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2112.11389 | Samujjwal Ghosh | Samujjwal Ghosh, Subhadeep Maji, Maunendra Sankar Desarkar | Supervised Graph Contrastive Pretraining for Text Classification | A condensed version of this paper has been accepted to ACM SAC'22.
DOI: https://doi.org/10.1145/3477314.3507194 | null | 10.1145/3477314.3507194 | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Contrastive pretraining techniques for text classification has been largely
studied in an unsupervised setting. However, oftentimes labeled data from
related tasks which share label semantics with current task is available. We
hypothesize that using this labeled data effectively can lead to better
generalization on current task. In this paper, we propose a novel way to
effectively utilize labeled data from related tasks with a graph based
supervised contrastive learning approach. We formulate a token-graph by
extrapolating the supervised information from examples to tokens. Our
formulation results in an embedding space where tokens with high/low
probability of belonging to same class are near/further-away from one another.
We also develop detailed theoretical insights which serve as a motivation for
our method. In our experiments with $13$ datasets, we show our method
outperforms pretraining schemes by $2.5\%$ and also example-level contrastive
learning based formulation by $1.8\%$ on average. In addition, we show
cross-domain effectiveness of our method in a zero-shot setting by $3.91\%$ on
average. Lastly, we also demonstrate our method can be used as a noisy teacher
in a knowledge distillation setting to significantly improve performance of
transformer based models in low labeled data regime by $4.57\%$ on average.
| [
{
"created": "Tue, 21 Dec 2021 17:47:14 GMT",
"version": "v1"
}
] | 2021-12-22 | [
[
"Ghosh",
"Samujjwal",
""
],
[
"Maji",
"Subhadeep",
""
],
[
"Desarkar",
"Maunendra Sankar",
""
]
] | Contrastive pretraining techniques for text classification has been largely studied in an unsupervised setting. However, oftentimes labeled data from related tasks which share label semantics with current task is available. We hypothesize that using this labeled data effectively can lead to better generalization on current task. In this paper, we propose a novel way to effectively utilize labeled data from related tasks with a graph based supervised contrastive learning approach. We formulate a token-graph by extrapolating the supervised information from examples to tokens. Our formulation results in an embedding space where tokens with high/low probability of belonging to same class are near/further-away from one another. We also develop detailed theoretical insights which serve as a motivation for our method. In our experiments with $13$ datasets, we show our method outperforms pretraining schemes by $2.5\%$ and also example-level contrastive learning based formulation by $1.8\%$ on average. In addition, we show cross-domain effectiveness of our method in a zero-shot setting by $3.91\%$ on average. Lastly, we also demonstrate our method can be used as a noisy teacher in a knowledge distillation setting to significantly improve performance of transformer based models in low labeled data regime by $4.57\%$ on average. |
2310.18205 | Shubham Mittal | Shubham Mittal, Megha Sundriyal, Preslav Nakov | Lost in Translation, Found in Spans: Identifying Claims in Multilingual
Social Media | EMNLP 2023 (main) | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Claim span identification (CSI) is an important step in fact-checking
pipelines, aiming to identify text segments that contain a checkworthy claim or
assertion in a social media post. Despite its importance to journalists and
human fact-checkers, it remains a severely understudied problem, and the scarce
research on this topic so far has only focused on English. Here we aim to
bridge this gap by creating a novel dataset, X-CLAIM, consisting of 7K
real-world claims collected from numerous social media platforms in five Indian
languages and English. We report strong baselines with state-of-the-art
encoder-only language models (e.g., XLM-R) and we demonstrate the benefits of
training on multiple languages over alternative cross-lingual transfer methods
such as zero-shot transfer, or training on translated data, from a
high-resource language such as English. We evaluate generative large language
models from the GPT series using prompting methods on the X-CLAIM dataset and
we find that they underperform the smaller encoder-only language models for
low-resource languages.
| [
{
"created": "Fri, 27 Oct 2023 15:28:12 GMT",
"version": "v1"
}
] | 2023-10-30 | [
[
"Mittal",
"Shubham",
""
],
[
"Sundriyal",
"Megha",
""
],
[
"Nakov",
"Preslav",
""
]
] | Claim span identification (CSI) is an important step in fact-checking pipelines, aiming to identify text segments that contain a checkworthy claim or assertion in a social media post. Despite its importance to journalists and human fact-checkers, it remains a severely understudied problem, and the scarce research on this topic so far has only focused on English. Here we aim to bridge this gap by creating a novel dataset, X-CLAIM, consisting of 7K real-world claims collected from numerous social media platforms in five Indian languages and English. We report strong baselines with state-of-the-art encoder-only language models (e.g., XLM-R) and we demonstrate the benefits of training on multiple languages over alternative cross-lingual transfer methods such as zero-shot transfer, or training on translated data, from a high-resource language such as English. We evaluate generative large language models from the GPT series using prompting methods on the X-CLAIM dataset and we find that they underperform the smaller encoder-only language models for low-resource languages. |
1512.01843 | Kamran Keykhosravi | Kamran Keykhosravi, Erik Agrell, Giuseppe Durisi | Rates Achievable on a Fiber-Optical Split-Step Fourier Channel | null | null | null | null | cs.IT math.IT physics.optics | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A lower bound on the capacity of the split-step Fourier channel is derived.
The channel under study is a concatenation of smaller segments, within which
three operations are performed on the signal, namely, nonlinearity, linearity,
and noise addition. Simulation results indicate that for a fixed number of
segments, our lower bound saturates in the high-power regime and that the
larger the number of segments is, the higher is the saturation point. We also
obtain an alternative lower bound, which is less tight but has a simple
closed-form expression. This bound allows us to conclude that the saturation
point grows unbounded with the number of segments. Specifically, it grows as
$c+(1/2)\log(K)$, where $K$ is the number of segments and $c$ is a constant.
The connection between our channel model and the nonlinear Schr\"odinger
equation is discussed.
| [
{
"created": "Sun, 6 Dec 2015 22:05:26 GMT",
"version": "v1"
},
{
"created": "Tue, 29 Dec 2015 20:11:19 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Oct 2016 21:01:27 GMT",
"version": "v3"
},
{
"created": "Fri, 21 Oct 2016 15:28:38 GMT",
"version": "v4"
}
] | 2016-10-24 | [
[
"Keykhosravi",
"Kamran",
""
],
[
"Agrell",
"Erik",
""
],
[
"Durisi",
"Giuseppe",
""
]
] | A lower bound on the capacity of the split-step Fourier channel is derived. The channel under study is a concatenation of smaller segments, within which three operations are performed on the signal, namely, nonlinearity, linearity, and noise addition. Simulation results indicate that for a fixed number of segments, our lower bound saturates in the high-power regime and that the larger the number of segments is, the higher is the saturation point. We also obtain an alternative lower bound, which is less tight but has a simple closed-form expression. This bound allows us to conclude that the saturation point grows unbounded with the number of segments. Specifically, it grows as $c+(1/2)\log(K)$, where $K$ is the number of segments and $c$ is a constant. The connection between our channel model and the nonlinear Schr\"odinger equation is discussed. |
2312.05086 | Asma Bensalah | Asma Bensalah, Antonio Parziale, Giuseppe De Gregorio, Angelo
Marcelli, Alicia Forn\'es, and Llad\'os | I Can't Believe It's Not Better: In-air Movement For Alzheimer
Handwriting Synthetic Generation | null | null | 10.1007/978-3-031-19745-1_20 | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | During recent years, there here has been a boom in terms of deep learning use
for handwriting analysis and recognition. One main application for handwriting
analysis is early detection and diagnosis in the health field. Unfortunately,
most real case problems still suffer a scarcity of data, which makes difficult
the use of deep learning-based models. To alleviate this problem, some works
resort to synthetic data generation. Lately, more works are directed towards
guided data synthetic generation, a generation that uses the domain and data
knowledge to generate realistic data that can be useful to train deep learning
models. In this work, we combine the domain knowledge about the Alzheimer's
disease for handwriting and use it for a more guided data generation.
Concretely, we have explored the use of in-air movements for synthetic data
generation.
| [
{
"created": "Fri, 8 Dec 2023 15:14:41 GMT",
"version": "v1"
}
] | 2023-12-11 | [
[
"Bensalah",
"Asma",
""
],
[
"Parziale",
"Antonio",
""
],
[
"De Gregorio",
"Giuseppe",
""
],
[
"Marcelli",
"Angelo",
""
],
[
"Fornés",
"Alicia",
""
],
[
"Lladós",
"",
""
]
] | During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer's disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation. |
2007.11427 | Karl Norrman | Karl Norrman, Vaishnavi Sundararajan and Alessandro Bruni | Formal Analysis of EDHOC Key Establishment for Constrained IoT Devices | 12 pages; version 3 is the version accepted to SECRYPT 2021 | In Proceedings of the 18th International Conference on Security
and Cryptography (2021), ISBN 978-989-758-524-1, ISSN 2184-7711, pages
210-221 | null | null | cs.CR cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Constrained IoT devices are becoming ubiquitous in society and there is a
need for secure communication protocols that respect the constraints under
which these devices operate. EDHOC is an authenticated key establishment
protocol for constrained IoT devices, currently being standardized by the
Internet Engineering Task Force (IETF). A rudimentary version of EDHOC with
only two key establishment methods was formally analyzed in 2018. Since then,
the protocol has evolved significantly and several new key establishment
methods have been added. In this paper, we present a formal analysis of all
EDHOC methods in an enhanced symbolic Dolev-Yao model using the Tamarin tool.
We show that not all methods satisfy the authentication notion injective of
agreement, but that they all do satisfy a notion of implicit authentication, as
well as Perfect Forward Secrecy (PFS) of the session key material. We identify
other weaknesses to which we propose improvements. For example, a party may
intend to establish a session key with a certain peer, but end up establishing
it with another, trusted but compromised, peer. We communicated our findings
and proposals to the IETF, which has incorporated some of these in newer
versions of the standard.
| [
{
"created": "Wed, 22 Jul 2020 13:35:49 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Sep 2020 17:46:56 GMT",
"version": "v2"
},
{
"created": "Thu, 15 Jul 2021 13:41:41 GMT",
"version": "v3"
}
] | 2021-07-16 | [
[
"Norrman",
"Karl",
""
],
[
"Sundararajan",
"Vaishnavi",
""
],
[
"Bruni",
"Alessandro",
""
]
] | Constrained IoT devices are becoming ubiquitous in society and there is a need for secure communication protocols that respect the constraints under which these devices operate. EDHOC is an authenticated key establishment protocol for constrained IoT devices, currently being standardized by the Internet Engineering Task Force (IETF). A rudimentary version of EDHOC with only two key establishment methods was formally analyzed in 2018. Since then, the protocol has evolved significantly and several new key establishment methods have been added. In this paper, we present a formal analysis of all EDHOC methods in an enhanced symbolic Dolev-Yao model using the Tamarin tool. We show that not all methods satisfy the authentication notion injective of agreement, but that they all do satisfy a notion of implicit authentication, as well as Perfect Forward Secrecy (PFS) of the session key material. We identify other weaknesses to which we propose improvements. For example, a party may intend to establish a session key with a certain peer, but end up establishing it with another, trusted but compromised, peer. We communicated our findings and proposals to the IETF, which has incorporated some of these in newer versions of the standard. |
2304.14295 | Amer Mouawad | Michael R. Fellows, Mario Grobler, Nicole Megow, Amer E. Mouawad,
Vijayaragunathan Ramamoorthi, Frances A. Rosamond, Daniel Schmand, Sebastian
Siebertz | On Solution Discovery via Reconfiguration | null | null | null | null | cs.CC cs.DM cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The dynamics of real-world applications and systems require efficient methods
for improving infeasible solutions or restoring corrupted ones by making
modifications to the current state of a system in a restricted way. We propose
a new framework of solution discovery via reconfiguration for constructing a
feasible solution for a given problem by executing a sequence of small
modifications starting from a given state. Our framework integrates and
formalizes different aspects of classical local search, reoptimization, and
combinatorial reconfiguration. We exemplify our framework on a multitude of
fundamental combinatorial problems, namely Vertex Cover, Independent Set,
Dominating Set, and Coloring. We study the classical as well as the
parameterized complexity of the solution discovery variants of those problems
and explore the boundary between tractable and intractable instances.
| [
{
"created": "Thu, 27 Apr 2023 15:58:41 GMT",
"version": "v1"
}
] | 2023-04-28 | [
[
"Fellows",
"Michael R.",
""
],
[
"Grobler",
"Mario",
""
],
[
"Megow",
"Nicole",
""
],
[
"Mouawad",
"Amer E.",
""
],
[
"Ramamoorthi",
"Vijayaragunathan",
""
],
[
"Rosamond",
"Frances A.",
""
],
[
"Schmand",
"Daniel",
""
],
[
"Siebertz",
"Sebastian",
""
]
] | The dynamics of real-world applications and systems require efficient methods for improving infeasible solutions or restoring corrupted ones by making modifications to the current state of a system in a restricted way. We propose a new framework of solution discovery via reconfiguration for constructing a feasible solution for a given problem by executing a sequence of small modifications starting from a given state. Our framework integrates and formalizes different aspects of classical local search, reoptimization, and combinatorial reconfiguration. We exemplify our framework on a multitude of fundamental combinatorial problems, namely Vertex Cover, Independent Set, Dominating Set, and Coloring. We study the classical as well as the parameterized complexity of the solution discovery variants of those problems and explore the boundary between tractable and intractable instances. |
2011.14298 | Alphin J Thottupattu | Alphin J. Thottupattu, Jayanthi Sivaswamy, Venkateswaran P. Krishnan | A method for large diffeomorphic registration via broken geodesics | 18 pages and 9 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anatomical variabilities seen in longitudinal data or inter-subject data is
usually described by the underlying deformation, captured by non-rigid
registration of these images. Stationary Velocity Field (SVF) based non-rigid
registration algorithms are widely used for registration. SVF based methods
form a metric-free framework which captures a finite dimensional submanifold of
deformations embedded in the infinite dimensional smooth manifold of
diffeomorphisms. However, these methods cover only a limited degree of
deformations. In this paper, we address this limitation and define an
approximate metric space for the manifold of diffeomorphisms $\mathcal{G}$. We
propose a method to break down the large deformation into finite compositions
of small deformations. This results in a broken geodesic path on $\mathcal{G}$
and its length now forms an approximate registration metric. We illustrate the
method using a simple, intensity-based, log-demon implementation. Validation
results of the proposed method show that it can capture large and complex
deformations while producing qualitatively better results than the
state-of-the-art methods. The results also demonstrate that the proposed
registration metric is a good indicator of the degree of deformation.
| [
{
"created": "Sun, 29 Nov 2020 06:14:53 GMT",
"version": "v1"
},
{
"created": "Sun, 3 Jan 2021 05:49:37 GMT",
"version": "v2"
}
] | 2021-01-05 | [
[
"Thottupattu",
"Alphin J.",
""
],
[
"Sivaswamy",
"Jayanthi",
""
],
[
"Krishnan",
"Venkateswaran P.",
""
]
] | Anatomical variabilities seen in longitudinal data or inter-subject data is usually described by the underlying deformation, captured by non-rigid registration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. SVF based methods form a metric-free framework which captures a finite dimensional submanifold of deformations embedded in the infinite dimensional smooth manifold of diffeomorphisms. However, these methods cover only a limited degree of deformations. In this paper, we address this limitation and define an approximate metric space for the manifold of diffeomorphisms $\mathcal{G}$. We propose a method to break down the large deformation into finite compositions of small deformations. This results in a broken geodesic path on $\mathcal{G}$ and its length now forms an approximate registration metric. We illustrate the method using a simple, intensity-based, log-demon implementation. Validation results of the proposed method show that it can capture large and complex deformations while producing qualitatively better results than the state-of-the-art methods. The results also demonstrate that the proposed registration metric is a good indicator of the degree of deformation. |
0807.3483 | Arnaud Martin | Arnaud Martin (E3I2) | Implementing general belief function framework with a practical
codification for low complexity | Advances and Applications of DSmT for Information Fusion, Florentin
Smarandache & Jean Dezert (Ed.) (2008) Pnd | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this chapter, we propose a new practical codification of the elements of
the Venn diagram in order to easily manipulate the focal elements. In order to
reduce the complexity, the eventual constraints must be integrated in the
codification at the beginning. Hence, we only consider a reduced hyper power
set $D_r^\Theta$ that can be $2^\Theta$ or $D^\Theta$. We describe all the
steps of a general belief function framework. The step of decision is
particularly studied, indeed, when we can decide on intersections of the
singletons of the discernment space no actual decision functions are easily to
use. Hence, two approaches are proposed, an extension of previous one and an
approach based on the specificity of the elements on which to decide. The
principal goal of this chapter is to provide practical codes of a general
belief function framework for the researchers and users needing the belief
function theory.
| [
{
"created": "Tue, 22 Jul 2008 13:50:22 GMT",
"version": "v1"
}
] | 2008-07-23 | [
[
"Martin",
"Arnaud",
"",
"E3I2"
]
] | In this chapter, we propose a new practical codification of the elements of the Venn diagram in order to easily manipulate the focal elements. In order to reduce the complexity, the eventual constraints must be integrated in the codification at the beginning. Hence, we only consider a reduced hyper power set $D_r^\Theta$ that can be $2^\Theta$ or $D^\Theta$. We describe all the steps of a general belief function framework. The step of decision is particularly studied, indeed, when we can decide on intersections of the singletons of the discernment space no actual decision functions are easily to use. Hence, two approaches are proposed, an extension of previous one and an approach based on the specificity of the elements on which to decide. The principal goal of this chapter is to provide practical codes of a general belief function framework for the researchers and users needing the belief function theory. |
2203.12646 | Christopher Harth-Kitzerow | Christopher Harth-Kitzerow, Georg Carle, Fan Fei, Andre Luckow,
Johannes Klepsch | CRGC -- A Practical Framework for Constructing Reusable Garbled Circuits | 13 pages, 7 figures | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we introduce two schemes to construct reusable garbled circuits
(RGCs) in the semi-honest setting. Our completely reusable garbled circuit
(CRGC) scheme allows the generator (party A) to construct and send an
obfuscated boolean circuit along with an encoded input to the evaluator (party
B). In contrast to Yao's Garbled Circuit protocol, B can securely evaluate the
same CRGC with an arbitrary number of inputs. As a tradeoff, CRGCs predictably
leak some input bits of A to B. We also propose a partially reusable garbled
circuit (PRGC) scheme that divides a circuit into reusable and non-reusable
sections. PRGCs do not leak input bits of A. We benchmark our CRGC
implementation against the state-of-the-art garbled circuit libraries EMP SH2PC
and TinyGarble2. Using our framework, evaluating a CRGC is up to twenty times
faster, albeit with weaker privacy guarantees, than evaluating an equivalent
garbled circuit constructed by the two existing libraries. Our open-source
library can convert any C++ function to a CRGC at approx. 80 million gates per
second and repeatedly evaluate a CRGC at approx. 350 million gates per second.
Additionally, a compressed CRGC is approx. 75% smaller in file size than the
unobfuscated boolean circuit.
| [
{
"created": "Wed, 23 Mar 2022 18:11:16 GMT",
"version": "v1"
},
{
"created": "Sun, 27 Mar 2022 14:07:05 GMT",
"version": "v2"
},
{
"created": "Fri, 29 Apr 2022 09:19:11 GMT",
"version": "v3"
},
{
"created": "Fri, 6 May 2022 13:44:12 GMT",
"version": "v4"
}
] | 2022-05-09 | [
[
"Harth-Kitzerow",
"Christopher",
""
],
[
"Carle",
"Georg",
""
],
[
"Fei",
"Fan",
""
],
[
"Luckow",
"Andre",
""
],
[
"Klepsch",
"Johannes",
""
]
] | In this work, we introduce two schemes to construct reusable garbled circuits (RGCs) in the semi-honest setting. Our completely reusable garbled circuit (CRGC) scheme allows the generator (party A) to construct and send an obfuscated boolean circuit along with an encoded input to the evaluator (party B). In contrast to Yao's Garbled Circuit protocol, B can securely evaluate the same CRGC with an arbitrary number of inputs. As a tradeoff, CRGCs predictably leak some input bits of A to B. We also propose a partially reusable garbled circuit (PRGC) scheme that divides a circuit into reusable and non-reusable sections. PRGCs do not leak input bits of A. We benchmark our CRGC implementation against the state-of-the-art garbled circuit libraries EMP SH2PC and TinyGarble2. Using our framework, evaluating a CRGC is up to twenty times faster, albeit with weaker privacy guarantees, than evaluating an equivalent garbled circuit constructed by the two existing libraries. Our open-source library can convert any C++ function to a CRGC at approx. 80 million gates per second and repeatedly evaluate a CRGC at approx. 350 million gates per second. Additionally, a compressed CRGC is approx. 75% smaller in file size than the unobfuscated boolean circuit. |
0710.5194 | Masoud Ebrahimi | Masoud Ebrahimi and Amir K. Khandani | Rate-Constrained Wireless Networks with Fading Channels:
Interference-Limited and Noise-Limited Regimes | Submitted to IEEE Trans. Information Theory | null | null | null | cs.IT math.IT | null | A network of $n$ wireless communication links is considered in a Rayleigh
fading environment. It is assumed that each link can be active and transmit
with a constant power $P$ or remain silent. The objective is to maximize the
number of active links such that each active link can transmit with a constant
rate $\lambda$. An upper bound is derived that shows the number of active links
scales at most like $\frac{1}{\lambda} \log n$. To obtain a lower bound, a
decentralized link activation strategy is described and analyzed. It is shown
that for small values of $\lambda$, the number of supported links by this
strategy meets the upper bound; however, as $\lambda$ grows, this number
becomes far below the upper bound. To shrink the gap between the upper bound
and the achievability result, a modified link activation strategy is proposed
and analyzed based on some results from random graph theory. It is shown that
this modified strategy performs very close to the optimum. Specifically, this
strategy is \emph{asymptotically almost surely} optimum when $\lambda$
approaches $\infty$ or 0. It turns out the optimality results are obtained in
an interference-limited regime. It is demonstrated that, by proper selection of
the algorithm parameters, the proposed scheme also allows the network to
operate in a noise-limited regime in which the transmission rates can be
adjusted by the transmission powers. The price for this flexibility is a
decrease in the throughput scaling law by a multiplicative factor of $\log \log
n$.
| [
{
"created": "Fri, 26 Oct 2007 23:36:59 GMT",
"version": "v1"
}
] | 2007-10-30 | [
[
"Ebrahimi",
"Masoud",
""
],
[
"Khandani",
"Amir K.",
""
]
] | A network of $n$ wireless communication links is considered in a Rayleigh fading environment. It is assumed that each link can be active and transmit with a constant power $P$ or remain silent. The objective is to maximize the number of active links such that each active link can transmit with a constant rate $\lambda$. An upper bound is derived that shows the number of active links scales at most like $\frac{1}{\lambda} \log n$. To obtain a lower bound, a decentralized link activation strategy is described and analyzed. It is shown that for small values of $\lambda$, the number of supported links by this strategy meets the upper bound; however, as $\lambda$ grows, this number becomes far below the upper bound. To shrink the gap between the upper bound and the achievability result, a modified link activation strategy is proposed and analyzed based on some results from random graph theory. It is shown that this modified strategy performs very close to the optimum. Specifically, this strategy is \emph{asymptotically almost surely} optimum when $\lambda$ approaches $\infty$ or 0. It turns out the optimality results are obtained in an interference-limited regime. It is demonstrated that, by proper selection of the algorithm parameters, the proposed scheme also allows the network to operate in a noise-limited regime in which the transmission rates can be adjusted by the transmission powers. The price for this flexibility is a decrease in the throughput scaling law by a multiplicative factor of $\log \log n$. |
1712.07814 | Yingxiang Sun | Yingxiang Sun, Jiajia Chen, Chau Yuen, and Susanto Rahardja | Indoor Sound Source Localization with Probabilistic Neural Network | 10 pages, accepted by IEEE Transactions on Industrial Electronics | IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp.
6403-6413, Aug. 2018 | 10.1109/TIE.2017.2786219 | null | cs.SD cs.LG eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is known that adverse environments such as high reverberation and low
signal-to-noise ratio (SNR) pose a great challenge to indoor sound source
localization. To address this challenge, in this paper, we propose a sound
source localization algorithm based on probabilistic neural network, namely
Generalized cross correlation Classification Algorithm (GCA). Experimental
results for adverse environments with high reverberation time T60 up to 600ms
and low SNR such as -10dB show that, the average azimuth angle error and
elevation angle error by GCA are only 4.6 degrees and 3.1 degrees respectively.
Compared with three recently published algorithms, GCA has increased the
success rate on direction of arrival estimation significantly with good
robustness to environmental changes. These results show that the proposed GCA
can localize accurately and robustly for diverse indoor applications where the
site acoustic features can be studied prior to the localization stage.
| [
{
"created": "Thu, 21 Dec 2017 07:26:53 GMT",
"version": "v1"
}
] | 2018-12-05 | [
[
"Sun",
"Yingxiang",
""
],
[
"Chen",
"Jiajia",
""
],
[
"Yuen",
"Chau",
""
],
[
"Rahardja",
"Susanto",
""
]
] | It is known that adverse environments such as high reverberation and low signal-to-noise ratio (SNR) pose a great challenge to indoor sound source localization. To address this challenge, in this paper, we propose a sound source localization algorithm based on probabilistic neural network, namely Generalized cross correlation Classification Algorithm (GCA). Experimental results for adverse environments with high reverberation time T60 up to 600ms and low SNR such as -10dB show that, the average azimuth angle error and elevation angle error by GCA are only 4.6 degrees and 3.1 degrees respectively. Compared with three recently published algorithms, GCA has increased the success rate on direction of arrival estimation significantly with good robustness to environmental changes. These results show that the proposed GCA can localize accurately and robustly for diverse indoor applications where the site acoustic features can be studied prior to the localization stage. |
2402.03050 | Rupak Raj Ghimire | Rupak Raj Ghimire and Bal Krishna Bal and Prakash Poudyal | A Comprehensive Study of the Current State-of-the-Art in Nepali
Automatic Speech Recognition Systems | Accepted in International Conference on Technologies for Computer,
Electrical, Electronics & Communication (ICT-CEEL 2023) | null | null | null | cs.SD cs.CL eess.AS | http://creativecommons.org/licenses/by/4.0/ | In this paper, we examine the research conducted in the field of Nepali
Automatic Speech Recognition (ASR). The primary objective of this survey is to
conduct a comprehensive review of the works on Nepali Automatic Speech
Recognition Systems completed to date, explore the different datasets used,
examine the technology utilized, and take account of the obstacles encountered
in implementing the Nepali ASR system. In tandem with the global trends of
ever-increasing research on speech recognition based research, the number of
Nepalese ASR-related projects are also growing. Nevertheless, the investigation
of language and acoustic models of the Nepali language has not received
adequate attention compared to languages that possess ample resources. In this
context, we provide a framework as well as directions for future
investigations.
| [
{
"created": "Mon, 5 Feb 2024 14:34:14 GMT",
"version": "v1"
}
] | 2024-02-06 | [
[
"Ghimire",
"Rupak Raj",
""
],
[
"Bal",
"Bal Krishna",
""
],
[
"Poudyal",
"Prakash",
""
]
] | In this paper, we examine the research conducted in the field of Nepali Automatic Speech Recognition (ASR). The primary objective of this survey is to conduct a comprehensive review of the works on Nepali Automatic Speech Recognition Systems completed to date, explore the different datasets used, examine the technology utilized, and take account of the obstacles encountered in implementing the Nepali ASR system. In tandem with the global trends of ever-increasing research on speech recognition based research, the number of Nepalese ASR-related projects are also growing. Nevertheless, the investigation of language and acoustic models of the Nepali language has not received adequate attention compared to languages that possess ample resources. In this context, we provide a framework as well as directions for future investigations. |
2402.06411 | V\'ictor Osma-Ruiz | Guillermo Garcia-Barrios, Eduardo Latorre Iglesias, Juana M.
Gutierrez-Arriola, Ruben Fraile, Nicolas Saenz-Lechon, Victor Jose Osma-Ruiz | Exploiting spatial diversity for increasing the robustness of sound
source localization systems against reverberation | null | null | 10.1016/j.apacoust.2022.109138 | null | cs.SD eess.AS eess.SP | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Acoustic reverberation is one of the most relevant factors that hampers the
localization of a sound source inside a room. To date, several approaches have
been proposed to deal with it, but have not always been evaluated under
realistic conditions. This paper proposes exploiting spatial diversity as an
alternative approach to achieve robustness against reverberation. The
theoretical arguments supporting this approach are first presented and later
confirmed by means of simulation results and real measurements. Simulations are
run for reverberation times up to 2 s, thus providing results with a wider
range of validity than in other previous research works. It is concluded that
the use of systems consisting of several, sufficiently separated, small arrays
leads to the best results in reverberant environments. Some recommendations are
given regarding the choice of the array sizes, the separation among them, and
the way to combine SRP-PHAT maps obtained from diverse arrays.
| [
{
"created": "Fri, 9 Feb 2024 13:57:02 GMT",
"version": "v1"
}
] | 2024-02-12 | [
[
"Garcia-Barrios",
"Guillermo",
""
],
[
"Iglesias",
"Eduardo Latorre",
""
],
[
"Gutierrez-Arriola",
"Juana M.",
""
],
[
"Fraile",
"Ruben",
""
],
[
"Saenz-Lechon",
"Nicolas",
""
],
[
"Osma-Ruiz",
"Victor Jose",
""
]
] | Acoustic reverberation is one of the most relevant factors that hampers the localization of a sound source inside a room. To date, several approaches have been proposed to deal with it, but have not always been evaluated under realistic conditions. This paper proposes exploiting spatial diversity as an alternative approach to achieve robustness against reverberation. The theoretical arguments supporting this approach are first presented and later confirmed by means of simulation results and real measurements. Simulations are run for reverberation times up to 2 s, thus providing results with a wider range of validity than in other previous research works. It is concluded that the use of systems consisting of several, sufficiently separated, small arrays leads to the best results in reverberant environments. Some recommendations are given regarding the choice of the array sizes, the separation among them, and the way to combine SRP-PHAT maps obtained from diverse arrays. |
2209.07031 | Shuai Hua | Shuai Hua, Xinxin Li, Yunpeng Jing, Qunfeng Liu | A semantic hierarchical graph neural network for text classification | 10 pages, 3 figures | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The key to the text classification task is language representation and
important information extraction, and there are many related studies. In recent
years, the research on graph neural network (GNN) in text classification has
gradually emerged and shown its advantages, but the existing models mainly
focus on directly inputting words as graph nodes into the GNN models ignoring
the different levels of semantic structure information in the samples. To
address the issue, we propose a new hierarchical graph neural network (HieGNN)
which extracts corresponding information from word-level, sentence-level and
document-level respectively. Experimental results on several benchmark datasets
achieve better or similar results compared to several baseline methods, which
demonstrate that our model is able to obtain more useful information for
classification from samples.
| [
{
"created": "Thu, 15 Sep 2022 03:59:31 GMT",
"version": "v1"
}
] | 2022-09-16 | [
[
"Hua",
"Shuai",
""
],
[
"Li",
"Xinxin",
""
],
[
"Jing",
"Yunpeng",
""
],
[
"Liu",
"Qunfeng",
""
]
] | The key to the text classification task is language representation and important information extraction, and there are many related studies. In recent years, the research on graph neural network (GNN) in text classification has gradually emerged and shown its advantages, but the existing models mainly focus on directly inputting words as graph nodes into the GNN models ignoring the different levels of semantic structure information in the samples. To address the issue, we propose a new hierarchical graph neural network (HieGNN) which extracts corresponding information from word-level, sentence-level and document-level respectively. Experimental results on several benchmark datasets achieve better or similar results compared to several baseline methods, which demonstrate that our model is able to obtain more useful information for classification from samples. |
1907.13376 | Hossein A. Rahmani | Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra
Baratchi, Mohsen Afsharchi, Fabio Crestani | Category-Aware Location Embedding for Point-of-Interest Recommendation | 4 pages, 1 figures | null | 10.1145/3341981.3344240 10.1145/3341981.3344240 10.1145/3341981.3344240 | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, Point of interest (POI) recommendation has gained ever-increasing
importance in various Location-Based Social Networks (LBSNs). With the recent
advances of neural models, much work has sought to leverage neural networks to
learn neural embeddings in a pre-training phase that achieve an improved
representation of POIs and consequently a better recommendation. However,
previous studies fail to capture crucial information about POIs such as
categorical information.
In this paper, we propose a novel neural model that generates a POI embedding
incorporating sequential and categorical information from POIs. Our model
consists of a check-in module and a category module. The check-in module
captures the geographical influence of POIs derived from the sequence of users'
check-ins, while the category module captures the characteristics of POIs
derived from the category information. To validate the efficacy of the model,
we experimented with two large-scale LBSN datasets. Our experimental results
demonstrate that our approach significantly outperforms state-of-the-art POI
recommendation methods.
| [
{
"created": "Wed, 31 Jul 2019 09:14:16 GMT",
"version": "v1"
}
] | 2019-08-01 | [
[
"Rahmani",
"Hossein A.",
""
],
[
"Aliannejadi",
"Mohammad",
""
],
[
"Zadeh",
"Rasoul Mirzaei",
""
],
[
"Baratchi",
"Mitra",
""
],
[
"Afsharchi",
"Mohsen",
""
],
[
"Crestani",
"Fabio",
""
]
] | Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation. However, previous studies fail to capture crucial information about POIs such as categorical information. In this paper, we propose a novel neural model that generates a POI embedding incorporating sequential and categorical information from POIs. Our model consists of a check-in module and a category module. The check-in module captures the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POIs derived from the category information. To validate the efficacy of the model, we experimented with two large-scale LBSN datasets. Our experimental results demonstrate that our approach significantly outperforms state-of-the-art POI recommendation methods. |
2003.13786 | Asish Mukhopadhyay | Sudiksha Khanduja, Aayushi Srivastava, Md. Zamilur Rahman and Asish
Mukhopadhyay | Generating Weakly Chordal Graphs from Arbitrary Graphs | 15 pages, 29 figures | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a scheme for generating a weakly chordal graph from a randomly
generated input graph, G = (V, E). We reduce G to a chordal graph H by adding
fill-edges, using the minimum vertex degree heuristic. Since H is necessarily a
weakly chordal graph, we use an algorithm for deleting edges from a weakly
chordal graph that preserves the weak chordality property of H. The edges that
are candidates for deletion are the fill-edges that were inserted into G. In
order to delete a maximal number of fill-edges, we maintain these in a queue. A
fill-edge is removed from the front of the queue, which we then try to delete
from H. If this violates the weak chordality property of H, we reinsert this
edge at the back of the queue. This loop continues till no more fill-edges can
be removed from H. Operationally, we implement this by defining a deletion
round as one in which the edge at the back of the queue is at the front.We stop
when the size of the queue does not change over two successive deletion rounds
and output H.
| [
{
"created": "Fri, 20 Mar 2020 02:45:21 GMT",
"version": "v1"
}
] | 2020-04-01 | [
[
"Khanduja",
"Sudiksha",
""
],
[
"Srivastava",
"Aayushi",
""
],
[
"Rahman",
"Md. Zamilur",
""
],
[
"Mukhopadhyay",
"Asish",
""
]
] | We propose a scheme for generating a weakly chordal graph from a randomly generated input graph, G = (V, E). We reduce G to a chordal graph H by adding fill-edges, using the minimum vertex degree heuristic. Since H is necessarily a weakly chordal graph, we use an algorithm for deleting edges from a weakly chordal graph that preserves the weak chordality property of H. The edges that are candidates for deletion are the fill-edges that were inserted into G. In order to delete a maximal number of fill-edges, we maintain these in a queue. A fill-edge is removed from the front of the queue, which we then try to delete from H. If this violates the weak chordality property of H, we reinsert this edge at the back of the queue. This loop continues till no more fill-edges can be removed from H. Operationally, we implement this by defining a deletion round as one in which the edge at the back of the queue is at the front.We stop when the size of the queue does not change over two successive deletion rounds and output H. |
0906.3920 | EPTCS | Claudio Guidi, Fabrizio Montesi | Reasoning About a Service-oriented Programming Paradigm | null | EPTCS 2, 2009, pp. 67-81 | 10.4204/EPTCS.2.6 | null | cs.PL cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper is about a new way for programming distributed applications: the
service-oriented one. It is a concept paper based upon our experience in
developing a theory and a language for programming services. Both the
theoretical formalization and the language interpreter showed us the evidence
that a new programming paradigm exists. In this paper we illustrate the basic
features it is characterized by.
| [
{
"created": "Mon, 22 Jun 2009 05:49:12 GMT",
"version": "v1"
}
] | 2009-06-23 | [
[
"Guidi",
"Claudio",
""
],
[
"Montesi",
"Fabrizio",
""
]
] | This paper is about a new way for programming distributed applications: the service-oriented one. It is a concept paper based upon our experience in developing a theory and a language for programming services. Both the theoretical formalization and the language interpreter showed us the evidence that a new programming paradigm exists. In this paper we illustrate the basic features it is characterized by. |
2011.11052 | Ahror Belaid | Hicham Messaoudi, Ahror Belaid, Mohamed Lamine Allaoui, Ahcene Zetout,
Mohand Said Allili, Souhil Tliba, Douraied Ben Salem, Pierre-Henri Conze | Efficient embedding network for 3D brain tumor segmentation | Multimodal Brain Tumor Segmentation Challenge 2020 | Multimodal Brain Tumor Segmentation Challenge 2020 (BRATS)
BrainLes 2020 | null | 30 | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | 3D medical image processing with deep learning greatly suffers from a lack of
data. Thus, studies carried out in this field are limited compared to works
related to 2D natural image analysis, where very large datasets exist. As a
result, powerful and efficient 2D convolutional neural networks have been
developed and trained. In this paper, we investigate a way to transfer the
performance of a two-dimensional classiffication network for the purpose of
three-dimensional semantic segmentation of brain tumors. We propose an
asymmetric U-Net network by incorporating the EfficientNet model as part of the
encoding branch. As the input data is in 3D, the first layers of the encoder
are devoted to the reduction of the third dimension in order to fit the input
of the EfficientNet network. Experimental results on validation and test data
from the BraTS 2020 challenge demonstrate that the proposed method achieve
promising performance.
| [
{
"created": "Sun, 22 Nov 2020 16:17:29 GMT",
"version": "v1"
}
] | 2020-11-24 | [
[
"Messaoudi",
"Hicham",
""
],
[
"Belaid",
"Ahror",
""
],
[
"Allaoui",
"Mohamed Lamine",
""
],
[
"Zetout",
"Ahcene",
""
],
[
"Allili",
"Mohand Said",
""
],
[
"Tliba",
"Souhil",
""
],
[
"Salem",
"Douraied Ben",
""
],
[
"Conze",
"Pierre-Henri",
""
]
] | 3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural image analysis, where very large datasets exist. As a result, powerful and efficient 2D convolutional neural networks have been developed and trained. In this paper, we investigate a way to transfer the performance of a two-dimensional classiffication network for the purpose of three-dimensional semantic segmentation of brain tumors. We propose an asymmetric U-Net network by incorporating the EfficientNet model as part of the encoding branch. As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network. Experimental results on validation and test data from the BraTS 2020 challenge demonstrate that the proposed method achieve promising performance. |
2103.14600 | Alper Kamil Bozkurt | Alper Kamil Bozkurt, Yu Wang, Miroslav Pajic | Model-Free Learning of Safe yet Effective Controllers | null | null | null | null | cs.RO cs.FL cs.LG cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the problem of learning safe control policies that are also
effective; i.e., maximizing the probability of satisfying a linear temporal
logic (LTL) specification of a task, and the discounted reward capturing the
(classic) control performance. We consider unknown environments modeled as
Markov decision processes. We propose a model-free reinforcement learning
algorithm that learns a policy that first maximizes the probability of ensuring
safety, then the probability of satisfying the given LTL specification and
lastly, the sum of discounted Quality of Control rewards. Finally, we
illustrate applicability of our RL-based approach.
| [
{
"created": "Fri, 26 Mar 2021 17:05:12 GMT",
"version": "v1"
},
{
"created": "Sun, 26 Sep 2021 22:57:53 GMT",
"version": "v2"
}
] | 2021-09-28 | [
[
"Bozkurt",
"Alper Kamil",
""
],
[
"Wang",
"Yu",
""
],
[
"Pajic",
"Miroslav",
""
]
] | We study the problem of learning safe control policies that are also effective; i.e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance. We consider unknown environments modeled as Markov decision processes. We propose a model-free reinforcement learning algorithm that learns a policy that first maximizes the probability of ensuring safety, then the probability of satisfying the given LTL specification and lastly, the sum of discounted Quality of Control rewards. Finally, we illustrate applicability of our RL-based approach. |
2310.05934 | Se Jin Park | Se Jin Park, Joanna Hong, Minsu Kim, Yong Man Ro | DF-3DFace: One-to-Many Speech Synchronized 3D Face Animation with
Diffusion | null | null | null | null | cs.CV cs.AI cs.MM eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Speech-driven 3D facial animation has gained significant attention for its
ability to create realistic and expressive facial animations in 3D space based
on speech. Learning-based methods have shown promising progress in achieving
accurate facial motion synchronized with speech. However, one-to-many nature of
speech-to-3D facial synthesis has not been fully explored: while the lip
accurately synchronizes with the speech content, other facial attributes beyond
speech-related motions are variable with respect to the speech. To account for
the potential variance in the facial attributes within a single speech, we
propose DF-3DFace, a diffusion-driven speech-to-3D face mesh synthesis.
DF-3DFace captures the complex one-to-many relationships between speech and 3D
face based on diffusion. It concurrently achieves aligned lip motion by
exploiting audio-mesh synchronization and masked conditioning. Furthermore, the
proposed method jointly models identity and pose in addition to facial motions
so that it can generate 3D face animation without requiring a reference
identity mesh and produce natural head poses. We contribute a new large-scale
3D facial mesh dataset, 3D-HDTF to enable the synthesis of variations in
identities, poses, and facial motions of 3D face mesh. Extensive experiments
demonstrate that our method successfully generates highly variable facial
shapes and motions from speech and simultaneously achieves more realistic
facial animation than the state-of-the-art methods.
| [
{
"created": "Wed, 23 Aug 2023 04:14:55 GMT",
"version": "v1"
}
] | 2023-10-11 | [
[
"Park",
"Se Jin",
""
],
[
"Hong",
"Joanna",
""
],
[
"Kim",
"Minsu",
""
],
[
"Ro",
"Yong Man",
""
]
] | Speech-driven 3D facial animation has gained significant attention for its ability to create realistic and expressive facial animations in 3D space based on speech. Learning-based methods have shown promising progress in achieving accurate facial motion synchronized with speech. However, one-to-many nature of speech-to-3D facial synthesis has not been fully explored: while the lip accurately synchronizes with the speech content, other facial attributes beyond speech-related motions are variable with respect to the speech. To account for the potential variance in the facial attributes within a single speech, we propose DF-3DFace, a diffusion-driven speech-to-3D face mesh synthesis. DF-3DFace captures the complex one-to-many relationships between speech and 3D face based on diffusion. It concurrently achieves aligned lip motion by exploiting audio-mesh synchronization and masked conditioning. Furthermore, the proposed method jointly models identity and pose in addition to facial motions so that it can generate 3D face animation without requiring a reference identity mesh and produce natural head poses. We contribute a new large-scale 3D facial mesh dataset, 3D-HDTF to enable the synthesis of variations in identities, poses, and facial motions of 3D face mesh. Extensive experiments demonstrate that our method successfully generates highly variable facial shapes and motions from speech and simultaneously achieves more realistic facial animation than the state-of-the-art methods. |
2002.00252 | Kevin Vermeulen | Kevin Vermeulen, Burim Ljuma, Vamsi Addanki, Matthieu Gouel, Olivier
Fourmaux, Timur Friedman and Reza Rejaie | Alias Resolution Based on ICMP Rate Limiting | Preprint to appear in Proceedings of Passive and Active Measurement
(PAM 2020) Conference, Eugene, OR, March 2020 | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Alias resolution techniques (e.g., Midar) associate, mostly through active
measurement, a set of IP addresses as belonging to a common router. These
techniques rely on distinct router features that can serve as a signature.
Their applicability is affected by router support of the features and the
robustness of the signature. This paper presents a new alias resolution tool
called Limited Ltd. that exploits ICMP rate limiting, a feature that is
increasingly supported by modern routers that has not previously been used for
alias resolution. It sends ICMP probes toward target interfaces in order to
trigger rate limiting, extracting features from the probe reply loss traces. It
uses a machine learning classifier to designate pairs of interfaces as aliases.
We describe the details of the algorithm used by Limited Ltd. and illustrate
its feasibility and accuracy. Limited Ltd. not only is the first tool that can
perform alias resolution on IPv6 routers that do not generate monotonically
increasing fragmentation IDs (e.g., Juniper routers) but it also complements
the state-of-the-art techniques for IPv4 alias resolution. All of our code and
the collected dataset are publicly available.
| [
{
"created": "Sat, 1 Feb 2020 18:11:19 GMT",
"version": "v1"
}
] | 2020-02-04 | [
[
"Vermeulen",
"Kevin",
""
],
[
"Ljuma",
"Burim",
""
],
[
"Addanki",
"Vamsi",
""
],
[
"Gouel",
"Matthieu",
""
],
[
"Fourmaux",
"Olivier",
""
],
[
"Friedman",
"Timur",
""
],
[
"Rejaie",
"Reza",
""
]
] | Alias resolution techniques (e.g., Midar) associate, mostly through active measurement, a set of IP addresses as belonging to a common router. These techniques rely on distinct router features that can serve as a signature. Their applicability is affected by router support of the features and the robustness of the signature. This paper presents a new alias resolution tool called Limited Ltd. that exploits ICMP rate limiting, a feature that is increasingly supported by modern routers that has not previously been used for alias resolution. It sends ICMP probes toward target interfaces in order to trigger rate limiting, extracting features from the probe reply loss traces. It uses a machine learning classifier to designate pairs of interfaces as aliases. We describe the details of the algorithm used by Limited Ltd. and illustrate its feasibility and accuracy. Limited Ltd. not only is the first tool that can perform alias resolution on IPv6 routers that do not generate monotonically increasing fragmentation IDs (e.g., Juniper routers) but it also complements the state-of-the-art techniques for IPv4 alias resolution. All of our code and the collected dataset are publicly available. |
1408.0540 | Awais Khawar | Awais Khawar, Ahmed Abdelhadi, and T. Charles Clancy | Target Detection Performance of Spectrum Sharing MIMO Radars | submitted to IEEE transactions. Distribution Statement A: Approved
for public release; distribution is unlimited | null | 10.1109/JSEN.2015.2424393 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Future wireless communication systems are envisioned to share radio frequency
(RF) spectrum, with other services such as radars, in order to meet the growing
spectrum demands. In this paper, we consider co-channel spectrum sharing
between cellular systems and radars. We address the problem of target detection
by radars that are subject to shape its waveform in a way that it does not
cause interference to cellular systems. We consider a multiple-input
multiple-output (MIMO) radar and a MIMO cellular communication system with $\mc
K$ base stations (BS). We propose a spectrum sharing algorithm which steers
radar nulls, by projecting radar waveform onto the null space of interference
channel, towards a `selected' BS, thus, protecting it from radar interference.
This BS is selected, among $\mc K$ BSs, on the basis of guaranteeing minimum
waveform degradation. We study target detection capabilities of this null-space
projected (NSP) waveform and compare it with the orthogonal waveform. We derive
the generalized likelihood ratio test (GLRT) for target detection and derive
detector statistic for NSP and orthogonal waveform. The target detection
performance for NSP and orthogonal waveform is studied theoretically and via
Monte Carlo simulations.
| [
{
"created": "Sun, 3 Aug 2014 20:33:10 GMT",
"version": "v1"
},
{
"created": "Thu, 14 Aug 2014 19:31:49 GMT",
"version": "v2"
}
] | 2016-11-18 | [
[
"Khawar",
"Awais",
""
],
[
"Abdelhadi",
"Ahmed",
""
],
[
"Clancy",
"T. Charles",
""
]
] | Future wireless communication systems are envisioned to share radio frequency (RF) spectrum, with other services such as radars, in order to meet the growing spectrum demands. In this paper, we consider co-channel spectrum sharing between cellular systems and radars. We address the problem of target detection by radars that are subject to shape its waveform in a way that it does not cause interference to cellular systems. We consider a multiple-input multiple-output (MIMO) radar and a MIMO cellular communication system with $\mc K$ base stations (BS). We propose a spectrum sharing algorithm which steers radar nulls, by projecting radar waveform onto the null space of interference channel, towards a `selected' BS, thus, protecting it from radar interference. This BS is selected, among $\mc K$ BSs, on the basis of guaranteeing minimum waveform degradation. We study target detection capabilities of this null-space projected (NSP) waveform and compare it with the orthogonal waveform. We derive the generalized likelihood ratio test (GLRT) for target detection and derive detector statistic for NSP and orthogonal waveform. The target detection performance for NSP and orthogonal waveform is studied theoretically and via Monte Carlo simulations. |
2010.07374 | Jean-Samuel Leboeuf | Jean-Samuel Leboeuf, Fr\'ed\'eric LeBlanc and Mario Marchand | Decision trees as partitioning machines to characterize their
generalization properties | 9 pages, 5 appendices | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Decision trees are popular machine learning models that are simple to build
and easy to interpret. Even though algorithms to learn decision trees date back
to almost 50 years, key properties affecting their generalization error are
still weakly bounded. Hence, we revisit binary decision trees on real-valued
features from the perspective of partitions of the data. We introduce the
notion of partitioning function, and we relate it to the growth function and to
the VC dimension. Using this new concept, we are able to find the exact VC
dimension of decision stumps, which is given by the largest integer $d$ such
that $2\ell \ge \binom{d}{\left\lfloor\frac{d}{2}\right\rfloor}$, where $\ell$
is the number of real-valued features. We provide a recursive expression to
bound the partitioning functions, resulting in a upper bound on the growth
function of any decision tree structure. This allows us to show that the VC
dimension of a binary tree structure with $N$ internal nodes is of order $N
\log(N\ell)$. Finally, we elaborate a pruning algorithm based on these results
that performs better than the CART algorithm on a number of datasets, with the
advantage that no cross-validation is required.
| [
{
"created": "Wed, 14 Oct 2020 19:25:58 GMT",
"version": "v1"
}
] | 2020-10-16 | [
[
"Leboeuf",
"Jean-Samuel",
""
],
[
"LeBlanc",
"Frédéric",
""
],
[
"Marchand",
"Mario",
""
]
] | Decision trees are popular machine learning models that are simple to build and easy to interpret. Even though algorithms to learn decision trees date back to almost 50 years, key properties affecting their generalization error are still weakly bounded. Hence, we revisit binary decision trees on real-valued features from the perspective of partitions of the data. We introduce the notion of partitioning function, and we relate it to the growth function and to the VC dimension. Using this new concept, we are able to find the exact VC dimension of decision stumps, which is given by the largest integer $d$ such that $2\ell \ge \binom{d}{\left\lfloor\frac{d}{2}\right\rfloor}$, where $\ell$ is the number of real-valued features. We provide a recursive expression to bound the partitioning functions, resulting in a upper bound on the growth function of any decision tree structure. This allows us to show that the VC dimension of a binary tree structure with $N$ internal nodes is of order $N \log(N\ell)$. Finally, we elaborate a pruning algorithm based on these results that performs better than the CART algorithm on a number of datasets, with the advantage that no cross-validation is required. |
2402.12660 | Liumeng Xue | Liumeng Xue, Chaoren Wang, Mingxuan Wang, Xueyao Zhang, Jun Han,
Zhizheng Wu | SingVisio: Visual Analytics of Diffusion Model for Singing Voice
Conversion | null | null | null | null | cs.SD cs.HC eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this study, we present SingVisio, an interactive visual analysis system
that aims to explain the diffusion model used in singing voice conversion.
SingVisio provides a visual display of the generation process in diffusion
models, showcasing the step-by-step denoising of the noisy spectrum and its
transformation into a clean spectrum that captures the desired singer's timbre.
The system also facilitates side-by-side comparisons of different conditions,
such as source content, melody, and target timbre, highlighting the impact of
these conditions on the diffusion generation process and resulting conversions.
Through comprehensive evaluations, SingVisio demonstrates its effectiveness in
terms of system design, functionality, explainability, and user-friendliness.
It offers users of various backgrounds valuable learning experiences and
insights into the diffusion model for singing voice conversion.
| [
{
"created": "Tue, 20 Feb 2024 02:16:24 GMT",
"version": "v1"
}
] | 2024-02-21 | [
[
"Xue",
"Liumeng",
""
],
[
"Wang",
"Chaoren",
""
],
[
"Wang",
"Mingxuan",
""
],
[
"Zhang",
"Xueyao",
""
],
[
"Han",
"Jun",
""
],
[
"Wu",
"Zhizheng",
""
]
] | In this study, we present SingVisio, an interactive visual analysis system that aims to explain the diffusion model used in singing voice conversion. SingVisio provides a visual display of the generation process in diffusion models, showcasing the step-by-step denoising of the noisy spectrum and its transformation into a clean spectrum that captures the desired singer's timbre. The system also facilitates side-by-side comparisons of different conditions, such as source content, melody, and target timbre, highlighting the impact of these conditions on the diffusion generation process and resulting conversions. Through comprehensive evaluations, SingVisio demonstrates its effectiveness in terms of system design, functionality, explainability, and user-friendliness. It offers users of various backgrounds valuable learning experiences and insights into the diffusion model for singing voice conversion. |
1707.04504 | Helena Peic Tukuljac | Helena Peic Tukuljac, Herve Lissek and Pierre Vandergheynst | Localization of Sound Sources in a Room with One Microphone | null | null | null | null | cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Estimation of the location of sound sources is usually done using microphone
arrays. Such settings provide an environment where we know the difference
between the received signals among different microphones in the terms of phase
or attenuation, which enables localization of the sound sources. In our
solution we exploit the properties of the room transfer function in order to
localize a sound source inside a room with only one microphone. The shape of
the room and the position of the microphone are assumed to be known. The design
guidelines and limitations of the sensing matrix are given. Implementation is
based on the sparsity in the terms of voxels in a room that are occupied by a
source. What is especially interesting about our solution is that we provide
localization of the sound sources not only in the horizontal plane, but in the
terms of the 3D coordinates inside the room.
| [
{
"created": "Fri, 14 Jul 2017 13:25:44 GMT",
"version": "v1"
}
] | 2017-07-17 | [
[
"Tukuljac",
"Helena Peic",
""
],
[
"Lissek",
"Herve",
""
],
[
"Vandergheynst",
"Pierre",
""
]
] | Estimation of the location of sound sources is usually done using microphone arrays. Such settings provide an environment where we know the difference between the received signals among different microphones in the terms of phase or attenuation, which enables localization of the sound sources. In our solution we exploit the properties of the room transfer function in order to localize a sound source inside a room with only one microphone. The shape of the room and the position of the microphone are assumed to be known. The design guidelines and limitations of the sensing matrix are given. Implementation is based on the sparsity in the terms of voxels in a room that are occupied by a source. What is especially interesting about our solution is that we provide localization of the sound sources not only in the horizontal plane, but in the terms of the 3D coordinates inside the room. |
2008.09753 | Tai-Xiang Jiang | Yi-Si Luo, Xi-Le Zhao, Tai-Xiang Jiang, Yu-Bang Zheng, Yi Chang | Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral
Constrained Deep Image Prior | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Recently, convolutional neural network (CNN)-based methods are proposed for
hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as
the deep image prior (DIP) have received much attention because these methods
do not require any training data. However, DIP suffers from the
semi-convergence behavior, i.e., the iteration of DIP needs to terminate by
referring to the ground-truth image at the optimal iteration point. In this
paper, we propose the spatial-spectral constrained deep image prior (S2DIP) for
HSI mixed noise removal. Specifically, we incorporate DIP with a
spatial-spectral total variation (SSTV) term to fully preserve the
spatial-spectral local smoothness of the HSI and an $\ell_1$-norm term to
capture the complex sparse noise. The proposed S2DIP jointly leverages the
expressive power brought from the deep CNN without any training data and
exploits the HSI and noise structures via hand-crafted priors. Thus, our method
avoids the semi-convergence behavior, showing higher stabilities than DIP.
Meanwhile, our method largely enhances the HSI denoising ability of DIP. To
tackle the proposed denoising model, we develop an alternating direction
multiplier method algorithm. Extensive experiments demonstrate that the
proposed S2DIP outperforms optimization-based and supervised CNN-based
state-of-the-art HSI denoising methods.
| [
{
"created": "Sat, 22 Aug 2020 04:25:08 GMT",
"version": "v1"
},
{
"created": "Thu, 10 Jun 2021 14:22:11 GMT",
"version": "v2"
}
] | 2021-06-11 | [
[
"Luo",
"Yi-Si",
""
],
[
"Zhao",
"Xi-Le",
""
],
[
"Jiang",
"Tai-Xiang",
""
],
[
"Zheng",
"Yu-Bang",
""
],
[
"Chang",
"Yi",
""
]
] | Recently, convolutional neural network (CNN)-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as the deep image prior (DIP) have received much attention because these methods do not require any training data. However, DIP suffers from the semi-convergence behavior, i.e., the iteration of DIP needs to terminate by referring to the ground-truth image at the optimal iteration point. In this paper, we propose the spatial-spectral constrained deep image prior (S2DIP) for HSI mixed noise removal. Specifically, we incorporate DIP with a spatial-spectral total variation (SSTV) term to fully preserve the spatial-spectral local smoothness of the HSI and an $\ell_1$-norm term to capture the complex sparse noise. The proposed S2DIP jointly leverages the expressive power brought from the deep CNN without any training data and exploits the HSI and noise structures via hand-crafted priors. Thus, our method avoids the semi-convergence behavior, showing higher stabilities than DIP. Meanwhile, our method largely enhances the HSI denoising ability of DIP. To tackle the proposed denoising model, we develop an alternating direction multiplier method algorithm. Extensive experiments demonstrate that the proposed S2DIP outperforms optimization-based and supervised CNN-based state-of-the-art HSI denoising methods. |
2105.01901 | Nicolas Kuhn Dr. | Kuhn Nicolas and Fernandes David and Dubois Emmanuel and Pradas David | Impact of channel access and transport mechanisms on QoE in
GEO-satellite based LTE backhauling systems | 5 pages, 5 Figures, 6 Tables | null | null | null | cs.NI | http://creativecommons.org/publicdomain/zero/1.0/ | Backhauling services through satellite systems have doubled between 2012 and
2018. There is an increasing demand for this service for which satellite
systems typically allocate a fixed resource. This solution may not help in
optimizing the usage of the scarce satellite resource.
This study measures the relevance of using dynamic resource allocation
mechanisms for backhaul services through satellite systems. The satellite
system is emulated with OpenSAND, the LTE system with Amarisoft and the
experiments are orchestrated by OpenBACH. We compare the relevance of applying
TCP PEP mechanisms and dynamic resource allocations for different traffic
services by measuring the QoE for web browsing, data transfer and VoIP
applications.
The main conclusions are the following. When the system is congested, PEP and
layer-2 access mechanisms do not provide significant improvements. When the
system is not congested, data transfer can be greatly improved through
protocols and channel access mechanism optimization. Tuning the Constant Rate
Assignment can help in reducing the cost of the resource and provide QoE
improvements when the network is not loaded.
| [
{
"created": "Wed, 5 May 2021 07:27:29 GMT",
"version": "v1"
}
] | 2021-05-06 | [
[
"Nicolas",
"Kuhn",
""
],
[
"David",
"Fernandes",
""
],
[
"Emmanuel",
"Dubois",
""
],
[
"David",
"Pradas",
""
]
] | Backhauling services through satellite systems have doubled between 2012 and 2018. There is an increasing demand for this service for which satellite systems typically allocate a fixed resource. This solution may not help in optimizing the usage of the scarce satellite resource. This study measures the relevance of using dynamic resource allocation mechanisms for backhaul services through satellite systems. The satellite system is emulated with OpenSAND, the LTE system with Amarisoft and the experiments are orchestrated by OpenBACH. We compare the relevance of applying TCP PEP mechanisms and dynamic resource allocations for different traffic services by measuring the QoE for web browsing, data transfer and VoIP applications. The main conclusions are the following. When the system is congested, PEP and layer-2 access mechanisms do not provide significant improvements. When the system is not congested, data transfer can be greatly improved through protocols and channel access mechanism optimization. Tuning the Constant Rate Assignment can help in reducing the cost of the resource and provide QoE improvements when the network is not loaded. |
2108.07190 | Jiska Classen | Jiska Classen and Matthias Hollick | Happy MitM: Fun and Toys in Every Bluetooth Device | null | WiSec 2021: Proceedings of the 14th ACM Conference on Security and
Privacy in Wireless and Mobile Networks | 10.1145/3448300.3467822 | null | cs.CR cs.NI | http://creativecommons.org/licenses/by/4.0/ | Bluetooth pairing establishes trust on first use between two devices by
creating a shared key. Similar to certificate warnings in TLS, the Bluetooth
specification requires warning users upon issues with this key, because this
can indicate ongoing Machine-in-the-Middle (MitM) attacks. This paper uncovers
that none of the major Bluetooth stacks warns users, which violates the
specification. Clear warnings would protect users from recently published and
potential future security issues in Bluetooth authentication and encryption.
| [
{
"created": "Mon, 16 Aug 2021 15:56:08 GMT",
"version": "v1"
}
] | 2021-08-17 | [
[
"Classen",
"Jiska",
""
],
[
"Hollick",
"Matthias",
""
]
] | Bluetooth pairing establishes trust on first use between two devices by creating a shared key. Similar to certificate warnings in TLS, the Bluetooth specification requires warning users upon issues with this key, because this can indicate ongoing Machine-in-the-Middle (MitM) attacks. This paper uncovers that none of the major Bluetooth stacks warns users, which violates the specification. Clear warnings would protect users from recently published and potential future security issues in Bluetooth authentication and encryption. |
1805.05409 | Jason Anastasopoulos | L. Jason Anastasopoulos and Andrew B. Whitford | Machine Learning for Public Administration Research, with Application to
Organizational Reputation | null | null | null | null | cs.CY cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning methods have gained a great deal of popularity in recent
years among public administration scholars and practitioners. These techniques
open the door to the analysis of text, image and other types of data that allow
us to test foundational theories of public administration and to develop new
theories. Despite the excitement surrounding machine learning methods, clarity
regarding their proper use and potential pitfalls is lacking. This paper
attempts to fill this gap in the literature through providing a machine
learning "guide to practice" for public administration scholars and
practitioners. Here, we take a foundational view of machine learning and
describe how these methods can enrich public administration research and
practice through their ability develop new measures, tap into new sources of
data and conduct statistical inference and causal inference in a principled
manner. We then turn our attention to the pitfalls of using these methods such
as unvalidated measures and lack of interpretability. Finally, we demonstrate
how machine learning techniques can help us learn about organizational
reputation in federal agencies through an illustrated example using tweets from
13 executive federal agencies.
| [
{
"created": "Fri, 11 May 2018 14:30:30 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Sep 2018 15:32:10 GMT",
"version": "v2"
}
] | 2018-09-12 | [
[
"Anastasopoulos",
"L. Jason",
""
],
[
"Whitford",
"Andrew B.",
""
]
] | Machine learning methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. Despite the excitement surrounding machine learning methods, clarity regarding their proper use and potential pitfalls is lacking. This paper attempts to fill this gap in the literature through providing a machine learning "guide to practice" for public administration scholars and practitioners. Here, we take a foundational view of machine learning and describe how these methods can enrich public administration research and practice through their ability develop new measures, tap into new sources of data and conduct statistical inference and causal inference in a principled manner. We then turn our attention to the pitfalls of using these methods such as unvalidated measures and lack of interpretability. Finally, we demonstrate how machine learning techniques can help us learn about organizational reputation in federal agencies through an illustrated example using tweets from 13 executive federal agencies. |
1911.11361 | Yifan Wu | Yifan Wu, George Tucker, Ofir Nachum | Behavior Regularized Offline Reinforcement Learning | null | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In reinforcement learning (RL) research, it is common to assume access to
direct online interactions with the environment. However in many real-world
applications, access to the environment is limited to a fixed offline dataset
of logged experience. In such settings, standard RL algorithms have been shown
to diverge or otherwise yield poor performance. Accordingly, recent work has
suggested a number of remedies to these issues. In this work, we introduce a
general framework, behavior regularized actor critic (BRAC), to empirically
evaluate recently proposed methods as well as a number of simple baselines
across a variety of offline continuous control tasks. Surprisingly, we find
that many of the technical complexities introduced in recent methods are
unnecessary to achieve strong performance. Additional ablations provide
insights into which design choices matter most in the offline RL setting.
| [
{
"created": "Tue, 26 Nov 2019 06:11:34 GMT",
"version": "v1"
}
] | 2019-11-27 | [
[
"Wu",
"Yifan",
""
],
[
"Tucker",
"George",
""
],
[
"Nachum",
"Ofir",
""
]
] | In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment. However in many real-world applications, access to the environment is limited to a fixed offline dataset of logged experience. In such settings, standard RL algorithms have been shown to diverge or otherwise yield poor performance. Accordingly, recent work has suggested a number of remedies to these issues. In this work, we introduce a general framework, behavior regularized actor critic (BRAC), to empirically evaluate recently proposed methods as well as a number of simple baselines across a variety of offline continuous control tasks. Surprisingly, we find that many of the technical complexities introduced in recent methods are unnecessary to achieve strong performance. Additional ablations provide insights into which design choices matter most in the offline RL setting. |
1202.6095 | Henry Pfister | Yung-Yih Jian, Henry D. Pfister, Krishna R. Narayanan | Approaching Capacity at High-Rates with Iterative Hard-Decision Decoding | 22 pages, this version accepted to the IEEE Transactions on
Information Theory | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A variety of low-density parity-check (LDPC) ensembles have now been observed
to approach capacity with message-passing decoding. However, all of them use
soft (i.e., non-binary) messages and a posteriori probability (APP) decoding of
their component codes. In this paper, we show that one can approach capacity at
high rates using iterative hard-decision decoding (HDD) of generalized product
codes. Specifically, a class of spatially-coupled GLDPC codes with BCH
component codes is considered, and it is observed that, in the high-rate
regime, they can approach capacity under the proposed iterative HDD. These
codes can be seen as generalized product codes and are closely related to
braided block codes. An iterative HDD algorithm is proposed that enables one to
analyze the performance of these codes via density evolution (DE).
| [
{
"created": "Tue, 28 Feb 2012 00:10:52 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Aug 2012 19:09:02 GMT",
"version": "v2"
},
{
"created": "Sun, 31 May 2015 00:51:00 GMT",
"version": "v3"
},
{
"created": "Wed, 17 May 2017 15:41:52 GMT",
"version": "v4"
}
] | 2017-05-18 | [
[
"Jian",
"Yung-Yih",
""
],
[
"Pfister",
"Henry D.",
""
],
[
"Narayanan",
"Krishna R.",
""
]
] | A variety of low-density parity-check (LDPC) ensembles have now been observed to approach capacity with message-passing decoding. However, all of them use soft (i.e., non-binary) messages and a posteriori probability (APP) decoding of their component codes. In this paper, we show that one can approach capacity at high rates using iterative hard-decision decoding (HDD) of generalized product codes. Specifically, a class of spatially-coupled GLDPC codes with BCH component codes is considered, and it is observed that, in the high-rate regime, they can approach capacity under the proposed iterative HDD. These codes can be seen as generalized product codes and are closely related to braided block codes. An iterative HDD algorithm is proposed that enables one to analyze the performance of these codes via density evolution (DE). |
2310.18617 | Branislav Kveton | Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain,
Branislav Kveton, and Ge Liu | Pessimistic Off-Policy Multi-Objective Optimization | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-objective optimization is a type of decision making problems where
multiple conflicting objectives are optimized. We study offline optimization of
multi-objective policies from data collected by an existing policy. We propose
a pessimistic estimator for the multi-objective policy values that can be
easily plugged into existing formulas for hypervolume computation and
optimized. The estimator is based on inverse propensity scores (IPS), and
improves upon a naive IPS estimator in both theory and experiments. Our
analysis is general, and applies beyond our IPS estimators and methods for
optimizing them. The pessimistic estimator can be optimized by policy gradients
and performs well in all of our experiments.
| [
{
"created": "Sat, 28 Oct 2023 06:50:15 GMT",
"version": "v1"
}
] | 2023-10-31 | [
[
"Alizadeh",
"Shima",
""
],
[
"Bhargava",
"Aniruddha",
""
],
[
"Gopalswamy",
"Karthick",
""
],
[
"Jain",
"Lalit",
""
],
[
"Kveton",
"Branislav",
""
],
[
"Liu",
"Ge",
""
]
] | Multi-objective optimization is a type of decision making problems where multiple conflicting objectives are optimized. We study offline optimization of multi-objective policies from data collected by an existing policy. We propose a pessimistic estimator for the multi-objective policy values that can be easily plugged into existing formulas for hypervolume computation and optimized. The estimator is based on inverse propensity scores (IPS), and improves upon a naive IPS estimator in both theory and experiments. Our analysis is general, and applies beyond our IPS estimators and methods for optimizing them. The pessimistic estimator can be optimized by policy gradients and performs well in all of our experiments. |
2204.04026 | Anne Lauscher | Carolin Holtermann, Anne Lauscher, Simone Paolo Ponzetto | Fair and Argumentative Language Modeling for Computational Argumentation | ACL 2022 | null | null | null | cs.CL | http://creativecommons.org/licenses/by-sa/4.0/ | Although much work in NLP has focused on measuring and mitigating
stereotypical bias in semantic spaces, research addressing bias in
computational argumentation is still in its infancy. In this paper, we address
this research gap and conduct a thorough investigation of bias in argumentative
language models. To this end, we introduce ABBA, a novel resource for bias
measurement specifically tailored to argumentation. We employ our resource to
assess the effect of argumentative fine-tuning and debiasing on the intrinsic
bias found in transformer-based language models using a lightweight
adapter-based approach that is more sustainable and parameter-efficient than
full fine-tuning. Finally, we analyze the potential impact of language model
debiasing on the performance in argument quality prediction, a downstream task
of computational argumentation. Our results show that we are able to
successfully and sustainably remove bias in general and argumentative language
models while preserving (and sometimes improving) model performance in
downstream tasks. We make all experimental code and data available at
https://github.com/umanlp/FairArgumentativeLM.
| [
{
"created": "Fri, 8 Apr 2022 12:23:46 GMT",
"version": "v1"
}
] | 2022-04-11 | [
[
"Holtermann",
"Carolin",
""
],
[
"Lauscher",
"Anne",
""
],
[
"Ponzetto",
"Simone Paolo",
""
]
] | Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its infancy. In this paper, we address this research gap and conduct a thorough investigation of bias in argumentative language models. To this end, we introduce ABBA, a novel resource for bias measurement specifically tailored to argumentation. We employ our resource to assess the effect of argumentative fine-tuning and debiasing on the intrinsic bias found in transformer-based language models using a lightweight adapter-based approach that is more sustainable and parameter-efficient than full fine-tuning. Finally, we analyze the potential impact of language model debiasing on the performance in argument quality prediction, a downstream task of computational argumentation. Our results show that we are able to successfully and sustainably remove bias in general and argumentative language models while preserving (and sometimes improving) model performance in downstream tasks. We make all experimental code and data available at https://github.com/umanlp/FairArgumentativeLM. |
1906.07194 | Cristian Danescu-Niculescu-Mizil | Justine Zhang, Robert Filbin, Christine Morrison, Jaclyn Weiser,
Cristian Danescu-Niculescu-Mizil | Finding Your Voice: The Linguistic Development of Mental Health
Counselors | To appear at ACL 2019, 12 pages, 2 figures; code available through
the Cornell Conversational Analysis Toolkit (https://convokit.cornell.edu) | null | null | null | cs.CL cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mental health counseling is an enterprise with profound societal importance
where conversations play a primary role. In order to acquire the conversational
skills needed to face a challenging range of situations, mental health
counselors must rely on training and on continued experience with actual
clients. However, in the absence of large scale longitudinal studies, the
nature and significance of this developmental process remain unclear. For
example, prior literature suggests that experience might not translate into
consequential changes in counselor behavior. This has led some to even argue
that counseling is a profession without expertise.
In this work, we develop a computational framework to quantify the extent to
which individuals change their linguistic behavior with experience and to study
the nature of this evolution. We use our framework to conduct a large
longitudinal study of mental health counseling conversations, tracking over
3,400 counselors across their tenure. We reveal that overall, counselors do
indeed change their conversational behavior to become more diverse across
interactions, developing an individual voice that distinguishes them from other
counselors. Furthermore, a finer-grained investigation shows that the rate and
nature of this diversification vary across functionally different
conversational components.
| [
{
"created": "Mon, 17 Jun 2019 18:00:04 GMT",
"version": "v1"
}
] | 2019-06-19 | [
[
"Zhang",
"Justine",
""
],
[
"Filbin",
"Robert",
""
],
[
"Morrison",
"Christine",
""
],
[
"Weiser",
"Jaclyn",
""
],
[
"Danescu-Niculescu-Mizil",
"Cristian",
""
]
] | Mental health counseling is an enterprise with profound societal importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components. |
1501.03975 | Vijay Manikandan Janakiraman | Vijay Manikandan Janakiraman and XuanLong Nguyen and Dennis Assanis | Stochastic Gradient Based Extreme Learning Machines For Online Learning
of Advanced Combustion Engines | This paper was written as an extract from my PhD thesis (July 2013)
and so references may not be to date as of this submission (Jan 2015). The
article is in review and contains 10 figures, 35 references | null | null | null | cs.NE cs.LG cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, a stochastic gradient based online learning algorithm for
Extreme Learning Machines (ELM) is developed (SG-ELM). A stability criterion
based on Lyapunov approach is used to prove both asymptotic stability of
estimation error and stability in the estimated parameters suitable for
identification of nonlinear dynamic systems. The developed algorithm not only
guarantees stability, but also reduces the computational demand compared to the
OS-ELM approach based on recursive least squares. In order to demonstrate the
effectiveness of the algorithm on a real-world scenario, an advanced combustion
engine identification problem is considered. The algorithm is applied to two
case studies: An online regression learning for system identification of a
Homogeneous Charge Compression Ignition (HCCI) Engine and an online
classification learning (with class imbalance) for identifying the dynamic
operating envelope of the HCCI Engine. The results indicate that the accuracy
of the proposed SG-ELM is comparable to that of the state-of-the-art but adds
stability and a reduction in computational effort.
| [
{
"created": "Fri, 16 Jan 2015 13:18:34 GMT",
"version": "v1"
}
] | 2015-01-19 | [
[
"Janakiraman",
"Vijay Manikandan",
""
],
[
"Nguyen",
"XuanLong",
""
],
[
"Assanis",
"Dennis",
""
]
] | In this article, a stochastic gradient based online learning algorithm for Extreme Learning Machines (ELM) is developed (SG-ELM). A stability criterion based on Lyapunov approach is used to prove both asymptotic stability of estimation error and stability in the estimated parameters suitable for identification of nonlinear dynamic systems. The developed algorithm not only guarantees stability, but also reduces the computational demand compared to the OS-ELM approach based on recursive least squares. In order to demonstrate the effectiveness of the algorithm on a real-world scenario, an advanced combustion engine identification problem is considered. The algorithm is applied to two case studies: An online regression learning for system identification of a Homogeneous Charge Compression Ignition (HCCI) Engine and an online classification learning (with class imbalance) for identifying the dynamic operating envelope of the HCCI Engine. The results indicate that the accuracy of the proposed SG-ELM is comparable to that of the state-of-the-art but adds stability and a reduction in computational effort. |
2204.08242 | Jarek Duda Dr | Jarek Duda | Fast optimization of common basis for matrix set through Common Singular
Value Decomposition | 4 pages, 3 figures | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | SVD (singular value decomposition) is one of the basic tools of machine
learning, allowing to optimize basis for a given matrix. However, sometimes we
have a set of matrices $\{A_k\}_k$ instead, and would like to optimize a single
common basis for them: find orthogonal matrices $U$, $V$, such that $\{U^T A_k
V\}$ set of matrices is somehow simpler. For example DCT-II is orthonormal
basis of functions commonly used in image/video compression - as discussed
here, this kind of basis can be quickly automatically optimized for a given
dataset. While also discussed gradient descent optimization might be
computationally costly, there is proposed CSVD (common SVD): fast general
approach based on SVD. Specifically, we choose $U$ as built of eigenvectors of
$\sum_i (w_k)^q (A_k A_k^T)^p$ and $V$ of $\sum_k (w_k)^q (A_k^T A_k)^p$, where
$w_k$ are their weights, $p,q>0$ are some chosen powers e.g. 1/2, optionally
with normalization e.g. $A \to A - rc^T$ where $r_i=\sum_j A_{ij}, c_j =\sum_i
A_{ij}$.
| [
{
"created": "Mon, 18 Apr 2022 10:18:51 GMT",
"version": "v1"
}
] | 2022-04-19 | [
[
"Duda",
"Jarek",
""
]
] | SVD (singular value decomposition) is one of the basic tools of machine learning, allowing to optimize basis for a given matrix. However, sometimes we have a set of matrices $\{A_k\}_k$ instead, and would like to optimize a single common basis for them: find orthogonal matrices $U$, $V$, such that $\{U^T A_k V\}$ set of matrices is somehow simpler. For example DCT-II is orthonormal basis of functions commonly used in image/video compression - as discussed here, this kind of basis can be quickly automatically optimized for a given dataset. While also discussed gradient descent optimization might be computationally costly, there is proposed CSVD (common SVD): fast general approach based on SVD. Specifically, we choose $U$ as built of eigenvectors of $\sum_i (w_k)^q (A_k A_k^T)^p$ and $V$ of $\sum_k (w_k)^q (A_k^T A_k)^p$, where $w_k$ are their weights, $p,q>0$ are some chosen powers e.g. 1/2, optionally with normalization e.g. $A \to A - rc^T$ where $r_i=\sum_j A_{ij}, c_j =\sum_i A_{ij}$. |
1304.7819 | Michael Adrir Scott | Michael 'Adrir' Scott | Vocalnayno: Designing a Game-Based Intervention to Support Reading
Development in Primary Schools | Presented at the 6th European Conference on Games-Based Learning, Oct
4-5, 2012, Cork, Ireland | Proceedings of the 6th European Conference on Games-Based
Learning. ACPI: Reading, UK. 654--657 | null | null | cs.CY cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Encouraging children to read frequently and helping them to develop their
reading skills as effectively as possible can be a challenge for some primary
schools. This research questions whether the use of a game-based intervention
can integrate into the existing teaching culture to aid volunteer teaching
assistants in achieving a more significant impact on pupil reading development.
A prototype based on the initial process of requirements gathering is presented
using Multimedia Fusion Developer 2. The design incorporates a game-like
exercise where a foam volcano character releases bubbles containing letters and
words. Pupils must read these aloud in order to burst them open, which is
recorded as a metric of reading ability.
| [
{
"created": "Mon, 29 Apr 2013 23:58:35 GMT",
"version": "v1"
}
] | 2013-05-01 | [
[
"Scott",
"Michael 'Adrir'",
""
]
] | Encouraging children to read frequently and helping them to develop their reading skills as effectively as possible can be a challenge for some primary schools. This research questions whether the use of a game-based intervention can integrate into the existing teaching culture to aid volunteer teaching assistants in achieving a more significant impact on pupil reading development. A prototype based on the initial process of requirements gathering is presented using Multimedia Fusion Developer 2. The design incorporates a game-like exercise where a foam volcano character releases bubbles containing letters and words. Pupils must read these aloud in order to burst them open, which is recorded as a metric of reading ability. |
2106.00157 | Bei Wang | Lin Yan, Talha Bin Masood, Raghavendra Sridharamurthy, Farhan Rasheed,
Vijay Natarajan, Ingrid Hotz, Bei Wang | Scalar Field Comparison with Topological Descriptors: Properties and
Applications for Scientific Visualization | null | null | 10.1111/cgf.14331 | null | cs.HC cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In topological data analysis and visualization, topological descriptors such
as persistence diagrams, merge trees, contour trees, Reeb graphs, and
Morse-Smale complexes play an essential role in capturing the shape of scalar
field data. We present a state-of-the-art report on scalar field comparison
using topological descriptors. We provide a taxonomy of existing approaches
based on visualization tasks associated with three categories of data: single
fields, time-varying fields, and ensembles. These tasks include symmetry
detection, periodicity detection, key event/feature detection, feature
tracking, clustering, and structure statistics. Our main contributions include
the formulation of a set of desirable mathematical and computational properties
of comparative measures, and the classification of visualization tasks and
applications that are enabled by these measures.
| [
{
"created": "Tue, 1 Jun 2021 00:34:18 GMT",
"version": "v1"
}
] | 2024-06-06 | [
[
"Yan",
"Lin",
""
],
[
"Masood",
"Talha Bin",
""
],
[
"Sridharamurthy",
"Raghavendra",
""
],
[
"Rasheed",
"Farhan",
""
],
[
"Natarajan",
"Vijay",
""
],
[
"Hotz",
"Ingrid",
""
],
[
"Wang",
"Bei",
""
]
] | In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse-Smale complexes play an essential role in capturing the shape of scalar field data. We present a state-of-the-art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time-varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures. |
2309.02027 | Katerina Schindlerova Hlavackova-Schindler | Katerina Hlavackova-Schindler, Anna Melnykova, Irene Tubikanec | Granger Causal Inference in Multivariate Hawkes Processes by Minimum
Message Length | 26 pages, 5 figures | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used
to model various real-life phenomena: earthquakes, operations on stock markets,
neuronal activity, virus propagation and many others. In this paper, we focus
on MHPs with exponential decay kernels and estimate connectivity graphs, which
represent the Granger causal relations between their components. We approach
this inference problem by proposing an optimization criterion and model
selection algorithm based on the minimum message length (MML) principle. MML
compares Granger causal models using the Occam's razor principle in the
following way: even when models have a comparable goodness-of-fit to the
observed data, the one generating the most concise explanation of the data is
preferred. While most of the state-of-art methods using lasso-type penalization
tend to overfitting in scenarios with short time horizons, the proposed
MML-based method achieves high F1 scores in these settings. We conduct a
numerical study comparing the proposed algorithm to other related classical and
state-of-art methods, where we achieve the highest F1 scores in specific sparse
graph settings. We illustrate the proposed method also on G7 sovereign bond
data and obtain causal connections, which are in agreement with the expert
knowledge available in the literature.
| [
{
"created": "Tue, 5 Sep 2023 08:13:34 GMT",
"version": "v1"
},
{
"created": "Wed, 10 Apr 2024 19:03:58 GMT",
"version": "v2"
}
] | 2024-04-12 | [
[
"Hlavackova-Schindler",
"Katerina",
""
],
[
"Melnykova",
"Anna",
""
],
[
"Tubikanec",
"Irene",
""
]
] | Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used to model various real-life phenomena: earthquakes, operations on stock markets, neuronal activity, virus propagation and many others. In this paper, we focus on MHPs with exponential decay kernels and estimate connectivity graphs, which represent the Granger causal relations between their components. We approach this inference problem by proposing an optimization criterion and model selection algorithm based on the minimum message length (MML) principle. MML compares Granger causal models using the Occam's razor principle in the following way: even when models have a comparable goodness-of-fit to the observed data, the one generating the most concise explanation of the data is preferred. While most of the state-of-art methods using lasso-type penalization tend to overfitting in scenarios with short time horizons, the proposed MML-based method achieves high F1 scores in these settings. We conduct a numerical study comparing the proposed algorithm to other related classical and state-of-art methods, where we achieve the highest F1 scores in specific sparse graph settings. We illustrate the proposed method also on G7 sovereign bond data and obtain causal connections, which are in agreement with the expert knowledge available in the literature. |
1602.06657 | Kaushik Sarkar | Kaushik Sarkar, Hari Sundaram | Influencing Busy People in a Social Network | null | null | 10.1371/journal.pone.0162014 | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We identify influential early adopters in a social network, where individuals
are resource constrained, to maximize the spread of multiple, costly behaviors.
A solution to this problem is especially important for viral marketing. The
problem of maximizing influence in a social network is challenging since it is
computationally intractable. We make three contributions. First, propose a new
model of collective behavior that incorporates individual intent, knowledge of
neighbors actions and resource constraints. Second, we show that the multiple
behavior influence maximization is NP-hard. Furthermore, we show that the
problem is submodular, implying the existence of a greedy solution that
approximates the optimal solution to within a constant. However, since the
greedy algorithm is expensive for large networks, we propose efficient
heuristics to identify the influential individuals, including heuristics to
assign behaviors to the different early adopters. We test our approach on
synthetic and real-world topologies with excellent results. We evaluate the
effectiveness under three metrics: unique number of participants, total number
of active behaviors and network resource utilization. Our heuristics produce
15-51% increase in expected resource utilization over the naive approach.
| [
{
"created": "Mon, 22 Feb 2016 06:17:38 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Mar 2016 03:29:10 GMT",
"version": "v2"
}
] | 2017-02-08 | [
[
"Sarkar",
"Kaushik",
""
],
[
"Sundaram",
"Hari",
""
]
] | We identify influential early adopters in a social network, where individuals are resource constrained, to maximize the spread of multiple, costly behaviors. A solution to this problem is especially important for viral marketing. The problem of maximizing influence in a social network is challenging since it is computationally intractable. We make three contributions. First, propose a new model of collective behavior that incorporates individual intent, knowledge of neighbors actions and resource constraints. Second, we show that the multiple behavior influence maximization is NP-hard. Furthermore, we show that the problem is submodular, implying the existence of a greedy solution that approximates the optimal solution to within a constant. However, since the greedy algorithm is expensive for large networks, we propose efficient heuristics to identify the influential individuals, including heuristics to assign behaviors to the different early adopters. We test our approach on synthetic and real-world topologies with excellent results. We evaluate the effectiveness under three metrics: unique number of participants, total number of active behaviors and network resource utilization. Our heuristics produce 15-51% increase in expected resource utilization over the naive approach. |
2206.12100 | Zahra Ghodsi | Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani, Xinqiao Zhang, Ke
Huang, Farinaz Koushanfar | zPROBE: Zero Peek Robustness Checks for Federated Learning | ICCV 2023 | null | null | null | cs.LG cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Privacy-preserving federated learning allows multiple users to jointly train
a model with coordination of a central server. The server only learns the final
aggregation result, thus the users' (private) training data is not leaked from
the individual model updates. However, keeping the individual updates private
allows malicious users to perform Byzantine attacks and degrade the accuracy
without being detected. Best existing defenses against Byzantine workers rely
on robust rank-based statistics, e.g., median, to find malicious updates.
However, implementing privacy-preserving rank-based statistics is nontrivial
and not scalable in the secure domain, as it requires sorting all individual
updates. We establish the first private robustness check that uses high break
point rank-based statistics on aggregated model updates. By exploiting
randomized clustering, we significantly improve the scalability of our defense
without compromising privacy. We leverage our statistical bounds in
zero-knowledge proofs to detect and remove malicious updates without revealing
the private user updates. Our novel framework, zPROBE, enables Byzantine
resilient and secure federated learning. Empirical evaluations demonstrate that
zPROBE provides a low overhead solution to defend against state-of-the-art
Byzantine attacks while preserving privacy.
| [
{
"created": "Fri, 24 Jun 2022 06:20:37 GMT",
"version": "v1"
},
{
"created": "Tue, 25 Oct 2022 19:42:48 GMT",
"version": "v2"
},
{
"created": "Tue, 5 Sep 2023 17:14:01 GMT",
"version": "v3"
}
] | 2023-09-06 | [
[
"Ghodsi",
"Zahra",
""
],
[
"Javaheripi",
"Mojan",
""
],
[
"Sheybani",
"Nojan",
""
],
[
"Zhang",
"Xinqiao",
""
],
[
"Huang",
"Ke",
""
],
[
"Koushanfar",
"Farinaz",
""
]
] | Privacy-preserving federated learning allows multiple users to jointly train a model with coordination of a central server. The server only learns the final aggregation result, thus the users' (private) training data is not leaked from the individual model updates. However, keeping the individual updates private allows malicious users to perform Byzantine attacks and degrade the accuracy without being detected. Best existing defenses against Byzantine workers rely on robust rank-based statistics, e.g., median, to find malicious updates. However, implementing privacy-preserving rank-based statistics is nontrivial and not scalable in the secure domain, as it requires sorting all individual updates. We establish the first private robustness check that uses high break point rank-based statistics on aggregated model updates. By exploiting randomized clustering, we significantly improve the scalability of our defense without compromising privacy. We leverage our statistical bounds in zero-knowledge proofs to detect and remove malicious updates without revealing the private user updates. Our novel framework, zPROBE, enables Byzantine resilient and secure federated learning. Empirical evaluations demonstrate that zPROBE provides a low overhead solution to defend against state-of-the-art Byzantine attacks while preserving privacy. |
2406.09722 | Sidike Paheding | Abhilash Durgam, Sidike Paheding, Vikas Dhiman, Vijay Devabhaktuni | Cross-view geo-localization: a survey | null | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Cross-view geo-localization has garnered notable attention in the realm of
computer vision, spurred by the widespread availability of copious geotagged
datasets and the advancements in machine learning techniques. This paper
provides a thorough survey of cutting-edge methodologies, techniques, and
associated challenges that are integral to this domain, with a focus on
feature-based and deep learning strategies. Feature-based methods capitalize on
unique features to establish correspondences across disparate viewpoints,
whereas deep learning-based methodologies deploy convolutional neural networks
to embed view-invariant attributes. This work also delineates the multifaceted
challenges encountered in cross-view geo-localization, such as variations in
viewpoints and illumination, the occurrence of occlusions, and it elucidates
innovative solutions that have been formulated to tackle these issues.
Furthermore, we delineate benchmark datasets and relevant evaluation metrics,
and also perform a comparative analysis of state-of-the-art techniques.
Finally, we conclude the paper with a discussion on prospective avenues for
future research and the burgeoning applications of cross-view geo-localization
in an intricately interconnected global landscape.
| [
{
"created": "Fri, 14 Jun 2024 05:14:54 GMT",
"version": "v1"
}
] | 2024-06-17 | [
[
"Durgam",
"Abhilash",
""
],
[
"Paheding",
"Sidike",
""
],
[
"Dhiman",
"Vikas",
""
],
[
"Devabhaktuni",
"Vijay",
""
]
] | Cross-view geo-localization has garnered notable attention in the realm of computer vision, spurred by the widespread availability of copious geotagged datasets and the advancements in machine learning techniques. This paper provides a thorough survey of cutting-edge methodologies, techniques, and associated challenges that are integral to this domain, with a focus on feature-based and deep learning strategies. Feature-based methods capitalize on unique features to establish correspondences across disparate viewpoints, whereas deep learning-based methodologies deploy convolutional neural networks to embed view-invariant attributes. This work also delineates the multifaceted challenges encountered in cross-view geo-localization, such as variations in viewpoints and illumination, the occurrence of occlusions, and it elucidates innovative solutions that have been formulated to tackle these issues. Furthermore, we delineate benchmark datasets and relevant evaluation metrics, and also perform a comparative analysis of state-of-the-art techniques. Finally, we conclude the paper with a discussion on prospective avenues for future research and the burgeoning applications of cross-view geo-localization in an intricately interconnected global landscape. |
2206.13734 | Dingwen Tao | Chengming Zhang, Tong Geng, Anqi Guo, Jiannan Tian, Martin Herbordt,
Ang Li, Dingwen Tao | H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP
Architecture | 8 pages, 8 figures, 4 tables, accepted by FPL'22 | null | null | null | cs.AR cs.LG | http://creativecommons.org/licenses/by/4.0/ | Graph Neural Networks (GNNs) have drawn tremendous attention due to their
unique capability to extend Machine Learning (ML) approaches to applications
broadly-defined as having unstructured data, especially graphs. Compared with
other Machine Learning (ML) modalities, the acceleration of Graph Neural
Networks (GNNs) is more challenging due to the irregularity and heterogeneity
derived from graph typologies. Existing efforts, however, have focused mainly
on handling graphs' irregularity and have not studied their heterogeneity.
To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine)
based hybrid accelerator that leverages the emerging heterogeneity of Xilinx
Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve
high-performance GNN inference. In particular, H-GCN partitions each graph into
three subgraphs based on its inherent heterogeneity, and processes them using
PL and AIE, respectively. To further improve performance, we explore the
sparsity support of AIE and develop an efficient density-aware method to
automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the
systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN
achieves, on average, speedups of 1.1~2.3X.
| [
{
"created": "Tue, 28 Jun 2022 03:37:31 GMT",
"version": "v1"
}
] | 2022-06-29 | [
[
"Zhang",
"Chengming",
""
],
[
"Geng",
"Tong",
""
],
[
"Guo",
"Anqi",
""
],
[
"Tian",
"Jiannan",
""
],
[
"Herbordt",
"Martin",
""
],
[
"Li",
"Ang",
""
],
[
"Tao",
"Dingwen",
""
]
] | Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs' irregularity and have not studied their heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine) based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity, and processes them using PL and AIE, respectively. To further improve performance, we explore the sparsity support of AIE and develop an efficient density-aware method to automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN achieves, on average, speedups of 1.1~2.3X. |
2205.00638 | Chenchen Ding | Chenchen Ding | A Two Parameters Equation for Word Rank-Frequency Relation | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Let $f (\cdot)$ be the absolute frequency of words and $r$ be the rank of
words in decreasing order of frequency, then the following function can fit the
rank-frequency relation \[ f (r;s,t) = \left(\frac{r_{\tt max}}{r}\right)^{1-s}
\left(\frac{r_{\tt max}+t \cdot r_{\tt exp}}{r+t \cdot r_{\tt
exp}}\right)^{1+(1+t)s} \] where $r_{\tt max}$ and $r_{\tt exp}$ are the
maximum and the expectation of the rank, respectively; $s>0$ and $t>0$ are
parameters estimated from data. On well-behaved data, there should be $s<1$ and
$s \cdot t < 1$.
| [
{
"created": "Mon, 2 May 2022 04:07:59 GMT",
"version": "v1"
}
] | 2022-05-03 | [
[
"Ding",
"Chenchen",
""
]
] | Let $f (\cdot)$ be the absolute frequency of words and $r$ be the rank of words in decreasing order of frequency, then the following function can fit the rank-frequency relation \[ f (r;s,t) = \left(\frac{r_{\tt max}}{r}\right)^{1-s} \left(\frac{r_{\tt max}+t \cdot r_{\tt exp}}{r+t \cdot r_{\tt exp}}\right)^{1+(1+t)s} \] where $r_{\tt max}$ and $r_{\tt exp}$ are the maximum and the expectation of the rank, respectively; $s>0$ and $t>0$ are parameters estimated from data. On well-behaved data, there should be $s<1$ and $s \cdot t < 1$. |
1301.0803 | Zhen Liu | Zhen Liu, Jia-Lin He, Jaideep Srivastava | Cliques in complex networks reveal link formation and community
evolution | null | null | null | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Missing link prediction in indirected and un-weighted network is an open and
challenge problem which has been studied intensively in recent years. In this
paper, we studied the relationships between community structure and link
formation and proposed a Fast Block probabilistic Model(FBM). In accordance
with the experiments on four real world networks, we have yielded very good
accuracy of missing link prediction and huge improvement in computing
efficiency compared to conventional methods. By analyzing the mechanism of link
formation, we also discovered that clique structure plays a significant role to
help us understand how links grow in communities. Therefore, we summarized
three principles which are proved to be able to well explain the mechanism of
link formation and network evolution from the theory of graph topology.
| [
{
"created": "Fri, 4 Jan 2013 18:56:45 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Mar 2013 00:21:17 GMT",
"version": "v2"
}
] | 2013-03-07 | [
[
"Liu",
"Zhen",
""
],
[
"He",
"Jia-Lin",
""
],
[
"Srivastava",
"Jaideep",
""
]
] | Missing link prediction in indirected and un-weighted network is an open and challenge problem which has been studied intensively in recent years. In this paper, we studied the relationships between community structure and link formation and proposed a Fast Block probabilistic Model(FBM). In accordance with the experiments on four real world networks, we have yielded very good accuracy of missing link prediction and huge improvement in computing efficiency compared to conventional methods. By analyzing the mechanism of link formation, we also discovered that clique structure plays a significant role to help us understand how links grow in communities. Therefore, we summarized three principles which are proved to be able to well explain the mechanism of link formation and network evolution from the theory of graph topology. |
1809.08372 | Matthew Valenti | Enass Hriba and Matthew C. Valenti | The Impact of Correlated Blocking on Millimeter-Wave Personal Networks | 7 pages, 8 figures, in IEEE Military Commun. Conf. (MILCOM), 2018 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to its potential to support high data rates at low latency with
reasonable interference isolation, millimeter-wave (mmWave) communications has
emerged as a promising solution for wireless personal-area networks (WPAN) and
an enabler for emerging applications such as high-resolution untethered virtual
reality. At mmWave, signals are prone to blockage by objects in the
environment, including human bodies. Most mmWave systems utilize directional
antennas in order to overcome the significant path loss. In this paper, we
consider the effects of blockage and antenna directivity on the performance of
a mmWave WPAN. Similar to related work, we assume that the interferers are in
arbitrary locations and the blockages are drawn from a random point process.
However, unlike related work that assumes independent blocking, we carefully
account for the possibility of correlated blocking, which arises when two
interferers are close to each other and therefore an obstruction that blocks
the first interferer may likely block the second interferer. Closed form
expressions for the blockage correlation coefficient and the distribution of
the SINR are provided for the case of two dominant interferers and a fixed
number of blockages drawn from a binomial point process. Finally, the effects
of antenna directivity and the spatial randomness of the interferers are taken
into account, resulting in SINR curves that fully account for correlated
blocking, which are compared against curves that neglect correlation. The
results provide insight into the validity of the commonly held assumption of
independent blocking and the improved accuracy that can be obtained when the
blocking correlation is taken into account.
| [
{
"created": "Sat, 22 Sep 2018 02:53:01 GMT",
"version": "v1"
}
] | 2018-09-25 | [
[
"Hriba",
"Enass",
""
],
[
"Valenti",
"Matthew C.",
""
]
] | Due to its potential to support high data rates at low latency with reasonable interference isolation, millimeter-wave (mmWave) communications has emerged as a promising solution for wireless personal-area networks (WPAN) and an enabler for emerging applications such as high-resolution untethered virtual reality. At mmWave, signals are prone to blockage by objects in the environment, including human bodies. Most mmWave systems utilize directional antennas in order to overcome the significant path loss. In this paper, we consider the effects of blockage and antenna directivity on the performance of a mmWave WPAN. Similar to related work, we assume that the interferers are in arbitrary locations and the blockages are drawn from a random point process. However, unlike related work that assumes independent blocking, we carefully account for the possibility of correlated blocking, which arises when two interferers are close to each other and therefore an obstruction that blocks the first interferer may likely block the second interferer. Closed form expressions for the blockage correlation coefficient and the distribution of the SINR are provided for the case of two dominant interferers and a fixed number of blockages drawn from a binomial point process. Finally, the effects of antenna directivity and the spatial randomness of the interferers are taken into account, resulting in SINR curves that fully account for correlated blocking, which are compared against curves that neglect correlation. The results provide insight into the validity of the commonly held assumption of independent blocking and the improved accuracy that can be obtained when the blocking correlation is taken into account. |
2403.01683 | Qingyao Tian | Qingyao Tian, Huai Liao, Xinyan Huang, Jian Chen, Zihui Zhang, Bingyu
Yang, Sebastien Ourselin and Hongbin Liu | DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually
Navigated Bronchoscopy | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-time 6 DOF localization of bronchoscopes is crucial for enhancing
intervention quality. However, current vision-based technologies struggle to
balance between generalization to unseen data and computational speed. In this
study, we propose a Depth-based Dual-Loop framework for real-time Visually
Navigated Bronchoscopy (DD-VNB) that can generalize across patient cases
without the need of re-training. The DD-VNB framework integrates two key
modules: depth estimation and dual-loop localization. To address the domain gap
among patients, we propose a knowledge-embedded depth estimation network that
maps endoscope frames to depth, ensuring generalization by eliminating
patient-specific textures. The network embeds view synthesis knowledge into a
cycle adversarial architecture for scale-constrained monocular depth
estimation. For real-time performance, our localization module embeds a fast
ego-motion estimation network into the loop of depth registration. The
ego-motion inference network estimates the pose change of the bronchoscope in
high frequency while depth registration against the pre-operative 3D model
provides absolute pose periodically. Specifically, the relative pose changes
are fed into the registration process as the initial guess to boost its
accuracy and speed. Experiments on phantom and in-vivo data from patients
demonstrate the effectiveness of our framework: 1) monocular depth estimation
outperforms SOTA, 2) localization achieves an accuracy of Absolute Tracking
Error (ATE) of 4.7 $\pm$ 3.17 mm in phantom and 6.49 $\pm$ 3.88 mm in patient
data, 3) with a frame-rate approaching video capture speed, 4) without the
necessity of case-wise network retraining. The framework's superior speed and
accuracy demonstrate its promising clinical potential for real-time
bronchoscopic navigation.
| [
{
"created": "Mon, 4 Mar 2024 02:29:02 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Mar 2024 07:25:48 GMT",
"version": "v2"
}
] | 2024-03-18 | [
[
"Tian",
"Qingyao",
""
],
[
"Liao",
"Huai",
""
],
[
"Huang",
"Xinyan",
""
],
[
"Chen",
"Jian",
""
],
[
"Zhang",
"Zihui",
""
],
[
"Yang",
"Bingyu",
""
],
[
"Ourselin",
"Sebastien",
""
],
[
"Liu",
"Hongbin",
""
]
] | Real-time 6 DOF localization of bronchoscopes is crucial for enhancing intervention quality. However, current vision-based technologies struggle to balance between generalization to unseen data and computational speed. In this study, we propose a Depth-based Dual-Loop framework for real-time Visually Navigated Bronchoscopy (DD-VNB) that can generalize across patient cases without the need of re-training. The DD-VNB framework integrates two key modules: depth estimation and dual-loop localization. To address the domain gap among patients, we propose a knowledge-embedded depth estimation network that maps endoscope frames to depth, ensuring generalization by eliminating patient-specific textures. The network embeds view synthesis knowledge into a cycle adversarial architecture for scale-constrained monocular depth estimation. For real-time performance, our localization module embeds a fast ego-motion estimation network into the loop of depth registration. The ego-motion inference network estimates the pose change of the bronchoscope in high frequency while depth registration against the pre-operative 3D model provides absolute pose periodically. Specifically, the relative pose changes are fed into the registration process as the initial guess to boost its accuracy and speed. Experiments on phantom and in-vivo data from patients demonstrate the effectiveness of our framework: 1) monocular depth estimation outperforms SOTA, 2) localization achieves an accuracy of Absolute Tracking Error (ATE) of 4.7 $\pm$ 3.17 mm in phantom and 6.49 $\pm$ 3.88 mm in patient data, 3) with a frame-rate approaching video capture speed, 4) without the necessity of case-wise network retraining. The framework's superior speed and accuracy demonstrate its promising clinical potential for real-time bronchoscopic navigation. |
1510.04132 | Rossi Kamal Mr | Rossi Kamal, Choong Seon Hong | Connected Big Data Measurement | null | null | null | null | cs.NI | http://creativecommons.org/licenses/by/4.0/ | In this paper, we have summarized how resilient Big Data monetization scheme
outperforms state-of-the art schemes by maintaining a balance between CDS size
and routing.
| [
{
"created": "Wed, 14 Oct 2015 14:55:41 GMT",
"version": "v1"
}
] | 2015-10-15 | [
[
"Kamal",
"Rossi",
""
],
[
"Hong",
"Choong Seon",
""
]
] | In this paper, we have summarized how resilient Big Data monetization scheme outperforms state-of-the art schemes by maintaining a balance between CDS size and routing. |
2004.09900 | Harvineet Singh | Harvineet Singh, Moumita Sinha, Atanu R. Sinha, Sahil Garg, Neha
Banerjee | An RNN-Survival Model to Decide Email Send Times | 11 pages, 3 figures, 2 tables | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Email communications are ubiquitous. Firms control send times of emails and
thereby the instants at which emails reach recipients (it is assumed email is
received instantaneously from the send time). However, they do not control the
duration it takes for recipients to open emails, labeled as time-to-open.
Importantly, among emails that are opened, most occur within a short window
from their send times. We posit that emails are likely to be opened sooner when
send times are convenient for recipients, while for other send times, emails
can get ignored. Thus, to compute appropriate send times it is important to
predict times-to-open accurately. We propose a recurrent neural network (RNN)
in a survival model framework to predict times-to-open, for each recipient.
Using that we compute appropriate send times. We experiment on a data set of
emails sent to a million customers over five months. The sequence of emails
received by a person from a sender is a result of interactions with past emails
from the sender, and hence contain useful signal that inform our model. This
sequential dependence affords our proposed RNN-Survival (RNN-S) approach to
outperform survival analysis approaches in predicting times-to-open. We show
that best times to send emails can be computed accurately from predicted
times-to-open. This approach allows a firm to tune send times of emails, which
is in its control, to favorably influence open rates and engagement.
| [
{
"created": "Tue, 21 Apr 2020 10:53:14 GMT",
"version": "v1"
}
] | 2020-04-22 | [
[
"Singh",
"Harvineet",
""
],
[
"Sinha",
"Moumita",
""
],
[
"Sinha",
"Atanu R.",
""
],
[
"Garg",
"Sahil",
""
],
[
"Banerjee",
"Neha",
""
]
] | Email communications are ubiquitous. Firms control send times of emails and thereby the instants at which emails reach recipients (it is assumed email is received instantaneously from the send time). However, they do not control the duration it takes for recipients to open emails, labeled as time-to-open. Importantly, among emails that are opened, most occur within a short window from their send times. We posit that emails are likely to be opened sooner when send times are convenient for recipients, while for other send times, emails can get ignored. Thus, to compute appropriate send times it is important to predict times-to-open accurately. We propose a recurrent neural network (RNN) in a survival model framework to predict times-to-open, for each recipient. Using that we compute appropriate send times. We experiment on a data set of emails sent to a million customers over five months. The sequence of emails received by a person from a sender is a result of interactions with past emails from the sender, and hence contain useful signal that inform our model. This sequential dependence affords our proposed RNN-Survival (RNN-S) approach to outperform survival analysis approaches in predicting times-to-open. We show that best times to send emails can be computed accurately from predicted times-to-open. This approach allows a firm to tune send times of emails, which is in its control, to favorably influence open rates and engagement. |
2012.12093 | Liangdong Lu | Liangdong Lu, Ruihu Li, Qiang Fu, Chen Xuan, Wenping Ma | Optimal Ternary Linear Complementary Dual Codes | arXiv admin note: substantial text overlap with arXiv:2010.10166 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Linear complementary dual (LCD) codes introduced by Massey are the codes
whose intersections with their dual codes are trivial. It can help to improve
the security of the information processed by sensitive devices, especially
against side-channel attacks (SCA) and fault invasive attacks. In this paper,
By construction of puncturing, extending, shortening and combination codes,
many good ternary LCD codes are presented. We give a Table 1 with the values of
$d_{LCD}(n,k)$ for length $ n \leq 20$. In addition, Many of these ternary LCD
codes given in this paper are optimal which are saturating the lower or upper
bound of Grassl's codetable in \cite{Grassl} and some of them are nearly
optimal.
| [
{
"created": "Thu, 3 Dec 2020 06:18:52 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Dec 2020 09:23:28 GMT",
"version": "v2"
}
] | 2020-12-29 | [
[
"Lu",
"Liangdong",
""
],
[
"Li",
"Ruihu",
""
],
[
"Fu",
"Qiang",
""
],
[
"Xuan",
"Chen",
""
],
[
"Ma",
"Wenping",
""
]
] | Linear complementary dual (LCD) codes introduced by Massey are the codes whose intersections with their dual codes are trivial. It can help to improve the security of the information processed by sensitive devices, especially against side-channel attacks (SCA) and fault invasive attacks. In this paper, By construction of puncturing, extending, shortening and combination codes, many good ternary LCD codes are presented. We give a Table 1 with the values of $d_{LCD}(n,k)$ for length $ n \leq 20$. In addition, Many of these ternary LCD codes given in this paper are optimal which are saturating the lower or upper bound of Grassl's codetable in \cite{Grassl} and some of them are nearly optimal. |
1903.09518 | Arthur Gaudron | Gaudron Arthur (CAOR) | Trial of an AI: Empowering people to explore law and science challenges | null | IFIM's International Journal on Law & Regulation of Artificial
Intelligence & Robotics, 2019, 1 (1) | null | null | cs.OH cs.AI cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence represents many things: a new market to conquer or a
quality label for tech companies, a threat for traditional industries, a menace
for democracy, or a blessing for our busy everyday life. The press abounds in
examples illustrating these aspects, but one should draw not hasty and
premature conclusions. The first successes in AI have been a surprise for
society at large-including researchers in the field. Today, after the initial
stupefaction, we have examples of the system reactions: traditional companies
are heavily investing in AI, social platforms are monitored during elections,
data collection is more and more regulated, etc. The resilience of an
organization (i.e. its capacity to resist to a shock) relies deeply on the
perception of its environment. Future problems have to be anticipated, while
unforeseen events occurring have to be quickly identified in order to be
mitigated as fast as possible. The author states that this clear perception
starts with a common definition of AI in terms of capacities and limits. AI
practitioners should make notions and concepts accessible to the general public
and the impacted fields (e.g. industries, law, education). It is a truism that
only law experts would have the potential to estimate IA impacts on judicial
system. However, questions remain on how to connect different kind of expertise
and what is the appropriate level of detail required for the knowledge
exchanges. And the same consideration is true for dissemination towards
society. Ultimately, society will live with decisions made by the "experts". It
sounds wise to involve society in the decision process rather than risking to
pay consequences later. Therefore, society also needs the key concepts to
understand AI impact on their life. This was the purpose of the trial of an IA
that took place in October 2018 at the Court of Appeal of Paris: gathering
experts from various fields to expose challenges in law and science towards a
general public.
| [
{
"created": "Tue, 5 Mar 2019 07:22:29 GMT",
"version": "v1"
}
] | 2019-03-25 | [
[
"Arthur",
"Gaudron",
"",
"CAOR"
]
] | Artificial Intelligence represents many things: a new market to conquer or a quality label for tech companies, a threat for traditional industries, a menace for democracy, or a blessing for our busy everyday life. The press abounds in examples illustrating these aspects, but one should draw not hasty and premature conclusions. The first successes in AI have been a surprise for society at large-including researchers in the field. Today, after the initial stupefaction, we have examples of the system reactions: traditional companies are heavily investing in AI, social platforms are monitored during elections, data collection is more and more regulated, etc. The resilience of an organization (i.e. its capacity to resist to a shock) relies deeply on the perception of its environment. Future problems have to be anticipated, while unforeseen events occurring have to be quickly identified in order to be mitigated as fast as possible. The author states that this clear perception starts with a common definition of AI in terms of capacities and limits. AI practitioners should make notions and concepts accessible to the general public and the impacted fields (e.g. industries, law, education). It is a truism that only law experts would have the potential to estimate IA impacts on judicial system. However, questions remain on how to connect different kind of expertise and what is the appropriate level of detail required for the knowledge exchanges. And the same consideration is true for dissemination towards society. Ultimately, society will live with decisions made by the "experts". It sounds wise to involve society in the decision process rather than risking to pay consequences later. Therefore, society also needs the key concepts to understand AI impact on their life. This was the purpose of the trial of an IA that took place in October 2018 at the Court of Appeal of Paris: gathering experts from various fields to expose challenges in law and science towards a general public. |
2209.02270 | F. Serhan Dani\c{s} | F. Serhan Dani\c{s}, A. Teoman Naskali, A. Taylan Cemgil, Cem Ersoy | An Indoor Localization Dataset and Data Collection Framework with High
Precision Position Annotation | 30 pages | F. Serhan Dani\c{s}, A. Teoman Naskali, A. Taylan Cemgil, Cem
Ersoy, "An indoor localization dataset and data collection framework with
high precision position annotation", Pervasive and Mobile Computing, Volume
81, 101554, 2022 | 10.1016/j.pmcj.2022.101554 | null | cs.LG cs.CV cs.NI cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | We introduce a novel technique and an associated high resolution dataset that
aims to precisely evaluate wireless signal based indoor positioning algorithms.
The technique implements an augmented reality (AR) based positioning system
that is used to annotate the wireless signal parameter data samples with high
precision position data. We track the position of a practical and low cost
navigable setup of cameras and a Bluetooth Low Energy (BLE) beacon in an area
decorated with AR markers. We maximize the performance of the AR-based
localization by using a redundant number of markers. Video streams captured by
the cameras are subjected to a series of marker recognition, subset selection
and filtering operations to yield highly precise pose estimations. Our results
show that we can reduce the positional error of the AR localization system to a
rate under 0.05 meters. The position data are then used to annotate the BLE
data that are captured simultaneously by the sensors stationed in the
environment, hence, constructing a wireless signal data set with the ground
truth, which allows a wireless signal based localization system to be evaluated
accurately.
| [
{
"created": "Tue, 6 Sep 2022 07:41:11 GMT",
"version": "v1"
}
] | 2022-09-09 | [
[
"Daniş",
"F. Serhan",
""
],
[
"Naskali",
"A. Teoman",
""
],
[
"Cemgil",
"A. Taylan",
""
],
[
"Ersoy",
"Cem",
""
]
] | We introduce a novel technique and an associated high resolution dataset that aims to precisely evaluate wireless signal based indoor positioning algorithms. The technique implements an augmented reality (AR) based positioning system that is used to annotate the wireless signal parameter data samples with high precision position data. We track the position of a practical and low cost navigable setup of cameras and a Bluetooth Low Energy (BLE) beacon in an area decorated with AR markers. We maximize the performance of the AR-based localization by using a redundant number of markers. Video streams captured by the cameras are subjected to a series of marker recognition, subset selection and filtering operations to yield highly precise pose estimations. Our results show that we can reduce the positional error of the AR localization system to a rate under 0.05 meters. The position data are then used to annotate the BLE data that are captured simultaneously by the sensors stationed in the environment, hence, constructing a wireless signal data set with the ground truth, which allows a wireless signal based localization system to be evaluated accurately. |
cs/0012022 | Neil J. Gunther | Neil J. Gunther | Performance and Scalability Models for a Hypergrowth e-Commerce Web Site | 15 pages; To appear in the book entitled "Performance Engineering -
State of the Art and Current Trends," Lecture Notes in Computer Science,
Springer-Verlag Heidelberg, 2001 | null | null | null | cs.PF cs.DC cs.SE | null | The performance of successful Web-based e-commerce services has all the
allure of a roller-coaster ride: accelerated fiscal growth combined with the
ever-present danger of running out of server capacity. This chapter presents a
case study based on the author's own capacity planning engagement with one of
the hottest e-commerce Web sites in the world. Several spreadsheet techniques
are presented for forecasting both short-term and long-term trends in the
consumption of server capacity. Two new performance metrics are introduced for
site planning and procurement: the effective demand, and the doubling period.
| [
{
"created": "Tue, 26 Dec 2000 22:42:39 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Gunther",
"Neil J.",
""
]
] | The performance of successful Web-based e-commerce services has all the allure of a roller-coaster ride: accelerated fiscal growth combined with the ever-present danger of running out of server capacity. This chapter presents a case study based on the author's own capacity planning engagement with one of the hottest e-commerce Web sites in the world. Several spreadsheet techniques are presented for forecasting both short-term and long-term trends in the consumption of server capacity. Two new performance metrics are introduced for site planning and procurement: the effective demand, and the doubling period. |
2108.11204 | Lukasz Kucinski | Konrad Czechowski, Tomasz Odrzyg\'o\'zd\'z, Marek Zbysi\'nski,
Micha{\l} Zawalski, Krzysztof Olejnik, Yuhuai Wu, {\L}ukasz Kuci\'nski, Piotr
Mi{\l}o\'s | Subgoal Search For Complex Reasoning Tasks | NeurIPS 2021 | null | null | null | cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Humans excel in solving complex reasoning tasks through a mental process of
moving from one idea to a related one. Inspired by this, we propose Subgoal
Search (kSubS) method. Its key component is a learned subgoal generator that
produces a diversity of subgoals that are both achievable and closer to the
solution. Using subgoals reduces the search space and induces a high-level
search graph suitable for efficient planning. In this paper, we implement kSubS
using a transformer-based subgoal module coupled with the classical best-first
search framework. We show that a simple approach of generating $k$-th step
ahead subgoals is surprisingly efficient on three challenging domains: two
popular puzzle games, Sokoban and the Rubik's Cube, and an inequality proving
benchmark INT. kSubS achieves strong results including state-of-the-art on INT
within a modest computational budget.
| [
{
"created": "Wed, 25 Aug 2021 12:40:04 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Oct 2021 15:35:09 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Apr 2024 15:35:04 GMT",
"version": "v3"
}
] | 2024-04-04 | [
[
"Czechowski",
"Konrad",
""
],
[
"Odrzygóźdź",
"Tomasz",
""
],
[
"Zbysiński",
"Marek",
""
],
[
"Zawalski",
"Michał",
""
],
[
"Olejnik",
"Krzysztof",
""
],
[
"Wu",
"Yuhuai",
""
],
[
"Kuciński",
"Łukasz",
""
],
[
"Miłoś",
"Piotr",
""
]
] | Humans excel in solving complex reasoning tasks through a mental process of moving from one idea to a related one. Inspired by this, we propose Subgoal Search (kSubS) method. Its key component is a learned subgoal generator that produces a diversity of subgoals that are both achievable and closer to the solution. Using subgoals reduces the search space and induces a high-level search graph suitable for efficient planning. In this paper, we implement kSubS using a transformer-based subgoal module coupled with the classical best-first search framework. We show that a simple approach of generating $k$-th step ahead subgoals is surprisingly efficient on three challenging domains: two popular puzzle games, Sokoban and the Rubik's Cube, and an inequality proving benchmark INT. kSubS achieves strong results including state-of-the-art on INT within a modest computational budget. |
2104.08450 | Xiyun Li | Xiyun Li and Yong Xu and Meng Yu and Shi-Xiong Zhang and Jiaming Xu
and Bo Xu and Dong Yu | MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation | null | null | null | null | cs.SD cs.AI eess.AS | http://creativecommons.org/licenses/by/4.0/ | Recently, our proposed recurrent neural network (RNN) based all deep learning
minimum variance distortionless response (ADL-MVDR) beamformer method yielded
superior performance over the conventional MVDR by replacing the matrix
inversion and eigenvalue decomposition with two recurrent neural networks. In
this work, we present a self-attentive RNN beamformer to further improve our
previous RNN-based beamformer by leveraging on the powerful modeling capability
of self-attention. Temporal-spatial self-attention module is proposed to better
learn the beamforming weights from the speech and noise spatial covariance
matrices. The temporal self-attention module could help RNN to learn global
statistics of covariance matrices. The spatial self-attention module is
designed to attend on the cross-channel correlation in the covariance matrices.
Furthermore, a multi-channel input with multi-speaker directional features and
multi-speaker speech separation outputs (MIMO) model is developed to improve
the inference efficiency. The evaluations demonstrate that our proposed MIMO
self-attentive RNN beamformer improves both the automatic speech recognition
(ASR) accuracy and the perceptual estimation of speech quality (PESQ) against
prior arts.
| [
{
"created": "Sat, 17 Apr 2021 05:02:04 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Apr 2021 08:18:36 GMT",
"version": "v2"
}
] | 2021-04-27 | [
[
"Li",
"Xiyun",
""
],
[
"Xu",
"Yong",
""
],
[
"Yu",
"Meng",
""
],
[
"Zhang",
"Shi-Xiong",
""
],
[
"Xu",
"Jiaming",
""
],
[
"Xu",
"Bo",
""
],
[
"Yu",
"Dong",
""
]
] | Recently, our proposed recurrent neural network (RNN) based all deep learning minimum variance distortionless response (ADL-MVDR) beamformer method yielded superior performance over the conventional MVDR by replacing the matrix inversion and eigenvalue decomposition with two recurrent neural networks. In this work, we present a self-attentive RNN beamformer to further improve our previous RNN-based beamformer by leveraging on the powerful modeling capability of self-attention. Temporal-spatial self-attention module is proposed to better learn the beamforming weights from the speech and noise spatial covariance matrices. The temporal self-attention module could help RNN to learn global statistics of covariance matrices. The spatial self-attention module is designed to attend on the cross-channel correlation in the covariance matrices. Furthermore, a multi-channel input with multi-speaker directional features and multi-speaker speech separation outputs (MIMO) model is developed to improve the inference efficiency. The evaluations demonstrate that our proposed MIMO self-attentive RNN beamformer improves both the automatic speech recognition (ASR) accuracy and the perceptual estimation of speech quality (PESQ) against prior arts. |
1601.01298 | Hamideh Vosoughpour Yazdchi | Anna Lubiw, Jack Snoeyink, Hamideh Vosoughpour | Visibility Graphs, Dismantlability, and the Cops and Robbers Game | 23 pages | null | null | null | cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study versions of cop and robber pursuit-evasion games on the visibility
graphs of polygons, and inside polygons with straight and curved sides. Each
player has full information about the other player's location, players take
turns, and the robber is captured when the cop arrives at the same point as the
robber. In visibility graphs we show the cop can always win because visibility
graphs are dismantlable, which is interesting as one of the few results
relating visibility graphs to other known graph classes. We extend this to show
that the cop wins games in which players move along straight line segments
inside any polygon and, more generally, inside any simply connected planar
region with a reasonable boundary. Essentially, our problem is a type of
pursuit-evasion using the link metric rather than the Euclidean metric, and our
result provides an interesting class of infinite cop-win graphs.
| [
{
"created": "Wed, 6 Jan 2016 20:26:31 GMT",
"version": "v1"
}
] | 2016-01-07 | [
[
"Lubiw",
"Anna",
""
],
[
"Snoeyink",
"Jack",
""
],
[
"Vosoughpour",
"Hamideh",
""
]
] | We study versions of cop and robber pursuit-evasion games on the visibility graphs of polygons, and inside polygons with straight and curved sides. Each player has full information about the other player's location, players take turns, and the robber is captured when the cop arrives at the same point as the robber. In visibility graphs we show the cop can always win because visibility graphs are dismantlable, which is interesting as one of the few results relating visibility graphs to other known graph classes. We extend this to show that the cop wins games in which players move along straight line segments inside any polygon and, more generally, inside any simply connected planar region with a reasonable boundary. Essentially, our problem is a type of pursuit-evasion using the link metric rather than the Euclidean metric, and our result provides an interesting class of infinite cop-win graphs. |
2202.09559 | Zhengqing Miao | Zhengqing Miao, Xin Zhang, Carlo Menon, Yelong Zheng, Meirong Zhao,
Dong Ming | Priming Cross-Session Motor Imagery Classification with A Universal Deep
Domain Adaptation Framework | 17 pages, 5figures | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motor imagery (MI) is a common brain computer interface (BCI) paradigm. EEG
is non-stationary with low signal-to-noise, classifying motor imagery tasks of
the same participant from different EEG recording sessions is generally
challenging, as EEG data distribution may vary tremendously among different
acquisition sessions. Although it is intuitive to consider the cross-session MI
classification as a domain adaptation problem, the rationale and feasible
approach is not elucidated. In this paper, we propose a Siamese deep domain
adaptation (SDDA) framework for cross-session MI classification based on
mathematical models in domain adaptation theory. The proposed framework can be
easily applied to most existing artificial neural networks without altering the
network structure, which facilitates our method with great flexibility and
transferability. In the proposed framework, domain invariants were firstly
constructed jointly with channel normalization and Euclidean alignment. Then,
embedding features from source and target domain were mapped into the
Reproducing Kernel Hilbert Space (RKHS) and aligned accordingly. A cosine-based
center loss was also integrated into the framework to improve the
generalizability of the SDDA. The proposed framework was validated with two
classic and popular convolutional neural networks from BCI research field
(EEGNet and ConvNet) in two MI-EEG public datasets (BCI Competition IV IIA,
IIB). Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework
was able to boost the MI classification accuracy by 15.2%, 10.2% respectively
in IIA dataset, and 5.5%, 4.2% in IIB dataset. The final MI classification
accuracy reached 82.01% in IIA dataset and 87.52% in IIB, which outperformed
the state-of-the-art methods in the literature.
| [
{
"created": "Sat, 19 Feb 2022 09:30:08 GMT",
"version": "v1"
},
{
"created": "Wed, 26 Jul 2023 01:36:38 GMT",
"version": "v2"
}
] | 2023-07-27 | [
[
"Miao",
"Zhengqing",
""
],
[
"Zhang",
"Xin",
""
],
[
"Menon",
"Carlo",
""
],
[
"Zheng",
"Yelong",
""
],
[
"Zhao",
"Meirong",
""
],
[
"Ming",
"Dong",
""
]
] | Motor imagery (MI) is a common brain computer interface (BCI) paradigm. EEG is non-stationary with low signal-to-noise, classifying motor imagery tasks of the same participant from different EEG recording sessions is generally challenging, as EEG data distribution may vary tremendously among different acquisition sessions. Although it is intuitive to consider the cross-session MI classification as a domain adaptation problem, the rationale and feasible approach is not elucidated. In this paper, we propose a Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory. The proposed framework can be easily applied to most existing artificial neural networks without altering the network structure, which facilitates our method with great flexibility and transferability. In the proposed framework, domain invariants were firstly constructed jointly with channel normalization and Euclidean alignment. Then, embedding features from source and target domain were mapped into the Reproducing Kernel Hilbert Space (RKHS) and aligned accordingly. A cosine-based center loss was also integrated into the framework to improve the generalizability of the SDDA. The proposed framework was validated with two classic and popular convolutional neural networks from BCI research field (EEGNet and ConvNet) in two MI-EEG public datasets (BCI Competition IV IIA, IIB). Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15.2%, 10.2% respectively in IIA dataset, and 5.5%, 4.2% in IIB dataset. The final MI classification accuracy reached 82.01% in IIA dataset and 87.52% in IIB, which outperformed the state-of-the-art methods in the literature. |
2403.05399 | Daniele Meli | Cristian Morasso, Daniele Meli, Yann Divet, Salvatore Sessa,
Alessandro Farinelli | Planning and Inverse Kinematics of Hyper-Redundant Manipulators with
VO-FABRIK | In publication in Springer Proceedings for the European Robotics
Forum 2024 | null | null | null | cs.RO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Hyper-redundant Robotic Manipulators (HRMs) offer great dexterity and
flexibility of operation, but solving Inverse Kinematics (IK) is challenging.
In this work, we introduce VO-FABRIK, an algorithm combining Forward and
Backward Reaching Inverse Kinematics (FABRIK) for repeatable deterministic IK
computation, and an approach inspired from velocity obstacles to perform path
planning under collision and joint limits constraints. We show preliminary
results on an industrial HRM with 19 actuated joints. Our algorithm achieves
good performance where a state-of-the-art IK solver fails.
| [
{
"created": "Fri, 8 Mar 2024 15:53:03 GMT",
"version": "v1"
}
] | 2024-03-11 | [
[
"Morasso",
"Cristian",
""
],
[
"Meli",
"Daniele",
""
],
[
"Divet",
"Yann",
""
],
[
"Sessa",
"Salvatore",
""
],
[
"Farinelli",
"Alessandro",
""
]
] | Hyper-redundant Robotic Manipulators (HRMs) offer great dexterity and flexibility of operation, but solving Inverse Kinematics (IK) is challenging. In this work, we introduce VO-FABRIK, an algorithm combining Forward and Backward Reaching Inverse Kinematics (FABRIK) for repeatable deterministic IK computation, and an approach inspired from velocity obstacles to perform path planning under collision and joint limits constraints. We show preliminary results on an industrial HRM with 19 actuated joints. Our algorithm achieves good performance where a state-of-the-art IK solver fails. |
1911.00399 | Joshua Chen | Joshua Chen | An Implementation of Homotopy Type Theory in Isabelle/Pure | Masters thesis | null | null | null | cs.LO math.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this Masters thesis we present an implementation of a fragment of "book
HoTT" as an object logic for the interactive proof assistant Isabelle. We also
give a mathematical description of the underlying theory of the Isabelle/Pure
logical framework, and discuss various issues and design decisions that arise
when attempting to encode intensional dependent type theory with universes
inside a simple type-theoretic logical foundation.
| [
{
"created": "Thu, 31 Oct 2019 14:46:31 GMT",
"version": "v1"
}
] | 2019-11-04 | [
[
"Chen",
"Joshua",
""
]
] | In this Masters thesis we present an implementation of a fragment of "book HoTT" as an object logic for the interactive proof assistant Isabelle. We also give a mathematical description of the underlying theory of the Isabelle/Pure logical framework, and discuss various issues and design decisions that arise when attempting to encode intensional dependent type theory with universes inside a simple type-theoretic logical foundation. |
2112.03340 | Yiren Jian | Yiren Jian, Lorenzo Torresani | Label Hallucination for Few-Shot Classification | Accepted by AAAI 2022. Code is available:
https://github.com/yiren-jian/LabelHalluc | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Few-shot classification requires adapting knowledge learned from a large
annotated base dataset to recognize novel unseen classes, each represented by
few labeled examples. In such a scenario, pretraining a network with high
capacity on the large dataset and then finetuning it on the few examples causes
severe overfitting. At the same time, training a simple linear classifier on
top of "frozen" features learned from the large labeled dataset fails to adapt
the model to the properties of the novel classes, effectively inducing
underfitting. In this paper we propose an alternative approach to both of these
two popular strategies. First, our method pseudo-labels the entire large
dataset using the linear classifier trained on the novel classes. This
effectively "hallucinates" the novel classes in the large dataset, despite the
novel categories not being present in the base database (novel and base classes
are disjoint). Then, it finetunes the entire model with a distillation loss on
the pseudo-labeled base examples, in addition to the standard cross-entropy
loss on the novel dataset. This step effectively trains the network to
recognize contextual and appearance cues that are useful for the novel-category
recognition but using the entire large-scale base dataset and thus overcoming
the inherent data-scarcity problem of few-shot learning. Despite the simplicity
of the approach, we show that that our method outperforms the state-of-the-art
on four well-established few-shot classification benchmarks.
| [
{
"created": "Mon, 6 Dec 2021 20:18:41 GMT",
"version": "v1"
}
] | 2021-12-08 | [
[
"Jian",
"Yiren",
""
],
[
"Torresani",
"Lorenzo",
""
]
] | Few-shot classification requires adapting knowledge learned from a large annotated base dataset to recognize novel unseen classes, each represented by few labeled examples. In such a scenario, pretraining a network with high capacity on the large dataset and then finetuning it on the few examples causes severe overfitting. At the same time, training a simple linear classifier on top of "frozen" features learned from the large labeled dataset fails to adapt the model to the properties of the novel classes, effectively inducing underfitting. In this paper we propose an alternative approach to both of these two popular strategies. First, our method pseudo-labels the entire large dataset using the linear classifier trained on the novel classes. This effectively "hallucinates" the novel classes in the large dataset, despite the novel categories not being present in the base database (novel and base classes are disjoint). Then, it finetunes the entire model with a distillation loss on the pseudo-labeled base examples, in addition to the standard cross-entropy loss on the novel dataset. This step effectively trains the network to recognize contextual and appearance cues that are useful for the novel-category recognition but using the entire large-scale base dataset and thus overcoming the inherent data-scarcity problem of few-shot learning. Despite the simplicity of the approach, we show that that our method outperforms the state-of-the-art on four well-established few-shot classification benchmarks. |
2408.01269 | Lutao Jiang | Lutao Jiang, Hangyu Li and Lin Wang | A General Framework to Boost 3D GS Initialization for Text-to-3D
Generation by Lexical Richness | null | ACM MM 2024 | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Text-to-3D content creation has recently received much attention, especially
with the prevalence of 3D Gaussians Splatting. In general, GS-based methods
comprise two key stages: initialization and rendering optimization. To achieve
initialization, existing works directly apply random sphere initialization or
3D diffusion models, e.g., Point-E, to derive the initial shapes. However, such
strategies suffer from two critical yet challenging problems: 1) the final
shapes are still similar to the initial ones even after training; 2) shapes can
be produced only from simple texts, e.g., "a dog", not for lexically richer
texts, e.g., "a dog is sitting on the top of the airplane". To address these
problems, this paper proposes a novel general framework to boost the 3D GS
Initialization for text-to-3D generation upon the lexical richness. Our key
idea is to aggregate 3D Gaussians into spatially uniform voxels to represent
complex shapes while enabling the spatial interaction among the 3D Gaussians
and semantic interaction between Gaussians and texts. Specifically, we first
construct a voxelized representation, where each voxel holds a 3D Gaussian with
its position, scale, and rotation fixed while setting opacity as the sole
factor to determine a position's occupancy. We then design an initialization
network mainly consisting of two novel components: 1) Global Information
Perception (GIP) block and 2) Gaussians-Text Fusion (GTF) block. Such a design
enables each 3D Gaussian to assimilate the spatial information from other areas
and semantic information from texts. Extensive experiments show the superiority
of our framework of high-quality 3D GS initialization against the existing
methods, e.g., Shap-E, by taking lexically simple, medium, and hard texts.
Also, our framework can be seamlessly plugged into SoTA training frameworks,
e.g., LucidDreamer, for semantically consistent text-to-3D generation.
| [
{
"created": "Fri, 2 Aug 2024 13:46:15 GMT",
"version": "v1"
}
] | 2024-08-05 | [
[
"Jiang",
"Lutao",
""
],
[
"Li",
"Hangyu",
""
],
[
"Wang",
"Lin",
""
]
] | Text-to-3D content creation has recently received much attention, especially with the prevalence of 3D Gaussians Splatting. In general, GS-based methods comprise two key stages: initialization and rendering optimization. To achieve initialization, existing works directly apply random sphere initialization or 3D diffusion models, e.g., Point-E, to derive the initial shapes. However, such strategies suffer from two critical yet challenging problems: 1) the final shapes are still similar to the initial ones even after training; 2) shapes can be produced only from simple texts, e.g., "a dog", not for lexically richer texts, e.g., "a dog is sitting on the top of the airplane". To address these problems, this paper proposes a novel general framework to boost the 3D GS Initialization for text-to-3D generation upon the lexical richness. Our key idea is to aggregate 3D Gaussians into spatially uniform voxels to represent complex shapes while enabling the spatial interaction among the 3D Gaussians and semantic interaction between Gaussians and texts. Specifically, we first construct a voxelized representation, where each voxel holds a 3D Gaussian with its position, scale, and rotation fixed while setting opacity as the sole factor to determine a position's occupancy. We then design an initialization network mainly consisting of two novel components: 1) Global Information Perception (GIP) block and 2) Gaussians-Text Fusion (GTF) block. Such a design enables each 3D Gaussian to assimilate the spatial information from other areas and semantic information from texts. Extensive experiments show the superiority of our framework of high-quality 3D GS initialization against the existing methods, e.g., Shap-E, by taking lexically simple, medium, and hard texts. Also, our framework can be seamlessly plugged into SoTA training frameworks, e.g., LucidDreamer, for semantically consistent text-to-3D generation. |
2003.05785 | Davoud Mougouei | Davoud Mougouei and David M W Powers | Dependency-Aware Software Requirements Selection using Fuzzy Graphs and
Integer Programming | arXiv admin note: text overlap with arXiv:2003.04806 | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Software requirements selection aims to find an optimal subset of the
requirements with the highest value while respecting the project constraints.
But the value of a requirement may depend on the presence or absence of other
requirements in the optimal subset. Such Value Dependencies, however, are
imprecise and hard to capture. In this paper, we propose a method based on
integer programming and fuzzy graphs to account for value dependencies and
their imprecision in software requirements selection. The proposed method,
referred to as Dependency-Aware Software Requirements Selection (DARS), is
comprised of three components: (i) an automated technique for the
identification of value dependencies from user preferences, (ii) a modeling
technique based on fuzzy graphs that allows for capturing the imprecision of
value dependencies, and (iii) an Integer Linear Programming (ILP) model that
takes into account user preferences and value dependencies identified from
those preferences to reduce the risk of value loss in software projects. Our
work is verified by studying a real-world software project. The results show
that our proposed method reduces the value loss in software projects and is
scalable to large requirement sets.
| [
{
"created": "Wed, 11 Mar 2020 02:09:34 GMT",
"version": "v1"
}
] | 2020-03-13 | [
[
"Mougouei",
"Davoud",
""
],
[
"Powers",
"David M W",
""
]
] | Software requirements selection aims to find an optimal subset of the requirements with the highest value while respecting the project constraints. But the value of a requirement may depend on the presence or absence of other requirements in the optimal subset. Such Value Dependencies, however, are imprecise and hard to capture. In this paper, we propose a method based on integer programming and fuzzy graphs to account for value dependencies and their imprecision in software requirements selection. The proposed method, referred to as Dependency-Aware Software Requirements Selection (DARS), is comprised of three components: (i) an automated technique for the identification of value dependencies from user preferences, (ii) a modeling technique based on fuzzy graphs that allows for capturing the imprecision of value dependencies, and (iii) an Integer Linear Programming (ILP) model that takes into account user preferences and value dependencies identified from those preferences to reduce the risk of value loss in software projects. Our work is verified by studying a real-world software project. The results show that our proposed method reduces the value loss in software projects and is scalable to large requirement sets. |
1106.0669 | M. L. Ginsberg | M. L. Ginsberg | GIB: Imperfect Information in a Computationally Challenging Game | null | Journal Of Artificial Intelligence Research, Volume 14, pages
303-358, 2001 | 10.1613/jair.820 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates the problems arising in the construction of a program
to play the game of contract bridge. These problems include both the difficulty
of solving the game's perfect information variant, and techniques needed to
address the fact that bridge is not, in fact, a perfect information game. GIB,
the program being described, involves five separate technical advances:
partition search, the practical application of Monte Carlo techniques to
realistic problems, a focus on achievable sets to solve problems inherent in
the Monte Carlo approach, an extension of alpha-beta pruning from total orders
to arbitrary distributive lattices, and the use of squeaky wheel optimization
to find approximately optimal solutions to cardplay problems. GIB is currently
believed to be of approximately expert caliber, and is currently the strongest
computer bridge program in the world.
| [
{
"created": "Fri, 3 Jun 2011 14:53:55 GMT",
"version": "v1"
}
] | 2011-06-06 | [
[
"Ginsberg",
"M. L.",
""
]
] | This paper investigates the problems arising in the construction of a program to play the game of contract bridge. These problems include both the difficulty of solving the game's perfect information variant, and techniques needed to address the fact that bridge is not, in fact, a perfect information game. GIB, the program being described, involves five separate technical advances: partition search, the practical application of Monte Carlo techniques to realistic problems, a focus on achievable sets to solve problems inherent in the Monte Carlo approach, an extension of alpha-beta pruning from total orders to arbitrary distributive lattices, and the use of squeaky wheel optimization to find approximately optimal solutions to cardplay problems. GIB is currently believed to be of approximately expert caliber, and is currently the strongest computer bridge program in the world. |
2009.10515 | Kostas Kolomvatsos | Panagiotis Oikonomou, Kostas Kolomvatsos, Nikos Tziritas, Georgios
Theodoropoulos, Thanasis Loukopoulos, Georgios Stamoulis | A Fuzzy Logic Controller for Tasks Scheduling Using Unreliable Cloud
Resources | null | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Cloud infrastructure offers to end users a broad set of heterogenous
computational resources using the pay-as-you-go model. These virtualized
resources can be provisioned using different pricing models like the unreliable
model where resources are provided at a fraction of the cost but with no
guarantee for an uninterrupted processing. However, the enormous gamut of
opportunities comes with a great caveat as resource management and scheduling
decisions are increasingly complicated. Moreover, the presented uncertainty in
optimally selecting resources has also a negatively impact on the quality of
solutions delivered by scheduling algorithms. In this paper, we present a
dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS
algorithm) for the management of scientific workflows in Cloud. Our model
minimizes both the makespan and the monetary cost by dynamically selecting
reliable or unreliable virtualized resources. For covering the uncertainty in
decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing
model of the resources that will host every task. We evaluate the performance
of the proposed algorithm using real workflow applications being tested under
the assumption of different probabilities regarding the revocation of
unreliable resources. Numerical results depict the performance of the proposed
approach and a comparative assessment reveals the position of the paper in the
relevant literature.
| [
{
"created": "Tue, 22 Sep 2020 13:15:19 GMT",
"version": "v1"
}
] | 2020-09-23 | [
[
"Oikonomou",
"Panagiotis",
""
],
[
"Kolomvatsos",
"Kostas",
""
],
[
"Tziritas",
"Nikos",
""
],
[
"Theodoropoulos",
"Georgios",
""
],
[
"Loukopoulos",
"Thanasis",
""
],
[
"Stamoulis",
"Georgios",
""
]
] | The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature. |
1005.0052 | Byung-Hak Kim | Byung-Hak Kim and Henry D. Pfister | On the Joint Decoding of LDPC Codes and Finite-State Channels via Linear
Programming | To appear in Proc. 2010 IEEE Int. Symp. Information Theory, Ausin,
TX, June 12-18, 2010 (a small error in the reference corrected) | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, the linear programming (LP) decoder for binary linear codes,
introduced by Feldman, et al. is extended to joint-decoding of binary-input
finite-state channels. In particular, we provide a rigorous definition of LP
joint-decoding pseudo-codewords (JD-PCWs) that enables evaluation of the
pairwise error probability between codewords and JD-PCWs. This leads naturally
to a provable upper bound on decoder failure probability. If the channel is a
finite-state intersymbol interference channel, then the LP joint decoder also
has the maximum-likelihood (ML) certificate property and all integer valued
solutions are codewords. In this case, the performance loss relative to ML
decoding can be explained completely by fractional valued JD-PCWs.
| [
{
"created": "Sat, 1 May 2010 07:30:55 GMT",
"version": "v1"
},
{
"created": "Fri, 7 May 2010 19:32:31 GMT",
"version": "v2"
},
{
"created": "Mon, 7 Jun 2010 20:42:05 GMT",
"version": "v3"
}
] | 2010-06-09 | [
[
"Kim",
"Byung-Hak",
""
],
[
"Pfister",
"Henry D.",
""
]
] | In this paper, the linear programming (LP) decoder for binary linear codes, introduced by Feldman, et al. is extended to joint-decoding of binary-input finite-state channels. In particular, we provide a rigorous definition of LP joint-decoding pseudo-codewords (JD-PCWs) that enables evaluation of the pairwise error probability between codewords and JD-PCWs. This leads naturally to a provable upper bound on decoder failure probability. If the channel is a finite-state intersymbol interference channel, then the LP joint decoder also has the maximum-likelihood (ML) certificate property and all integer valued solutions are codewords. In this case, the performance loss relative to ML decoding can be explained completely by fractional valued JD-PCWs. |
2310.13098 | Piotr Gramacki | Piotr Gramacki, Kacper Le\'sniara, Kamil Raczycki, Szymon Wo\'zniak,
Marcin Przymus, Piotr Szyma\'nski | SRAI: Towards Standardization of Geospatial AI | Accepted for the 6th ACM SIGSPATIAL International Workshop on AI for
Geographic Knowledge Discovery (GeoAI 2023) | null | 10.1145/3615886.3627740 | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Spatial Representations for Artificial Intelligence (srai) is a Python
library for working with geospatial data. The library can download geospatial
data, split a given area into micro-regions using multiple algorithms and train
an embedding model using various architectures. It includes baseline models as
well as more complex methods from published works. Those capabilities make it
possible to use srai in a complete pipeline for geospatial task solving. The
proposed library is the first step to standardize the geospatial AI domain
toolset. It is fully open-source and published under Apache 2.0 licence.
| [
{
"created": "Thu, 19 Oct 2023 18:56:04 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Oct 2023 15:03:50 GMT",
"version": "v2"
}
] | 2023-11-22 | [
[
"Gramacki",
"Piotr",
""
],
[
"Leśniara",
"Kacper",
""
],
[
"Raczycki",
"Kamil",
""
],
[
"Woźniak",
"Szymon",
""
],
[
"Przymus",
"Marcin",
""
],
[
"Szymański",
"Piotr",
""
]
] | Spatial Representations for Artificial Intelligence (srai) is a Python library for working with geospatial data. The library can download geospatial data, split a given area into micro-regions using multiple algorithms and train an embedding model using various architectures. It includes baseline models as well as more complex methods from published works. Those capabilities make it possible to use srai in a complete pipeline for geospatial task solving. The proposed library is the first step to standardize the geospatial AI domain toolset. It is fully open-source and published under Apache 2.0 licence. |
2203.04142 | Latif Salum | Latif Salum | A Reply to "On Salum's Algorithm for X3SAT" | null | null | null | null | cs.CC | http://creativecommons.org/licenses/by/4.0/ | This paper is a reply to "On Salum's Algorithm for X3SAT" (arXiv:2104.02886)
| [
{
"created": "Mon, 6 Dec 2021 11:46:20 GMT",
"version": "v1"
},
{
"created": "Wed, 9 Mar 2022 13:21:28 GMT",
"version": "v2"
}
] | 2022-03-10 | [
[
"Salum",
"Latif",
""
]
] | This paper is a reply to "On Salum's Algorithm for X3SAT" (arXiv:2104.02886) |
2308.03463 | Zhongjie Duan | Zhongjie Duan, Lizhou You, Chengyu Wang, Cen Chen, Ziheng Wu, Weining
Qian, Jun Huang | DiffSynth: Latent In-Iteration Deflickering for Realistic Video
Synthesis | 9 pages, 6 figures | null | null | null | cs.CV cs.MM | http://creativecommons.org/licenses/by/4.0/ | In recent years, diffusion models have emerged as the most powerful approach
in image synthesis. However, applying these models directly to video synthesis
presents challenges, as it often leads to noticeable flickering contents.
Although recently proposed zero-shot methods can alleviate flicker to some
extent, we still struggle to generate coherent videos. In this paper, we
propose DiffSynth, a novel approach that aims to convert image synthesis
pipelines to video synthesis pipelines. DiffSynth consists of two key
components: a latent in-iteration deflickering framework and a video
deflickering algorithm. The latent in-iteration deflickering framework applies
video deflickering to the latent space of diffusion models, effectively
preventing flicker accumulation in intermediate steps. Additionally, we propose
a video deflickering algorithm, named patch blending algorithm, that remaps
objects in different frames and blends them together to enhance video
consistency. One of the notable advantages of DiffSynth is its general
applicability to various video synthesis tasks, including text-guided video
stylization, fashion video synthesis, image-guided video stylization, video
restoring, and 3D rendering. In the task of text-guided video stylization, we
make it possible to synthesize high-quality videos without cherry-picking. The
experimental results demonstrate the effectiveness of DiffSynth. All videos can
be viewed on our project page. Source codes will also be released.
| [
{
"created": "Mon, 7 Aug 2023 10:41:52 GMT",
"version": "v1"
},
{
"created": "Tue, 8 Aug 2023 07:54:55 GMT",
"version": "v2"
},
{
"created": "Thu, 10 Aug 2023 02:26:16 GMT",
"version": "v3"
}
] | 2023-08-11 | [
[
"Duan",
"Zhongjie",
""
],
[
"You",
"Lizhou",
""
],
[
"Wang",
"Chengyu",
""
],
[
"Chen",
"Cen",
""
],
[
"Wu",
"Ziheng",
""
],
[
"Qian",
"Weining",
""
],
[
"Huang",
"Jun",
""
]
] | In recent years, diffusion models have emerged as the most powerful approach in image synthesis. However, applying these models directly to video synthesis presents challenges, as it often leads to noticeable flickering contents. Although recently proposed zero-shot methods can alleviate flicker to some extent, we still struggle to generate coherent videos. In this paper, we propose DiffSynth, a novel approach that aims to convert image synthesis pipelines to video synthesis pipelines. DiffSynth consists of two key components: a latent in-iteration deflickering framework and a video deflickering algorithm. The latent in-iteration deflickering framework applies video deflickering to the latent space of diffusion models, effectively preventing flicker accumulation in intermediate steps. Additionally, we propose a video deflickering algorithm, named patch blending algorithm, that remaps objects in different frames and blends them together to enhance video consistency. One of the notable advantages of DiffSynth is its general applicability to various video synthesis tasks, including text-guided video stylization, fashion video synthesis, image-guided video stylization, video restoring, and 3D rendering. In the task of text-guided video stylization, we make it possible to synthesize high-quality videos without cherry-picking. The experimental results demonstrate the effectiveness of DiffSynth. All videos can be viewed on our project page. Source codes will also be released. |
2012.08977 | Hyemin Ahn | Hyemin Ahn, Obin Kwon, Kyoungdo Kim, Jaeyeon Jeong, Howoong Jun,
Hongjung Lee, Dongheui Lee, Songhwai Oh | Visually Grounding Language Instruction for History-Dependent
Manipulation | 8 pages, 5 figures | null | null | null | cs.RO cs.LG | http://creativecommons.org/licenses/by/4.0/ | This paper emphasizes the importance of a robot's ability to refer to its
task history, especially when it executes a series of pick-and-place
manipulations by following language instructions given one by one. The
advantage of referring to the manipulation history can be categorized into two
folds: (1) the language instructions omitting details but using expressions
referring to the past can be interpreted, and (2) the visual information of
objects occluded by previous manipulations can be inferred. For this, we
introduce a history-dependent manipulation task which objective is to visually
ground a series of language instructions for proper pick-and-place
manipulations by referring to the past. We also suggest a relevant dataset and
model which can be a baseline, and show that our model trained with the
proposed dataset can also be applied to the real world based on the CycleGAN.
Our dataset and code are publicly available on the project website:
https://sites.google.com/view/history-dependent-manipulation.
| [
{
"created": "Wed, 16 Dec 2020 14:11:15 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Mar 2022 14:48:08 GMT",
"version": "v2"
}
] | 2022-03-15 | [
[
"Ahn",
"Hyemin",
""
],
[
"Kwon",
"Obin",
""
],
[
"Kim",
"Kyoungdo",
""
],
[
"Jeong",
"Jaeyeon",
""
],
[
"Jun",
"Howoong",
""
],
[
"Lee",
"Hongjung",
""
],
[
"Lee",
"Dongheui",
""
],
[
"Oh",
"Songhwai",
""
]
] | This paper emphasizes the importance of a robot's ability to refer to its task history, especially when it executes a series of pick-and-place manipulations by following language instructions given one by one. The advantage of referring to the manipulation history can be categorized into two folds: (1) the language instructions omitting details but using expressions referring to the past can be interpreted, and (2) the visual information of objects occluded by previous manipulations can be inferred. For this, we introduce a history-dependent manipulation task which objective is to visually ground a series of language instructions for proper pick-and-place manipulations by referring to the past. We also suggest a relevant dataset and model which can be a baseline, and show that our model trained with the proposed dataset can also be applied to the real world based on the CycleGAN. Our dataset and code are publicly available on the project website: https://sites.google.com/view/history-dependent-manipulation. |
2306.10006 | Wolfgang Paier | Wolfgang Paier and Anna Hilsmann and Peter Eisert | Unsupervised Learning of Style-Aware Facial Animation from Real Acting
Performances | 16 pages, submitted to Graphical Models (Feb 2023) | null | null | null | cs.CV cs.GR cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This paper presents a novel approach for text/speech-driven animation of a
photo-realistic head model based on blend-shape geometry, dynamic textures, and
neural rendering. Training a VAE for geometry and texture yields a parametric
model for accurate capturing and realistic synthesis of facial expressions from
a latent feature vector. Our animation method is based on a conditional CNN
that transforms text or speech into a sequence of animation parameters. In
contrast to previous approaches, our animation model learns
disentangling/synthesizing different acting-styles in an unsupervised manner,
requiring only phonetic labels that describe the content of training sequences.
For realistic real-time rendering, we train a U-Net that refines
rasterization-based renderings by computing improved pixel colors and a
foreground matte. We compare our framework qualitatively/quantitatively against
recent methods for head modeling as well as facial animation and evaluate the
perceived rendering/animation quality in a user-study, which indicates large
improvements compared to state-of-the-art approaches
| [
{
"created": "Fri, 16 Jun 2023 17:58:04 GMT",
"version": "v1"
},
{
"created": "Mon, 10 Jul 2023 13:58:20 GMT",
"version": "v2"
},
{
"created": "Fri, 1 Sep 2023 18:08:05 GMT",
"version": "v3"
}
] | 2023-09-06 | [
[
"Paier",
"Wolfgang",
""
],
[
"Hilsmann",
"Anna",
""
],
[
"Eisert",
"Peter",
""
]
] | This paper presents a novel approach for text/speech-driven animation of a photo-realistic head model based on blend-shape geometry, dynamic textures, and neural rendering. Training a VAE for geometry and texture yields a parametric model for accurate capturing and realistic synthesis of facial expressions from a latent feature vector. Our animation method is based on a conditional CNN that transforms text or speech into a sequence of animation parameters. In contrast to previous approaches, our animation model learns disentangling/synthesizing different acting-styles in an unsupervised manner, requiring only phonetic labels that describe the content of training sequences. For realistic real-time rendering, we train a U-Net that refines rasterization-based renderings by computing improved pixel colors and a foreground matte. We compare our framework qualitatively/quantitatively against recent methods for head modeling as well as facial animation and evaluate the perceived rendering/animation quality in a user-study, which indicates large improvements compared to state-of-the-art approaches |
2003.09354 | Varun Tolani | Varun Tolani, Somil Bansal, Aleksandra Faust, Claire Tomlin | Visual Navigation Among Humans with Optimal Control as a Supervisor | Project Website: https://smlbansal.github.io/LB-WayPtNav-DH/ | null | null | null | cs.RO cs.AI cs.CV cs.LG cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real world visual navigation requires robots to operate in unfamiliar,
human-occupied dynamic environments. Navigation around humans is especially
difficult because it requires anticipating their future motion, which can be
quite challenging. We propose an approach that combines learning-based
perception with model-based optimal control to navigate among humans based only
on monocular, first-person RGB images. Our approach is enabled by our novel
data-generation tool, HumANav that allows for photorealistic renderings of
indoor environment scenes with humans in them, which are then used to train the
perception module entirely in simulation. Through simulations and experiments
on a mobile robot, we demonstrate that the learned navigation policies can
anticipate and react to humans without explicitly predicting future human
motion, generalize to previously unseen environments and human behaviors, and
transfer directly from simulation to reality. Videos describing our approach
and experiments, as well as a demo of HumANav are available on the project
website.
| [
{
"created": "Fri, 20 Mar 2020 16:13:47 GMT",
"version": "v1"
},
{
"created": "Fri, 12 Feb 2021 21:09:24 GMT",
"version": "v2"
}
] | 2021-02-16 | [
[
"Tolani",
"Varun",
""
],
[
"Bansal",
"Somil",
""
],
[
"Faust",
"Aleksandra",
""
],
[
"Tomlin",
"Claire",
""
]
] | Real world visual navigation requires robots to operate in unfamiliar, human-occupied dynamic environments. Navigation around humans is especially difficult because it requires anticipating their future motion, which can be quite challenging. We propose an approach that combines learning-based perception with model-based optimal control to navigate among humans based only on monocular, first-person RGB images. Our approach is enabled by our novel data-generation tool, HumANav that allows for photorealistic renderings of indoor environment scenes with humans in them, which are then used to train the perception module entirely in simulation. Through simulations and experiments on a mobile robot, we demonstrate that the learned navigation policies can anticipate and react to humans without explicitly predicting future human motion, generalize to previously unseen environments and human behaviors, and transfer directly from simulation to reality. Videos describing our approach and experiments, as well as a demo of HumANav are available on the project website. |
2106.00089 | Fernando Gama | Fernando Gama, Brendon G. Anderson, Somayeh Sojoudi | Node-Variant Graph Filters in Graph Neural Networks | null | null | null | null | cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph neural networks (GNNs) have been successfully employed in a myriad of
applications involving graph signals. Theoretical findings establish that GNNs
use nonlinear activation functions to create low-eigenvalue frequency content
that can be processed in a stable manner by subsequent graph convolutional
filters. However, the exact shape of the frequency content created by nonlinear
functions is not known and cannot be learned. In this work, we use node-variant
graph filters (NVGFs) -- which are linear filters capable of creating
frequencies -- as a means of investigating the role that frequency creation
plays in GNNs. We show that, by replacing nonlinear activation functions by
NVGFs, frequency creation mechanisms can be designed or learned. By doing so,
the role of frequency creation is separated from the nonlinear nature of
traditional GNNs. Simulations on graph signal processing problems are carried
out to pinpoint the role of frequency creation.
| [
{
"created": "Mon, 31 May 2021 20:26:53 GMT",
"version": "v1"
},
{
"created": "Fri, 4 Mar 2022 22:04:02 GMT",
"version": "v2"
}
] | 2022-03-08 | [
[
"Gama",
"Fernando",
""
],
[
"Anderson",
"Brendon G.",
""
],
[
"Sojoudi",
"Somayeh",
""
]
] | Graph neural networks (GNNs) have been successfully employed in a myriad of applications involving graph signals. Theoretical findings establish that GNNs use nonlinear activation functions to create low-eigenvalue frequency content that can be processed in a stable manner by subsequent graph convolutional filters. However, the exact shape of the frequency content created by nonlinear functions is not known and cannot be learned. In this work, we use node-variant graph filters (NVGFs) -- which are linear filters capable of creating frequencies -- as a means of investigating the role that frequency creation plays in GNNs. We show that, by replacing nonlinear activation functions by NVGFs, frequency creation mechanisms can be designed or learned. By doing so, the role of frequency creation is separated from the nonlinear nature of traditional GNNs. Simulations on graph signal processing problems are carried out to pinpoint the role of frequency creation. |
1903.03104 | James Bagrow | Abigail Hotaling and James Bagrow | Accurate inference of crowdsourcing properties when using efficient
allocation strategies | 17 pages, 6 figures, 1 table | Scientific Reports 12, 6849 (2022) | 10.1038/s41598-022-10794-9 | null | cs.LG cs.HC stat.AP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Allocation strategies improve the efficiency of crowdsourcing by decreasing
the work needed to complete individual tasks accurately. However, these
algorithms introduce bias by preferentially allocating workers onto easy tasks,
leading to sets of completed tasks that are no longer representative of all
tasks. This bias challenges inference of problem-wide properties such as
typical task difficulty or crowd properties such as worker completion times,
important information that goes beyond the crowd responses themselves. Here we
study inference about problem properties when using an allocation algorithm to
improve crowd efficiency. We introduce Decision-Explicit Probability Sampling
(DEPS), a novel method to perform inference of problem properties while
accounting for the potential bias introduced by an allocation strategy.
Experiments on real and synthetic crowdsourcing data show that DEPS outperforms
baseline inference methods while still leveraging the efficiency gains of the
allocation method. The ability to perform accurate inference of general
properties when using non-representative data allows crowdsourcers to extract
more knowledge out of a given crowdsourced dataset.
| [
{
"created": "Thu, 7 Mar 2019 18:58:34 GMT",
"version": "v1"
},
{
"created": "Wed, 27 Apr 2022 12:42:36 GMT",
"version": "v2"
}
] | 2022-04-28 | [
[
"Hotaling",
"Abigail",
""
],
[
"Bagrow",
"James",
""
]
] | Allocation strategies improve the efficiency of crowdsourcing by decreasing the work needed to complete individual tasks accurately. However, these algorithms introduce bias by preferentially allocating workers onto easy tasks, leading to sets of completed tasks that are no longer representative of all tasks. This bias challenges inference of problem-wide properties such as typical task difficulty or crowd properties such as worker completion times, important information that goes beyond the crowd responses themselves. Here we study inference about problem properties when using an allocation algorithm to improve crowd efficiency. We introduce Decision-Explicit Probability Sampling (DEPS), a novel method to perform inference of problem properties while accounting for the potential bias introduced by an allocation strategy. Experiments on real and synthetic crowdsourcing data show that DEPS outperforms baseline inference methods while still leveraging the efficiency gains of the allocation method. The ability to perform accurate inference of general properties when using non-representative data allows crowdsourcers to extract more knowledge out of a given crowdsourced dataset. |
1803.06604 | Haichuan Yang | Ke Ren, Haichuan Yang, Yu Zhao, Mingshan Xue, Hongyu Miao, Shuai
Huang, Ji Liu | A Robust AUC Maximization Framework with Simultaneous Outlier Detection
and Feature Selection for Positive-Unlabeled Classification | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The positive-unlabeled (PU) classification is a common scenario in real-world
applications such as healthcare, text classification, and bioinformatics, in
which we only observe a few samples labeled as "positive" together with a large
volume of "unlabeled" samples that may contain both positive and negative
samples. Building robust classifier for the PU problem is very challenging,
especially for complex data where the negative samples overwhelm and mislabeled
samples or corrupted features exist. To address these three issues, we propose
a robust learning framework that unifies AUC maximization (a robust metric for
biased labels), outlier detection (for excluding wrong labels), and feature
selection (for excluding corrupted features). The generalization error bounds
are provided for the proposed model that give valuable insight into the
theoretical performance of the method and lead to useful practical guidance,
e.g., to train a model, we find that the included unlabeled samples are
sufficient as long as the sample size is comparable to the number of positive
samples in the training process. Empirical comparisons and two real-world
applications on surgical site infection (SSI) and EEG seizure detection are
also conducted to show the effectiveness of the proposed model.
| [
{
"created": "Sun, 18 Mar 2018 05:09:53 GMT",
"version": "v1"
}
] | 2018-03-20 | [
[
"Ren",
"Ke",
""
],
[
"Yang",
"Haichuan",
""
],
[
"Zhao",
"Yu",
""
],
[
"Xue",
"Mingshan",
""
],
[
"Miao",
"Hongyu",
""
],
[
"Huang",
"Shuai",
""
],
[
"Liu",
"Ji",
""
]
] | The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unlabeled" samples that may contain both positive and negative samples. Building robust classifier for the PU problem is very challenging, especially for complex data where the negative samples overwhelm and mislabeled samples or corrupted features exist. To address these three issues, we propose a robust learning framework that unifies AUC maximization (a robust metric for biased labels), outlier detection (for excluding wrong labels), and feature selection (for excluding corrupted features). The generalization error bounds are provided for the proposed model that give valuable insight into the theoretical performance of the method and lead to useful practical guidance, e.g., to train a model, we find that the included unlabeled samples are sufficient as long as the sample size is comparable to the number of positive samples in the training process. Empirical comparisons and two real-world applications on surgical site infection (SSI) and EEG seizure detection are also conducted to show the effectiveness of the proposed model. |
2311.00444 | Vahan Hovhannisyan | Peter A. Zachares, Vahan Hovhannisyan, Alan Mosca, Yarin Gal | Form follows Function: Text-to-Text Conditional Graph Generation based
on Functional Requirements | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | This work focuses on the novel problem setting of generating graphs
conditioned on a description of the graph's functional requirements in a
downstream task. We pose the problem as a text-to-text generation problem and
focus on the approach of fine-tuning a pretrained large language model (LLM) to
generate graphs. We propose an inductive bias which incorporates information
about the structure of the graph into the LLM's generation process by
incorporating message passing layers into an LLM's architecture. To evaluate
our proposed method, we design a novel set of experiments using publicly
available and widely studied molecule and knowledge graph data sets. Results
suggest our proposed approach generates graphs which more closely meet the
requested functional requirements, outperforming baselines developed on similar
tasks by a statistically significant margin.
| [
{
"created": "Wed, 1 Nov 2023 11:12:02 GMT",
"version": "v1"
}
] | 2023-11-02 | [
[
"Zachares",
"Peter A.",
""
],
[
"Hovhannisyan",
"Vahan",
""
],
[
"Mosca",
"Alan",
""
],
[
"Gal",
"Yarin",
""
]
] | This work focuses on the novel problem setting of generating graphs conditioned on a description of the graph's functional requirements in a downstream task. We pose the problem as a text-to-text generation problem and focus on the approach of fine-tuning a pretrained large language model (LLM) to generate graphs. We propose an inductive bias which incorporates information about the structure of the graph into the LLM's generation process by incorporating message passing layers into an LLM's architecture. To evaluate our proposed method, we design a novel set of experiments using publicly available and widely studied molecule and knowledge graph data sets. Results suggest our proposed approach generates graphs which more closely meet the requested functional requirements, outperforming baselines developed on similar tasks by a statistically significant margin. |
2305.16038 | Arthur Jacot | Zihan Wang, Arthur Jacot | Implicit bias of SGD in $L_{2}$-regularized linear DNNs: One-way jumps
from high to low rank | null | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) with more than
one hidden layers has multiple local minima, corresponding to matrices with
different ranks. In tasks such as matrix completion, the goal is to converge to
the local minimum with the smallest rank that still fits the training data.
While rank-underestimating minima can be avoided since they do not fit the
data, GD might get stuck at rank-overestimating minima. We show that with SGD,
there is always a probability to jump from a higher rank minimum to a lower
rank one, but the probability of jumping back is zero. More precisely, we
define a sequence of sets $B_{1}\subset B_{2}\subset\cdots\subset B_{R}$ so
that $B_{r}$ contains all minima of rank $r$ or less (and not more) that are
absorbing for small enough ridge parameters $\lambda$ and learning rates
$\eta$: SGD has prob. 0 of leaving $B_{r}$, and from any starting point there
is a non-zero prob. for SGD to go in $B_{r}$.
| [
{
"created": "Thu, 25 May 2023 13:17:32 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Sep 2023 13:18:59 GMT",
"version": "v2"
}
] | 2023-10-02 | [
[
"Wang",
"Zihan",
""
],
[
"Jacot",
"Arthur",
""
]
] | The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) with more than one hidden layers has multiple local minima, corresponding to matrices with different ranks. In tasks such as matrix completion, the goal is to converge to the local minimum with the smallest rank that still fits the training data. While rank-underestimating minima can be avoided since they do not fit the data, GD might get stuck at rank-overestimating minima. We show that with SGD, there is always a probability to jump from a higher rank minimum to a lower rank one, but the probability of jumping back is zero. More precisely, we define a sequence of sets $B_{1}\subset B_{2}\subset\cdots\subset B_{R}$ so that $B_{r}$ contains all minima of rank $r$ or less (and not more) that are absorbing for small enough ridge parameters $\lambda$ and learning rates $\eta$: SGD has prob. 0 of leaving $B_{r}$, and from any starting point there is a non-zero prob. for SGD to go in $B_{r}$. |
1311.7283 | Dmitry N. Kozlov | Dmitry N. Kozlov | Topology of the view complex | accepted for publication in Homotopy, Homology Appl | null | null | null | cs.DC math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we consider a family of simplicial complexes, which we call the
view complexes. Our choice of objects of study is motivated by theoretical
distributed computing, since the view complex is a key simplicial construction
used for protocol complexes in the snapshot computational model. We show that
the view complex $\view$ can be collapsed to the well-known complex
$\chi(\Delta^n)$, called standard chromatic subdivision of a simplex, and that
$\chi(\Delta^n)$ is itself collapsible. Furthermore, we show that the collapses
can be performed simultaneously in entire orbits of the natural symmetric group
action. Our results yield a purely combinatorial and constructive understanding
of the topology of view complexes, at the same time as they enhance our
knowledge about the standard chromatic subdivision of a simplex.
| [
{
"created": "Thu, 28 Nov 2013 11:43:44 GMT",
"version": "v1"
},
{
"created": "Thu, 26 Jun 2014 13:12:55 GMT",
"version": "v2"
},
{
"created": "Fri, 5 Dec 2014 13:24:44 GMT",
"version": "v3"
}
] | 2014-12-08 | [
[
"Kozlov",
"Dmitry N.",
""
]
] | In this paper we consider a family of simplicial complexes, which we call the view complexes. Our choice of objects of study is motivated by theoretical distributed computing, since the view complex is a key simplicial construction used for protocol complexes in the snapshot computational model. We show that the view complex $\view$ can be collapsed to the well-known complex $\chi(\Delta^n)$, called standard chromatic subdivision of a simplex, and that $\chi(\Delta^n)$ is itself collapsible. Furthermore, we show that the collapses can be performed simultaneously in entire orbits of the natural symmetric group action. Our results yield a purely combinatorial and constructive understanding of the topology of view complexes, at the same time as they enhance our knowledge about the standard chromatic subdivision of a simplex. |
2112.07599 | Givi Meishvili | Givi Meishvili, Attila Szab\'o, Simon Jenni, Paolo Favaro | Learning to Deblur and Rotate Motion-Blurred Faces | British Machine Vision Conference 2021 | null | null | null | cs.CV cs.AI cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a solution to the novel task of rendering sharp videos from new
viewpoints from a single motion-blurred image of a face. Our method handles the
complexity of face blur by implicitly learning the geometry and motion of faces
through the joint training on three large datasets: FFHQ and 300VW, which are
publicly available, and a new Bern Multi-View Face Dataset (BMFD) that we
built. The first two datasets provide a large variety of faces and allow our
model to generalize better. BMFD instead allows us to introduce multi-view
constraints, which are crucial to synthesizing sharp videos from a new camera
view. It consists of high frame rate synchronized videos from multiple views of
several subjects displaying a wide range of facial expressions. We use the high
frame rate videos to simulate realistic motion blur through averaging. Thanks
to this dataset, we train a neural network to reconstruct a 3D video
representation from a single image and the corresponding face gaze. We then
provide a camera viewpoint relative to the estimated gaze and the blurry image
as input to an encoder-decoder network to generate a video of sharp frames with
a novel camera viewpoint. We demonstrate our approach on test subjects of our
multi-view dataset and VIDTIMIT.
| [
{
"created": "Tue, 14 Dec 2021 17:51:19 GMT",
"version": "v1"
}
] | 2021-12-15 | [
[
"Meishvili",
"Givi",
""
],
[
"Szabó",
"Attila",
""
],
[
"Jenni",
"Simon",
""
],
[
"Favaro",
"Paolo",
""
]
] | We propose a solution to the novel task of rendering sharp videos from new viewpoints from a single motion-blurred image of a face. Our method handles the complexity of face blur by implicitly learning the geometry and motion of faces through the joint training on three large datasets: FFHQ and 300VW, which are publicly available, and a new Bern Multi-View Face Dataset (BMFD) that we built. The first two datasets provide a large variety of faces and allow our model to generalize better. BMFD instead allows us to introduce multi-view constraints, which are crucial to synthesizing sharp videos from a new camera view. It consists of high frame rate synchronized videos from multiple views of several subjects displaying a wide range of facial expressions. We use the high frame rate videos to simulate realistic motion blur through averaging. Thanks to this dataset, we train a neural network to reconstruct a 3D video representation from a single image and the corresponding face gaze. We then provide a camera viewpoint relative to the estimated gaze and the blurry image as input to an encoder-decoder network to generate a video of sharp frames with a novel camera viewpoint. We demonstrate our approach on test subjects of our multi-view dataset and VIDTIMIT. |
2111.10541 | Hanning Gao | Hanning Gao, Lingfei Wu, Po Hu, Zhihua Wei, Fangli Xu and Bo Long | Graph-augmented Learning to Rank for Querying Large-scale Knowledge
Graph | Accepted by AACL 2022 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graph question answering (KGQA) based on information retrieval aims
to answer a question by retrieving answer from a large-scale knowledge graph.
Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that
may contain candidate answer, and then search for the exact answer in the KSG.
However, the KSG may contain thousands of candidate nodes since the knowledge
graph involved in querying is often of large scale, thus decreasing the
performance of answer selection. To tackle this problem, we first propose to
partition the retrieved KSG to several smaller sub-KSGs via a new subgraph
partition algorithm and then present a graph-augmented learning to rank model
to select the top-ranked sub-KSGs from them. Our proposed model combines a
novel subgraph matching networks to capture global interactions in both
question and subgraphs, and an Enhanced Bilateral Multi-Perspective Matching
model is proposed to capture local interactions. Finally, we apply an answer
selection model on the full KSG and the top-ranked sub-KSGs respectively to
validate the effectiveness of our proposed graph-augmented learning to rank
method. The experimental results on multiple benchmark datasets have
demonstrated the effectiveness of our approach.
| [
{
"created": "Sat, 20 Nov 2021 08:27:37 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Apr 2022 01:34:30 GMT",
"version": "v2"
},
{
"created": "Tue, 3 May 2022 12:47:41 GMT",
"version": "v3"
},
{
"created": "Wed, 5 Oct 2022 00:52:01 GMT",
"version": "v4"
}
] | 2022-10-06 | [
[
"Gao",
"Hanning",
""
],
[
"Wu",
"Lingfei",
""
],
[
"Hu",
"Po",
""
],
[
"Wei",
"Zhihua",
""
],
[
"Xu",
"Fangli",
""
],
[
"Long",
"Bo",
""
]
] | Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that may contain candidate answer, and then search for the exact answer in the KSG. However, the KSG may contain thousands of candidate nodes since the knowledge graph involved in querying is often of large scale, thus decreasing the performance of answer selection. To tackle this problem, we first propose to partition the retrieved KSG to several smaller sub-KSGs via a new subgraph partition algorithm and then present a graph-augmented learning to rank model to select the top-ranked sub-KSGs from them. Our proposed model combines a novel subgraph matching networks to capture global interactions in both question and subgraphs, and an Enhanced Bilateral Multi-Perspective Matching model is proposed to capture local interactions. Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method. The experimental results on multiple benchmark datasets have demonstrated the effectiveness of our approach. |
1711.08589 | Benjamin Klein | Benjamin Klein and Lior Wolf | End-to-End Supervised Product Quantization for Image Search and
Retrieval | null | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Product Quantization, a dictionary based hashing method, is one of the
leading unsupervised hashing techniques. While it ignores the labels, it
harnesses the features to construct look up tables that can approximate the
feature space. In recent years, several works have achieved state of the art
results on hashing benchmarks by learning binary representations in a
supervised manner. This work presents Deep Product Quantization (DPQ), a
technique that leads to more accurate retrieval and classification than the
latest state of the art methods, while having similar computational complexity
and memory footprint as the Product Quantization method. To our knowledge, this
is the first work to introduce a dictionary-based representation that is
inspired by Product Quantization and which is learned end-to-end, and thus
benefits from the supervised signal. DPQ explicitly learns soft and hard
representations to enable an efficient and accurate asymmetric search, by using
a straight-through estimator. Our method obtains state of the art results on an
extensive array of retrieval and classification experiments.
| [
{
"created": "Thu, 23 Nov 2017 06:40:28 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Jan 2020 22:56:50 GMT",
"version": "v2"
}
] | 2020-01-22 | [
[
"Klein",
"Benjamin",
""
],
[
"Wolf",
"Lior",
""
]
] | Product Quantization, a dictionary based hashing method, is one of the leading unsupervised hashing techniques. While it ignores the labels, it harnesses the features to construct look up tables that can approximate the feature space. In recent years, several works have achieved state of the art results on hashing benchmarks by learning binary representations in a supervised manner. This work presents Deep Product Quantization (DPQ), a technique that leads to more accurate retrieval and classification than the latest state of the art methods, while having similar computational complexity and memory footprint as the Product Quantization method. To our knowledge, this is the first work to introduce a dictionary-based representation that is inspired by Product Quantization and which is learned end-to-end, and thus benefits from the supervised signal. DPQ explicitly learns soft and hard representations to enable an efficient and accurate asymmetric search, by using a straight-through estimator. Our method obtains state of the art results on an extensive array of retrieval and classification experiments. |
2005.13829 | Yitong Ji | Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li | A Re-visit of the Popularity Baseline in Recommender Systems | Accepted by SIGIR2020 | null | 10.1145/3397271.3401233 | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Popularity is often included in experimental evaluation to provide a
reference performance for a recommendation task. To understand how popularity
baseline is defined and evaluated, we sample 12 papers from top-tier
conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits.
We note that the widely adopted MostPop baseline simply ranks items based on
the number of interactions in the training data. We argue that the current
evaluation of popularity (i) does not reflect the popular items at the time
when a user interacts with the system, and (ii) may recommend items released
after a user's last interaction with the system. On the widely used MovieLens
dataset, we show that the performance of popularity could be significantly
improved by 70% or more, if we consider the popular items at the time point
when a user interacts with the system. We further show that, on MovieLens
dataset, the users having lower tendencies on movies tend to follow the crowd
and rate more popular movies. Movie lovers who rate a large number of movies,
rate movies based on their own preferences and interests. Through this study,
we call for a re-visit of the popularity baseline in recommender system to
better reflect its effectiveness.
| [
{
"created": "Thu, 28 May 2020 08:04:40 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Jun 2020 06:37:06 GMT",
"version": "v2"
}
] | 2020-06-03 | [
[
"Ji",
"Yitong",
""
],
[
"Sun",
"Aixin",
""
],
[
"Zhang",
"Jie",
""
],
[
"Li",
"Chenliang",
""
]
] | Popularity is often included in experimental evaluation to provide a reference performance for a recommendation task. To understand how popularity baseline is defined and evaluated, we sample 12 papers from top-tier conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits. We note that the widely adopted MostPop baseline simply ranks items based on the number of interactions in the training data. We argue that the current evaluation of popularity (i) does not reflect the popular items at the time when a user interacts with the system, and (ii) may recommend items released after a user's last interaction with the system. On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system. We further show that, on MovieLens dataset, the users having lower tendencies on movies tend to follow the crowd and rate more popular movies. Movie lovers who rate a large number of movies, rate movies based on their own preferences and interests. Through this study, we call for a re-visit of the popularity baseline in recommender system to better reflect its effectiveness. |
2308.15870 | EPTCS | Christian Hatschka (TU Vienna), Agata Ciabattoni (TU Vienna), Thomas
Eiter (TU Vienna) | Deontic Paradoxes in ASP with Weak Constraints | In Proceedings ICLP 2023, arXiv:2308.14898 | EPTCS 385, 2023, pp. 367-380 | 10.4204/EPTCS.385.39 | null | cs.LO cs.AI cs.CY cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rise of powerful AI technology for a range of applications that are
sensitive to legal, social, and ethical norms demands decision-making support
in presence of norms and regulations. Normative reasoning is the realm of
deontic logics, that are challenged by well-known benchmark problems (deontic
paradoxes), and lack efficient computational tools. In this paper, we use
Answer Set Programming (ASP) for addressing these shortcomings and showcase how
to encode and resolve several well-known deontic paradoxes utilizing weak
constraints. By abstracting and generalizing this encoding, we present a
methodology for translating normative systems in ASP with weak constraints.
This methodology is applied to "ethical" versions of Pac-man, where we obtain a
comparable performance with related works, but ethically preferable results.
| [
{
"created": "Wed, 30 Aug 2023 08:56:54 GMT",
"version": "v1"
}
] | 2023-08-31 | [
[
"Hatschka",
"Christian",
"",
"TU Vienna"
],
[
"Ciabattoni",
"Agata",
"",
"TU Vienna"
],
[
"Eiter",
"Thomas",
"",
"TU Vienna"
]
] | The rise of powerful AI technology for a range of applications that are sensitive to legal, social, and ethical norms demands decision-making support in presence of norms and regulations. Normative reasoning is the realm of deontic logics, that are challenged by well-known benchmark problems (deontic paradoxes), and lack efficient computational tools. In this paper, we use Answer Set Programming (ASP) for addressing these shortcomings and showcase how to encode and resolve several well-known deontic paradoxes utilizing weak constraints. By abstracting and generalizing this encoding, we present a methodology for translating normative systems in ASP with weak constraints. This methodology is applied to "ethical" versions of Pac-man, where we obtain a comparable performance with related works, but ethically preferable results. |
2204.05959 | Jeffrey Young | Sara Karamati, Clayton Hughes, K. Scott Hemmert, Ryan E. Grant, W.
Whit Schonbein, Scott Levy, Thomas M. Conte, Jeffrey Young, Richard W. Vuduc | "Smarter" NICs for faster molecular dynamics: a case study | null | null | null | null | cs.DC cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work evaluates the benefits of using a "smart" network interface card
(SmartNIC) as a compute accelerator for the example of the MiniMD molecular
dynamics proxy application. The accelerator is NVIDIA's BlueField-2 card, which
includes an 8-core Arm processor along with a small amount of DRAM and storage.
We test the networking and data movement performance of these cards compared to
a standard Intel server host using microbenchmarks and MiniMD. In MiniMD, we
identify two distinct classes of computation, namely core computation and
maintenance computation, which are executed in sequence. We restructure the
algorithm and code to weaken this dependence and increase task parallelism,
thereby making it possible to increase utilization of the BlueField-2
concurrently with the host. We evaluate our implementation on a cluster
consisting of 16 dual-socket Intel Broadwell host nodes with one BlueField-2
per host-node. Our results show that while the overall compute performance of
BlueField-2 is limited, using them with a modified MiniMD algorithm allows for
up to 20% speedup over the host CPU baseline with no loss in simulation
accuracy.
| [
{
"created": "Tue, 12 Apr 2022 17:17:05 GMT",
"version": "v1"
}
] | 2022-04-13 | [
[
"Karamati",
"Sara",
""
],
[
"Hughes",
"Clayton",
""
],
[
"Hemmert",
"K. Scott",
""
],
[
"Grant",
"Ryan E.",
""
],
[
"Schonbein",
"W. Whit",
""
],
[
"Levy",
"Scott",
""
],
[
"Conte",
"Thomas M.",
""
],
[
"Young",
"Jeffrey",
""
],
[
"Vuduc",
"Richard W.",
""
]
] | This work evaluates the benefits of using a "smart" network interface card (SmartNIC) as a compute accelerator for the example of the MiniMD molecular dynamics proxy application. The accelerator is NVIDIA's BlueField-2 card, which includes an 8-core Arm processor along with a small amount of DRAM and storage. We test the networking and data movement performance of these cards compared to a standard Intel server host using microbenchmarks and MiniMD. In MiniMD, we identify two distinct classes of computation, namely core computation and maintenance computation, which are executed in sequence. We restructure the algorithm and code to weaken this dependence and increase task parallelism, thereby making it possible to increase utilization of the BlueField-2 concurrently with the host. We evaluate our implementation on a cluster consisting of 16 dual-socket Intel Broadwell host nodes with one BlueField-2 per host-node. Our results show that while the overall compute performance of BlueField-2 is limited, using them with a modified MiniMD algorithm allows for up to 20% speedup over the host CPU baseline with no loss in simulation accuracy. |
cs/0102013 | Hirotada Kobayashi | Hirotada Kobayashi, Keiji Matsumoto | Quantum Multi-Prover Interactive Proof Systems with Limited Prior
Entanglement | LaTeX2e, 19 pages, 2 figures, title changed, some of the sections are
fully revised, journal version in Journal of Computer and System Sciences | Journal of Computer and System Sciences, 66(3):429--450, 2003 | null | null | cs.CC quant-ph | null | This paper gives the first formal treatment of a quantum analogue of
multi-prover interactive proof systems. It is proved that the class of
languages having quantum multi-prover interactive proof systems is necessarily
contained in NEXP, under the assumption that provers are allowed to share at
most polynomially many prior-entangled qubits. This implies that, in
particular, if provers do not share any prior entanglement with each other, the
class of languages having quantum multi-prover interactive proof systems is
equal to NEXP. Related to these, it is shown that, in the case a prover does
not have his private qubits, the class of languages having quantum
single-prover interactive proof systems is also equal to NEXP.
| [
{
"created": "Mon, 19 Feb 2001 19:46:12 GMT",
"version": "v1"
},
{
"created": "Thu, 12 Apr 2001 11:31:46 GMT",
"version": "v2"
},
{
"created": "Tue, 15 May 2001 12:32:31 GMT",
"version": "v3"
},
{
"created": "Fri, 16 Nov 2001 13:34:35 GMT",
"version": "v4"
},
{
"created": "Tue, 10 Jun 2003 17:07:59 GMT",
"version": "v5"
}
] | 2007-05-23 | [
[
"Kobayashi",
"Hirotada",
""
],
[
"Matsumoto",
"Keiji",
""
]
] | This paper gives the first formal treatment of a quantum analogue of multi-prover interactive proof systems. It is proved that the class of languages having quantum multi-prover interactive proof systems is necessarily contained in NEXP, under the assumption that provers are allowed to share at most polynomially many prior-entangled qubits. This implies that, in particular, if provers do not share any prior entanglement with each other, the class of languages having quantum multi-prover interactive proof systems is equal to NEXP. Related to these, it is shown that, in the case a prover does not have his private qubits, the class of languages having quantum single-prover interactive proof systems is also equal to NEXP. |
1805.05980 | Kendeas Theofanous Mr | Kendeas Theofanous | Dynamic Walkng of Legged Machines | null | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Locomotion of legged machines faces the problems of model complexity and
computational costs. Algorithms based on complex models and/or reinforcement
learning exist to solve the walking control task. In this project, we aim to
develop a bipedal walking control system based on a simple model the Linear
Inverted Pendulum model. In order to simplify the complex process of
controlling legged locomotion, we make use of the technique of splitting the
control into three parts as height control, forward velocity control and
balance control. The forward velocity of the body has a linear relationship
with the foot placement, therefore we use a linear function to realise foot
placement. Our control system achieves stable walking gait in a simulated
environment, where our bipedal robot walks more than 200 steps with a cyclic
pattern in a stable, dynamic and almost natural manner. The experimental data
are presented and analysed.
| [
{
"created": "Tue, 15 May 2018 18:25:49 GMT",
"version": "v1"
}
] | 2018-05-17 | [
[
"Theofanous",
"Kendeas",
""
]
] | Locomotion of legged machines faces the problems of model complexity and computational costs. Algorithms based on complex models and/or reinforcement learning exist to solve the walking control task. In this project, we aim to develop a bipedal walking control system based on a simple model the Linear Inverted Pendulum model. In order to simplify the complex process of controlling legged locomotion, we make use of the technique of splitting the control into three parts as height control, forward velocity control and balance control. The forward velocity of the body has a linear relationship with the foot placement, therefore we use a linear function to realise foot placement. Our control system achieves stable walking gait in a simulated environment, where our bipedal robot walks more than 200 steps with a cyclic pattern in a stable, dynamic and almost natural manner. The experimental data are presented and analysed. |
2112.06106 | Donsuk Lee | Donsuk Lee, Pranav Gujarathi, Justin N. Wood | Controlled-rearing studies of newborn chicks and deep neural networks | NeurIPS 2021 Workshop on Shared Visual Representations in Human &
Machine Intelligence | null | null | null | cs.CV cs.AI q-bio.NC | http://creativecommons.org/licenses/by/4.0/ | Convolutional neural networks (CNNs) can now achieve human-level performance
on challenging object recognition tasks. CNNs are also the leading quantitative
models in terms of predicting neural and behavioral responses in visual
recognition tasks. However, there is a widely accepted critique of CNN models:
unlike newborn animals, which learn rapidly and efficiently, CNNs are thought
to be "data hungry," requiring massive amounts of training data to develop
accurate models for object recognition. This critique challenges the promise of
using CNNs as models of visual development. Here, we directly examined whether
CNNs are more data hungry than newborn animals by performing parallel
controlled-rearing experiments on newborn chicks and CNNs. We raised newborn
chicks in strictly controlled visual environments, then simulated the training
data available in that environment by constructing a virtual animal chamber in
a video game engine. We recorded the visual images acquired by an agent moving
through the virtual chamber and used those images to train CNNs. When CNNs
received similar visual training data as chicks, the CNNs successfully solved
the same challenging view-invariant object recognition tasks as the chicks.
Thus, the CNNs were not more data hungry than animals: both CNNs and chicks
successfully developed robust object models from training data of a single
object.
| [
{
"created": "Sun, 12 Dec 2021 00:45:07 GMT",
"version": "v1"
}
] | 2021-12-14 | [
[
"Lee",
"Donsuk",
""
],
[
"Gujarathi",
"Pranav",
""
],
[
"Wood",
"Justin N.",
""
]
] | Convolutional neural networks (CNNs) can now achieve human-level performance on challenging object recognition tasks. CNNs are also the leading quantitative models in terms of predicting neural and behavioral responses in visual recognition tasks. However, there is a widely accepted critique of CNN models: unlike newborn animals, which learn rapidly and efficiently, CNNs are thought to be "data hungry," requiring massive amounts of training data to develop accurate models for object recognition. This critique challenges the promise of using CNNs as models of visual development. Here, we directly examined whether CNNs are more data hungry than newborn animals by performing parallel controlled-rearing experiments on newborn chicks and CNNs. We raised newborn chicks in strictly controlled visual environments, then simulated the training data available in that environment by constructing a virtual animal chamber in a video game engine. We recorded the visual images acquired by an agent moving through the virtual chamber and used those images to train CNNs. When CNNs received similar visual training data as chicks, the CNNs successfully solved the same challenging view-invariant object recognition tasks as the chicks. Thus, the CNNs were not more data hungry than animals: both CNNs and chicks successfully developed robust object models from training data of a single object. |
1410.1864 | Maurice Margenstern | Maurice Margenstern | A weakly universal cellular automaton in the heptagrid with three states | 27 pages, 21 figures. arXiv admin note: substantial text overlap with
arXiv:1403.2373 | null | null | null | cs.DM nlin.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we construct a cellular automaton on the heptagrid which is
planar, weakly universal and which have three states only. This result improves
the best result which was with four states.
| [
{
"created": "Tue, 7 Oct 2014 19:54:18 GMT",
"version": "v1"
}
] | 2014-10-08 | [
[
"Margenstern",
"Maurice",
""
]
] | In this paper, we construct a cellular automaton on the heptagrid which is planar, weakly universal and which have three states only. This result improves the best result which was with four states. |
2301.13311 | Ahmed Alkhateeb | Yu Zhang, Tawfik Osman, and Ahmed Alkhateeb | A Digital Twin Assisted Framework for Interference Nulling in Millimeter
Wave MIMO Systems | arXiv admin note: substantial text overlap with arXiv:2209.04509 | null | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined
beamforming codebooks for both initial access and data transmission. However,
most of the existing codebooks adopt pre-defined beams that focus mainly on
improving the gain of their target users, without taking interference into
account, which could incur critical performance degradation in dense networks.
To address this problem, in this paper, we propose a sample-efficient digital
twin-assisted beam pattern design framework that learns how to form the beam
pattern to reject the signals from the interfering directions. The proposed
approach does not require any explicit channel knowledge or any coordination
with the interferers. The adoption of the digital twin improves the sample
efficiency by better leveraging the underlying signal relationship and by
incorporating a demand-based data acquisition strategy. Simulation results show
that the developed signal model-based learning framework can significantly
reduce the actual interaction with the radio environment (i.e., the number of
measurements) compared to the model-unaware design, leading to a more practical
and efficient interference-aware beam design approach.
| [
{
"created": "Mon, 30 Jan 2023 22:10:15 GMT",
"version": "v1"
}
] | 2023-02-01 | [
[
"Zhang",
"Yu",
""
],
[
"Osman",
"Tawfik",
""
],
[
"Alkhateeb",
"Ahmed",
""
]
] | Millimeter wave (mmWave) and terahertz MIMO systems rely on pre-defined beamforming codebooks for both initial access and data transmission. However, most of the existing codebooks adopt pre-defined beams that focus mainly on improving the gain of their target users, without taking interference into account, which could incur critical performance degradation in dense networks. To address this problem, in this paper, we propose a sample-efficient digital twin-assisted beam pattern design framework that learns how to form the beam pattern to reject the signals from the interfering directions. The proposed approach does not require any explicit channel knowledge or any coordination with the interferers. The adoption of the digital twin improves the sample efficiency by better leveraging the underlying signal relationship and by incorporating a demand-based data acquisition strategy. Simulation results show that the developed signal model-based learning framework can significantly reduce the actual interaction with the radio environment (i.e., the number of measurements) compared to the model-unaware design, leading to a more practical and efficient interference-aware beam design approach. |
2104.07972 | Vincent Micheli | Vincent Micheli, Fran\c{c}ois Fleuret | Language Models are Few-Shot Butlers | EMNLP 2021 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pretrained language models demonstrate strong performance in most NLP tasks
when fine-tuned on small task-specific datasets. Hence, these autoregressive
models constitute ideal agents to operate in text-based environments where
language understanding and generative capabilities are essential. Nonetheless,
collecting expert demonstrations in such environments is a time-consuming
endeavour. We introduce a two-stage procedure to learn from a small set of
demonstrations and further improve by interacting with an environment. We show
that language models fine-tuned with only 1.2% of the expert demonstrations and
a simple reinforcement learning algorithm achieve a 51% absolute improvement in
success rate over existing methods in the ALFWorld environment.
| [
{
"created": "Fri, 16 Apr 2021 08:47:07 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Sep 2021 11:49:49 GMT",
"version": "v2"
}
] | 2021-09-21 | [
[
"Micheli",
"Vincent",
""
],
[
"Fleuret",
"François",
""
]
] | Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. Hence, these autoregressive models constitute ideal agents to operate in text-based environments where language understanding and generative capabilities are essential. Nonetheless, collecting expert demonstrations in such environments is a time-consuming endeavour. We introduce a two-stage procedure to learn from a small set of demonstrations and further improve by interacting with an environment. We show that language models fine-tuned with only 1.2% of the expert demonstrations and a simple reinforcement learning algorithm achieve a 51% absolute improvement in success rate over existing methods in the ALFWorld environment. |
0811.1301 | Amit Bhosle | Amit M. Bhosle and Teofilo F. Gonzalez | Distributed Algorithms for Computing Alternate Paths Avoiding Failed
Nodes and Links | 8 pages, 2 columns, 1 figure | null | null | null | cs.DC cs.DS cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A recent study characterizing failures in computer networks shows that
transient single element (node/link) failures are the dominant failures in
large communication networks like the Internet. Thus, having the routing paths
globally recomputed on a failure does not pay off since the failed element
recovers fairly quickly, and the recomputed routing paths need to be discarded.
In this paper, we present the first distributed algorithm that computes the
alternate paths required by some "proactive recovery schemes" for handling
transient failures. Our algorithm computes paths that avoid a failed node, and
provides an alternate path to a particular destination from an upstream
neighbor of the failed node. With minor modifications, we can have the
algorithm compute alternate paths that avoid a failed link as well. To the best
of our knowledge all previous algorithms proposed for computing alternate paths
are centralized, and need complete information of the network graph as input to
the algorithm.
| [
{
"created": "Sun, 9 Nov 2008 03:34:39 GMT",
"version": "v1"
}
] | 2008-11-11 | [
[
"Bhosle",
"Amit M.",
""
],
[
"Gonzalez",
"Teofilo F.",
""
]
] | A recent study characterizing failures in computer networks shows that transient single element (node/link) failures are the dominant failures in large communication networks like the Internet. Thus, having the routing paths globally recomputed on a failure does not pay off since the failed element recovers fairly quickly, and the recomputed routing paths need to be discarded. In this paper, we present the first distributed algorithm that computes the alternate paths required by some "proactive recovery schemes" for handling transient failures. Our algorithm computes paths that avoid a failed node, and provides an alternate path to a particular destination from an upstream neighbor of the failed node. With minor modifications, we can have the algorithm compute alternate paths that avoid a failed link as well. To the best of our knowledge all previous algorithms proposed for computing alternate paths are centralized, and need complete information of the network graph as input to the algorithm. |
2406.01917 | Anindya Sarkar | Anindya Sarkar, Srikumar Sastry, Aleksis Pirinen, Chongjie Zhang,
Nathan Jacobs, Yevgeniy Vorobeychik | GOMAA-Geo: GOal Modality Agnostic Active Geo-localization | 23 pages, 17 figures | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We consider the task of active geo-localization (AGL) in which an agent uses
a sequence of visual cues observed during aerial navigation to find a target
specified through multiple possible modalities. This could emulate a UAV
involved in a search-and-rescue operation navigating through an area, observing
a stream of aerial images as it goes. The AGL task is associated with two
important challenges. Firstly, an agent must deal with a goal specification in
one of multiple modalities (e.g., through a natural language description) while
the search cues are provided in other modalities (aerial imagery). The second
challenge is limited localization time (e.g., limited battery life, urgency) so
that the goal must be localized as efficiently as possible, i.e. the agent must
effectively leverage its sequentially observed aerial views when searching for
the goal. To address these challenges, we propose GOMAA-Geo - a goal modality
agnostic active geo-localization agent - for zero-shot generalization between
different goal modalities. Our approach combines cross-modality contrastive
learning to align representations across modalities with supervised foundation
model pretraining and reinforcement learning to obtain highly effective
navigation and localization policies. Through extensive evaluations, we show
that GOMAA-Geo outperforms alternative learnable approaches and that it
generalizes across datasets - e.g., to disaster-hit areas without seeing a
single disaster scenario during training - and goal modalities - e.g., to
ground-level imagery or textual descriptions, despite only being trained with
goals specified as aerial views. Code and models are publicly available at
https://github.com/mvrl/GOMAA-Geo/tree/main.
| [
{
"created": "Tue, 4 Jun 2024 02:59:36 GMT",
"version": "v1"
}
] | 2024-06-05 | [
[
"Sarkar",
"Anindya",
""
],
[
"Sastry",
"Srikumar",
""
],
[
"Pirinen",
"Aleksis",
""
],
[
"Zhang",
"Chongjie",
""
],
[
"Jacobs",
"Nathan",
""
],
[
"Vorobeychik",
"Yevgeniy",
""
]
] | We consider the task of active geo-localization (AGL) in which an agent uses a sequence of visual cues observed during aerial navigation to find a target specified through multiple possible modalities. This could emulate a UAV involved in a search-and-rescue operation navigating through an area, observing a stream of aerial images as it goes. The AGL task is associated with two important challenges. Firstly, an agent must deal with a goal specification in one of multiple modalities (e.g., through a natural language description) while the search cues are provided in other modalities (aerial imagery). The second challenge is limited localization time (e.g., limited battery life, urgency) so that the goal must be localized as efficiently as possible, i.e. the agent must effectively leverage its sequentially observed aerial views when searching for the goal. To address these challenges, we propose GOMAA-Geo - a goal modality agnostic active geo-localization agent - for zero-shot generalization between different goal modalities. Our approach combines cross-modality contrastive learning to align representations across modalities with supervised foundation model pretraining and reinforcement learning to obtain highly effective navigation and localization policies. Through extensive evaluations, we show that GOMAA-Geo outperforms alternative learnable approaches and that it generalizes across datasets - e.g., to disaster-hit areas without seeing a single disaster scenario during training - and goal modalities - e.g., to ground-level imagery or textual descriptions, despite only being trained with goals specified as aerial views. Code and models are publicly available at https://github.com/mvrl/GOMAA-Geo/tree/main. |
2405.17991 | Roy Miles | Roy Miles, Pradyumna Reddy, Ismail Elezi, Jiankang Deng | VeLoRA: Memory Efficient Training using Rank-1 Sub-Token Projections | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs) have recently emerged as powerful tools for
tackling many language-processing tasks. Despite their success, training and
fine-tuning these models is still far too computationally and memory intensive.
In this paper, we identify and characterise the important components needed for
effective model convergence using gradient descent. In doing so we find that
the intermediate activations used to implement backpropagation can be
excessively compressed without incurring any degradation in performance. This
result leads us to a cheap and memory-efficient algorithm for both fine-tuning
and pre-training LLMs. The proposed algorithm simply divides the tokens up into
smaller sub-tokens before projecting them onto a fixed 1-dimensional subspace
during the forward pass. These features are then coarsely reconstructed during
the backward pass to implement the update rules. We confirm the effectiveness
of our algorithm as being complimentary to many state-of-the-art PEFT methods
on the VTAB-1k fine-tuning benchmark. Furthermore, we outperform QLoRA for
fine-tuning LLaMA and show competitive performance against other
memory-efficient pre-training methods on the large-scale C4 dataset.
| [
{
"created": "Tue, 28 May 2024 09:23:14 GMT",
"version": "v1"
}
] | 2024-05-29 | [
[
"Miles",
"Roy",
""
],
[
"Reddy",
"Pradyumna",
""
],
[
"Elezi",
"Ismail",
""
],
[
"Deng",
"Jiankang",
""
]
] | Large language models (LLMs) have recently emerged as powerful tools for tackling many language-processing tasks. Despite their success, training and fine-tuning these models is still far too computationally and memory intensive. In this paper, we identify and characterise the important components needed for effective model convergence using gradient descent. In doing so we find that the intermediate activations used to implement backpropagation can be excessively compressed without incurring any degradation in performance. This result leads us to a cheap and memory-efficient algorithm for both fine-tuning and pre-training LLMs. The proposed algorithm simply divides the tokens up into smaller sub-tokens before projecting them onto a fixed 1-dimensional subspace during the forward pass. These features are then coarsely reconstructed during the backward pass to implement the update rules. We confirm the effectiveness of our algorithm as being complimentary to many state-of-the-art PEFT methods on the VTAB-1k fine-tuning benchmark. Furthermore, we outperform QLoRA for fine-tuning LLaMA and show competitive performance against other memory-efficient pre-training methods on the large-scale C4 dataset. |
2102.10801 | Wei Lin | Qunxi Zhu, Yao Guo, Wei Lin | Neural Delay Differential Equations | Accepted as a poster in ICLR 2021 (submitted 28 Sep 2020, revised 22
Nov 2020, accepted 08 Jan 2021) | null | null | null | cs.LG cs.AI math.DS nlin.CD | http://creativecommons.org/licenses/by/4.0/ | Neural Ordinary Differential Equations (NODEs), a framework of
continuous-depth neural networks, have been widely applied, showing exceptional
efficacy in coping with some representative datasets. Recently, an augmented
framework has been successfully developed for conquering some limitations
emergent in application of the original framework. Here we propose a new class
of continuous-depth neural networks with delay, named as Neural Delay
Differential Equations (NDDEs), and, for computing the corresponding gradients,
we use the adjoint sensitivity method to obtain the delayed dynamics of the
adjoint. Since the differential equations with delays are usually seen as
dynamical systems of infinite dimension possessing more fruitful dynamics, the
NDDEs, compared to the NODEs, own a stronger capacity of nonlinear
representations. Indeed, we analytically validate that the NDDEs are of
universal approximators, and further articulate an extension of the NDDEs,
where the initial function of the NDDEs is supposed to satisfy ODEs. More
importantly, we use several illustrative examples to demonstrate the
outstanding capacities of the NDDEs and the NDDEs with ODEs' initial value.
Specifically, (1) we successfully model the delayed dynamics where the
trajectories in the lower-dimensional phase space could be mutually
intersected, while the traditional NODEs without any argumentation are not
directly applicable for such modeling, and (2) we achieve lower loss and higher
accuracy not only for the data produced synthetically by complex models but
also for the real-world image datasets, i.e., CIFAR10, MNIST, and SVHN. Our
results on the NDDEs reveal that appropriately articulating the elements of
dynamical systems into the network design is truly beneficial to promoting the
network performance.
| [
{
"created": "Mon, 22 Feb 2021 06:53:51 GMT",
"version": "v1"
}
] | 2021-02-23 | [
[
"Zhu",
"Qunxi",
""
],
[
"Guo",
"Yao",
""
],
[
"Lin",
"Wei",
""
]
] | Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural networks, have been widely applied, showing exceptional efficacy in coping with some representative datasets. Recently, an augmented framework has been successfully developed for conquering some limitations emergent in application of the original framework. Here we propose a new class of continuous-depth neural networks with delay, named as Neural Delay Differential Equations (NDDEs), and, for computing the corresponding gradients, we use the adjoint sensitivity method to obtain the delayed dynamics of the adjoint. Since the differential equations with delays are usually seen as dynamical systems of infinite dimension possessing more fruitful dynamics, the NDDEs, compared to the NODEs, own a stronger capacity of nonlinear representations. Indeed, we analytically validate that the NDDEs are of universal approximators, and further articulate an extension of the NDDEs, where the initial function of the NDDEs is supposed to satisfy ODEs. More importantly, we use several illustrative examples to demonstrate the outstanding capacities of the NDDEs and the NDDEs with ODEs' initial value. Specifically, (1) we successfully model the delayed dynamics where the trajectories in the lower-dimensional phase space could be mutually intersected, while the traditional NODEs without any argumentation are not directly applicable for such modeling, and (2) we achieve lower loss and higher accuracy not only for the data produced synthetically by complex models but also for the real-world image datasets, i.e., CIFAR10, MNIST, and SVHN. Our results on the NDDEs reveal that appropriately articulating the elements of dynamical systems into the network design is truly beneficial to promoting the network performance. |
2307.00552 | R\'emy Chaput | R\'emy Chaput, Olivier Boissier, Mathieu Guillermin | Adaptive reinforcement learning of multi-agent ethically-aligned
behaviours: the QSOM and QDSOM algorithms | 30 pages, 7 figures, 7 tables | null | null | null | cs.LG cs.AI cs.CY cs.MA | http://creativecommons.org/licenses/by-sa/4.0/ | The numerous deployed Artificial Intelligence systems need to be aligned with
our ethical considerations. However, such ethical considerations might change
as time passes: our society is not fixed, and our social mores evolve. This
makes it difficult for these AI systems; in the Machine Ethics field
especially, it has remained an under-studied challenge. In this paper, we
present two algorithms, named QSOM and QDSOM, which are able to adapt to
changes in the environment, and especially in the reward function, which
represents the ethical considerations that we want these systems to be aligned
with. They associate the well-known Q-Table to (Dynamic) Self-Organizing Maps
to handle the continuous and multi-dimensional state and action spaces. We
evaluate them on a use-case of multi-agent energy repartition within a small
Smart Grid neighborhood, and prove their ability to adapt, and their higher
performance compared to baseline Reinforcement Learning algorithms.
| [
{
"created": "Sun, 2 Jul 2023 12:22:02 GMT",
"version": "v1"
}
] | 2023-07-04 | [
[
"Chaput",
"Rémy",
""
],
[
"Boissier",
"Olivier",
""
],
[
"Guillermin",
"Mathieu",
""
]
] | The numerous deployed Artificial Intelligence systems need to be aligned with our ethical considerations. However, such ethical considerations might change as time passes: our society is not fixed, and our social mores evolve. This makes it difficult for these AI systems; in the Machine Ethics field especially, it has remained an under-studied challenge. In this paper, we present two algorithms, named QSOM and QDSOM, which are able to adapt to changes in the environment, and especially in the reward function, which represents the ethical considerations that we want these systems to be aligned with. They associate the well-known Q-Table to (Dynamic) Self-Organizing Maps to handle the continuous and multi-dimensional state and action spaces. We evaluate them on a use-case of multi-agent energy repartition within a small Smart Grid neighborhood, and prove their ability to adapt, and their higher performance compared to baseline Reinforcement Learning algorithms. |
1905.09068 | Konstantinos Nikolaidis | Konstantinos Nikolaidis, Stein Kristiansen, Vera Goebel, Thomas
Plagemann, Knut Liest{\o}l, Mohan Kankanhalli | Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea
Detection | null | ECML-PKDD 2019 | null | null | cs.LG eess.SP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Supervised machine learning applications in the health domain often face the
problem of insufficient training datasets. The quantity of labelled data is
small due to privacy concerns and the cost of data acquisition and labelling by
a medical expert. Furthermore, it is quite common that collected data are
unbalanced and getting enough data to personalize models for individuals is
very expensive or even infeasible. This paper addresses these problems by (1)
designing a recurrent Generative Adversarial Network to generate realistic
synthetic data and to augment the original dataset, (2) enabling the generation
of balanced datasets based on heavily unbalanced dataset, and (3) to control
the data generation in such a way that the generated data resembles data from
specific individuals. We apply these solutions for sleep apnea detection and
study in the evaluation the performance of four well-known techniques, i.e.,
K-Nearest Neighbour, Random Forest, Multi-Layer Perceptron, and Support Vector
Machine. All classifiers exhibit in the experiments a consistent increase in
sensitivity and a kappa statistic increase by between 0.007 and 0.182.
| [
{
"created": "Wed, 22 May 2019 11:01:34 GMT",
"version": "v1"
}
] | 2021-12-10 | [
[
"Nikolaidis",
"Konstantinos",
""
],
[
"Kristiansen",
"Stein",
""
],
[
"Goebel",
"Vera",
""
],
[
"Plagemann",
"Thomas",
""
],
[
"Liestøl",
"Knut",
""
],
[
"Kankanhalli",
"Mohan",
""
]
] | Supervised machine learning applications in the health domain often face the problem of insufficient training datasets. The quantity of labelled data is small due to privacy concerns and the cost of data acquisition and labelling by a medical expert. Furthermore, it is quite common that collected data are unbalanced and getting enough data to personalize models for individuals is very expensive or even infeasible. This paper addresses these problems by (1) designing a recurrent Generative Adversarial Network to generate realistic synthetic data and to augment the original dataset, (2) enabling the generation of balanced datasets based on heavily unbalanced dataset, and (3) to control the data generation in such a way that the generated data resembles data from specific individuals. We apply these solutions for sleep apnea detection and study in the evaluation the performance of four well-known techniques, i.e., K-Nearest Neighbour, Random Forest, Multi-Layer Perceptron, and Support Vector Machine. All classifiers exhibit in the experiments a consistent increase in sensitivity and a kappa statistic increase by between 0.007 and 0.182. |
1910.06493 | Benjamin Adams | Mathew Darling, Benjamin Adams, Caroline Orchiston, Thomas Wilson,
Brendon Bradley | Understanding population fluctuations through volunteered geographic
information and novel indicators: The experience of Rakiura, Stewart Island,
New Zealand | 8 pages, GeoComputation 2019 | null | 10.17608/k6.auckland.9846323.v1 | null | cs.SI | http://creativecommons.org/licenses/by/4.0/ | In an era of heterogeneous data, novel methods and volunteered geographic
information provide opportunities to understand how people interact with a
place. However, it is not enough to simply have such heterogeneous data,
instead an understanding of its usability and reliability needs to be
undertaken. Here, we draw upon the case study of Rakiura, Stewart Island where
manifested passenger numbers across the Foveaux Strait are known. We have built
a population model to ground truth such novel indicators. In our preliminary
study, we find that a number of indicators offer the opportunity to understand
fluctuations in populations. Some indicators (such as wastewater volumes) can
suggest relative changes in populations in a raw form. While other indicators
(such as TripAdvisor reviews or Instagram posts) require further data
enrichment to get insights into population fluctuations. This research forms
part of a larger research project looking to test and apply such novel
indicators to inform disaster risk assessments.
| [
{
"created": "Tue, 15 Oct 2019 02:43:03 GMT",
"version": "v1"
}
] | 2019-10-16 | [
[
"Darling",
"Mathew",
""
],
[
"Adams",
"Benjamin",
""
],
[
"Orchiston",
"Caroline",
""
],
[
"Wilson",
"Thomas",
""
],
[
"Bradley",
"Brendon",
""
]
] | In an era of heterogeneous data, novel methods and volunteered geographic information provide opportunities to understand how people interact with a place. However, it is not enough to simply have such heterogeneous data, instead an understanding of its usability and reliability needs to be undertaken. Here, we draw upon the case study of Rakiura, Stewart Island where manifested passenger numbers across the Foveaux Strait are known. We have built a population model to ground truth such novel indicators. In our preliminary study, we find that a number of indicators offer the opportunity to understand fluctuations in populations. Some indicators (such as wastewater volumes) can suggest relative changes in populations in a raw form. While other indicators (such as TripAdvisor reviews or Instagram posts) require further data enrichment to get insights into population fluctuations. This research forms part of a larger research project looking to test and apply such novel indicators to inform disaster risk assessments. |
2008.05563 | Naimul Mefraz Khan | Bita Houshmand, Naimul Khan | Facial Expression Recognition Under Partial Occlusion from Virtual
Reality Headsets based on Transfer Learning | To be presented at the IEEE BigMM 2020 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Facial expressions of emotion are a major channel in our daily
communications, and it has been subject of intense research in recent years. To
automatically infer facial expressions, convolutional neural network based
approaches has become widely adopted due to their proven applicability to
Facial Expression Recognition (FER) task.On the other hand Virtual Reality (VR)
has gained popularity as an immersive multimedia platform, where FER can
provide enriched media experiences. However, recognizing facial expression
while wearing a head-mounted VR headset is a challenging task due to the upper
half of the face being completely occluded. In this paper we attempt to
overcome these issues and focus on facial expression recognition in presence of
a severe occlusion where the user is wearing a head-mounted display in a VR
setting. We propose a geometric model to simulate occlusion resulting from a
Samsung Gear VR headset that can be applied to existing FER datasets. Then, we
adopt a transfer learning approach, starting from two pretrained networks,
namely VGG and ResNet. We further fine-tune the networks on FER+ and RAF-DB
datasets. Experimental results show that our approach achieves comparable
results to existing methods while training on three modified benchmark datasets
that adhere to realistic occlusion resulting from wearing a commodity VR
headset. Code for this paper is available at:
https://github.com/bita-github/MRP-FER
| [
{
"created": "Wed, 12 Aug 2020 20:25:07 GMT",
"version": "v1"
}
] | 2020-08-14 | [
[
"Houshmand",
"Bita",
""
],
[
"Khan",
"Naimul",
""
]
] | Facial expressions of emotion are a major channel in our daily communications, and it has been subject of intense research in recent years. To automatically infer facial expressions, convolutional neural network based approaches has become widely adopted due to their proven applicability to Facial Expression Recognition (FER) task.On the other hand Virtual Reality (VR) has gained popularity as an immersive multimedia platform, where FER can provide enriched media experiences. However, recognizing facial expression while wearing a head-mounted VR headset is a challenging task due to the upper half of the face being completely occluded. In this paper we attempt to overcome these issues and focus on facial expression recognition in presence of a severe occlusion where the user is wearing a head-mounted display in a VR setting. We propose a geometric model to simulate occlusion resulting from a Samsung Gear VR headset that can be applied to existing FER datasets. Then, we adopt a transfer learning approach, starting from two pretrained networks, namely VGG and ResNet. We further fine-tune the networks on FER+ and RAF-DB datasets. Experimental results show that our approach achieves comparable results to existing methods while training on three modified benchmark datasets that adhere to realistic occlusion resulting from wearing a commodity VR headset. Code for this paper is available at: https://github.com/bita-github/MRP-FER |
1810.03048 | Gilwoo Lee | Gilwoo Lee, Sanjiban Choudhury, Brian Hou, Siddhartha S. Srinivasa | Bayes-CPACE: PAC Optimal Exploration in Continuous Space Bayes-Adaptive
Markov Decision Processes | null | null | null | null | cs.LG cs.RO stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision
Processes (BAMDPs) in continuous state and action spaces, to the best of our
knowledge. The BAMDP framework elegantly addresses model uncertainty by
incorporating Bayesian belief updates into long-term expected return. However,
computing an exact optimal Bayesian policy is intractable. Our key insight is
to compute a near-optimal value function by covering the continuous
state-belief-action space with a finite set of representative samples and
exploiting the Lipschitz continuity of the value function. We prove the
near-optimality of our algorithm and analyze a number of schemes that boost the
algorithm's efficiency. Finally, we empirically validate our approach on a
number of discrete and continuous BAMDPs and show that the learned policy has
consistently competitive performance against baseline approaches.
| [
{
"created": "Sat, 6 Oct 2018 20:37:38 GMT",
"version": "v1"
}
] | 2018-10-09 | [
[
"Lee",
"Gilwoo",
""
],
[
"Choudhury",
"Sanjiban",
""
],
[
"Hou",
"Brian",
""
],
[
"Srinivasa",
"Siddhartha S.",
""
]
] | We present the first PAC optimal algorithm for Bayes-Adaptive Markov Decision Processes (BAMDPs) in continuous state and action spaces, to the best of our knowledge. The BAMDP framework elegantly addresses model uncertainty by incorporating Bayesian belief updates into long-term expected return. However, computing an exact optimal Bayesian policy is intractable. Our key insight is to compute a near-optimal value function by covering the continuous state-belief-action space with a finite set of representative samples and exploiting the Lipschitz continuity of the value function. We prove the near-optimality of our algorithm and analyze a number of schemes that boost the algorithm's efficiency. Finally, we empirically validate our approach on a number of discrete and continuous BAMDPs and show that the learned policy has consistently competitive performance against baseline approaches. |
2312.05404 | Debo Cheng | Debo Cheng (1), Yang Xie (2), Ziqi Xu (1), Jiuyong Li (1), Lin Liu
(1), Jixue Liu (1), Yinghao Zhang (2) and Zaiwen Feng (2) ((1) UniSA STEM,
University of South Australia, Adelaide, Australia and (2) College of
Informatics, Huazhong Agricultural University, Wuhan, China) | Disentangled Latent Representation Learning for Tackling the Confounding
M-Bias Problem in Causal Inference | 10 pages, 3 figures and 5 tables. Accepted by ICDM2023 | null | null | null | cs.LG cs.AI stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In causal inference, it is a fundamental task to estimate the causal effect
from observational data. However, latent confounders pose major challenges in
causal inference in observational data, for example, confounding bias and
M-bias. Recent data-driven causal effect estimators tackle the confounding bias
problem via balanced representation learning, but assume no M-bias in the
system, thus they fail to handle the M-bias. In this paper, we identify a
challenging and unsolved problem caused by a variable that leads to confounding
bias and M-bias simultaneously. To address this problem with co-occurring
M-bias and confounding bias, we propose a novel Disentangled Latent
Representation learning framework for learning latent representations from
proxy variables for unbiased Causal effect Estimation (DLRCE) from
observational data. Specifically, DLRCE learns three sets of latent
representations from the measured proxy variables to adjust for the confounding
bias and M-bias. Extensive experiments on both synthetic and three real-world
datasets demonstrate that DLRCE significantly outperforms the state-of-the-art
estimators in the case of the presence of both confounding bias and M-bias.
| [
{
"created": "Fri, 8 Dec 2023 23:25:45 GMT",
"version": "v1"
}
] | 2023-12-12 | [
[
"Cheng",
"Debo",
""
],
[
"Xie",
"Yang",
""
],
[
"Xu",
"Ziqi",
""
],
[
"Li",
"Jiuyong",
""
],
[
"Liu",
"Lin",
""
],
[
"Liu",
"Jixue",
""
],
[
"Zhang",
"Yinghao",
""
],
[
"Feng",
"Zaiwen",
""
]
] | In causal inference, it is a fundamental task to estimate the causal effect from observational data. However, latent confounders pose major challenges in causal inference in observational data, for example, confounding bias and M-bias. Recent data-driven causal effect estimators tackle the confounding bias problem via balanced representation learning, but assume no M-bias in the system, thus they fail to handle the M-bias. In this paper, we identify a challenging and unsolved problem caused by a variable that leads to confounding bias and M-bias simultaneously. To address this problem with co-occurring M-bias and confounding bias, we propose a novel Disentangled Latent Representation learning framework for learning latent representations from proxy variables for unbiased Causal effect Estimation (DLRCE) from observational data. Specifically, DLRCE learns three sets of latent representations from the measured proxy variables to adjust for the confounding bias and M-bias. Extensive experiments on both synthetic and three real-world datasets demonstrate that DLRCE significantly outperforms the state-of-the-art estimators in the case of the presence of both confounding bias and M-bias. |
2008.00710 | Yuting He | Yuting He, Tiantian Li, Guanyu Yang, Youyong Kong, Yang Chen, Huazhong
Shu, Jean-Louis Coatrieux, Jean-Louis Dillenseger, Shuo Li | Deep Complementary Joint Model for Complex Scene Registration and
Few-shot Segmentation on Medical Images | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning-based medical image registration and segmentation joint models
utilize the complementarity (augmentation data or weakly supervised data from
registration, region constraints from segmentation) to bring mutual improvement
in complex scene and few-shot situation. However, further adoption of the joint
models are hindered: 1) the diversity of augmentation data is reduced limiting
the further enhancement of segmentation, 2) misaligned regions in weakly
supervised data disturb the training process, 3) lack of label-based region
constraints in few-shot situation limits the registration performance. We
propose a novel Deep Complementary Joint Model (DeepRS) for complex scene
registration and few-shot segmentation. We embed a perturbation factor in the
registration to increase the activity of deformation thus maintaining the
augmentation data diversity. We take a pixel-wise discriminator to extract
alignment confidence maps which highlight aligned regions in weakly supervised
data so the misaligned regions' disturbance will be suppressed via weighting.
The outputs from segmentation model are utilized to implement deep-based region
constraints thus relieving the label requirements and bringing fine
registration. Extensive experiments on the CT dataset of MM-WHS 2017 Challenge
show great advantages of our DeepRS that outperforms the existing
state-of-the-art models.
| [
{
"created": "Mon, 3 Aug 2020 08:25:59 GMT",
"version": "v1"
}
] | 2020-08-04 | [
[
"He",
"Yuting",
""
],
[
"Li",
"Tiantian",
""
],
[
"Yang",
"Guanyu",
""
],
[
"Kong",
"Youyong",
""
],
[
"Chen",
"Yang",
""
],
[
"Shu",
"Huazhong",
""
],
[
"Coatrieux",
"Jean-Louis",
""
],
[
"Dillenseger",
"Jean-Louis",
""
],
[
"Li",
"Shuo",
""
]
] | Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation. However, further adoption of the joint models are hindered: 1) the diversity of augmentation data is reduced limiting the further enhancement of segmentation, 2) misaligned regions in weakly supervised data disturb the training process, 3) lack of label-based region constraints in few-shot situation limits the registration performance. We propose a novel Deep Complementary Joint Model (DeepRS) for complex scene registration and few-shot segmentation. We embed a perturbation factor in the registration to increase the activity of deformation thus maintaining the augmentation data diversity. We take a pixel-wise discriminator to extract alignment confidence maps which highlight aligned regions in weakly supervised data so the misaligned regions' disturbance will be suppressed via weighting. The outputs from segmentation model are utilized to implement deep-based region constraints thus relieving the label requirements and bringing fine registration. Extensive experiments on the CT dataset of MM-WHS 2017 Challenge show great advantages of our DeepRS that outperforms the existing state-of-the-art models. |
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