavior of a HfO$_2$/Ti-based memristive cell. |
1603.05214 | Tadeusz Litak | Stefan Milius, Tadeusz Litak | Guard Your Daggers and Traces: Properties of Guarded (Co-)recursion | invited to a special issue of Fundamenta Informaticae (FiCS'13).
arXiv admin note: text overlap with arXiv:1309.0895 | null | 10.3233/FI-2017-1475 | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motivated by the recent interest in models of guarded (co-)recursion, we
study their equational properties. We formulate axioms for guarded fixpoint
operators generalizing the axioms of iteration theories of Bloom and \'Esik.
Models of these axioms include both standard (e.g., cpo-based) models of
iteration theories and models of guarded recursion such as complete metric
spaces or the topos of trees studied by Birkedal et al. We show that the
standard result on the satisfaction of all Conway axioms by a unique dagger
operation generalizes to the guarded setting. We also introduce the notion of
guarded trace operator on a category, and we prove that guarded trace and
guarded fixpoint operators are in one-to-one correspondence. Our results are
intended as first steps leading, hopefully, towards future description of
classifying theories for guarded recursion.
| [
{
"created": "Wed, 16 Mar 2016 18:39:53 GMT",
"version": "v1"
}
] | 2018-08-21 | [
[
"Milius",
"Stefan",
""
],
[
"Litak",
"Tadeusz",
""
]
] | Motivated by the recent interest in models of guarded (co-)recursion, we study their equational properties. We formulate axioms for guarded fixpoint operators generalizing the axioms of iteration theories of Bloom and \'Esik. Models of these axioms include both standard (e.g., cpo-based) models of iteration theories and models of guarded recursion such as complete metric spaces or the topos of trees studied by Birkedal et al. We show that the standard result on the satisfaction of all Conway axioms by a unique dagger operation generalizes to the guarded setting. We also introduce the notion of guarded trace operator on a category, and we prove that guarded trace and guarded fixpoint operators are in one-to-one correspondence. Our results are intended as first steps leading, hopefully, towards future description of classifying theories for guarded recursion. |
1211.6216 | Jos\'e Verschae | Nicole Megow and Jos\'e Verschae | Dual techniques for scheduling on a machine with varying speed | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study scheduling problems on a machine with varying speed. Assuming a
known speed function we ask for a cost-efficient scheduling solution. Our main
result is a PTAS for minimizing the total weighted completion time in this
setting. This also implies a PTAS for the closely related problem of scheduling
to minimize generalized global cost functions. The key to our results is a
re-interpretation of the problem within the well-known two-dimensional Gantt
chart: instead of the standard approach of scheduling in the {\em
time-dimension}, we construct scheduling solutions in the weight-dimension.
We also consider a dynamic problem variant in which deciding upon the speed
is part of the scheduling problem and we are interested in the tradeoff between
scheduling cost and speed-scaling cost, which is typically the energy
consumption. We observe that the optimal order is independent of the energy
consumption and that the problem can be reduced to the setting where the speed
of the machine is fixed, and thus admits a PTAS. Furthermore, we provide an
FPTAS for the NP-hard problem variant in which the machine can run only on a
fixed number of discrete speeds. Finally, we show how our results can be used
to obtain a~$(2+\eps)$-approximation for scheduling preemptive jobs with
release dates on multiple identical parallel machines.
| [
{
"created": "Tue, 27 Nov 2012 05:45:55 GMT",
"version": "v1"
},
{
"created": "Mon, 11 Feb 2013 23:22:12 GMT",
"version": "v2"
},
{
"created": "Tue, 4 Mar 2014 21:20:25 GMT",
"version": "v3"
}
] | 2014-03-06 | [
[
"Megow",
"Nicole",
""
],
[
"Verschae",
"José",
""
]
] | We study scheduling problems on a machine with varying speed. Assuming a known speed function we ask for a cost-efficient scheduling solution. Our main result is a PTAS for minimizing the total weighted completion time in this setting. This also implies a PTAS for the closely related problem of scheduling to minimize generalized global cost functions. The key to our results is a re-interpretation of the problem within the well-known two-dimensional Gantt chart: instead of the standard approach of scheduling in the {\em time-dimension}, we construct scheduling solutions in the weight-dimension. We also consider a dynamic problem variant in which deciding upon the speed is part of the scheduling problem and we are interested in the tradeoff between scheduling cost and speed-scaling cost, which is typically the energy consumption. We observe that the optimal order is independent of the energy consumption and that the problem can be reduced to the setting where the speed of the machine is fixed, and thus admits a PTAS. Furthermore, we provide an FPTAS for the NP-hard problem variant in which the machine can run only on a fixed number of discrete speeds. Finally, we show how our results can be used to obtain a~$(2+\eps)$-approximation for scheduling preemptive jobs with release dates on multiple identical parallel machines. |
2407.00717 | Xikun Zhang | Xikun Zhang, Dongjin Song, Yushan Jiang, Yixin Chen, Dacheng Tao | Learning System Dynamics without Forgetting | null | null | null | null | cs.LG cs.AI cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Predicting the trajectories of systems with unknown dynamics (\textit{i.e.}
the governing rules) is crucial in various research fields, including physics
and biology. This challenge has gathered significant attention from diverse
communities. Most existing works focus on learning fixed system dynamics within
one single system. However, real-world applications often involve multiple
systems with different types of dynamics or evolving systems with
non-stationary dynamics (dynamics shifts). When data from those systems are
continuously collected and sequentially fed to machine learning models for
training, these models tend to be biased toward the most recently learned
dynamics, leading to catastrophic forgetting of previously observed/learned
system dynamics. To this end, we aim to learn system dynamics via continual
learning. Specifically, we present a novel framework of Mode-switching Graph
ODE (MS-GODE), which can continually learn varying dynamics and encode the
system-specific dynamics into binary masks over the model parameters. During
the inference stage, the model can select the most confident mask based on the
observational data to identify the system and predict future trajectories
accordingly. Empirically, we systematically investigate the task configurations
and compare the proposed MS-GODE with state-of-the-art techniques. More
importantly, we construct a novel benchmark of biological dynamic systems,
featuring diverse systems with disparate dynamics and significantly enriching
the research field of machine learning for dynamic systems.
| [
{
"created": "Sun, 30 Jun 2024 14:55:18 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Zhang",
"Xikun",
""
],
[
"Song",
"Dongjin",
""
],
[
"Jiang",
"Yushan",
""
],
[
"Chen",
"Yixin",
""
],
[
"Tao",
"Dacheng",
""
]
] | Predicting the trajectories of systems with unknown dynamics (\textit{i.e.} the governing rules) is crucial in various research fields, including physics and biology. This challenge has gathered significant attention from diverse communities. Most existing works focus on learning fixed system dynamics within one single system. However, real-world applications often involve multiple systems with different types of dynamics or evolving systems with non-stationary dynamics (dynamics shifts). When data from those systems are continuously collected and sequentially fed to machine learning models for training, these models tend to be biased toward the most recently learned dynamics, leading to catastrophic forgetting of previously observed/learned system dynamics. To this end, we aim to learn system dynamics via continual learning. Specifically, we present a novel framework of Mode-switching Graph ODE (MS-GODE), which can continually learn varying dynamics and encode the system-specific dynamics into binary masks over the model parameters. During the inference stage, the model can select the most confident mask based on the observational data to identify the system and predict future trajectories accordingly. Empirically, we systematically investigate the task configurations and compare the proposed MS-GODE with state-of-the-art techniques. More importantly, we construct a novel benchmark of biological dynamic systems, featuring diverse systems with disparate dynamics and significantly enriching the research field of machine learning for dynamic systems. |
2105.01697 | Noel Csomay-Shanklin | Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai, Andrew J. Taylor, Aaron
D. Ames | Episodic Learning for Safe Bipedal Locomotion with Control Barrier
Functions and Projection-to-State Safety | 13 pages, 4 figures, to appear at the Conference on Learning for
Dynamics and Control 2021 | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper combines episodic learning and control barrier functions in the
setting of bipedal locomotion. The safety guarantees that control barrier
functions provide are only valid with perfect model knowledge; however, this
assumption cannot be met on hardware platforms. To address this, we utilize the
notion of projection-to-state safety paired with a machine learning framework
in an attempt to learn the model uncertainty as it affects the barrier
functions. The proposed approach is demonstrated both in simulation and on
hardware for the AMBER-3M bipedal robot in the context of the stepping-stone
problem, which requires precise foot placement while walking dynamically.
| [
{
"created": "Tue, 4 May 2021 18:33:28 GMT",
"version": "v1"
}
] | 2021-05-06 | [
[
"Csomay-Shanklin",
"Noel",
""
],
[
"Cosner",
"Ryan K.",
""
],
[
"Dai",
"Min",
""
],
[
"Taylor",
"Andrew J.",
""
],
[
"Ames",
"Aaron D.",
""
]
] | This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of projection-to-state safety paired with a machine learning framework in an attempt to learn the model uncertainty as it affects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem, which requires precise foot placement while walking dynamically. |
2302.08062 | Chengbin Hou | Chengbin Hou, Xinyu Lin, Hanhui Huang, Sheng Xu, Junxuan Fan, Yukun
Shi, Hairong Lv | Fossil Image Identification using Deep Learning Ensembles of Data
Augmented Multiviews | published in Methods in Ecology and Evolution | Methods in Ecology and Evolution, 14, 3020-3034 (2023) | 10.1111/2041-210X.14229 | null | cs.CV cs.AI q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identification of fossil species is crucial to evolutionary studies. Recent
advances from deep learning have shown promising prospects in fossil image
identification. However, the quantity and quality of labeled fossil images are
often limited due to fossil preservation, conditioned sampling, and expensive
and inconsistent label annotation by domain experts, which pose great
challenges to training deep learning based image classification models. To
address these challenges, we follow the idea of the wisdom of crowds and
propose a multiview ensemble framework, which collects Original (O), Gray (G),
and Skeleton (S) views of each fossil image reflecting its different
characteristics to train multiple base models, and then makes the final
decision via soft voting. Experiments on the largest fusulinid dataset with
2400 images show that the proposed OGS consistently outperforms baselines
(using a single model for each view), and obtains superior or comparable
performance compared to OOO (using three base models for three the same
Original views). Besides, as the training data decreases, the proposed
framework achieves more gains. While considering the identification consistency
estimation with respect to human experts, OGS receives the highest agreement
with the original labels of dataset and with the re-identifications of two
human experts. The validation performance provides a quantitative estimation of
consistency across different experts and genera. We conclude that the proposed
framework can present state-of-the-art performance in the fusulinid fossil
identification case study. This framework is designed for general fossil
identification and it is expected to see applications to other fossil datasets
in future work. The source code is publicly available at
https://github.com/houchengbin/Fossil-Image-Identification to benefit future
research in fossil image identification.
| [
{
"created": "Thu, 16 Feb 2023 03:57:21 GMT",
"version": "v1"
},
{
"created": "Wed, 20 Sep 2023 08:53:59 GMT",
"version": "v2"
},
{
"created": "Fri, 2 Feb 2024 02:04:32 GMT",
"version": "v3"
}
] | 2024-02-05 | [
[
"Hou",
"Chengbin",
""
],
[
"Lin",
"Xinyu",
""
],
[
"Huang",
"Hanhui",
""
],
[
"Xu",
"Sheng",
""
],
[
"Fan",
"Junxuan",
""
],
[
"Shi",
"Yukun",
""
],
[
"Lv",
"Hairong",
""
]
] | Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labeled fossil images are often limited due to fossil preservation, conditioned sampling, and expensive and inconsistent label annotation by domain experts, which pose great challenges to training deep learning based image classification models. To address these challenges, we follow the idea of the wisdom of crowds and propose a multiview ensemble framework, which collects Original (O), Gray (G), and Skeleton (S) views of each fossil image reflecting its different characteristics to train multiple base models, and then makes the final decision via soft voting. Experiments on the largest fusulinid dataset with 2400 images show that the proposed OGS consistently outperforms baselines (using a single model for each view), and obtains superior or comparable performance compared to OOO (using three base models for three the same Original views). Besides, as the training data decreases, the proposed framework achieves more gains. While considering the identification consistency estimation with respect to human experts, OGS receives the highest agreement with the original labels of dataset and with the re-identifications of two human experts. The validation performance provides a quantitative estimation of consistency across different experts and genera. We conclude that the proposed framework can present state-of-the-art performance in the fusulinid fossil identification case study. This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work. The source code is publicly available at https://github.com/houchengbin/Fossil-Image-Identification to benefit future research in fossil image identification. |
2112.06068 | Peter Plantinga | Peter Plantinga, Deblin Bagchi, Eric Fosler-Lussier | Perceptual Loss with Recognition Model for Single-Channel Enhancement
and Robust ASR | null | null | null | null | cs.SD eess.AS | http://creativecommons.org/licenses/by/4.0/ | Single-channel speech enhancement approaches do not always improve automatic
recognition rates in the presence of noise, because they can introduce
distortions unhelpful for recognition. Following a trend towards end-to-end
training of sequential neural network models, several research groups have
addressed this problem with joint training of front-end enhancement module with
back-end recognition module. While this approach ensures enhancement outputs
are helpful for recognition, the enhancement model can overfit to the training
data, weakening the recognition model in the presence of unseen noise. To
address this, we used a pre-trained acoustic model to generate a perceptual
loss that makes speech enhancement more aware of the phonetic properties of the
signal. This approach keeps some benefits of joint training, while alleviating
the overfitting problem. Experiments on Voicebank + DEMAND dataset for
enhancement show that this approach achieves a new state of the art for some
objective enhancement scores. In combination with distortion-independent
training, our approach gets a WER of 2.80\% on the test set, which is more than
20\% relative better recognition performance than joint training, and 14\%
relative better than distortion-independent mask training.
| [
{
"created": "Sat, 11 Dec 2021 20:44:26 GMT",
"version": "v1"
}
] | 2021-12-14 | [
[
"Plantinga",
"Peter",
""
],
[
"Bagchi",
"Deblin",
""
],
[
"Fosler-Lussier",
"Eric",
""
]
] | Single-channel speech enhancement approaches do not always improve automatic recognition rates in the presence of noise, because they can introduce distortions unhelpful for recognition. Following a trend towards end-to-end training of sequential neural network models, several research groups have addressed this problem with joint training of front-end enhancement module with back-end recognition module. While this approach ensures enhancement outputs are helpful for recognition, the enhancement model can overfit to the training data, weakening the recognition model in the presence of unseen noise. To address this, we used a pre-trained acoustic model to generate a perceptual loss that makes speech enhancement more aware of the phonetic properties of the signal. This approach keeps some benefits of joint training, while alleviating the overfitting problem. Experiments on Voicebank + DEMAND dataset for enhancement show that this approach achieves a new state of the art for some objective enhancement scores. In combination with distortion-independent training, our approach gets a WER of 2.80\% on the test set, which is more than 20\% relative better recognition performance than joint training, and 14\% relative better than distortion-independent mask training. |
2205.14252 | Aditya Vaidya | Aditya R. Vaidya, Shailee Jain, Alexander G. Huth | Self-supervised models of audio effectively explain human cortical
responses to speech | Accepted to the International Conference on Machine Learning (ICML)
2022 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Self-supervised language models are very effective at predicting high-level
cortical responses during language comprehension. However, the best current
models of lower-level auditory processing in the human brain rely on either
hand-constructed acoustic filters or representations from supervised audio
neural networks. In this work, we capitalize on the progress of self-supervised
speech representation learning (SSL) to create new state-of-the-art models of
the human auditory system. Compared against acoustic baselines, phonemic
features, and supervised models, representations from the middle layers of
self-supervised models (APC, wav2vec, wav2vec 2.0, and HuBERT) consistently
yield the best prediction performance for fMRI recordings within the auditory
cortex (AC). Brain areas involved in low-level auditory processing exhibit a
preference for earlier SSL model layers, whereas higher-level semantic areas
prefer later layers. We show that these trends are due to the models' ability
to encode information at multiple linguistic levels (acoustic, phonetic, and
lexical) along their representation depth. Overall, these results show that
self-supervised models effectively capture the hierarchy of information
relevant to different stages of speech processing in human cortex.
| [
{
"created": "Fri, 27 May 2022 22:04:02 GMT",
"version": "v1"
}
] | 2022-05-31 | [
[
"Vaidya",
"Aditya R.",
""
],
[
"Jain",
"Shailee",
""
],
[
"Huth",
"Alexander G.",
""
]
] | Self-supervised language models are very effective at predicting high-level cortical responses during language comprehension. However, the best current models of lower-level auditory processing in the human brain rely on either hand-constructed acoustic filters or representations from supervised audio neural networks. In this work, we capitalize on the progress of self-supervised speech representation learning (SSL) to create new state-of-the-art models of the human auditory system. Compared against acoustic baselines, phonemic features, and supervised models, representations from the middle layers of self-supervised models (APC, wav2vec, wav2vec 2.0, and HuBERT) consistently yield the best prediction performance for fMRI recordings within the auditory cortex (AC). Brain areas involved in low-level auditory processing exhibit a preference for earlier SSL model layers, whereas higher-level semantic areas prefer later layers. We show that these trends are due to the models' ability to encode information at multiple linguistic levels (acoustic, phonetic, and lexical) along their representation depth. Overall, these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex. |
2105.05557 | Daniel Wiegreffe | Christopher Schr\"oder, Kim B\"urgl, Yves Annanias, Andreas Niekler,
Lydia M\"uller, Daniel Wiegreffe, Christian Bender, Christoph Mengs, Gerik
Scheuermann, Gerhard Heyer | Supporting Land Reuse of Former Open Pit Mining Sites using Text
Classification and Active Learning | null | Proceedings of the 59th Annual Meeting of the Association for
Computational Linguistics and the 11th International Joint Conference on
Natural Language Processing (Volume 1: Long Papers), 2021 | 10.18653/v1/2021.acl-long.320 | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Open pit mines left many regions worldwide inhospitable or uninhabitable. To
put these regions back into use, entire stretches of land must be
renaturalized. For the sustainable subsequent use or transfer to a new primary
use, many contaminated sites and soil information have to be permanently
managed. In most cases, this information is available in the form of expert
reports in unstructured data collections or file folders, which in the best
case are digitized. Due to size and complexity of the data, it is difficult for
a single person to have an overview of this data in order to be able to make
reliable statements. This is one of the most important obstacles to the rapid
transfer of these areas to after-use. An information-based approach to this
issue supports fulfilling several Sustainable Development Goals regarding
environment issues, health and climate action. We use a stack of Optical
Character Recognition, Text Classification, Active Learning and Geographic
Information System Visualization to effectively mine and visualize this
information. Subsequently, we link the extracted information to geographic
coordinates and visualize them using a Geographic Information System. Active
Learning plays a vital role because our dataset provides no training data. In
total, we process nine categories and actively learn their representation in
our dataset. We evaluate the OCR, Active Learning and Text Classification
separately to report the performance of the system. Active Learning and text
classification results are twofold: Whereas our categories about restrictions
work sufficient ($>$.85 F1), the seven topic-oriented categories were
complicated for human coders and hence the results achieved mediocre evaluation
scores ($<$.70 F1).
| [
{
"created": "Wed, 12 May 2021 10:18:14 GMT",
"version": "v1"
},
{
"created": "Thu, 13 May 2021 10:47:44 GMT",
"version": "v2"
},
{
"created": "Thu, 2 Dec 2021 10:17:25 GMT",
"version": "v3"
},
{
"created": "Tue, 22 Mar 2022 12:02:01 GMT",
"version": "v4"
}
] | 2022-03-23 | [
[
"Schröder",
"Christopher",
""
],
[
"Bürgl",
"Kim",
""
],
[
"Annanias",
"Yves",
""
],
[
"Niekler",
"Andreas",
""
],
[
"Müller",
"Lydia",
""
],
[
"Wiegreffe",
"Daniel",
""
],
[
"Bender",
"Christian",
""
],
[
"Mengs",
"Christoph",
""
],
[
"Scheuermann",
"Gerik",
""
],
[
"Heyer",
"Gerhard",
""
]
] | Open pit mines left many regions worldwide inhospitable or uninhabitable. To put these regions back into use, entire stretches of land must be renaturalized. For the sustainable subsequent use or transfer to a new primary use, many contaminated sites and soil information have to be permanently managed. In most cases, this information is available in the form of expert reports in unstructured data collections or file folders, which in the best case are digitized. Due to size and complexity of the data, it is difficult for a single person to have an overview of this data in order to be able to make reliable statements. This is one of the most important obstacles to the rapid transfer of these areas to after-use. An information-based approach to this issue supports fulfilling several Sustainable Development Goals regarding environment issues, health and climate action. We use a stack of Optical Character Recognition, Text Classification, Active Learning and Geographic Information System Visualization to effectively mine and visualize this information. Subsequently, we link the extracted information to geographic coordinates and visualize them using a Geographic Information System. Active Learning plays a vital role because our dataset provides no training data. In total, we process nine categories and actively learn their representation in our dataset. We evaluate the OCR, Active Learning and Text Classification separately to report the performance of the system. Active Learning and text classification results are twofold: Whereas our categories about restrictions work sufficient ($>$.85 F1), the seven topic-oriented categories were complicated for human coders and hence the results achieved mediocre evaluation scores ($<$.70 F1). |
2210.08976 | Tobias Wenzel | Tobias Wenzel | Global technology access in biolabs -- from DIY trend to an open source
transformation | null | PLoS Biol 21(1): e3001931 (2023) | 10.1371/journal.pbio.3001931 | null | cs.CY cs.AR q-bio.OT | http://creativecommons.org/licenses/by/4.0/ | This article illustrates how open hardware solutions are implemented by
researchers as a strategy to access technology for cutting-edge research.
Specifically, it is discussed what kind of open technologies are most enabling
in scientific environments characterized by economic and infrastructural
constraints. It is demonstrated that do-it-yourself (DIY) technologies are
already wide spread, in particular in countries with lower science funding,
which in turn is the basis for the development of open technologies. Beyond
financial accessibility, open hardware can be transformational to the
technology access of laboratories through advantages in local production and
direct knowledge transfer. Central drivers of the adoption of appropriate
technologies in biolabs globally are open sharing, digital fabrication, local
production, standard parts use, and detailed documentation.
| [
{
"created": "Fri, 30 Sep 2022 16:34:27 GMT",
"version": "v1"
}
] | 2023-01-19 | [
[
"Wenzel",
"Tobias",
""
]
] | This article illustrates how open hardware solutions are implemented by researchers as a strategy to access technology for cutting-edge research. Specifically, it is discussed what kind of open technologies are most enabling in scientific environments characterized by economic and infrastructural constraints. It is demonstrated that do-it-yourself (DIY) technologies are already wide spread, in particular in countries with lower science funding, which in turn is the basis for the development of open technologies. Beyond financial accessibility, open hardware can be transformational to the technology access of laboratories through advantages in local production and direct knowledge transfer. Central drivers of the adoption of appropriate technologies in biolabs globally are open sharing, digital fabrication, local production, standard parts use, and detailed documentation. |
2302.13203 | Shengbo Wang | Shengbo Wang, Nian Si, Jose Blanchet, and Zhengyuan Zhou | A Finite Sample Complexity Bound for Distributionally Robust Q-learning | Accepted by AISTATS 2023 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a reinforcement learning setting in which the deployment
environment is different from the training environment. Applying a robust
Markov decision processes formulation, we extend the distributionally robust
$Q$-learning framework studied in Liu et al. [2022]. Further, we improve the
design and analysis of their multi-level Monte Carlo estimator. Assuming access
to a simulator, we prove that the worst-case expected sample complexity of our
algorithm to learn the optimal robust $Q$-function within an $\epsilon$ error
in the sup norm is upper bounded by $\tilde
O(|S||A|(1-\gamma)^{-5}\epsilon^{-2}p_{\wedge}^{-6}\delta^{-4})$, where
$\gamma$ is the discount rate, $p_{\wedge}$ is the non-zero minimal support
probability of the transition kernels and $\delta$ is the uncertainty size.
This is the first sample complexity result for the model-free robust RL
problem. Simulation studies further validate our theoretical results.
| [
{
"created": "Sun, 26 Feb 2023 01:15:32 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Mar 2023 00:52:20 GMT",
"version": "v2"
},
{
"created": "Wed, 31 Jul 2024 20:59:45 GMT",
"version": "v3"
}
] | 2024-08-02 | [
[
"Wang",
"Shengbo",
""
],
[
"Si",
"Nian",
""
],
[
"Blanchet",
"Jose",
""
],
[
"Zhou",
"Zhengyuan",
""
]
] | We consider a reinforcement learning setting in which the deployment environment is different from the training environment. Applying a robust Markov decision processes formulation, we extend the distributionally robust $Q$-learning framework studied in Liu et al. [2022]. Further, we improve the design and analysis of their multi-level Monte Carlo estimator. Assuming access to a simulator, we prove that the worst-case expected sample complexity of our algorithm to learn the optimal robust $Q$-function within an $\epsilon$ error in the sup norm is upper bounded by $\tilde O(|S||A|(1-\gamma)^{-5}\epsilon^{-2}p_{\wedge}^{-6}\delta^{-4})$, where $\gamma$ is the discount rate, $p_{\wedge}$ is the non-zero minimal support probability of the transition kernels and $\delta$ is the uncertainty size. This is the first sample complexity result for the model-free robust RL problem. Simulation studies further validate our theoretical results. |
2011.14479 | Huaxiong Li | Haoxing Chen and Huaxiong Li and Yaohui Li and Chunlin Chen | Multi-scale Adaptive Task Attention Network for Few-Shot Learning | null | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | The goal of few-shot learning is to classify unseen categories with few
labeled samples. Recently, the low-level information metric-learning based
methods have achieved satisfying performance, since local representations (LRs)
are more consistent between seen and unseen classes. However, most of these
methods deal with each category in the support set independently, which is not
sufficient to measure the relation between features, especially in a certain
task. Moreover, the low-level information-based metric learning method suffers
when dominant objects of different scales exist in a complex background. To
address these issues, this paper proposes a novel Multi-scale Adaptive Task
Attention Network (MATANet) for few-shot learning. Specifically, we first use a
multi-scale feature generator to generate multiple features at different
scales. Then, an adaptive task attention module is proposed to select the most
important LRs among the entire task. Afterwards, a similarity-to-class module
and a fusion layer are utilized to calculate a joint multi-scale similarity
between the query image and the support set. Extensive experiments on popular
benchmarks clearly show the effectiveness of the proposed MATANet compared with
state-of-the-art methods.
| [
{
"created": "Mon, 30 Nov 2020 00:36:01 GMT",
"version": "v1"
}
] | 2020-12-01 | [
[
"Chen",
"Haoxing",
""
],
[
"Li",
"Huaxiong",
""
],
[
"Li",
"Yaohui",
""
],
[
"Chen",
"Chunlin",
""
]
] | The goal of few-shot learning is to classify unseen categories with few labeled samples. Recently, the low-level information metric-learning based methods have achieved satisfying performance, since local representations (LRs) are more consistent between seen and unseen classes. However, most of these methods deal with each category in the support set independently, which is not sufficient to measure the relation between features, especially in a certain task. Moreover, the low-level information-based metric learning method suffers when dominant objects of different scales exist in a complex background. To address these issues, this paper proposes a novel Multi-scale Adaptive Task Attention Network (MATANet) for few-shot learning. Specifically, we first use a multi-scale feature generator to generate multiple features at different scales. Then, an adaptive task attention module is proposed to select the most important LRs among the entire task. Afterwards, a similarity-to-class module and a fusion layer are utilized to calculate a joint multi-scale similarity between the query image and the support set. Extensive experiments on popular benchmarks clearly show the effectiveness of the proposed MATANet compared with state-of-the-art methods. |
2408.06537 | Mara Finkelstein | Mara Finkelstein, David Vilar, and Markus Freitag | Introducing the NewsPaLM MBR and QE Dataset: LLM-Generated High-Quality
Parallel Data Outperforms Traditional Web-Crawled Data | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Recent research in neural machine translation (NMT) has shown that training
on high-quality machine-generated data can outperform training on
human-generated data. This work accompanies the first-ever release of a
LLM-generated, MBR-decoded and QE-reranked dataset with both sentence-level and
multi-sentence examples. We perform extensive experiments to demonstrate the
quality of our dataset in terms of its downstream impact on NMT model
performance. We find that training from scratch on our (machine-generated)
dataset outperforms training on the (web-crawled) WMT'23 training dataset
(which is 300 times larger), and also outperforms training on the top-quality
subset of the WMT'23 training dataset. We also find that performing
self-distillation by finetuning the LLM which generated this dataset
outperforms the LLM's strong few-shot baseline. These findings corroborate the
quality of our dataset, and demonstrate the value of high-quality
machine-generated data in improving performance of NMT models.
| [
{
"created": "Tue, 13 Aug 2024 00:06:56 GMT",
"version": "v1"
},
{
"created": "Wed, 14 Aug 2024 18:38:11 GMT",
"version": "v2"
}
] | 2024-08-16 | [
[
"Finkelstein",
"Mara",
""
],
[
"Vilar",
"David",
""
],
[
"Freitag",
"Markus",
""
]
] | Recent research in neural machine translation (NMT) has shown that training on high-quality machine-generated data can outperform training on human-generated data. This work accompanies the first-ever release of a LLM-generated, MBR-decoded and QE-reranked dataset with both sentence-level and multi-sentence examples. We perform extensive experiments to demonstrate the quality of our dataset in terms of its downstream impact on NMT model performance. We find that training from scratch on our (machine-generated) dataset outperforms training on the (web-crawled) WMT'23 training dataset (which is 300 times larger), and also outperforms training on the top-quality subset of the WMT'23 training dataset. We also find that performing self-distillation by finetuning the LLM which generated this dataset outperforms the LLM's strong few-shot baseline. These findings corroborate the quality of our dataset, and demonstrate the value of high-quality machine-generated data in improving performance of NMT models. |
2406.15126 | Lin Long | Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen,
Haobo Wang | On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A
Survey | A survey on LLMs-driven synthetic data generation, curation and
evaluation | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Within the evolving landscape of deep learning, the dilemma of data quantity
and quality has been a long-standing problem. The recent advent of Large
Language Models (LLMs) offers a data-centric solution to alleviate the
limitations of real-world data with synthetic data generation. However, current
investigations into this field lack a unified framework and mostly stay on the
surface. Therefore, this paper provides an organization of relevant studies
based on a generic workflow of synthetic data generation. By doing so, we
highlight the gaps within existing research and outline prospective avenues for
future study. This work aims to shepherd the academic and industrial
communities towards deeper, more methodical inquiries into the capabilities and
applications of LLMs-driven synthetic data generation.
| [
{
"created": "Fri, 14 Jun 2024 07:47:09 GMT",
"version": "v1"
}
] | 2024-06-24 | [
[
"Long",
"Lin",
""
],
[
"Wang",
"Rui",
""
],
[
"Xiao",
"Ruixuan",
""
],
[
"Zhao",
"Junbo",
""
],
[
"Ding",
"Xiao",
""
],
[
"Chen",
"Gang",
""
],
[
"Wang",
"Haobo",
""
]
] | Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation. |
1806.02377 | Ugur Kursuncu | Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan,
Amit Sheth and I. Budak Arpinar | Predictive Analysis on Twitter: Techniques and Applications | null | Emerging Research Challenges and Opportunities in Computational
Social Network Analysis and Mining. (2019) 67-104 | 10.1007/978-3-319-94105-9_4 | null | cs.SI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Predictive analysis of social media data has attracted considerable attention
from the research community as well as the business world because of the
essential and actionable information it can provide. Over the years, extensive
experimentation and analysis for insights have been carried out using Twitter
data in various domains such as healthcare, public health, politics, social
sciences, and demographics. In this chapter, we discuss techniques, approaches
and state-of-the-art applications of predictive analysis of Twitter data.
Specifically, we present fine-grained analysis involving aspects such as
sentiment, emotion, and the use of domain knowledge in the coarse-grained
analysis of Twitter data for making decisions and taking actions, and relate a
few success stories.
| [
{
"created": "Wed, 6 Jun 2018 18:41:32 GMT",
"version": "v1"
}
] | 2023-09-04 | [
[
"Kursuncu",
"Ugur",
""
],
[
"Gaur",
"Manas",
""
],
[
"Lokala",
"Usha",
""
],
[
"Thirunarayan",
"Krishnaprasad",
""
],
[
"Sheth",
"Amit",
""
],
[
"Arpinar",
"I. Budak",
""
]
] | Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories. |
2303.01595 | Ellis Solaiman | Adrian Delchev and Ioannis Sfyrakis and Ellis Solaiman | Developing a Compiler for EROP -- A Language for the Specification of
Smart Contracts, An Experience Report | null | null | null | null | cs.PL cs.DC cs.SE | http://creativecommons.org/licenses/by/4.0/ | A smart contract is a translation of a standard paper-based contract that can
be enforced and executed by a contract management system. At a high level of
abstraction, a contract is only a document that describes how the signing
parties are to behave in different scenarios; nevertheless, the translation of
a typical paper-based contract to its electronic counterpart has proved to be
both time-consuming and difficult. The requirement for a language capable of
capturing the core of a contract in simple phrases and definitions has been a
focus of study for many years. EROP (Events, Rights, Obligations, Prohibitions)
is a contract specification language that breaks a contract down into sets of
events, rights, obligations, and prohibitions.
| [
{
"created": "Thu, 2 Mar 2023 21:35:25 GMT",
"version": "v1"
}
] | 2023-03-06 | [
[
"Delchev",
"Adrian",
""
],
[
"Sfyrakis",
"Ioannis",
""
],
[
"Solaiman",
"Ellis",
""
]
] | A smart contract is a translation of a standard paper-based contract that can be enforced and executed by a contract management system. At a high level of abstraction, a contract is only a document that describes how the signing parties are to behave in different scenarios; nevertheless, the translation of a typical paper-based contract to its electronic counterpart has proved to be both time-consuming and difficult. The requirement for a language capable of capturing the core of a contract in simple phrases and definitions has been a focus of study for many years. EROP (Events, Rights, Obligations, Prohibitions) is a contract specification language that breaks a contract down into sets of events, rights, obligations, and prohibitions. |
1912.07800 | Ethan Chi | Bowen Jing, Ethan A. Chi, Jillian Tang | SGVAE: Sequential Graph Variational Autoencoder | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generative models of graphs are well-known, but many existing models are
limited in scalability and expressivity. We present a novel sequential
graphical variational autoencoder operating directly on graphical
representations of data. In our model, the encoding and decoding of a graph as
is framed as a sequential deconstruction and construction process,
respectively, enabling the the learning of a latent space. Experiments on a
cycle dataset show promise, but highlight the need for a relaxation of the
distribution over node permutations.
| [
{
"created": "Tue, 17 Dec 2019 03:19:47 GMT",
"version": "v1"
}
] | 2019-12-18 | [
[
"Jing",
"Bowen",
""
],
[
"Chi",
"Ethan A.",
""
],
[
"Tang",
"Jillian",
""
]
] | Generative models of graphs are well-known, but many existing models are limited in scalability and expressivity. We present a novel sequential graphical variational autoencoder operating directly on graphical representations of data. In our model, the encoding and decoding of a graph as is framed as a sequential deconstruction and construction process, respectively, enabling the the learning of a latent space. Experiments on a cycle dataset show promise, but highlight the need for a relaxation of the distribution over node permutations. |
1807.01956 | Markus M\"uller | Markus M\"uller, Sebastian St\"uker, and Alex Waibel | Neural Language Codes for Multilingual Acoustic Models | 5 pages, 3 figures, accepted at Interspeech 2018 | null | null | null | cs.CL cs.LG cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multilingual Speech Recognition is one of the most costly AI problems,
because each language (7,000+) and even different accents require their own
acoustic models to obtain best recognition performance. Even though they all
use the same phoneme symbols, each language and accent imposes its own coloring
or "twang". Many adaptive approaches have been proposed, but they require
further training, additional data and generally are inferior to monolingually
trained models. In this paper, we propose a different approach that uses a
large multilingual model that is \emph{modulated} by the codes generated by an
ancillary network that learns to code useful differences between the "twangs"
or human language.
We use Meta-Pi networks to have one network (the language code net) gate the
activity of neurons in another (the acoustic model nets). Our results show that
during recognition multilingual Meta-Pi networks quickly adapt to the proper
language coloring without retraining or new data, and perform better than
monolingually trained networks. The model was evaluated by training acoustic
modeling nets and modulating language code nets jointly and optimize them for
best recognition performance.
| [
{
"created": "Thu, 5 Jul 2018 12:15:34 GMT",
"version": "v1"
}
] | 2018-07-06 | [
[
"Müller",
"Markus",
""
],
[
"Stüker",
"Sebastian",
""
],
[
"Waibel",
"Alex",
""
]
] | Multilingual Speech Recognition is one of the most costly AI problems, because each language (7,000+) and even different accents require their own acoustic models to obtain best recognition performance. Even though they all use the same phoneme symbols, each language and accent imposes its own coloring or "twang". Many adaptive approaches have been proposed, but they require further training, additional data and generally are inferior to monolingually trained models. In this paper, we propose a different approach that uses a large multilingual model that is \emph{modulated} by the codes generated by an ancillary network that learns to code useful differences between the "twangs" or human language. We use Meta-Pi networks to have one network (the language code net) gate the activity of neurons in another (the acoustic model nets). Our results show that during recognition multilingual Meta-Pi networks quickly adapt to the proper language coloring without retraining or new data, and perform better than monolingually trained networks. The model was evaluated by training acoustic modeling nets and modulating language code nets jointly and optimize them for best recognition performance. |
2207.01920 | Jos\'e Marcelo Fernandes | J. Fernandes, J. S\'a Silva, A. Rodrigues, F. Boavida, R. Gaspar, C.
Godinho, R. Francisco | Social Sensing and Human in the Loop Profiling during Pandemics: the
Vitoria application | 23 pages, 12 figures and 4 tables | null | null | null | cs.HC cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As the number of smart devices that surround us increases, so do the
opportunities to leverage them to create socially- and context-aware systems.
Smart devices can be used for better understanding human behaviour and its
societal implications. As an example of a scenario in which the role of
socially aware systems is crucial, consider the SARS-CoV-2 pandemic. In this
paper we present an innovative Humanin-The-Loop Cyber Physical system that can
collect passive data from people, such as physical activity, sleep information,
and discrete location, as well as collect self-reported data, and provide
individualised user feedback. In this paper, we also present a three and a half
months field trial implemented in Portugal. This trial was part of a larger
scope project that was supported by the Portuguese National Health System, to
evaluate the indicators and effects of the pandemic. Results concerning various
applications usage statistics are presented, comparing the most used
applications, their objective and their usage pattern in work/non-work periods.
Additionally,the time-lagged cross correlation between some of the collected
metrics, Covid events, and media news, are explored. This type of applications
can be used not only in the context of Covid but also in future pandemics, to
assist individuals in self-regulation of their contagion risk, based on
personalized information, while also function as a means for raising
self-awareness of risks related to psychological wellbeing.
| [
{
"created": "Tue, 5 Jul 2022 09:55:00 GMT",
"version": "v1"
}
] | 2022-07-06 | [
[
"Fernandes",
"J.",
""
],
[
"Silva",
"J. Sá",
""
],
[
"Rodrigues",
"A.",
""
],
[
"Boavida",
"F.",
""
],
[
"Gaspar",
"R.",
""
],
[
"Godinho",
"C.",
""
],
[
"Francisco",
"R.",
""
]
] | As the number of smart devices that surround us increases, so do the opportunities to leverage them to create socially- and context-aware systems. Smart devices can be used for better understanding human behaviour and its societal implications. As an example of a scenario in which the role of socially aware systems is crucial, consider the SARS-CoV-2 pandemic. In this paper we present an innovative Humanin-The-Loop Cyber Physical system that can collect passive data from people, such as physical activity, sleep information, and discrete location, as well as collect self-reported data, and provide individualised user feedback. In this paper, we also present a three and a half months field trial implemented in Portugal. This trial was part of a larger scope project that was supported by the Portuguese National Health System, to evaluate the indicators and effects of the pandemic. Results concerning various applications usage statistics are presented, comparing the most used applications, their objective and their usage pattern in work/non-work periods. Additionally,the time-lagged cross correlation between some of the collected metrics, Covid events, and media news, are explored. This type of applications can be used not only in the context of Covid but also in future pandemics, to assist individuals in self-regulation of their contagion risk, based on personalized information, while also function as a means for raising self-awareness of risks related to psychological wellbeing. |
1712.03950 | Quanquan Gu | Yaodong Yu and Difan Zou and Quanquan Gu | Saving Gradient and Negative Curvature Computations: Finding Local
Minima More Efficiently | 31 pages, 1 table | null | null | null | cs.LG math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a family of nonconvex optimization algorithms that are able to
save gradient and negative curvature computations to a large extent, and are
guaranteed to find an approximate local minimum with improved runtime
complexity. At the core of our algorithms is the division of the entire domain
of the objective function into small and large gradient regions: our algorithms
only perform gradient descent based procedure in the large gradient region, and
only perform negative curvature descent in the small gradient region. Our novel
analysis shows that the proposed algorithms can escape the small gradient
region in only one negative curvature descent step whenever they enter it, and
thus they only need to perform at most $N_{\epsilon}$ negative curvature
direction computations, where $N_{\epsilon}$ is the number of times the
algorithms enter small gradient regions. For both deterministic and stochastic
settings, we show that the proposed algorithms can potentially beat the
state-of-the-art local minima finding algorithms. For the finite-sum setting,
our algorithm can also outperform the best algorithm in a certain regime.
| [
{
"created": "Mon, 11 Dec 2017 18:59:09 GMT",
"version": "v1"
}
] | 2017-12-12 | [
[
"Yu",
"Yaodong",
""
],
[
"Zou",
"Difan",
""
],
[
"Gu",
"Quanquan",
""
]
] | We propose a family of nonconvex optimization algorithms that are able to save gradient and negative curvature computations to a large extent, and are guaranteed to find an approximate local minimum with improved runtime complexity. At the core of our algorithms is the division of the entire domain of the objective function into small and large gradient regions: our algorithms only perform gradient descent based procedure in the large gradient region, and only perform negative curvature descent in the small gradient region. Our novel analysis shows that the proposed algorithms can escape the small gradient region in only one negative curvature descent step whenever they enter it, and thus they only need to perform at most $N_{\epsilon}$ negative curvature direction computations, where $N_{\epsilon}$ is the number of times the algorithms enter small gradient regions. For both deterministic and stochastic settings, we show that the proposed algorithms can potentially beat the state-of-the-art local minima finding algorithms. For the finite-sum setting, our algorithm can also outperform the best algorithm in a certain regime. |
1609.06065 | Yuan Cao | Yonglin Cao, Yuan Cao | Complete classification of $(\delta+\alpha u^2)$-constacyclic codes over
$\mathbb{F}_{2^m}[u]/\langle u^4\rangle$ of oddly even length | arXiv admin note: text overlap with arXiv:1511.02369 | null | null | FFA-16-175 | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Let $\mathbb{F}_{2^m}$ be a finite field of cardinality $2^m$,
$R=\mathbb{F}_{2^m}[u]/\langle u^4\rangle)$ and $n$ is an odd positive integer.
For any $\delta,\alpha\in \mathbb{F}_{2^m}^{\times}$, ideals of the ring
$R[x]/\langle x^{2n}-(\delta+\alpha u^2)\rangle$ are identified as
$(\delta+\alpha u^2)$-constacyclic codes of length $2n$ over $R$. In this
paper, an explicit representation and enumeration for all distinct
$(\delta+\alpha u^2)$-constacyclic codes of length $2n$ over $R$ are presented.
| [
{
"created": "Tue, 20 Sep 2016 09:28:04 GMT",
"version": "v1"
}
] | 2016-09-21 | [
[
"Cao",
"Yonglin",
""
],
[
"Cao",
"Yuan",
""
]
] | Let $\mathbb{F}_{2^m}$ be a finite field of cardinality $2^m$, $R=\mathbb{F}_{2^m}[u]/\langle u^4\rangle)$ and $n$ is an odd positive integer. For any $\delta,\alpha\in \mathbb{F}_{2^m}^{\times}$, ideals of the ring $R[x]/\langle x^{2n}-(\delta+\alpha u^2)\rangle$ are identified as $(\delta+\alpha u^2)$-constacyclic codes of length $2n$ over $R$. In this paper, an explicit representation and enumeration for all distinct $(\delta+\alpha u^2)$-constacyclic codes of length $2n$ over $R$ are presented. |
2210.00949 | Skander Karkar | Skander Karkar and Ibrahim Ayed and Emmanuel de B\'ezenac and Patrick
Gallinari | Block-wise Training of Residual Networks via the Minimizing Movement
Scheme | 1st International Workshop on Practical Deep Learning in the Wild at
AAAI 2022 | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | End-to-end backpropagation has a few shortcomings: it requires loading the
entire model during training, which can be impossible in constrained settings,
and suffers from three locking problems (forward locking, update locking and
backward locking), which prohibit training the layers in parallel. Solving
layer-wise optimization problems can address these problems and has been used
in on-device training of neural networks. We develop a layer-wise training
method, particularly welladapted to ResNets, inspired by the minimizing
movement scheme for gradient flows in distribution space. The method amounts to
a kinetic energy regularization of each block that makes the blocks optimal
transport maps and endows them with regularity. It works by alleviating the
stagnation problem observed in layer-wise training, whereby greedily-trained
early layers overfit and deeper layers stop increasing test accuracy after a
certain depth. We show on classification tasks that the test accuracy of
block-wise trained ResNets is improved when using our method, whether the
blocks are trained sequentially or in parallel.
| [
{
"created": "Mon, 3 Oct 2022 14:03:56 GMT",
"version": "v1"
},
{
"created": "Tue, 6 Jun 2023 13:48:11 GMT",
"version": "v2"
}
] | 2023-06-07 | [
[
"Karkar",
"Skander",
""
],
[
"Ayed",
"Ibrahim",
""
],
[
"de Bézenac",
"Emmanuel",
""
],
[
"Gallinari",
"Patrick",
""
]
] | End-to-end backpropagation has a few shortcomings: it requires loading the entire model during training, which can be impossible in constrained settings, and suffers from three locking problems (forward locking, update locking and backward locking), which prohibit training the layers in parallel. Solving layer-wise optimization problems can address these problems and has been used in on-device training of neural networks. We develop a layer-wise training method, particularly welladapted to ResNets, inspired by the minimizing movement scheme for gradient flows in distribution space. The method amounts to a kinetic energy regularization of each block that makes the blocks optimal transport maps and endows them with regularity. It works by alleviating the stagnation problem observed in layer-wise training, whereby greedily-trained early layers overfit and deeper layers stop increasing test accuracy after a certain depth. We show on classification tasks that the test accuracy of block-wise trained ResNets is improved when using our method, whether the blocks are trained sequentially or in parallel. |
2202.06935 | Sebastian Gehrmann | Sebastian Gehrmann, Elizabeth Clark, Thibault Sellam | Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation
Practices for Generated Text | null | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Evaluation practices in natural language generation (NLG) have many known
flaws, but improved evaluation approaches are rarely widely adopted. This issue
has become more urgent, since neural NLG models have improved to the point
where they can often no longer be distinguished based on the surface-level
features that older metrics rely on. This paper surveys the issues with human
and automatic model evaluations and with commonly used datasets in NLG that
have been pointed out over the past 20 years. We summarize, categorize, and
discuss how researchers have been addressing these issues and what their
findings mean for the current state of model evaluations. Building on those
insights, we lay out a long-term vision for NLG evaluation and propose concrete
steps for researchers to improve their evaluation processes. Finally, we
analyze 66 NLG papers from recent NLP conferences in how well they already
follow these suggestions and identify which areas require more drastic changes
to the status quo.
| [
{
"created": "Mon, 14 Feb 2022 18:51:07 GMT",
"version": "v1"
}
] | 2022-02-15 | [
[
"Gehrmann",
"Sebastian",
""
],
[
"Clark",
"Elizabeth",
""
],
[
"Sellam",
"Thibault",
""
]
] | Evaluation practices in natural language generation (NLG) have many known flaws, but improved evaluation approaches are rarely widely adopted. This issue has become more urgent, since neural NLG models have improved to the point where they can often no longer be distinguished based on the surface-level features that older metrics rely on. This paper surveys the issues with human and automatic model evaluations and with commonly used datasets in NLG that have been pointed out over the past 20 years. We summarize, categorize, and discuss how researchers have been addressing these issues and what their findings mean for the current state of model evaluations. Building on those insights, we lay out a long-term vision for NLG evaluation and propose concrete steps for researchers to improve their evaluation processes. Finally, we analyze 66 NLG papers from recent NLP conferences in how well they already follow these suggestions and identify which areas require more drastic changes to the status quo. |
2009.03136 | Matthew Ciolino | Josh Kalin, Matthew Ciolino, David Noever, Gerry Dozier | Black Box to White Box: Discover Model Characteristics Based on
Strategic Probing | 4 Pages, 3 Figure, IEEE Format, Ai4i 2020 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Machine Learning, White Box Adversarial Attacks rely on knowing underlying
knowledge about the model attributes. This works focuses on discovering to
distrinct pieces of model information: the underlying architecture and primary
training dataset. With the process in this paper, a structured set of input
probes and the output of the model become the training data for a deep
classifier. Two subdomains in Machine Learning are explored: image based
classifiers and text transformers with GPT-2. With image classification, the
focus is on exploring commonly deployed architectures and datasets available in
popular public libraries. Using a single transformer architecture with multiple
levels of parameters, text generation is explored by fine tuning off different
datasets. Each dataset explored in image and text are distinguishable from one
another. Diversity in text transformer outputs implies further research is
needed to successfully classify architecture attribution in text domain.
| [
{
"created": "Mon, 7 Sep 2020 14:44:28 GMT",
"version": "v1"
}
] | 2020-09-08 | [
[
"Kalin",
"Josh",
""
],
[
"Ciolino",
"Matthew",
""
],
[
"Noever",
"David",
""
],
[
"Dozier",
"Gerry",
""
]
] | In Machine Learning, White Box Adversarial Attacks rely on knowing underlying knowledge about the model attributes. This works focuses on discovering to distrinct pieces of model information: the underlying architecture and primary training dataset. With the process in this paper, a structured set of input probes and the output of the model become the training data for a deep classifier. Two subdomains in Machine Learning are explored: image based classifiers and text transformers with GPT-2. With image classification, the focus is on exploring commonly deployed architectures and datasets available in popular public libraries. Using a single transformer architecture with multiple levels of parameters, text generation is explored by fine tuning off different datasets. Each dataset explored in image and text are distinguishable from one another. Diversity in text transformer outputs implies further research is needed to successfully classify architecture attribution in text domain. |
2309.07473 | Chuanruo Ning | Chuanruo Ning, Ruihai Wu, Haoran Lu, Kaichun Mo, Hao Dong | Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories
of Articulated Objects | NeurIPS 2023 | null | null | null | cs.RO cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Articulated object manipulation is a fundamental yet challenging task in
robotics. Due to significant geometric and semantic variations across object
categories, previous manipulation models struggle to generalize to novel
categories. Few-shot learning is a promising solution for alleviating this
issue by allowing robots to perform a few interactions with unseen objects.
However, extant approaches often necessitate costly and inefficient test-time
interactions with each unseen instance. Recognizing this limitation, we observe
that despite their distinct shapes, different categories often share similar
local geometries essential for manipulation, such as pullable handles and
graspable edges - a factor typically underutilized in previous few-shot
learning works. To harness this commonality, we introduce 'Where2Explore', an
affordance learning framework that effectively explores novel categories with
minimal interactions on a limited number of instances. Our framework explicitly
estimates the geometric similarity across different categories, identifying
local areas that differ from shapes in the training categories for efficient
exploration while concurrently transferring affordance knowledge to similar
parts of the objects. Extensive experiments in simulated and real-world
environments demonstrate our framework's capacity for efficient few-shot
exploration and generalization.
| [
{
"created": "Thu, 14 Sep 2023 07:11:58 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Dec 2023 13:36:46 GMT",
"version": "v2"
}
] | 2023-12-18 | [
[
"Ning",
"Chuanruo",
""
],
[
"Wu",
"Ruihai",
""
],
[
"Lu",
"Haoran",
""
],
[
"Mo",
"Kaichun",
""
],
[
"Dong",
"Hao",
""
]
] | Articulated object manipulation is a fundamental yet challenging task in robotics. Due to significant geometric and semantic variations across object categories, previous manipulation models struggle to generalize to novel categories. Few-shot learning is a promising solution for alleviating this issue by allowing robots to perform a few interactions with unseen objects. However, extant approaches often necessitate costly and inefficient test-time interactions with each unseen instance. Recognizing this limitation, we observe that despite their distinct shapes, different categories often share similar local geometries essential for manipulation, such as pullable handles and graspable edges - a factor typically underutilized in previous few-shot learning works. To harness this commonality, we introduce 'Where2Explore', an affordance learning framework that effectively explores novel categories with minimal interactions on a limited number of instances. Our framework explicitly estimates the geometric similarity across different categories, identifying local areas that differ from shapes in the training categories for efficient exploration while concurrently transferring affordance knowledge to similar parts of the objects. Extensive experiments in simulated and real-world environments demonstrate our framework's capacity for efficient few-shot exploration and generalization. |
2407.12202 | Kento Kawaharazuka | Kento Kawaharazuka and Toru Ogawa and Cota Nabeshima | Tool Shape Optimization through Backpropagation of Neural Network | Accepted at IROS2020 | null | 10.1109/IROS45743.2020.9341583 | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When executing a certain task, human beings can choose or make an appropriate
tool to achieve the task. This research especially addresses the optimization
of tool shape for robotic tool-use. We propose a method in which a robot
obtains an optimized tool shape, tool trajectory, or both, depending on a given
task. The feature of our method is that a transition of the task state when the
robot moves a certain tool along a certain trajectory is represented by a deep
neural network. We applied this method to object manipulation tasks on a 2D
plane, and verified that appropriate tool shapes are generated by using this
novel method.
| [
{
"created": "Tue, 16 Jul 2024 22:01:59 GMT",
"version": "v1"
}
] | 2024-07-18 | [
[
"Kawaharazuka",
"Kento",
""
],
[
"Ogawa",
"Toru",
""
],
[
"Nabeshima",
"Cota",
""
]
] | When executing a certain task, human beings can choose or make an appropriate tool to achieve the task. This research especially addresses the optimization of tool shape for robotic tool-use. We propose a method in which a robot obtains an optimized tool shape, tool trajectory, or both, depending on a given task. The feature of our method is that a transition of the task state when the robot moves a certain tool along a certain trajectory is represented by a deep neural network. We applied this method to object manipulation tasks on a 2D plane, and verified that appropriate tool shapes are generated by using this novel method. |
2004.02546 | Erik H\"ark\"onen | Erik H\"ark\"onen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris | GANSpace: Discovering Interpretable GAN Controls | Accepted to NeurIPS 2020 | Advances in Neural Information Processing Systems 33 (NeurIPS
2020), 9841-9850 | null | null | cs.CV cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes a simple technique to analyze Generative Adversarial
Networks (GANs) and create interpretable controls for image synthesis, such as
change of viewpoint, aging, lighting, and time of day. We identify important
latent directions based on Principal Components Analysis (PCA) applied either
in latent space or feature space. Then, we show that a large number of
interpretable controls can be defined by layer-wise perturbation along the
principal directions. Moreover, we show that BigGAN can be controlled with
layer-wise inputs in a StyleGAN-like manner. We show results on different GANs
trained on various datasets, and demonstrate good qualitative matches to edit
directions found through earlier supervised approaches.
| [
{
"created": "Mon, 6 Apr 2020 10:41:44 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Jul 2020 11:10:27 GMT",
"version": "v2"
},
{
"created": "Mon, 14 Dec 2020 10:13:42 GMT",
"version": "v3"
}
] | 2022-07-05 | [
[
"Härkönen",
"Erik",
""
],
[
"Hertzmann",
"Aaron",
""
],
[
"Lehtinen",
"Jaakko",
""
],
[
"Paris",
"Sylvain",
""
]
] | This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day. We identify important latent directions based on Principal Components Analysis (PCA) applied either in latent space or feature space. Then, we show that a large number of interpretable controls can be defined by layer-wise perturbation along the principal directions. Moreover, we show that BigGAN can be controlled with layer-wise inputs in a StyleGAN-like manner. We show results on different GANs trained on various datasets, and demonstrate good qualitative matches to edit directions found through earlier supervised approaches. |
2203.12601 | Suraj Nair | Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav
Gupta | R3M: A Universal Visual Representation for Robot Manipulation | Conference on Robot Learning (CoRL) 2022 | null | null | null | cs.RO cs.AI cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study how visual representations pre-trained on diverse human video data
can enable data-efficient learning of downstream robotic manipulation tasks.
Concretely, we pre-train a visual representation using the Ego4D human video
dataset using a combination of time-contrastive learning, video-language
alignment, and an L1 penalty to encourage sparse and compact representations.
The resulting representation, R3M, can be used as a frozen perception module
for downstream policy learning. Across a suite of 12 simulated robot
manipulation tasks, we find that R3M improves task success by over 20% compared
to training from scratch and by over 10% compared to state-of-the-art visual
representations like CLIP and MoCo. Furthermore, R3M enables a Franka Emika
Panda arm to learn a range of manipulation tasks in a real, cluttered apartment
given just 20 demonstrations. Code and pre-trained models are available at
https://tinyurl.com/robotr3m.
| [
{
"created": "Wed, 23 Mar 2022 17:55:09 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Apr 2022 22:39:13 GMT",
"version": "v2"
},
{
"created": "Fri, 18 Nov 2022 05:57:09 GMT",
"version": "v3"
}
] | 2022-11-21 | [
[
"Nair",
"Suraj",
""
],
[
"Rajeswaran",
"Aravind",
""
],
[
"Kumar",
"Vikash",
""
],
[
"Finn",
"Chelsea",
""
],
[
"Gupta",
"Abhinav",
""
]
] | We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a visual representation using the Ego4D human video dataset using a combination of time-contrastive learning, video-language alignment, and an L1 penalty to encourage sparse and compact representations. The resulting representation, R3M, can be used as a frozen perception module for downstream policy learning. Across a suite of 12 simulated robot manipulation tasks, we find that R3M improves task success by over 20% compared to training from scratch and by over 10% compared to state-of-the-art visual representations like CLIP and MoCo. Furthermore, R3M enables a Franka Emika Panda arm to learn a range of manipulation tasks in a real, cluttered apartment given just 20 demonstrations. Code and pre-trained models are available at https://tinyurl.com/robotr3m. |
2106.04963 | Yang Tao | Tao Yang, Feifan Yang, Haolan Ouyang, Xiaojun Quan | Psycholinguistic Tripartite Graph Network for Personality Detection | Accepted by ACL 2021 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Most of the recent work on personality detection from online posts adopts
multifarious deep neural networks to represent the posts and builds predictive
models in a data-driven manner, without the exploitation of psycholinguistic
knowledge that may unveil the connections between one's language usage and his
psychological traits. In this paper, we propose a psycholinguistic
knowledge-based tripartite graph network, TrigNet, which consists of a
tripartite graph network and a BERT-based graph initializer. The graph network
injects structural psycholinguistic knowledge from LIWC, a computerized
instrument for psycholinguistic analysis, by constructing a heterogeneous
tripartite graph. The graph initializer is employed to provide initial
embeddings for the graph nodes. To reduce the computational cost in graph
learning, we further propose a novel flow graph attention network (GAT) that
only transmits messages between neighboring parties in the tripartite graph.
Benefiting from the tripartite graph, TrigNet can aggregate post information
from a psychological perspective, which is a novel way of exploiting domain
knowledge. Extensive experiments on two datasets show that TrigNet outperforms
the existing state-of-art model by 3.47 and 2.10 points in average F1.
Moreover, the flow GAT reduces the FLOPS and Memory measures by 38% and 32%,
respectively, in comparison to the original GAT in our setting.
| [
{
"created": "Wed, 9 Jun 2021 10:18:50 GMT",
"version": "v1"
}
] | 2021-06-10 | [
[
"Yang",
"Tao",
""
],
[
"Yang",
"Feifan",
""
],
[
"Ouyang",
"Haolan",
""
],
[
"Quan",
"Xiaojun",
""
]
] | Most of the recent work on personality detection from online posts adopts multifarious deep neural networks to represent the posts and builds predictive models in a data-driven manner, without the exploitation of psycholinguistic knowledge that may unveil the connections between one's language usage and his psychological traits. In this paper, we propose a psycholinguistic knowledge-based tripartite graph network, TrigNet, which consists of a tripartite graph network and a BERT-based graph initializer. The graph network injects structural psycholinguistic knowledge from LIWC, a computerized instrument for psycholinguistic analysis, by constructing a heterogeneous tripartite graph. The graph initializer is employed to provide initial embeddings for the graph nodes. To reduce the computational cost in graph learning, we further propose a novel flow graph attention network (GAT) that only transmits messages between neighboring parties in the tripartite graph. Benefiting from the tripartite graph, TrigNet can aggregate post information from a psychological perspective, which is a novel way of exploiting domain knowledge. Extensive experiments on two datasets show that TrigNet outperforms the existing state-of-art model by 3.47 and 2.10 points in average F1. Moreover, the flow GAT reduces the FLOPS and Memory measures by 38% and 32%, respectively, in comparison to the original GAT in our setting. |
1711.03488 | Oscar Carrasco | Shahid Mumtaz, Kazi Saidul, Huq Jonathan Rodriguez, Paulo Marques,
Ayman Radwan, Keith Briggs Michael Fitch BT, Andreas Georgakopoulos,
Ioannis-Prodromos Belikaidis, Panagiotis Vlacheas, Dimitrios Kelaidonis,
Evangelos Kosmatos, Serafim Kotrotsos, Stavroula Vassaki, Yiouli Kritikou,
Panagiotis Demestichas, Kostas Tsagkaris, Evangelia Tzifa, Aikaterini
Demesticha, Vera Stavroulaki, Athina Ropodi, Evangelos Argoudelis, Marinos
Galiatsatos, Aristotelis Margaris, George Paitaris, Dimitrios Kardaris,
Ioannis Kaffes, Haeyoung Lee Klaus, Moessner Unis Valerio, Frascolla Bismark,
Okyere Intel, Salva D\'iaz, Oscar Carrasco, Federico Miatton, Sistel Antonio,
Dedomenico Benoit, Miscopein Cea, Thanasis Oikonomou, Dimitrios Kritharidis,
Harald Weigold | D3.2: SPEED-5G enhanced functional and system architecture, scenarios
and performance evaluation metrics | null | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This deliverable contains a detailed description of the use cases considered
in SPEED-5G, which will be used as a basis for demonstration in project. These
use cases are Dynamic Channel selection, Load balancing, carrier aggregation.
This deliverable also explains the SPEED-5G architecture design principles,
which is based on software-defined networking and network function
virtualisation. The degree of virtualisation is further illustrated by a number
of novel contributions from involved partners. In the end, KPIs for each use
case are presented, along with the description of how these KPIs can support
5G-PPP KPIs.
| [
{
"created": "Thu, 9 Nov 2017 17:38:07 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Nov 2017 08:23:40 GMT",
"version": "v2"
}
] | 2017-11-16 | [
[
"Mumtaz",
"Shahid",
""
],
[
"Saidul",
"Kazi",
""
],
[
"Rodriguez",
"Huq Jonathan",
""
],
[
"Marques",
"Paulo",
""
],
[
"Radwan",
"Ayman",
""
],
[
"BT",
"Keith Briggs Michael Fitch",
""
],
[
"Georgakopoulos",
"Andreas",
""
],
[
"Belikaidis",
"Ioannis-Prodromos",
""
],
[
"Vlacheas",
"Panagiotis",
""
],
[
"Kelaidonis",
"Dimitrios",
""
],
[
"Kosmatos",
"Evangelos",
""
],
[
"Kotrotsos",
"Serafim",
""
],
[
"Vassaki",
"Stavroula",
""
],
[
"Kritikou",
"Yiouli",
""
],
[
"Demestichas",
"Panagiotis",
""
],
[
"Tsagkaris",
"Kostas",
""
],
[
"Tzifa",
"Evangelia",
""
],
[
"Demesticha",
"Aikaterini",
""
],
[
"Stavroulaki",
"Vera",
""
],
[
"Ropodi",
"Athina",
""
],
[
"Argoudelis",
"Evangelos",
""
],
[
"Galiatsatos",
"Marinos",
""
],
[
"Margaris",
"Aristotelis",
""
],
[
"Paitaris",
"George",
""
],
[
"Kardaris",
"Dimitrios",
""
],
[
"Kaffes",
"Ioannis",
""
],
[
"Klaus",
"Haeyoung Lee",
""
],
[
"Valerio",
"Moessner Unis",
""
],
[
"Bismark",
"Frascolla",
""
],
[
"Intel",
"Okyere",
""
],
[
"Díaz",
"Salva",
""
],
[
"Carrasco",
"Oscar",
""
],
[
"Miatton",
"Federico",
""
],
[
"Antonio",
"Sistel",
""
],
[
"Benoit",
"Dedomenico",
""
],
[
"Cea",
"Miscopein",
""
],
[
"Oikonomou",
"Thanasis",
""
],
[
"Kritharidis",
"Dimitrios",
""
],
[
"Weigold",
"Harald",
""
]
] | This deliverable contains a detailed description of the use cases considered in SPEED-5G, which will be used as a basis for demonstration in project. These use cases are Dynamic Channel selection, Load balancing, carrier aggregation. This deliverable also explains the SPEED-5G architecture design principles, which is based on software-defined networking and network function virtualisation. The degree of virtualisation is further illustrated by a number of novel contributions from involved partners. In the end, KPIs for each use case are presented, along with the description of how these KPIs can support 5G-PPP KPIs. |
1208.1326 | Brian Butler | Brian K. Butler and Paul H. Siegel | Numerical Issues Affecting LDPC Error Floors | 7 pages, 5 figures. Submitted to IEEE Globecom (Selected Area of
Communications Data Storage Track) | null | null | null | cs.IT cs.NA math.IT math.NA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerical issues related to the occurrence of error floors in floating-point
simulations of belief propagation (BP) decoders are examined. Careful
processing of messages corresponding to highly-certain bit values can sometimes
reduce error floors by several orders of magnitude. Computational solutions for
properly handling such messages are provided for the sum-product algorithm
(SPA) and several variants.
| [
{
"created": "Tue, 7 Aug 2012 02:41:54 GMT",
"version": "v1"
}
] | 2012-08-08 | [
[
"Butler",
"Brian K.",
""
],
[
"Siegel",
"Paul H.",
""
]
] | Numerical issues related to the occurrence of error floors in floating-point simulations of belief propagation (BP) decoders are examined. Careful processing of messages corresponding to highly-certain bit values can sometimes reduce error floors by several orders of magnitude. Computational solutions for properly handling such messages are provided for the sum-product algorithm (SPA) and several variants. |
2203.11447 | Jasper Brown | Jasper Brown, Cameron Clark, Sabrina Lomax, Khalid Rafique, Salah
Sukkarieh | Manipulating UAV Imagery for Satellite Model Training, Calibration and
Testing | 16 pages, 7 figures, 2 tables | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modern livestock farming is increasingly data driven and frequently relies on
efficient remote sensing to gather data over wide areas. High resolution
satellite imagery is one such data source, which is becoming more accessible
for farmers as coverage increases and cost falls. Such images can be used to
detect and track animals, monitor pasture changes, and understand land use.
Many of the data driven models being applied to these tasks require ground
truthing at resolutions higher than satellites can provide. Simultaneously,
there is a lack of available aerial imagery focused on farmland changes that
occur over days or weeks, such as herd movement. With this goal in mind, we
present a new multi-temporal dataset of high resolution UAV imagery which is
artificially degraded to match satellite data quality. An empirical blurring
metric is used to calibrate the degradation process against actual satellite
imagery of the area. UAV surveys were flown repeatedly over several weeks, for
specific farm locations. This 5cm/pixel data is sufficiently high resolution to
accurately ground truth cattle locations, and other factors such as grass
cover. From 33 wide area UAV surveys, 1869 patches were extracted and
artificially degraded using an accurate satellite optical model to simulate
satellite data. Geographic patches from multiple time periods are aligned and
presented as sets, providing a multi-temporal dataset that can be used for
detecting changes on farms. The geo-referenced images and 27,853 manually
annotated cattle labels are made publicly available.
| [
{
"created": "Tue, 22 Mar 2022 03:57:02 GMT",
"version": "v1"
}
] | 2022-04-12 | [
[
"Brown",
"Jasper",
""
],
[
"Clark",
"Cameron",
""
],
[
"Lomax",
"Sabrina",
""
],
[
"Rafique",
"Khalid",
""
],
[
"Sukkarieh",
"Salah",
""
]
] | Modern livestock farming is increasingly data driven and frequently relies on efficient remote sensing to gather data over wide areas. High resolution satellite imagery is one such data source, which is becoming more accessible for farmers as coverage increases and cost falls. Such images can be used to detect and track animals, monitor pasture changes, and understand land use. Many of the data driven models being applied to these tasks require ground truthing at resolutions higher than satellites can provide. Simultaneously, there is a lack of available aerial imagery focused on farmland changes that occur over days or weeks, such as herd movement. With this goal in mind, we present a new multi-temporal dataset of high resolution UAV imagery which is artificially degraded to match satellite data quality. An empirical blurring metric is used to calibrate the degradation process against actual satellite imagery of the area. UAV surveys were flown repeatedly over several weeks, for specific farm locations. This 5cm/pixel data is sufficiently high resolution to accurately ground truth cattle locations, and other factors such as grass cover. From 33 wide area UAV surveys, 1869 patches were extracted and artificially degraded using an accurate satellite optical model to simulate satellite data. Geographic patches from multiple time periods are aligned and presented as sets, providing a multi-temporal dataset that can be used for detecting changes on farms. The geo-referenced images and 27,853 manually annotated cattle labels are made publicly available. |
2301.05316 | Pedro Enrique Iturria Rivera Mr. | Md Arafat Habib, Hao Zhou, Pedro Enrique Iturria Rivera, Medhat
Elsayed, Majid Bavand, Raimundas Gaigalas, Steve Furr, Melike Erol-Kantarci | Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement
Learning | 6 pages, 6 figures and 1 table. Accepted in CCNC'23 | null | null | null | cs.NI | http://creativecommons.org/licenses/by/4.0/ | In 5G non-standalone mode, traffic steering is a critical technique to take
full advantage of 5G new radio while optimizing dual connectivity of 5G and LTE
networks in multiple radio access technology (RAT). An intelligent traffic
steering mechanism can play an important role to maintain seamless user
experience by choosing appropriate RAT (5G or LTE) dynamically for a specific
user traffic flow with certain QoS requirements. In this paper, we propose a
novel traffic steering mechanism based on Deep Q-learning that can automate
traffic steering decisions in a dynamic environment having multiple RATs, and
maintain diverse QoS requirements for different traffic classes. The proposed
method is compared with two baseline algorithms: a heuristic-based algorithm
and Q-learningbased traffic steering. Compared to the Q-learning and heuristic
baselines, our results show that the proposed algorithm achieves better
performance in terms of 6% and 10% higher average system throughput, and 23%
and 33% lower network delay, respectively.
| [
{
"created": "Thu, 12 Jan 2023 22:02:25 GMT",
"version": "v1"
}
] | 2023-01-16 | [
[
"Habib",
"Md Arafat",
""
],
[
"Zhou",
"Hao",
""
],
[
"Rivera",
"Pedro Enrique Iturria",
""
],
[
"Elsayed",
"Medhat",
""
],
[
"Bavand",
"Majid",
""
],
[
"Gaigalas",
"Raimundas",
""
],
[
"Furr",
"Steve",
""
],
[
"Erol-Kantarci",
"Melike",
""
]
] | In 5G non-standalone mode, traffic steering is a critical technique to take full advantage of 5G new radio while optimizing dual connectivity of 5G and LTE networks in multiple radio access technology (RAT). An intelligent traffic steering mechanism can play an important role to maintain seamless user experience by choosing appropriate RAT (5G or LTE) dynamically for a specific user traffic flow with certain QoS requirements. In this paper, we propose a novel traffic steering mechanism based on Deep Q-learning that can automate traffic steering decisions in a dynamic environment having multiple RATs, and maintain diverse QoS requirements for different traffic classes. The proposed method is compared with two baseline algorithms: a heuristic-based algorithm and Q-learningbased traffic steering. Compared to the Q-learning and heuristic baselines, our results show that the proposed algorithm achieves better performance in terms of 6% and 10% higher average system throughput, and 23% and 33% lower network delay, respectively. |
2112.13340 | Baofeng Wu | Baofeng Wu | Proof of a conjecture on a special class of matrices over commutative
rings of characteristic 2 | null | null | null | null | cs.CR cs.IT math.CO math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this note, we prove the conjecture posed by Keller and Rosemarin at
Eurocrypt 2021 on the nullity of a matrix polynomial of a block matrix with
Hadamard type blocks over commutative rings of characteristic 2. Therefore, it
confirms the conjectural optimal bound on the dimension of invariant subspace
of the Starkad cipher using the HADES design strategy. We also give
characterizations of the algebraic structure formed by Hadamard matrices over
commutative rings.
| [
{
"created": "Sun, 26 Dec 2021 09:39:32 GMT",
"version": "v1"
}
] | 2021-12-28 | [
[
"Wu",
"Baofeng",
""
]
] | In this note, we prove the conjecture posed by Keller and Rosemarin at Eurocrypt 2021 on the nullity of a matrix polynomial of a block matrix with Hadamard type blocks over commutative rings of characteristic 2. Therefore, it confirms the conjectural optimal bound on the dimension of invariant subspace of the Starkad cipher using the HADES design strategy. We also give characterizations of the algebraic structure formed by Hadamard matrices over commutative rings. |
1610.08309 | Edita Pelantova | Christiane Frougny, Marta Pavelka, Edita Pelantova, Milena Svobodova | On-line algorithms for multiplication and division in real and complex
numeration systems | Extended version of contribution on 23rd IEEE Symposium on Computer
Arithmetic ARITH23 | Discrete Mathematics & Theoretical Computer Science, Vol. 21 no. 3
, Discrete Algorithms (June 20, 2019) dmtcs:4313 | 10.23638/DMTCS-21-3-14 | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A positional numeration system is given by a base and by a set of digits. The
base is a real or complex number $\beta$ such that $|\beta|>1$, and the digit
set $A$ is a finite set of digits including $0$. Thus a number can be seen as a
finite or infinite string of digits. An on-line algorithm processes the input
piece-by-piece in a serial fashion. On-line arithmetic, introduced by Trivedi
and Ercegovac, is a mode of computation where operands and results flow through
arithmetic units in a digit serial manner, starting with the most significant
digit.
In this paper, we first formulate a generalized version of the on-line
algorithms for multiplication and division of Trivedi and Ercegovac for the
cases that $\beta$ is any real or complex number, and digits are real or
complex. We then define the so-called OL Property, and show that if $(\beta,
A)$ has the OL Property, then on-line multiplication and division are feasible
by the Trivedi-Ercegovac algorithms. For a real base $\beta$ and a digit set
$A$ of contiguous integers, the system $(\beta, A)$ has the OL Property if $\#
A > |\beta|$. For a complex base $\beta$ and symmetric digit set $A$ of
contiguous integers, the system $(\beta, A)$ has the OL Property if $\# A >
\beta\overline{\beta} + |\beta + \overline{\beta}|$. Provided that addition and
subtraction are realizable in parallel in the system $(\beta, A)$ and that
preprocessing of the denominator is possible, our on-line algorithms for
multiplication and division have linear time complexity. Three examples are
presented in detail: base $\beta=\frac{3+\sqrt{5}}{2}$ with digits
$A=\{-1,0,1\}$; base $\beta=2i$ with digits $A = \{-2,-1, 0,1,2\}$; and base
$\beta = -\frac{3}{2} + i \frac{\sqrt{3}}{2} = -1 + \omega$, where $\omega =
\exp{\frac{2i\pi}{3}}$, with digits $A = \{0, \pm 1, \pm \omega, \pm \omega^2
\}$.
| [
{
"created": "Wed, 26 Oct 2016 13:05:12 GMT",
"version": "v1"
},
{
"created": "Sun, 18 Feb 2018 11:04:16 GMT",
"version": "v2"
},
{
"created": "Fri, 25 Jan 2019 10:07:38 GMT",
"version": "v3"
},
{
"created": "Mon, 20 May 2019 09:12:15 GMT",
"version": "v4"
},
{
"created": "Tue, 11 Jun 2019 16:16:23 GMT",
"version": "v5"
}
] | 2023-06-22 | [
[
"Frougny",
"Christiane",
""
],
[
"Pavelka",
"Marta",
""
],
[
"Pelantova",
"Edita",
""
],
[
"Svobodova",
"Milena",
""
]
] | A positional numeration system is given by a base and by a set of digits. The base is a real or complex number $\beta$ such that $|\beta|>1$, and the digit set $A$ is a finite set of digits including $0$. Thus a number can be seen as a finite or infinite string of digits. An on-line algorithm processes the input piece-by-piece in a serial fashion. On-line arithmetic, introduced by Trivedi and Ercegovac, is a mode of computation where operands and results flow through arithmetic units in a digit serial manner, starting with the most significant digit. In this paper, we first formulate a generalized version of the on-line algorithms for multiplication and division of Trivedi and Ercegovac for the cases that $\beta$ is any real or complex number, and digits are real or complex. We then define the so-called OL Property, and show that if $(\beta, A)$ has the OL Property, then on-line multiplication and division are feasible by the Trivedi-Ercegovac algorithms. For a real base $\beta$ and a digit set $A$ of contiguous integers, the system $(\beta, A)$ has the OL Property if $\# A > |\beta|$. For a complex base $\beta$ and symmetric digit set $A$ of contiguous integers, the system $(\beta, A)$ has the OL Property if $\# A > \beta\overline{\beta} + |\beta + \overline{\beta}|$. Provided that addition and subtraction are realizable in parallel in the system $(\beta, A)$ and that preprocessing of the denominator is possible, our on-line algorithms for multiplication and division have linear time complexity. Three examples are presented in detail: base $\beta=\frac{3+\sqrt{5}}{2}$ with digits $A=\{-1,0,1\}$; base $\beta=2i$ with digits $A = \{-2,-1, 0,1,2\}$; and base $\beta = -\frac{3}{2} + i \frac{\sqrt{3}}{2} = -1 + \omega$, where $\omega = \exp{\frac{2i\pi}{3}}$, with digits $A = \{0, \pm 1, \pm \omega, \pm \omega^2 \}$. |
0805.3237 | Sebastien Collette | S. Collette and L. Cucu and J. Goossens | Integrating Job Parallelism in Real-Time Scheduling Theory | null | null | null | null | cs.OS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate the global scheduling of sporadic, implicit deadline,
real-time task systems on multiprocessor platforms. We provide a task model
which integrates job parallelism. We prove that the time-complexity of the
feasibility problem of these systems is linear relatively to the number of
(sporadic) tasks for a fixed number of processors. We propose a scheduling
algorithm theoretically optimal (i.e., preemptions and migrations neglected).
Moreover, we provide an exact feasibility utilization bound. Lastly, we propose
a technique to limit the number of migrations and preemptions.
| [
{
"created": "Wed, 21 May 2008 09:38:15 GMT",
"version": "v1"
}
] | 2008-05-22 | [
[
"Collette",
"S.",
""
],
[
"Cucu",
"L.",
""
],
[
"Goossens",
"J.",
""
]
] | We investigate the global scheduling of sporadic, implicit deadline, real-time task systems on multiprocessor platforms. We provide a task model which integrates job parallelism. We prove that the time-complexity of the feasibility problem of these systems is linear relatively to the number of (sporadic) tasks for a fixed number of processors. We propose a scheduling algorithm theoretically optimal (i.e., preemptions and migrations neglected). Moreover, we provide an exact feasibility utilization bound. Lastly, we propose a technique to limit the number of migrations and preemptions. |
1706.03952 | Charalambos Themistocleous | Jean-Philippe Bernardy and Charalambos Themistocleous | Modelling prosodic structure using Artificial Neural Networks | 4 pages, 3 figures, Experimental linguistics 2017 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The ability to accurately perceive whether a speaker is asking a question or
is making a statement is crucial for any successful interaction. However,
learning and classifying tonal patterns has been a challenging task for
automatic speech recognition and for models of tonal representation, as tonal
contours are characterized by significant variation. This paper provides a
classification model of Cypriot Greek questions and statements. We evaluate two
state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network
and a convolutional network (ConvNet). The ConvNet outperforms the LSTM in the
classification task and exhibited an excellent performance with 95%
classification accuracy.
| [
{
"created": "Tue, 13 Jun 2017 08:28:39 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Jun 2017 12:49:57 GMT",
"version": "v2"
}
] | 2017-06-16 | [
[
"Bernardy",
"Jean-Philippe",
""
],
[
"Themistocleous",
"Charalambos",
""
]
] | The ability to accurately perceive whether a speaker is asking a question or is making a statement is crucial for any successful interaction. However, learning and classifying tonal patterns has been a challenging task for automatic speech recognition and for models of tonal representation, as tonal contours are characterized by significant variation. This paper provides a classification model of Cypriot Greek questions and statements. We evaluate two state-of-the-art network architectures: a Long Short-Term Memory (LSTM) network and a convolutional network (ConvNet). The ConvNet outperforms the LSTM in the classification task and exhibited an excellent performance with 95% classification accuracy. |
2112.12142 | Priyam Shah Mr | Priyam Shah, Jie Ye, Xian-He Sun | Survey the storage systems used in HPC and BDA ecosystems | 13 pages, 10 figures, 7 tables | null | null | null | cs.DC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The advancement in HPC and BDA ecosystem demands a better understanding of
the storage systems to plan effective solutions. To make applications access
data more efficiently for computation, HPC and BDA ecosystems adopt different
storage systems. Each storage system has its pros and cons. Therefore, it is
worthwhile and interesting to explore the storage systems used in HPC and BDA
respectively. Also, it's inquisitive to understand how such storage systems can
handle data consistency and fault tolerance at a massive scale. In this paper,
we're surveying four storage systems Lustre, Ceph, HDFS, and CockroachDB.
Lustre and HDFS are some of the most prominent file systems in HPC and BDA
ecosystem. Ceph is an upcoming filesystem and is being used by supercomputers.
CockroachDB is based on NewSQL systems a technique that is being used in the
industry for BDA applications. The study helps us to understand the underlying
architecture of these storage systems and the building blocks used to create
them. The protocols and mechanisms used for data storage, data access, data
consistency, fault tolerance, and recovery from failover are also overviewed.
The comparative study will help system designers to understand the key features
and architectural goals of these storage systems to select better storage
system solutions.
| [
{
"created": "Wed, 22 Dec 2021 18:57:18 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Dec 2021 18:25:38 GMT",
"version": "v2"
}
] | 2021-12-24 | [
[
"Shah",
"Priyam",
""
],
[
"Ye",
"Jie",
""
],
[
"Sun",
"Xian-He",
""
]
] | The advancement in HPC and BDA ecosystem demands a better understanding of the storage systems to plan effective solutions. To make applications access data more efficiently for computation, HPC and BDA ecosystems adopt different storage systems. Each storage system has its pros and cons. Therefore, it is worthwhile and interesting to explore the storage systems used in HPC and BDA respectively. Also, it's inquisitive to understand how such storage systems can handle data consistency and fault tolerance at a massive scale. In this paper, we're surveying four storage systems Lustre, Ceph, HDFS, and CockroachDB. Lustre and HDFS are some of the most prominent file systems in HPC and BDA ecosystem. Ceph is an upcoming filesystem and is being used by supercomputers. CockroachDB is based on NewSQL systems a technique that is being used in the industry for BDA applications. The study helps us to understand the underlying architecture of these storage systems and the building blocks used to create them. The protocols and mechanisms used for data storage, data access, data consistency, fault tolerance, and recovery from failover are also overviewed. The comparative study will help system designers to understand the key features and architectural goals of these storage systems to select better storage system solutions. |
2302.04288 | Jiaqi Ma | Satyapriya Krishna, Jiaqi Ma, Himabindu Lakkaraju | Towards Bridging the Gaps between the Right to Explanation and the Right
to be Forgotten | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Right to Explanation and the Right to be Forgotten are two important
principles outlined to regulate algorithmic decision making and data usage in
real-world applications. While the right to explanation allows individuals to
request an actionable explanation for an algorithmic decision, the right to be
forgotten grants them the right to ask for their data to be deleted from all
the databases and models of an organization. Intuitively, enforcing the right
to be forgotten may trigger model updates which in turn invalidate previously
provided explanations, thus violating the right to explanation. In this work,
we investigate the technical implications arising due to the interference
between the two aforementioned regulatory principles, and propose the first
algorithmic framework to resolve the tension between them. To this end, we
formulate a novel optimization problem to generate explanations that are robust
to model updates due to the removal of training data instances by data deletion
requests. We then derive an efficient approximation algorithm to handle the
combinatorial complexity of this optimization problem. We theoretically
demonstrate that our method generates explanations that are provably robust to
worst-case data deletion requests with bounded costs in case of linear models
and certain classes of non-linear models. Extensive experimentation with
real-world datasets demonstrates the efficacy of the proposed framework.
| [
{
"created": "Wed, 8 Feb 2023 19:03:00 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Feb 2023 03:24:50 GMT",
"version": "v2"
}
] | 2023-02-13 | [
[
"Krishna",
"Satyapriya",
""
],
[
"Ma",
"Jiaqi",
""
],
[
"Lakkaraju",
"Himabindu",
""
]
] | The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an algorithmic decision, the right to be forgotten grants them the right to ask for their data to be deleted from all the databases and models of an organization. Intuitively, enforcing the right to be forgotten may trigger model updates which in turn invalidate previously provided explanations, thus violating the right to explanation. In this work, we investigate the technical implications arising due to the interference between the two aforementioned regulatory principles, and propose the first algorithmic framework to resolve the tension between them. To this end, we formulate a novel optimization problem to generate explanations that are robust to model updates due to the removal of training data instances by data deletion requests. We then derive an efficient approximation algorithm to handle the combinatorial complexity of this optimization problem. We theoretically demonstrate that our method generates explanations that are provably robust to worst-case data deletion requests with bounded costs in case of linear models and certain classes of non-linear models. Extensive experimentation with real-world datasets demonstrates the efficacy of the proposed framework. |
2202.09097 | Mangal Kothari | Aryan Sharma, Nitik Jain, and Mangal Kothari | Lightweight Multi-Drone Detection and 3D-Localization via YOLO | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | In this work, we present and evaluate a method to perform real-time multiple
drone detection and three-dimensional localization using state-of-the-art
tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer
vision approach eliminates the need for computationally expensive stereo
matching algorithms, thereby significantly reducing the memory footprint and
making it deployable on embedded systems. Our drone detection system is highly
modular (with support for various detection algorithms) and capable of
identifying multiple drones in a system, with real-time detection accuracy of
up to 77\% with an average FPS of 332 (on Nvidia Titan Xp). We also test the
complete pipeline in AirSim environment, detecting drones at a maximum distance
of 8 meters, with a mean error of $23\%$ of the distance. We also release the
source code for the project, with pre-trained models and the curated synthetic
stereo dataset.
| [
{
"created": "Fri, 18 Feb 2022 09:41:23 GMT",
"version": "v1"
}
] | 2022-02-21 | [
[
"Sharma",
"Aryan",
""
],
[
"Jain",
"Nitik",
""
],
[
"Kothari",
"Mangal",
""
]
] | In this work, we present and evaluate a method to perform real-time multiple drone detection and three-dimensional localization using state-of-the-art tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer vision approach eliminates the need for computationally expensive stereo matching algorithms, thereby significantly reducing the memory footprint and making it deployable on embedded systems. Our drone detection system is highly modular (with support for various detection algorithms) and capable of identifying multiple drones in a system, with real-time detection accuracy of up to 77\% with an average FPS of 332 (on Nvidia Titan Xp). We also test the complete pipeline in AirSim environment, detecting drones at a maximum distance of 8 meters, with a mean error of $23\%$ of the distance. We also release the source code for the project, with pre-trained models and the curated synthetic stereo dataset. |
1801.01769 | Suichan Li | Suichan Li | 3D-DETNet: a Single Stage Video-Based Vehicle Detector | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Video-based vehicle detection has received considerable attention over the
last ten years and there are many deep learning based detection methods which
can be applied to it. However, these methods are devised for still images and
applying them for video vehicle detection directly always obtains poor
performance. In this work, we propose a new single-stage video-based vehicle
detector integrated with 3DCovNet and focal loss, called 3D-DETNet. Draw
support from 3D Convolution network and focal loss, our method has ability to
capture motion information and is more suitable to detect vehicle in video than
other single-stage methods devised for static images. The multiple video frames
are initially fed to 3D-DETNet to generate multiple spatial feature maps, then
sub-model 3DConvNet takes spatial feature maps as input to capture temporal
information which is fed to final fully convolution model for predicting
locations of vehicles in video frames. We evaluate our method on UA-DETAC
vehicle detection dataset and our 3D-DETNet yields best performance and keeps a
higher detection speed of 26 fps compared with other competing methods.
| [
{
"created": "Fri, 5 Jan 2018 14:38:14 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jan 2018 09:06:07 GMT",
"version": "v2"
}
] | 2018-01-16 | [
[
"Li",
"Suichan",
""
]
] | Video-based vehicle detection has received considerable attention over the last ten years and there are many deep learning based detection methods which can be applied to it. However, these methods are devised for still images and applying them for video vehicle detection directly always obtains poor performance. In this work, we propose a new single-stage video-based vehicle detector integrated with 3DCovNet and focal loss, called 3D-DETNet. Draw support from 3D Convolution network and focal loss, our method has ability to capture motion information and is more suitable to detect vehicle in video than other single-stage methods devised for static images. The multiple video frames are initially fed to 3D-DETNet to generate multiple spatial feature maps, then sub-model 3DConvNet takes spatial feature maps as input to capture temporal information which is fed to final fully convolution model for predicting locations of vehicles in video frames. We evaluate our method on UA-DETAC vehicle detection dataset and our 3D-DETNet yields best performance and keeps a higher detection speed of 26 fps compared with other competing methods. |
1404.4465 | Peter Sanders | Florian Merz and Peter Sanders | PReaCH: A Fast Lightweight Reachability Index using Pruning and
Contraction Hierarchies | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop the data structure PReaCH (for Pruned Reachability Contraction
Hierarchies) which supports reachability queries in a directed graph, i.e., it
supports queries that ask whether two nodes in the graph are connected by a
directed path. PReaCH adapts the contraction hierarchy speedup techniques for
shortest path queries to the reachability setting. The resulting approach is
surprisingly simple and guarantees linear space and near linear preprocessing
time. Orthogonally to that, we improve existing pruning techniques for the
search by gathering more information from a single DFS-traversal of the graph.
PReaCH-indices significantly outperform previous data structures with
comparable preprocessing cost. Methods with faster queries need significantly
more preprocessing time in particular for the most difficult instances.
| [
{
"created": "Thu, 17 Apr 2014 09:55:59 GMT",
"version": "v1"
}
] | 2014-04-18 | [
[
"Merz",
"Florian",
""
],
[
"Sanders",
"Peter",
""
]
] | We develop the data structure PReaCH (for Pruned Reachability Contraction Hierarchies) which supports reachability queries in a directed graph, i.e., it supports queries that ask whether two nodes in the graph are connected by a directed path. PReaCH adapts the contraction hierarchy speedup techniques for shortest path queries to the reachability setting. The resulting approach is surprisingly simple and guarantees linear space and near linear preprocessing time. Orthogonally to that, we improve existing pruning techniques for the search by gathering more information from a single DFS-traversal of the graph. PReaCH-indices significantly outperform previous data structures with comparable preprocessing cost. Methods with faster queries need significantly more preprocessing time in particular for the most difficult instances. |
1712.07242 | Dan Kushnir | Dan Kushnir, Shirin Jalali, Iraj Saniee | Linear Time Clustering for High Dimensional Mixtures of Gaussian Clouds | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Clustering mixtures of Gaussian distributions is a fundamental and
challenging problem that is ubiquitous in various high-dimensional data
processing tasks. While state-of-the-art work on learning Gaussian mixture
models has focused primarily on improving separation bounds and their
generalization to arbitrary classes of mixture models, less emphasis has been
paid to practical computational efficiency of the proposed solutions. In this
paper, we propose a novel and highly efficient clustering algorithm for $n$
points drawn from a mixture of two arbitrary Gaussian distributions in
$\mathbb{R}^p$. The algorithm involves performing random 1-dimensional
projections until a direction is found that yields a user-specified clustering
error $e$. For a 1-dimensional separation parameter $\gamma$ satisfying
$\gamma=Q^{-1}(e)$, the expected number of such projections is shown to be
bounded by $o(\ln p)$, when $\gamma$ satisfies $\gamma\leq
c\sqrt{\ln{\ln{p}}}$, with $c$ as the separability parameter of the two
Gaussians in $\mathbb{R}^p$. Consequently, the expected overall running time of
the algorithm is linear in $n$ and quasi-linear in $p$ at $o(\ln{p})O(np)$, and
the sample complexity is independent of $p$. This result stands in contrast to
prior works which provide polynomial, with at-best quadratic, running time in
$p$ and $n$. We show that our bound on the expected number of 1-dimensional
projections extends to the case of three or more Gaussian components, and we
present a generalization of our results to mixture distributions beyond the
Gaussian model.
| [
{
"created": "Tue, 19 Dec 2017 22:23:53 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Dec 2017 17:35:38 GMT",
"version": "v2"
},
{
"created": "Thu, 1 Mar 2018 22:46:05 GMT",
"version": "v3"
}
] | 2018-03-05 | [
[
"Kushnir",
"Dan",
""
],
[
"Jalali",
"Shirin",
""
],
[
"Saniee",
"Iraj",
""
]
] | Clustering mixtures of Gaussian distributions is a fundamental and challenging problem that is ubiquitous in various high-dimensional data processing tasks. While state-of-the-art work on learning Gaussian mixture models has focused primarily on improving separation bounds and their generalization to arbitrary classes of mixture models, less emphasis has been paid to practical computational efficiency of the proposed solutions. In this paper, we propose a novel and highly efficient clustering algorithm for $n$ points drawn from a mixture of two arbitrary Gaussian distributions in $\mathbb{R}^p$. The algorithm involves performing random 1-dimensional projections until a direction is found that yields a user-specified clustering error $e$. For a 1-dimensional separation parameter $\gamma$ satisfying $\gamma=Q^{-1}(e)$, the expected number of such projections is shown to be bounded by $o(\ln p)$, when $\gamma$ satisfies $\gamma\leq c\sqrt{\ln{\ln{p}}}$, with $c$ as the separability parameter of the two Gaussians in $\mathbb{R}^p$. Consequently, the expected overall running time of the algorithm is linear in $n$ and quasi-linear in $p$ at $o(\ln{p})O(np)$, and the sample complexity is independent of $p$. This result stands in contrast to prior works which provide polynomial, with at-best quadratic, running time in $p$ and $n$. We show that our bound on the expected number of 1-dimensional projections extends to the case of three or more Gaussian components, and we present a generalization of our results to mixture distributions beyond the Gaussian model. |
2308.06241 | Mohammad Maksood Akhter | Mohammad Maksood Akhter, Devpriya Kanojia | Covid-19 Public Sentiment Analysis for Indian Tweets Classification | null | null | null | null | cs.CL cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When any extraordinary event takes place in the world wide area, it is the
social media that acts as the fastest carrier of the news along with the
consequences dealt with that event. One can gather much information through
social networks regarding the sentiments, behavior, and opinions of the people.
In this paper, we focus mainly on sentiment analysis of twitter data of India
which comprises of COVID-19 tweets. We show how Twitter data has been extracted
and then run sentimental analysis queries on it. This is helpful to analyze the
information in the tweets where opinions are highly unstructured,
heterogeneous, and are either positive or negative or neutral in some cases.
| [
{
"created": "Tue, 1 Aug 2023 09:29:55 GMT",
"version": "v1"
}
] | 2023-08-14 | [
[
"Akhter",
"Mohammad Maksood",
""
],
[
"Kanojia",
"Devpriya",
""
]
] | When any extraordinary event takes place in the world wide area, it is the social media that acts as the fastest carrier of the news along with the consequences dealt with that event. One can gather much information through social networks regarding the sentiments, behavior, and opinions of the people. In this paper, we focus mainly on sentiment analysis of twitter data of India which comprises of COVID-19 tweets. We show how Twitter data has been extracted and then run sentimental analysis queries on it. This is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and are either positive or negative or neutral in some cases. |
2304.14289 | Yuzhou Gu | Zongchen Chen, Yuzhou Gu | Fast Sampling of $b$-Matchings and $b$-Edge Covers | Added new results | null | null | null | cs.DS cs.DM math.CO math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For an integer $b \ge 1$, a $b$-matching (resp. $b$-edge cover) of a graph
$G=(V,E)$ is a subset $S\subseteq E$ of edges such that every vertex is
incident with at most (resp. at least) $b$ edges from $S$. We prove that for
any $b \ge 1$ the simple Glauber dynamics for sampling (weighted) $b$-matchings
and $b$-edge covers mixes in $O(n\log n)$ time on all $n$-vertex bounded-degree
graphs. This significantly improves upon previous results which have worse
running time and only work for $b$-matchings with $b \le 7$ and for $b$-edge
covers with $b \le 2$.
More generally, we prove spectral independence for a broad class of binary
symmetric Holant problems with log-concave signatures, including $b$-matchings,
$b$-edge covers, and antiferromagnetic $2$-spin edge models. We hence deduce
optimal mixing time of the Glauber dynamics from spectral independence.
The core of our proof is a recursive coupling inspired by (Chen and Zhang
'23) which upper bounds the Wasserstein $W_1$ distance between distributions
under different pinnings. Using a similar method, we also obtain the optimal
$O(n\log n)$ mixing time of the Glauber dynamics for the hardcore model on
$n$-vertex bounded-degree claw-free graphs, for any fugacity $\lambda$. This
improves over previous works which have at least cubic dependence on $n$.
| [
{
"created": "Thu, 27 Apr 2023 15:48:22 GMT",
"version": "v1"
},
{
"created": "Mon, 31 Jul 2023 18:48:30 GMT",
"version": "v2"
}
] | 2023-08-02 | [
[
"Chen",
"Zongchen",
""
],
[
"Gu",
"Yuzhou",
""
]
] | For an integer $b \ge 1$, a $b$-matching (resp. $b$-edge cover) of a graph $G=(V,E)$ is a subset $S\subseteq E$ of edges such that every vertex is incident with at most (resp. at least) $b$ edges from $S$. We prove that for any $b \ge 1$ the simple Glauber dynamics for sampling (weighted) $b$-matchings and $b$-edge covers mixes in $O(n\log n)$ time on all $n$-vertex bounded-degree graphs. This significantly improves upon previous results which have worse running time and only work for $b$-matchings with $b \le 7$ and for $b$-edge covers with $b \le 2$. More generally, we prove spectral independence for a broad class of binary symmetric Holant problems with log-concave signatures, including $b$-matchings, $b$-edge covers, and antiferromagnetic $2$-spin edge models. We hence deduce optimal mixing time of the Glauber dynamics from spectral independence. The core of our proof is a recursive coupling inspired by (Chen and Zhang '23) which upper bounds the Wasserstein $W_1$ distance between distributions under different pinnings. Using a similar method, we also obtain the optimal $O(n\log n)$ mixing time of the Glauber dynamics for the hardcore model on $n$-vertex bounded-degree claw-free graphs, for any fugacity $\lambda$. This improves over previous works which have at least cubic dependence on $n$. |
2406.06379 | Siyu An | Siyu An, Qin Li, Junru Lu, Di Yin and Xing Sun | FinVerse: An Autonomous Agent System for Versatile Financial Analysis | null | null | null | null | cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the significant advancements in cognitive intelligence driven by LLMs,
autonomous agent systems have attracted extensive attention. Despite this
growing interest, the development of stable and efficient agent systems poses
substantial practical challenges. In this paper, we introduce FinVerse, a
meticulously crafted agent system designed for a broad range of financial
topics. FinVerse integrates over 600 financial APIs, enabling access to more
accurate and extensive financial information compared to generalist agents. To
enhance financial information processing capabilities, FinVerse is equipped
with an embedded code interpreter, enabling the execution of complex data
analysis tasks with precision and efficiency. Our work includes an empirical
comparison of several LLMs in driving FinVerse. Specifically, we propose our
own scheme for training LLMs using SFT to optimize LLM performance within
FinVerse. Recognizing the scarcity of specialized datasets to build LLMs for
agents, we have constructed a dataset and plan to make it open-source,
providing a valuable resource for peer application developers. The demo video
has been released on YouTube at https://www.youtube.com/watch?v=sk8L9_Wv7J4
| [
{
"created": "Mon, 10 Jun 2024 15:40:23 GMT",
"version": "v1"
}
] | 2024-06-11 | [
[
"An",
"Siyu",
""
],
[
"Li",
"Qin",
""
],
[
"Lu",
"Junru",
""
],
[
"Yin",
"Di",
""
],
[
"Sun",
"Xing",
""
]
] | With the significant advancements in cognitive intelligence driven by LLMs, autonomous agent systems have attracted extensive attention. Despite this growing interest, the development of stable and efficient agent systems poses substantial practical challenges. In this paper, we introduce FinVerse, a meticulously crafted agent system designed for a broad range of financial topics. FinVerse integrates over 600 financial APIs, enabling access to more accurate and extensive financial information compared to generalist agents. To enhance financial information processing capabilities, FinVerse is equipped with an embedded code interpreter, enabling the execution of complex data analysis tasks with precision and efficiency. Our work includes an empirical comparison of several LLMs in driving FinVerse. Specifically, we propose our own scheme for training LLMs using SFT to optimize LLM performance within FinVerse. Recognizing the scarcity of specialized datasets to build LLMs for agents, we have constructed a dataset and plan to make it open-source, providing a valuable resource for peer application developers. The demo video has been released on YouTube at https://www.youtube.com/watch?v=sk8L9_Wv7J4 |
1808.10326 | Shen Li | Shen Li, Hengru Xu, Zhengdong Lu | Generalize Symbolic Knowledge With Neural Rule Engine | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As neural networks have dominated the state-of-the-art results in a wide
range of NLP tasks, it attracts considerable attention to improve the
performance of neural models by integrating symbolic knowledge. Different from
existing works, this paper investigates the combination of these two powerful
paradigms from the knowledge-driven side. We propose Neural Rule Engine (NRE),
which can learn knowledge explicitly from logic rules and then generalize them
implicitly with neural networks. NRE is implemented with neural module networks
in which each module represents an action of a logic rule. The experiments show
that NRE could greatly improve the generalization abilities of logic rules with
a significant increase in recall. Meanwhile, the precision is still maintained
at a high level.
| [
{
"created": "Thu, 30 Aug 2018 14:51:43 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Sep 2018 06:07:15 GMT",
"version": "v2"
},
{
"created": "Wed, 14 Aug 2019 07:15:49 GMT",
"version": "v3"
}
] | 2019-08-15 | [
[
"Li",
"Shen",
""
],
[
"Xu",
"Hengru",
""
],
[
"Lu",
"Zhengdong",
""
]
] | As neural networks have dominated the state-of-the-art results in a wide range of NLP tasks, it attracts considerable attention to improve the performance of neural models by integrating symbolic knowledge. Different from existing works, this paper investigates the combination of these two powerful paradigms from the knowledge-driven side. We propose Neural Rule Engine (NRE), which can learn knowledge explicitly from logic rules and then generalize them implicitly with neural networks. NRE is implemented with neural module networks in which each module represents an action of a logic rule. The experiments show that NRE could greatly improve the generalization abilities of logic rules with a significant increase in recall. Meanwhile, the precision is still maintained at a high level. |
1610.07719 | Chong Shangguan | Chong Shangguan, Jingxue Ma, Gennian Ge | New results for traitor tracing schemes | 9 pages, submitted | null | null | null | cs.IT math.CO math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the last two decades, several classes of codes are introduced to protect
the copyrighted digital data. They have important applications in the scenarios
like digital fingerprinting and broadcast encryption schemes. In this paper we
will discuss three important classes of such codes, namely, frameproof codes,
parent-identifying codes and traceability codes.
Firstly, suppose $N(t)$ is the minimal integer such that there exists a
binary $t$-frameproof code of length $N$ with cardinality larger than $N$, we
prove that $N(t)\ge\frac{15+\sqrt{33}}{24} (t-2)^2$, which is a great
improvement of the previously known bound $N(t)\ge\binom{t+1}{2}$. Moreover, we
find that the determination of $N(t)$ is closely related to a conjecture of
Erd\H{o}s, Frankl and F\"uredi posed in the 1980's, which implies the
conjectured value $N(t)=t^2+o(t^2)$. Secondly, we derive a new upper bound for
parent-identifying codes, which is superior than all previously known bounds.
Thirdly, we present an upper bound for 3-traceability codes, which shows that a
$q$-ary 3-traceability code of length $N$ can have at most $cq^{\lceil
N/9\rceil}$ codewords, where $c$ is a constant only related to the code length
$N$. It is the first meaningful upper bound for 3-traceability codes and our
result supports a conjecture of Blackburn et al. posed in 2010.
| [
{
"created": "Tue, 25 Oct 2016 03:52:05 GMT",
"version": "v1"
}
] | 2016-10-26 | [
[
"Shangguan",
"Chong",
""
],
[
"Ma",
"Jingxue",
""
],
[
"Ge",
"Gennian",
""
]
] | In the last two decades, several classes of codes are introduced to protect the copyrighted digital data. They have important applications in the scenarios like digital fingerprinting and broadcast encryption schemes. In this paper we will discuss three important classes of such codes, namely, frameproof codes, parent-identifying codes and traceability codes. Firstly, suppose $N(t)$ is the minimal integer such that there exists a binary $t$-frameproof code of length $N$ with cardinality larger than $N$, we prove that $N(t)\ge\frac{15+\sqrt{33}}{24} (t-2)^2$, which is a great improvement of the previously known bound $N(t)\ge\binom{t+1}{2}$. Moreover, we find that the determination of $N(t)$ is closely related to a conjecture of Erd\H{o}s, Frankl and F\"uredi posed in the 1980's, which implies the conjectured value $N(t)=t^2+o(t^2)$. Secondly, we derive a new upper bound for parent-identifying codes, which is superior than all previously known bounds. Thirdly, we present an upper bound for 3-traceability codes, which shows that a $q$-ary 3-traceability code of length $N$ can have at most $cq^{\lceil N/9\rceil}$ codewords, where $c$ is a constant only related to the code length $N$. It is the first meaningful upper bound for 3-traceability codes and our result supports a conjecture of Blackburn et al. posed in 2010. |
2312.10888 | Fangming Zhao | Fangming Zhao, Nikolaos Pappas, Chuan Ma, Xinghua Sun, Tony Q. S.
Quek, Howard H. Yang | Age-Threshold Slotted ALOHA for Optimizing Information Freshness in
Mobile Networks | 21 pages. Update version after peer review | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We optimize the Age of Information (AoI) in mobile networks using the
age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple
source-destination pairs, where each source sends a sequence of status update
packets to its destination over a shared spectrum. The TSA protocol stipulates
that a source node must remain silent until its AoI reaches a predefined
threshold, after which the node accesses the radio channel with a certain
probability. Using stochastic geometry tools, we derive analytical expressions
for the transmission success probability, mean peak AoI, and time-average AoI.
Subsequently, we obtain closed-form expressions for the optimal update rate and
age threshold that minimize the mean peak and time-average AoI, respectively.
In addition, we establish a scaling law for the mean peak AoI and time-average
AoI in mobile networks, revealing that the optimal mean peak AoI and
time-average AoI increase linearly with the deployment density. Notably, the
growth rate of time-average AoI under TSA is half of that under conventional
slotted ALOHA. When considering the optimal mean peak AoI, the TSA protocol
exhibits comparable performance to the traditional slotted ALOHA protocol.
These findings conclusively affirm the advantage of TSA in reducing
higher-order AoI, particularly in densely deployed networks.
| [
{
"created": "Mon, 18 Dec 2023 02:28:13 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Jun 2024 14:16:09 GMT",
"version": "v2"
}
] | 2024-06-06 | [
[
"Zhao",
"Fangming",
""
],
[
"Pappas",
"Nikolaos",
""
],
[
"Ma",
"Chuan",
""
],
[
"Sun",
"Xinghua",
""
],
[
"Quek",
"Tony Q. S.",
""
],
[
"Yang",
"Howard H.",
""
]
] | We optimize the Age of Information (AoI) in mobile networks using the age-threshold slotted ALOHA (TSA) protocol. The network comprises multiple source-destination pairs, where each source sends a sequence of status update packets to its destination over a shared spectrum. The TSA protocol stipulates that a source node must remain silent until its AoI reaches a predefined threshold, after which the node accesses the radio channel with a certain probability. Using stochastic geometry tools, we derive analytical expressions for the transmission success probability, mean peak AoI, and time-average AoI. Subsequently, we obtain closed-form expressions for the optimal update rate and age threshold that minimize the mean peak and time-average AoI, respectively. In addition, we establish a scaling law for the mean peak AoI and time-average AoI in mobile networks, revealing that the optimal mean peak AoI and time-average AoI increase linearly with the deployment density. Notably, the growth rate of time-average AoI under TSA is half of that under conventional slotted ALOHA. When considering the optimal mean peak AoI, the TSA protocol exhibits comparable performance to the traditional slotted ALOHA protocol. These findings conclusively affirm the advantage of TSA in reducing higher-order AoI, particularly in densely deployed networks. |
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