id stringlengths 9 10 | submitter stringlengths 1 64 ⌀ | authors stringlengths 4 20.7k | title stringlengths 4 246 | comments stringlengths 1 523 ⌀ | journal-ref stringlengths 4 404 ⌀ | doi stringlengths 11 153 ⌀ | report-no stringlengths 2 254 ⌀ | categories stringlengths 5 98 | license stringclasses 9 values | orig_abstract stringlengths 14 3.35k | versions listlengths 1 60 | update_date stringlengths 10 10 | authors_parsed listlengths 1 1.35k | abstract stringlengths 11 3.34k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1801.01383 | Sheng Zhang | Sheng Zhang, Bo Liao, and Fei Liao | Computation of Optimal Control Problems with Terminal Constraint via
Variation Evolution | arXiv admin note: substantial text overlap with arXiv:1709.02242,
arXiv:1712.09702, arXiv:1711.02998, arXiv:1703.10263 | null | null | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Enlightened from the inverse consideration of the stable continuous-time
dynamics evolution, the Variation Evolving Method (VEM) analogizes the optimal
solution to the equilibrium point of an infinite-dimensional dynamic system and
solves it in an asymptotically evolving way. In this paper, the compact version
of the VEM is further developed for the computation of Optimal Control Problems
(OCPs) with terminal constraint. The corresponding Evolution Partial
Differential Equation (EPDE), which describes the variation motion towards the
optimal solution, is derived, and the costate-free optimality conditions are
established. The explicit analytic expressions of the costates and the Lagrange
multipliers adjoining the terminal constraint, related to the states and the
control variables, are presented. With the semi-discrete method in the field of
PDE numerical calculation, the EPDE is discretized as finite-dimensional
Initial-value Problems (IVPs) to be solved, with common Ordinary Differential
Equation (ODE) numerical integration methods.
| [
{
"created": "Tue, 2 Jan 2018 23:01:42 GMT",
"version": "v1"
}
] | 2018-01-08 | [
[
"Zhang",
"Sheng",
""
],
[
"Liao",
"Bo",
""
],
[
"Liao",
"Fei",
""
]
] | Enlightened from the inverse consideration of the stable continuous-time dynamics evolution, the Variation Evolving Method (VEM) analogizes the optimal solution to the equilibrium point of an infinite-dimensional dynamic system and solves it in an asymptotically evolving way. In this paper, the compact version of the VEM is further developed for the computation of Optimal Control Problems (OCPs) with terminal constraint. The corresponding Evolution Partial Differential Equation (EPDE), which describes the variation motion towards the optimal solution, is derived, and the costate-free optimality conditions are established. The explicit analytic expressions of the costates and the Lagrange multipliers adjoining the terminal constraint, related to the states and the control variables, are presented. With the semi-discrete method in the field of PDE numerical calculation, the EPDE is discretized as finite-dimensional Initial-value Problems (IVPs) to be solved, with common Ordinary Differential Equation (ODE) numerical integration methods. |
2407.12710 | Mohammad-Amin Charusaie | Mohammad-Amin Charusaie, Samira Samadi | A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer
Problems | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Learn-to-Defer is a paradigm that enables learning algorithms to work not in
isolation but as a team with human experts. In this paradigm, we permit the
system to defer a subset of its tasks to the expert. Although there are
currently systems that follow this paradigm and are designed to optimize the
accuracy of the final human-AI team, the general methodology for developing
such systems under a set of constraints (e.g., algorithmic fairness, expert
intervention budget, defer of anomaly, etc.) remains largely unexplored. In
this paper, using a $d$-dimensional generalization to the fundamental lemma of
Neyman and Pearson (d-GNP), we obtain the Bayes optimal solution for
learn-to-defer systems under various constraints. Furthermore, we design a
generalizable algorithm to estimate that solution and apply this algorithm to
the COMPAS and ACSIncome datasets. Our algorithm shows improvements in terms of
constraint violation over a set of baselines.
| [
{
"created": "Wed, 17 Jul 2024 16:32:30 GMT",
"version": "v1"
}
] | 2024-07-18 | [
[
"Charusaie",
"Mohammad-Amin",
""
],
[
"Samadi",
"Samira",
""
]
] | Learn-to-Defer is a paradigm that enables learning algorithms to work not in isolation but as a team with human experts. In this paradigm, we permit the system to defer a subset of its tasks to the expert. Although there are currently systems that follow this paradigm and are designed to optimize the accuracy of the final human-AI team, the general methodology for developing such systems under a set of constraints (e.g., algorithmic fairness, expert intervention budget, defer of anomaly, etc.) remains largely unexplored. In this paper, using a $d$-dimensional generalization to the fundamental lemma of Neyman and Pearson (d-GNP), we obtain the Bayes optimal solution for learn-to-defer systems under various constraints. Furthermore, we design a generalizable algorithm to estimate that solution and apply this algorithm to the COMPAS and ACSIncome datasets. Our algorithm shows improvements in terms of constraint violation over a set of baselines. |
2006.11607 | Marco Molinaro | Thomas Kesselheim and Marco Molinaro | Knapsack Secretary with Bursty Adversary | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The random-order or secretary model is one of the most popular beyond-worst
case model for online algorithms. While it avoids the pessimism of the
traditional adversarial model, in practice we cannot expect the input to be
presented in perfectly random order. This has motivated research on ``best of
both worlds'' (algorithms with good performance on both purely stochastic and
purely adversarial inputs), or even better, on inputs that are a mix of both
stochastic and adversarial parts. Unfortunately the latter seems much harder to
achieve and very few results of this type are known.
Towards advancing our understanding of designing such robust algorithms, we
propose a random-order model with bursts of adversarial time steps. The
assumption of burstiness of unexpected patterns is reasonable in many contexts,
since changes (e.g. spike in a demand for a good) are often triggered by a
common external event. We then consider the Knapsack Secretary problem in this
model: there is a knapsack of size $k$ (e.g., available quantity of a good),
and in each of the $n$ time steps an item comes with its value and size in
$[0,1]$ and the algorithm needs to make an irrevocable decision whether to
accept or reject the item.
We design an algorithm that gives an approximation of $1 -
\tilde{O}(\Gamma/k)$ when the adversarial time steps can be covered by $\Gamma
\ge \sqrt{k}$ intervals of size $\tilde{O}(\frac{n}{k})$. In particular,
setting $\Gamma = \sqrt{k}$ gives a $(1 - O(\frac{\ln^2
k}{\sqrt{k}}))$-approximation that is resistant to up to a $\frac{\ln^2
k}{\sqrt{k}}$-fraction of the items being adversarial, which is almost optimal
even in the absence of adversarial items. Also, setting $\Gamma =
\tilde{\Omega}(k)$ gives a constant approximation that is resistant to up to a
constant fraction of items being adversarial.
| [
{
"created": "Sat, 20 Jun 2020 16:24:22 GMT",
"version": "v1"
}
] | 2020-06-23 | [
[
"Kesselheim",
"Thomas",
""
],
[
"Molinaro",
"Marco",
""
]
] | The random-order or secretary model is one of the most popular beyond-worst case model for online algorithms. While it avoids the pessimism of the traditional adversarial model, in practice we cannot expect the input to be presented in perfectly random order. This has motivated research on ``best of both worlds'' (algorithms with good performance on both purely stochastic and purely adversarial inputs), or even better, on inputs that are a mix of both stochastic and adversarial parts. Unfortunately the latter seems much harder to achieve and very few results of this type are known. Towards advancing our understanding of designing such robust algorithms, we propose a random-order model with bursts of adversarial time steps. The assumption of burstiness of unexpected patterns is reasonable in many contexts, since changes (e.g. spike in a demand for a good) are often triggered by a common external event. We then consider the Knapsack Secretary problem in this model: there is a knapsack of size $k$ (e.g., available quantity of a good), and in each of the $n$ time steps an item comes with its value and size in $[0,1]$ and the algorithm needs to make an irrevocable decision whether to accept or reject the item. We design an algorithm that gives an approximation of $1 - \tilde{O}(\Gamma/k)$ when the adversarial time steps can be covered by $\Gamma \ge \sqrt{k}$ intervals of size $\tilde{O}(\frac{n}{k})$. In particular, setting $\Gamma = \sqrt{k}$ gives a $(1 - O(\frac{\ln^2 k}{\sqrt{k}}))$-approximation that is resistant to up to a $\frac{\ln^2 k}{\sqrt{k}}$-fraction of the items being adversarial, which is almost optimal even in the absence of adversarial items. Also, setting $\Gamma = \tilde{\Omega}(k)$ gives a constant approximation that is resistant to up to a constant fraction of items being adversarial. |
2111.09917 | Davood Rafiei | Arif Hasnat, Davood Rafiei | Interactive Set Discovery | To appear in the Proceedings of the EDBT 2023 Conference | null | null | null | cs.DB | http://creativecommons.org/licenses/by/4.0/ | We study the problem of set discovery where given a few example tuples of a
desired set, we want to find the set in a collection of sets. A challenge is
that the example tuples may not uniquely identify a set, and a large number of
candidate sets may be returned. Our focus is on interactive exploration to set
discovery where additional example tuples from the candidate sets are shown and
the user either accepts or rejects them as members of the target set. The goal
is to find the target set with the least number of user interactions. The
problem can be cast as an optimization problem where we want to find a decision
tree that can guide the search to the target set with the least number of
questions to be answered by the user. We propose a general algorithm that is
capable of reaching an optimal solution and two variations of it that strike a
balance between the quality of a solution and the running time. We also propose
a novel pruning strategy that safely reduces the search space without
introducing false negatives. We evaluate the efficiency and the effectiveness
of our algorithms through an extensive experimental study using both real and
synthetic datasets and comparing them to previous approaches in the literature.
We show that our pruning strategy reduces the running time of the search
algorithms by 2-5 orders of magnitude.
| [
{
"created": "Thu, 18 Nov 2021 19:21:23 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Oct 2022 23:43:07 GMT",
"version": "v2"
}
] | 2022-10-05 | [
[
"Hasnat",
"Arif",
""
],
[
"Rafiei",
"Davood",
""
]
] | We study the problem of set discovery where given a few example tuples of a desired set, we want to find the set in a collection of sets. A challenge is that the example tuples may not uniquely identify a set, and a large number of candidate sets may be returned. Our focus is on interactive exploration to set discovery where additional example tuples from the candidate sets are shown and the user either accepts or rejects them as members of the target set. The goal is to find the target set with the least number of user interactions. The problem can be cast as an optimization problem where we want to find a decision tree that can guide the search to the target set with the least number of questions to be answered by the user. We propose a general algorithm that is capable of reaching an optimal solution and two variations of it that strike a balance between the quality of a solution and the running time. We also propose a novel pruning strategy that safely reduces the search space without introducing false negatives. We evaluate the efficiency and the effectiveness of our algorithms through an extensive experimental study using both real and synthetic datasets and comparing them to previous approaches in the literature. We show that our pruning strategy reduces the running time of the search algorithms by 2-5 orders of magnitude. |
2205.00033 | Jakob Schoeffer | Jakob Schoeffer | A Human-Centric Perspective on Fairness and Transparency in Algorithmic
Decision-Making | CHI Conference on Human Factors in Computing Systems Extended
Abstracts (CHI '22 Extended Abstracts), April 29--May 5, 2022, New Orleans,
LA, USA | null | 10.1145/3491101.3503811 | null | cs.AI cs.HC cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Automated decision systems (ADS) are increasingly used for consequential
decision-making. These systems often rely on sophisticated yet opaque machine
learning models, which do not allow for understanding how a given decision was
arrived at. This is not only problematic from a legal perspective, but
non-transparent systems are also prone to yield unfair outcomes because their
sanity is challenging to assess and calibrate in the first place -- which is
particularly worrisome for human decision-subjects. Based on this observation
and building upon existing work, I aim to make the following three main
contributions through my doctoral thesis: (a) understand how (potential)
decision-subjects perceive algorithmic decisions (with varying degrees of
transparency of the underlying ADS), as compared to similar decisions made by
humans; (b) evaluate different tools for transparent decision-making with
respect to their effectiveness in enabling people to appropriately assess the
quality and fairness of ADS; and (c) develop human-understandable technical
artifacts for fair automated decision-making. Over the course of the first half
of my PhD program, I have already addressed substantial pieces of (a) and (c),
whereas (b) will be the major focus of the second half.
| [
{
"created": "Fri, 29 Apr 2022 18:31:04 GMT",
"version": "v1"
}
] | 2022-05-03 | [
[
"Schoeffer",
"Jakob",
""
]
] | Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a given decision was arrived at. This is not only problematic from a legal perspective, but non-transparent systems are also prone to yield unfair outcomes because their sanity is challenging to assess and calibrate in the first place -- which is particularly worrisome for human decision-subjects. Based on this observation and building upon existing work, I aim to make the following three main contributions through my doctoral thesis: (a) understand how (potential) decision-subjects perceive algorithmic decisions (with varying degrees of transparency of the underlying ADS), as compared to similar decisions made by humans; (b) evaluate different tools for transparent decision-making with respect to their effectiveness in enabling people to appropriately assess the quality and fairness of ADS; and (c) develop human-understandable technical artifacts for fair automated decision-making. Over the course of the first half of my PhD program, I have already addressed substantial pieces of (a) and (c), whereas (b) will be the major focus of the second half. |
2011.02574 | Andrei Cramariuc | Le Chen, Yunke Ao, Florian Tschopp, Andrei Cramariuc, Michel Breyer,
Jen Jen Chung, Roland Siegwart, Cesar Cadena | Learning Trajectories for Visual-Inertial System Calibration via
Model-based Heuristic Deep Reinforcement Learning | null | Proceedings of the 4th Conference on Robot Learning (CoRL) 2020 | null | null | cs.RO cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual-inertial systems rely on precise calibrations of both camera
intrinsics and inter-sensor extrinsics, which typically require manually
performing complex motions in front of a calibration target. In this work we
present a novel approach to obtain favorable trajectories for visual-inertial
system calibration, using model-based deep reinforcement learning. Our key
contribution is to model the calibration process as a Markov decision process
and then use model-based deep reinforcement learning with particle swarm
optimization to establish a sequence of calibration trajectories to be
performed by a robot arm. Our experiments show that while maintaining similar
or shorter path lengths, the trajectories generated by our learned policy
result in lower calibration errors compared to random or handcrafted
trajectories.
| [
{
"created": "Wed, 4 Nov 2020 23:20:15 GMT",
"version": "v1"
}
] | 2021-02-17 | [
[
"Chen",
"Le",
""
],
[
"Ao",
"Yunke",
""
],
[
"Tschopp",
"Florian",
""
],
[
"Cramariuc",
"Andrei",
""
],
[
"Breyer",
"Michel",
""
],
[
"Chung",
"Jen Jen",
""
],
[
"Siegwart",
"Roland",
""
],
[
"Cadena",
"Cesar",
""
]
] | Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target. In this work we present a novel approach to obtain favorable trajectories for visual-inertial system calibration, using model-based deep reinforcement learning. Our key contribution is to model the calibration process as a Markov decision process and then use model-based deep reinforcement learning with particle swarm optimization to establish a sequence of calibration trajectories to be performed by a robot arm. Our experiments show that while maintaining similar or shorter path lengths, the trajectories generated by our learned policy result in lower calibration errors compared to random or handcrafted trajectories. |
2402.18807 | Chenglei Shen | Chenglei Shen and Guofu Xie and Xiao Zhang and Jun Xu | On the Decision-Making Abilities in Role-Playing using Large Language
Models | null | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models (LLMs) are now increasingly utilized for role-playing
tasks, especially in impersonating domain-specific experts, primarily through
role-playing prompts. When interacting in real-world scenarios, the
decision-making abilities of a role significantly shape its behavioral
patterns. In this paper, we concentrate on evaluating the decision-making
abilities of LLMs post role-playing thereby validating the efficacy of
role-playing. Our goal is to provide metrics and guidance for enhancing the
decision-making abilities of LLMs in role-playing tasks. Specifically, we first
use LLMs to generate virtual role descriptions corresponding to the 16
personality types of Myers-Briggs Type Indicator (abbreviated as MBTI)
representing a segmentation of the population. Then we design specific
quantitative operations to evaluate the decision-making abilities of LLMs post
role-playing from four aspects: adaptability, exploration$\&$exploitation
trade-off ability, reasoning ability, and safety. Finally, we analyze the
association between the performance of decision-making and the corresponding
MBTI types through GPT-4. Extensive experiments demonstrate stable differences
in the four aspects of decision-making abilities across distinct roles,
signifying a robust correlation between decision-making abilities and the roles
emulated by LLMs. These results underscore that LLMs can effectively
impersonate varied roles while embodying their genuine sociological
characteristics.
| [
{
"created": "Thu, 29 Feb 2024 02:22:23 GMT",
"version": "v1"
}
] | 2024-03-01 | [
[
"Shen",
"Chenglei",
""
],
[
"Xie",
"Guofu",
""
],
[
"Zhang",
"Xiao",
""
],
[
"Xu",
"Jun",
""
]
] | Large language models (LLMs) are now increasingly utilized for role-playing tasks, especially in impersonating domain-specific experts, primarily through role-playing prompts. When interacting in real-world scenarios, the decision-making abilities of a role significantly shape its behavioral patterns. In this paper, we concentrate on evaluating the decision-making abilities of LLMs post role-playing thereby validating the efficacy of role-playing. Our goal is to provide metrics and guidance for enhancing the decision-making abilities of LLMs in role-playing tasks. Specifically, we first use LLMs to generate virtual role descriptions corresponding to the 16 personality types of Myers-Briggs Type Indicator (abbreviated as MBTI) representing a segmentation of the population. Then we design specific quantitative operations to evaluate the decision-making abilities of LLMs post role-playing from four aspects: adaptability, exploration$\&$exploitation trade-off ability, reasoning ability, and safety. Finally, we analyze the association between the performance of decision-making and the corresponding MBTI types through GPT-4. Extensive experiments demonstrate stable differences in the four aspects of decision-making abilities across distinct roles, signifying a robust correlation between decision-making abilities and the roles emulated by LLMs. These results underscore that LLMs can effectively impersonate varied roles while embodying their genuine sociological characteristics. |
2208.07304 | Di Wu | D. Wu | Vehicle-road Cooperative Simulation and 3D Visualization System | null | null | null | null | cs.RO cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The safety of single-vehicle autonomous driving technology is limited due to
the limits of perception capability of on-board sensors. In contrast,
vehicle-road collaboration technology can overcome those limits and improve the
traffic safety and efficiency, by expanding the sensing range, improving the
perception accuracy, and reducing the response time. However, such a technology
is still under development; it requires rigorous testing and verification
methods to ensure the reliability and trustworthiness of the technology. In
this thesis, we focus on three major tasks: (1) analyze the functional
characteristics related to the scenarios of vehicle-road cooperations,
highlightening the differences between vehicle-road cooperative systems and
traditional single-vehicle autonomous driving systems; (2) refine and classifiy
the functional characteristics of vehicle-road cooperative systems; (3) design
and develop a simulation system, and provide a visual interface to facilitate
development and analysis. The efficiency and effectiveness the proposed method
are verfied by experiments.
| [
{
"created": "Thu, 14 Jul 2022 04:53:54 GMT",
"version": "v1"
}
] | 2022-08-16 | [
[
"Wu",
"D.",
""
]
] | The safety of single-vehicle autonomous driving technology is limited due to the limits of perception capability of on-board sensors. In contrast, vehicle-road collaboration technology can overcome those limits and improve the traffic safety and efficiency, by expanding the sensing range, improving the perception accuracy, and reducing the response time. However, such a technology is still under development; it requires rigorous testing and verification methods to ensure the reliability and trustworthiness of the technology. In this thesis, we focus on three major tasks: (1) analyze the functional characteristics related to the scenarios of vehicle-road cooperations, highlightening the differences between vehicle-road cooperative systems and traditional single-vehicle autonomous driving systems; (2) refine and classifiy the functional characteristics of vehicle-road cooperative systems; (3) design and develop a simulation system, and provide a visual interface to facilitate development and analysis. The efficiency and effectiveness the proposed method are verfied by experiments. |
2210.02821 | Igor Korkin | Denis Pogonin, Igor Korkin | Microsoft Defender Will Be Defended: MemoryRanger Prevents Blinding
Windows AV | 29 pages, 17 figures, 1 table, In Proceedings of the ADFSL 2022, USA | null | null | null | cs.CR cs.OS | http://creativecommons.org/licenses/by/4.0/ | Windows OS is facing a huge rise in kernel attacks. An overview of popular
techniques that result in loading kernel drivers will be presented. One of the
key targets of modern threats is disabling and blinding Microsoft Defender, a
default Windows AV. The analysis of recent driver-based attacks will be given,
the challenge is to block them. The survey of user- and kernel-level attacks on
Microsoft Defender will be given. One of the recently published attackers
techniques abuses Mandatory Integrity Control (MIC) and Security Reference
Monitor (SRM) by modifying Integrity Level and Debug Privileges for the
Microsoft Defender via syscalls. However, this user-mode attack can be blocked
via the Windows 'trust labels' mechanism. The presented paper discovered the
internals of MIC and SRM, including the analysis of Microsoft Defender during
malware detection. We show how attackers can attack Microsoft Defender using a
kernel-mode driver. This driver modifies the fields of the Token structure
allocated for the Microsoft Defender application. The presented attack resulted
in disabling Microsoft Defender, without terminating any of its processes and
without triggering any Windows security features, such as PatchGuard. The
customized hypervisor-based solution named MemoryRanger was used to protect the
Windows Defender kernel structures. The experiments show that MemoryRanger
successfully restricts access to the sensitive kernel data from illegal access
attempts with affordable performance degradation.
| [
{
"created": "Thu, 6 Oct 2022 11:25:05 GMT",
"version": "v1"
}
] | 2022-10-07 | [
[
"Pogonin",
"Denis",
""
],
[
"Korkin",
"Igor",
""
]
] | Windows OS is facing a huge rise in kernel attacks. An overview of popular techniques that result in loading kernel drivers will be presented. One of the key targets of modern threats is disabling and blinding Microsoft Defender, a default Windows AV. The analysis of recent driver-based attacks will be given, the challenge is to block them. The survey of user- and kernel-level attacks on Microsoft Defender will be given. One of the recently published attackers techniques abuses Mandatory Integrity Control (MIC) and Security Reference Monitor (SRM) by modifying Integrity Level and Debug Privileges for the Microsoft Defender via syscalls. However, this user-mode attack can be blocked via the Windows 'trust labels' mechanism. The presented paper discovered the internals of MIC and SRM, including the analysis of Microsoft Defender during malware detection. We show how attackers can attack Microsoft Defender using a kernel-mode driver. This driver modifies the fields of the Token structure allocated for the Microsoft Defender application. The presented attack resulted in disabling Microsoft Defender, without terminating any of its processes and without triggering any Windows security features, such as PatchGuard. The customized hypervisor-based solution named MemoryRanger was used to protect the Windows Defender kernel structures. The experiments show that MemoryRanger successfully restricts access to the sensitive kernel data from illegal access attempts with affordable performance degradation. |
1304.3179 | Seok-Hwan Park | Seok-Hwan Park, Osvaldo Simeone, Onur Sahin and Shlomo Shamai (Shitz) | Joint Precoding and Multivariate Backhaul Compression for the Downlink
of Cloud Radio Access Networks | Submitted to IEEE Transactions on Signal Processing | null | 10.1109/TSP.2013.2280111 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work studies the joint design of precoding and backhaul compression
strategies for the downlink of cloud radio access networks. In these systems, a
central encoder is connected to multiple multi-antenna base stations (BSs) via
finite-capacity backhaul links. At the central encoder, precoding is followed
by compression in order to produce the rate-limited bit streams delivered to
each BS over the corresponding backhaul link. In current state-of-the-art
approaches, the signals intended for different BSs are compressed
independently. In contrast, this work proposes to leverage joint compression,
also referred to as multivariate compression, of the signals of different BSs
in order to better control the effect of the additive quantization noises at
the mobile stations (MSs). The problem of maximizing the weighted sum-rate with
respect to both the precoding matrix and the joint correlation matrix of the
quantization noises is formulated subject to power and backhaul capacity
constraints. An iterative algorithm is proposed that achieves a stationary
point of the problem. Moreover, in order to enable the practical implementation
of multivariate compression across BSs, a novel architecture is proposed based
on successive steps of minimum mean-squared error (MMSE) estimation and per-BS
compression. Robust design with respect to imperfect channel state information
is also discussed. From numerical results, it is confirmed that the proposed
joint precoding and compression strategy outperforms conventional approaches
based on the separate design of precoding and compression or independent
compression across the BSs.
| [
{
"created": "Thu, 11 Apr 2013 02:15:18 GMT",
"version": "v1"
}
] | 2015-06-15 | [
[
"Park",
"Seok-Hwan",
"",
"Shitz"
],
[
"Simeone",
"Osvaldo",
"",
"Shitz"
],
[
"Sahin",
"Onur",
"",
"Shitz"
],
[
"Shamai",
"Shlomo",
"",
"Shitz"
]
] | This work studies the joint design of precoding and backhaul compression strategies for the downlink of cloud radio access networks. In these systems, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art approaches, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals of different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate with respect to both the precoding matrix and the joint correlation matrix of the quantization noises is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. Moreover, in order to enable the practical implementation of multivariate compression across BSs, a novel architecture is proposed based on successive steps of minimum mean-squared error (MMSE) estimation and per-BS compression. Robust design with respect to imperfect channel state information is also discussed. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on the separate design of precoding and compression or independent compression across the BSs. |
2310.12727 | Johann-Mattis List | Johann-Mattis List, Nathan W. Hill, Robert Forkel, Frederic Blum | Representing and Computing Uncertainty in Phonological Reconstruction | To appear in: Proceedings of the 4th Workshop on Computational
Approaches to Historical Language Change | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Despite the inherently fuzzy nature of reconstructions in historical
linguistics, most scholars do not represent their uncertainty when proposing
proto-forms. With the increasing success of recently proposed approaches to
automating certain aspects of the traditional comparative method, the formal
representation of proto-forms has also improved. This formalization makes it
possible to address both the representation and the computation of uncertainty.
Building on recent advances in supervised phonological reconstruction, during
which an algorithm learns how to reconstruct words in a given proto-language
relying on previously annotated data, and inspired by improved methods for
automated word prediction from cognate sets, we present a new framework that
allows for the representation of uncertainty in linguistic reconstruction and
also includes a workflow for the computation of fuzzy reconstructions from
linguistic data.
| [
{
"created": "Thu, 19 Oct 2023 13:27:42 GMT",
"version": "v1"
}
] | 2023-10-20 | [
[
"List",
"Johann-Mattis",
""
],
[
"Hill",
"Nathan W.",
""
],
[
"Forkel",
"Robert",
""
],
[
"Blum",
"Frederic",
""
]
] | Despite the inherently fuzzy nature of reconstructions in historical linguistics, most scholars do not represent their uncertainty when proposing proto-forms. With the increasing success of recently proposed approaches to automating certain aspects of the traditional comparative method, the formal representation of proto-forms has also improved. This formalization makes it possible to address both the representation and the computation of uncertainty. Building on recent advances in supervised phonological reconstruction, during which an algorithm learns how to reconstruct words in a given proto-language relying on previously annotated data, and inspired by improved methods for automated word prediction from cognate sets, we present a new framework that allows for the representation of uncertainty in linguistic reconstruction and also includes a workflow for the computation of fuzzy reconstructions from linguistic data. |
1801.05768 | Zhen Chen | Zhen Chen, Zhiying Wang and Syed Jafar | The Asymptotic Capacity of Private Search | null | null | null | null | cs.IT cs.CR cs.DS math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The private search problem is introduced, where a dataset comprised of $L$
i.i.d. records is replicated across $N$ non-colluding servers, each record
takes values uniformly from an alphabet of size $K$, and a user wishes to
search for all records that match a privately chosen value, without revealing
any information about the chosen value to any individual server. The capacity
of private search is the maximum number of bits of desired information that can
be retrieved per bit of download. The asymptotic (large $K$) capacity of
private search is shown to be $1-1/N$, even as the scope of private search is
further generalized to allow approximate (OR) search over a number of
realizations that grows with $K$. The results are based on the asymptotic
behavior of a new converse bound for private information retrieval with
arbitrarily dependent messages.
| [
{
"created": "Fri, 12 Jan 2018 05:29:38 GMT",
"version": "v1"
},
{
"created": "Thu, 18 Jan 2018 18:17:36 GMT",
"version": "v2"
}
] | 2018-01-19 | [
[
"Chen",
"Zhen",
""
],
[
"Wang",
"Zhiying",
""
],
[
"Jafar",
"Syed",
""
]
] | The private search problem is introduced, where a dataset comprised of $L$ i.i.d. records is replicated across $N$ non-colluding servers, each record takes values uniformly from an alphabet of size $K$, and a user wishes to search for all records that match a privately chosen value, without revealing any information about the chosen value to any individual server. The capacity of private search is the maximum number of bits of desired information that can be retrieved per bit of download. The asymptotic (large $K$) capacity of private search is shown to be $1-1/N$, even as the scope of private search is further generalized to allow approximate (OR) search over a number of realizations that grows with $K$. The results are based on the asymptotic behavior of a new converse bound for private information retrieval with arbitrarily dependent messages. |
2403.01157 | Lorenz Graf-Vlachy | Lorenz Graf-Vlachy, Stefan Wagner | Different Debt: An Addition to the Technical Debt Dataset and a
Demonstration Using Developer Personality | null | 7th International Conference on Technical Debt (TechDebt) 2024 | 10.1145/3644384.3644475 | null | cs.SE | http://creativecommons.org/licenses/by/4.0/ | Background: The "Technical Debt Dataset" (TDD) is a comprehensive dataset on
technical debt (TD) in the main branches of more than 30 Java projects.
However, some TD items produced by SonarQube are not included for many commits,
for instance because the commits failed to compile. This has limited previous
studies using the dataset. Aims and Method: In this paper, we provide an
addition to the dataset that includes an analysis of 278,320 commits of all
branches in a superset of 37 projects using Teamscale. We then demonstrate the
utility of the dataset by exploring the relationship between developer
personality by replicating a prior study. Results: The new dataset allows us to
use a larger sample than prior work could, and we analyze the personality of
111 developers and 5,497 of their commits. The relationships we find between
developer personality and the introduction and removal of TD differ from those
found in prior work. Conclusions: We offer a dataset that may enable future
studies into the topic of TD and we provide additional insights on how
developer personality relates to TD.
| [
{
"created": "Sat, 2 Mar 2024 10:11:07 GMT",
"version": "v1"
}
] | 2024-03-05 | [
[
"Graf-Vlachy",
"Lorenz",
""
],
[
"Wagner",
"Stefan",
""
]
] | Background: The "Technical Debt Dataset" (TDD) is a comprehensive dataset on technical debt (TD) in the main branches of more than 30 Java projects. However, some TD items produced by SonarQube are not included for many commits, for instance because the commits failed to compile. This has limited previous studies using the dataset. Aims and Method: In this paper, we provide an addition to the dataset that includes an analysis of 278,320 commits of all branches in a superset of 37 projects using Teamscale. We then demonstrate the utility of the dataset by exploring the relationship between developer personality by replicating a prior study. Results: The new dataset allows us to use a larger sample than prior work could, and we analyze the personality of 111 developers and 5,497 of their commits. The relationships we find between developer personality and the introduction and removal of TD differ from those found in prior work. Conclusions: We offer a dataset that may enable future studies into the topic of TD and we provide additional insights on how developer personality relates to TD. |
1411.7191 | S{\o}ren Dahlgaard | S{\o}ren Dahlgaard, Mathias B{\ae}k Tejs Knudsen, Eva Rotenberg,
Mikkel Thorup | Hashing for statistics over k-partitions | Appear at FOCS'15 | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we analyze a hash function for $k$-partitioning a set into
bins, obtaining strong concentration bounds for standard algorithms combining
statistics from each bin.
This generic method was originally introduced by Flajolet and
Martin~[FOCS'83] in order to save a factor $\Omega(k)$ of time per element over
$k$ independent samples when estimating the number of distinct elements in a
data stream. It was also used in the widely used HyperLogLog algorithm of
Flajolet et al.~[AOFA'97] and in large-scale machine learning by Li et
al.~[NIPS'12] for minwise estimation of set similarity.
The main issue of $k$-partition, is that the contents of different bins may
be highly correlated when using popular hash functions. This means that methods
of analyzing the marginal distribution for a single bin do not apply. Here we
show that a tabulation based hash function, mixed tabulation, does yield strong
concentration bounds on the most popular applications of $k$-partitioning
similar to those we would get using a truly random hash function. The analysis
is very involved and implies several new results of independent interest for
both simple and double tabulation, e.g. a simple and efficient construction for
invertible bloom filters and uniform hashing on a given set.
| [
{
"created": "Wed, 26 Nov 2014 11:36:15 GMT",
"version": "v1"
},
{
"created": "Sun, 26 Apr 2015 14:27:46 GMT",
"version": "v2"
},
{
"created": "Mon, 15 Feb 2016 16:06:53 GMT",
"version": "v3"
}
] | 2016-02-16 | [
[
"Dahlgaard",
"Søren",
""
],
[
"Knudsen",
"Mathias Bæk Tejs",
""
],
[
"Rotenberg",
"Eva",
""
],
[
"Thorup",
"Mikkel",
""
]
] | In this paper we analyze a hash function for $k$-partitioning a set into bins, obtaining strong concentration bounds for standard algorithms combining statistics from each bin. This generic method was originally introduced by Flajolet and Martin~[FOCS'83] in order to save a factor $\Omega(k)$ of time per element over $k$ independent samples when estimating the number of distinct elements in a data stream. It was also used in the widely used HyperLogLog algorithm of Flajolet et al.~[AOFA'97] and in large-scale machine learning by Li et al.~[NIPS'12] for minwise estimation of set similarity. The main issue of $k$-partition, is that the contents of different bins may be highly correlated when using popular hash functions. This means that methods of analyzing the marginal distribution for a single bin do not apply. Here we show that a tabulation based hash function, mixed tabulation, does yield strong concentration bounds on the most popular applications of $k$-partitioning similar to those we would get using a truly random hash function. The analysis is very involved and implies several new results of independent interest for both simple and double tabulation, e.g. a simple and efficient construction for invertible bloom filters and uniform hashing on a given set. |
2201.12489 | Zhijian Duan | Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil
Zaheer, Xiaotie Deng | A Context-Integrated Transformer-Based Neural Network for Auction Design | Accepted by ICML 2022. Code is available at
https://github.com/zjduan/CITransNet | null | null | null | cs.GT cs.LG cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One of the central problems in auction design is developing an
incentive-compatible mechanism that maximizes the auctioneer's expected
revenue. While theoretical approaches have encountered bottlenecks in
multi-item auctions, recently, there has been much progress on finding the
optimal mechanism through deep learning. However, these works either focus on a
fixed set of bidders and items, or restrict the auction to be symmetric. In
this work, we overcome such limitations by factoring \emph{public} contextual
information of bidders and items into the auction learning framework. We
propose $\mathtt{CITransNet}$, a context-integrated transformer-based neural
network for optimal auction design, which maintains permutation-equivariance
over bids and contexts while being able to find asymmetric solutions. We show
by extensive experiments that $\mathtt{CITransNet}$ can recover the known
optimal solutions in single-item settings, outperform strong baselines in
multi-item auctions, and generalize well to cases other than those in training.
| [
{
"created": "Sat, 29 Jan 2022 03:47:00 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Jun 2022 07:34:51 GMT",
"version": "v2"
},
{
"created": "Sun, 22 Jan 2023 07:26:18 GMT",
"version": "v3"
}
] | 2023-01-24 | [
[
"Duan",
"Zhijian",
""
],
[
"Tang",
"Jingwu",
""
],
[
"Yin",
"Yutong",
""
],
[
"Feng",
"Zhe",
""
],
[
"Yan",
"Xiang",
""
],
[
"Zaheer",
"Manzil",
""
],
[
"Deng",
"Xiaotie",
""
]
] | One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have encountered bottlenecks in multi-item auctions, recently, there has been much progress on finding the optimal mechanism through deep learning. However, these works either focus on a fixed set of bidders and items, or restrict the auction to be symmetric. In this work, we overcome such limitations by factoring \emph{public} contextual information of bidders and items into the auction learning framework. We propose $\mathtt{CITransNet}$, a context-integrated transformer-based neural network for optimal auction design, which maintains permutation-equivariance over bids and contexts while being able to find asymmetric solutions. We show by extensive experiments that $\mathtt{CITransNet}$ can recover the known optimal solutions in single-item settings, outperform strong baselines in multi-item auctions, and generalize well to cases other than those in training. |
1906.09962 | Richard Olaniyan | Muthucumaru Maheswaran, Robert Wenger, Richard Olaniyan, Salman Memon,
Olamilekan Fadahunsi and Richboy Echomgbe | A Language for Programming Edge Clouds for Next Generation IoT
Applications | 22 pages, 9 figures, journal | null | null | null | cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For effective use of edge computing in an IoT application, we need to
partition the application into tasks and map them into the cloud, fog (edge
server), device levels such that the resources at the different levels are
optimally used to meet the overall quality of service requirements. In this
paper, we consider four concerns about application-to-fog mapping: task
placement at different levels, data filtering to limit network loading, fog
fail-over, and data consistency, and reacting to hotspots at the edge. We
describe a programming language and middleware we created for edge computing
that addresses the above four concerns. The language has a distributed-node
programming model that allows programs to be written for a collection of nodes
organized into a cloud, fog, device hierarchy. The paper describes the major
design elements of the language and explains the prototype implementation. The
unique distributed-node programming model embodied in the language enables new
edge-oriented programming patterns that are highly suitable for cognitive or
data-intensive edge computing workloads. The paper presents result from an
initial evaluation of the language prototype and also a distributed shell and a
smart parking app that were developed using the programming language.
| [
{
"created": "Fri, 21 Jun 2019 01:33:55 GMT",
"version": "v1"
}
] | 2019-06-25 | [
[
"Maheswaran",
"Muthucumaru",
""
],
[
"Wenger",
"Robert",
""
],
[
"Olaniyan",
"Richard",
""
],
[
"Memon",
"Salman",
""
],
[
"Fadahunsi",
"Olamilekan",
""
],
[
"Echomgbe",
"Richboy",
""
]
] | For effective use of edge computing in an IoT application, we need to partition the application into tasks and map them into the cloud, fog (edge server), device levels such that the resources at the different levels are optimally used to meet the overall quality of service requirements. In this paper, we consider four concerns about application-to-fog mapping: task placement at different levels, data filtering to limit network loading, fog fail-over, and data consistency, and reacting to hotspots at the edge. We describe a programming language and middleware we created for edge computing that addresses the above four concerns. The language has a distributed-node programming model that allows programs to be written for a collection of nodes organized into a cloud, fog, device hierarchy. The paper describes the major design elements of the language and explains the prototype implementation. The unique distributed-node programming model embodied in the language enables new edge-oriented programming patterns that are highly suitable for cognitive or data-intensive edge computing workloads. The paper presents result from an initial evaluation of the language prototype and also a distributed shell and a smart parking app that were developed using the programming language. |
1711.08534 | William Wang | William Wang, Angelina Wang, Aviv Tamar, Xi Chen, Pieter Abbeel | Safer Classification by Synthesis | null | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The discriminative approach to classification using deep neural networks has
become the de-facto standard in various fields. Complementing recent
reservations about safety against adversarial examples, we show that
conventional discriminative methods can easily be fooled to provide incorrect
labels with very high confidence to out of distribution examples. We posit that
a generative approach is the natural remedy for this problem, and propose a
method for classification using generative models. At training time, we learn a
generative model for each class, while at test time, given an example to
classify, we query each generator for its most similar generation, and select
the class corresponding to the most similar one. Our approach is general and
can be used with expressive models such as GANs and VAEs. At test time, our
method accurately "knows when it does not know," and provides resilience to out
of distribution examples while maintaining competitive performance for standard
examples.
| [
{
"created": "Wed, 22 Nov 2017 23:32:20 GMT",
"version": "v1"
},
{
"created": "Mon, 23 Jul 2018 23:47:59 GMT",
"version": "v2"
}
] | 2018-07-25 | [
[
"Wang",
"William",
""
],
[
"Wang",
"Angelina",
""
],
[
"Tamar",
"Aviv",
""
],
[
"Chen",
"Xi",
""
],
[
"Abbeel",
"Pieter",
""
]
] | The discriminative approach to classification using deep neural networks has become the de-facto standard in various fields. Complementing recent reservations about safety against adversarial examples, we show that conventional discriminative methods can easily be fooled to provide incorrect labels with very high confidence to out of distribution examples. We posit that a generative approach is the natural remedy for this problem, and propose a method for classification using generative models. At training time, we learn a generative model for each class, while at test time, given an example to classify, we query each generator for its most similar generation, and select the class corresponding to the most similar one. Our approach is general and can be used with expressive models such as GANs and VAEs. At test time, our method accurately "knows when it does not know," and provides resilience to out of distribution examples while maintaining competitive performance for standard examples. |
2404.00962 | Haokai Hong | Haokai Hong, Wanyu Lin, and Kay Chen Tan | Diffusion-Driven Domain Adaptation for Generating 3D Molecules | 11 pages, 3 figures, and 3 tables | null | null | null | cs.LG physics.chem-ph q-bio.BM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Can we train a molecule generator that can generate 3D molecules from a new
domain, circumventing the need to collect data? This problem can be cast as the
problem of domain adaptive molecule generation. This work presents a novel and
principled diffusion-based approach, called GADM, that allows shifting a
generative model to desired new domains without the need to collect even a
single molecule. As the domain shift is typically caused by the structure
variations of molecules, e.g., scaffold variations, we leverage a designated
equivariant masked autoencoder (MAE) along with various masking strategies to
capture the structural-grained representations of the in-domain varieties. In
particular, with an asymmetric encoder-decoder module, the MAE can generalize
to unseen structure variations from the target domains. These structure
variations are encoded with an equivariant encoder and treated as domain
supervisors to control denoising. We show that, with these encoded
structural-grained domain supervisors, GADM can generate effective molecules
within the desired new domains. We conduct extensive experiments across various
domain adaptation tasks over benchmarking datasets. We show that our approach
can improve up to 65.6% in terms of success rate defined based on molecular
validity, uniqueness, and novelty compared to alternative baselines.
| [
{
"created": "Mon, 1 Apr 2024 07:12:27 GMT",
"version": "v1"
}
] | 2024-04-02 | [
[
"Hong",
"Haokai",
""
],
[
"Lin",
"Wanyu",
""
],
[
"Tan",
"Kay Chen",
""
]
] | Can we train a molecule generator that can generate 3D molecules from a new domain, circumventing the need to collect data? This problem can be cast as the problem of domain adaptive molecule generation. This work presents a novel and principled diffusion-based approach, called GADM, that allows shifting a generative model to desired new domains without the need to collect even a single molecule. As the domain shift is typically caused by the structure variations of molecules, e.g., scaffold variations, we leverage a designated equivariant masked autoencoder (MAE) along with various masking strategies to capture the structural-grained representations of the in-domain varieties. In particular, with an asymmetric encoder-decoder module, the MAE can generalize to unseen structure variations from the target domains. These structure variations are encoded with an equivariant encoder and treated as domain supervisors to control denoising. We show that, with these encoded structural-grained domain supervisors, GADM can generate effective molecules within the desired new domains. We conduct extensive experiments across various domain adaptation tasks over benchmarking datasets. We show that our approach can improve up to 65.6% in terms of success rate defined based on molecular validity, uniqueness, and novelty compared to alternative baselines. |
2107.14551 | Thilanka Munasinghe | Thilanka Munasinghe, HR Pasindu | Sensing and Mapping for Better Roads: Initial Plan for Using Federated
Learning and Implementing a Digital Twin to Identify the Road Conditions in a
Developing Country -- Sri Lanka | 4 pages, KDD Workshop on Data-driven Humanitarian Mapping held with
the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August
14, 2021 | null | null | null | cs.CY cs.DC cs.LG | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We propose how a developing country like Sri Lanka can benefit from
privacy-enabled machine learning techniques such as Federated Learning to
detect road conditions using crowd-sourced data collection and proposed the
idea of implementing a Digital Twin for the national road system in Sri Lanka.
Developing countries such as Sri Lanka are far behind in implementing smart
road systems and smart cities compared to the developed countries. The proposed
work discussed in this paper matches the UN Sustainable Development Goal (SDG)
9: "Build Resilient Infrastructure, Promote Inclusive and Sustainable
Industrialization and Foster Innovation". Our proposed work discusses how the
government and private sector vehicles that conduct routine trips to collect
crowd-sourced data using smartphone devices to identify the road conditions and
detect where the potholes, surface unevenness (roughness), and other major
distresses are located on the roads. We explore Mobile Edge Computing (MEC)
techniques that can bring machine learning intelligence closer to the edge
devices where produced data is stored and show how the applications of
Federated Learning can be made to detect and improve road conditions. During
the second phase of this study, we plan to implement a Digital Twin for the
road system in Sri Lanka. We intend to use data provided by both Dedicated and
Non-Dedicated systems in the proposed Digital Twin for the road system. As of
writing this paper, and best to our knowledge, there is no Digital Twin system
implemented for roads and other infrastructure systems in Sri Lanka. The
proposed Digital Twin will be one of the first implementations of such systems
in Sri Lanka. Lessons learned from this pilot project will benefit other
developing countries who wish to follow the same path and make data-driven
decisions.
| [
{
"created": "Fri, 30 Jul 2021 11:06:32 GMT",
"version": "v1"
}
] | 2021-08-02 | [
[
"Munasinghe",
"Thilanka",
""
],
[
"Pasindu",
"HR",
""
]
] | We propose how a developing country like Sri Lanka can benefit from privacy-enabled machine learning techniques such as Federated Learning to detect road conditions using crowd-sourced data collection and proposed the idea of implementing a Digital Twin for the national road system in Sri Lanka. Developing countries such as Sri Lanka are far behind in implementing smart road systems and smart cities compared to the developed countries. The proposed work discussed in this paper matches the UN Sustainable Development Goal (SDG) 9: "Build Resilient Infrastructure, Promote Inclusive and Sustainable Industrialization and Foster Innovation". Our proposed work discusses how the government and private sector vehicles that conduct routine trips to collect crowd-sourced data using smartphone devices to identify the road conditions and detect where the potholes, surface unevenness (roughness), and other major distresses are located on the roads. We explore Mobile Edge Computing (MEC) techniques that can bring machine learning intelligence closer to the edge devices where produced data is stored and show how the applications of Federated Learning can be made to detect and improve road conditions. During the second phase of this study, we plan to implement a Digital Twin for the road system in Sri Lanka. We intend to use data provided by both Dedicated and Non-Dedicated systems in the proposed Digital Twin for the road system. As of writing this paper, and best to our knowledge, there is no Digital Twin system implemented for roads and other infrastructure systems in Sri Lanka. The proposed Digital Twin will be one of the first implementations of such systems in Sri Lanka. Lessons learned from this pilot project will benefit other developing countries who wish to follow the same path and make data-driven decisions. |
2005.12662 | Zihao Wang | Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles
Raffaelli, Nicolas Guevara, Herv\'e Delingette | A Deep Learning based Fast Signed Distance Map Generation | null | null | null | MIDL/2020/ExtendedAbstract/b2N5ZuEouu | cs.GR cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Signed distance map (SDM) is a common representation of surfaces in medical
image analysis and machine learning. The computational complexity of SDM for 3D
parametric shapes is often a bottleneck in many applications, thus limiting
their interest. In this paper, we propose a learning based SDM generation
neural network which is demonstrated on a tridimensional cochlea shape model
parameterized by 4 shape parameters. The proposed SDM Neural Network generates
a cochlea signed distance map depending on four input parameters and we show
that the deep learning approach leads to a 60 fold improvement in the time of
computation compared to more classical SDM generation methods. Therefore, the
proposed approach achieves a good trade-off between accuracy and efficiency.
| [
{
"created": "Tue, 26 May 2020 12:36:19 GMT",
"version": "v1"
}
] | 2022-12-01 | [
[
"Wang",
"Zihao",
""
],
[
"Vandersteen",
"Clair",
""
],
[
"Demarcy",
"Thomas",
""
],
[
"Gnansia",
"Dan",
""
],
[
"Raffaelli",
"Charles",
""
],
[
"Guevara",
"Nicolas",
""
],
[
"Delingette",
"Hervé",
""
]
] | Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency. |
2308.04798 | Qiushi Guo | Qiushi Guo | Enhancing Mobile Privacy and Security: A Face Skin Patch-Based
Anti-Spoofing Approach | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As Facial Recognition System(FRS) is widely applied in areas such as access
control and mobile payments due to its convenience and high accuracy. The
security of facial recognition is also highly regarded. The Face anti-spoofing
system(FAS) for face recognition is an important component used to enhance the
security of face recognition systems. Traditional FAS used images containing
identity information to detect spoofing traces, however there is a risk of
privacy leakage during the transmission and storage of these images. Besides,
the encryption and decryption of these privacy-sensitive data takes too long
compared to inference time by FAS model. To address the above issues, we
propose a face anti-spoofing algorithm based on facial skin patches leveraging
pure facial skin patch images as input, which contain no privacy information,
no encryption or decryption is needed for these images. We conduct experiments
on several public datasets, the results prove that our algorithm has
demonstrated superiority in both accuracy and speed.
| [
{
"created": "Wed, 9 Aug 2023 08:36:13 GMT",
"version": "v1"
}
] | 2023-08-10 | [
[
"Guo",
"Qiushi",
""
]
] | As Facial Recognition System(FRS) is widely applied in areas such as access control and mobile payments due to its convenience and high accuracy. The security of facial recognition is also highly regarded. The Face anti-spoofing system(FAS) for face recognition is an important component used to enhance the security of face recognition systems. Traditional FAS used images containing identity information to detect spoofing traces, however there is a risk of privacy leakage during the transmission and storage of these images. Besides, the encryption and decryption of these privacy-sensitive data takes too long compared to inference time by FAS model. To address the above issues, we propose a face anti-spoofing algorithm based on facial skin patches leveraging pure facial skin patch images as input, which contain no privacy information, no encryption or decryption is needed for these images. We conduct experiments on several public datasets, the results prove that our algorithm has demonstrated superiority in both accuracy and speed. |
1503.01416 | Bulent Abali | Bulent Abali, Richard J. Eickemeyer, Hubertus Franke, Chung-Sheng Li,
Marc A. Taubenblatt | Disaggregated and optically interconnected memory: when will it be cost
effective? | 9 pages, 7 figures | null | null | null | cs.DC cs.AR cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The "Disaggregated Server" concept has been proposed for datacenters where
the same type server resources are aggregated in their respective pools, for
example a compute pool, memory pool, network pool, and a storage pool. Each
server is constructed dynamically by allocating the right amount of resources
from these pools according to the workload's requirements. Modularity, higher
packaging and cooling efficiencies, and higher resource utilization are among
the suggested benefits. With the emergence of very large datacenters, "clouds"
containing tens of thousands of servers, datacenter efficiency has become an
important topic. Few computer chip and systems vendors are working on and
making frequent announcements on silicon photonics and disaggregated memory
systems.
In this paper we study the trade-off between cost and performance of building
a disaggregated memory system where DRAM modules in the datacenter are pooled,
for example in memory-only chassis and racks. The compute pool and the memory
pool are interconnected by an optical interconnect to overcome the distance and
bandwidth issues of electrical fabrics. We construct a simple cost model that
includes the cost of latency, cost of bandwidth and the savings expected from a
disaggregated memory system. We then identify the level at which a
disaggregated memory system becomes cost competitive with a traditional direct
attached memory system.
Our analysis shows that a rack-scale disaggregated memory system will have a
non-trivial performance penalty, and at the datacenter scale the penalty is
impractically high, and the optical interconnect costs are at least a factor of
10 more expensive than where they should be when compared to the traditional
direct attached memory systems.
| [
{
"created": "Tue, 3 Mar 2015 18:38:33 GMT",
"version": "v1"
}
] | 2015-03-09 | [
[
"Abali",
"Bulent",
""
],
[
"Eickemeyer",
"Richard J.",
""
],
[
"Franke",
"Hubertus",
""
],
[
"Li",
"Chung-Sheng",
""
],
[
"Taubenblatt",
"Marc A.",
""
]
] | The "Disaggregated Server" concept has been proposed for datacenters where the same type server resources are aggregated in their respective pools, for example a compute pool, memory pool, network pool, and a storage pool. Each server is constructed dynamically by allocating the right amount of resources from these pools according to the workload's requirements. Modularity, higher packaging and cooling efficiencies, and higher resource utilization are among the suggested benefits. With the emergence of very large datacenters, "clouds" containing tens of thousands of servers, datacenter efficiency has become an important topic. Few computer chip and systems vendors are working on and making frequent announcements on silicon photonics and disaggregated memory systems. In this paper we study the trade-off between cost and performance of building a disaggregated memory system where DRAM modules in the datacenter are pooled, for example in memory-only chassis and racks. The compute pool and the memory pool are interconnected by an optical interconnect to overcome the distance and bandwidth issues of electrical fabrics. We construct a simple cost model that includes the cost of latency, cost of bandwidth and the savings expected from a disaggregated memory system. We then identify the level at which a disaggregated memory system becomes cost competitive with a traditional direct attached memory system. Our analysis shows that a rack-scale disaggregated memory system will have a non-trivial performance penalty, and at the datacenter scale the penalty is impractically high, and the optical interconnect costs are at least a factor of 10 more expensive than where they should be when compared to the traditional direct attached memory systems. |
2011.07778 | Ji Woong Kim | Ji Woong Kim, Peiyao Zhang, Peter Gehlbach, Iulian Iordachita, Marin
Kobilarov | Towards Autonomous Eye Surgery by Combining Deep Imitation Learning with
Optimal Control | Accepted to Conference on Robot Learning (CoRL) 2020 | null | null | null | cs.RO | http://creativecommons.org/licenses/by/4.0/ | During retinal microsurgery, precise manipulation of the delicate retinal
tissue is required for positive surgical outcome. However, accurate
manipulation and navigation of surgical tools remain difficult due to a
constrained workspace and the top-down view during the surgery, which limits
the surgeon's ability to estimate depth. To alleviate such difficulty, we
propose to automate the tool-navigation task by learning to predict relative
goal position on the retinal surface from the current tool-tip position. Given
an estimated target on the retina, we generate an optimal trajectory leading to
the predicted goal while imposing safety-related physical constraints aimed to
minimize tissue damage. As an extended task, we generate goal predictions to
various points across the retina to localize eye geometry and further generate
safe trajectories within the estimated confines. Through experiments in both
simulation and with several eye phantoms, we demonstrate that our framework can
permit navigation to various points on the retina within 0.089mm and 0.118mm in
xy error which is less than the human's surgeon mean tremor at the tool-tip of
0.180mm. All safety constraints were fulfilled and the algorithm was robust to
previously unseen eyes as well as unseen objects in the scene. Live video
demonstration is available here: https://youtu.be/n5j5jCCelXk
| [
{
"created": "Mon, 16 Nov 2020 08:20:16 GMT",
"version": "v1"
}
] | 2020-11-17 | [
[
"Kim",
"Ji Woong",
""
],
[
"Zhang",
"Peiyao",
""
],
[
"Gehlbach",
"Peter",
""
],
[
"Iordachita",
"Iulian",
""
],
[
"Kobilarov",
"Marin",
""
]
] | During retinal microsurgery, precise manipulation of the delicate retinal tissue is required for positive surgical outcome. However, accurate manipulation and navigation of surgical tools remain difficult due to a constrained workspace and the top-down view during the surgery, which limits the surgeon's ability to estimate depth. To alleviate such difficulty, we propose to automate the tool-navigation task by learning to predict relative goal position on the retinal surface from the current tool-tip position. Given an estimated target on the retina, we generate an optimal trajectory leading to the predicted goal while imposing safety-related physical constraints aimed to minimize tissue damage. As an extended task, we generate goal predictions to various points across the retina to localize eye geometry and further generate safe trajectories within the estimated confines. Through experiments in both simulation and with several eye phantoms, we demonstrate that our framework can permit navigation to various points on the retina within 0.089mm and 0.118mm in xy error which is less than the human's surgeon mean tremor at the tool-tip of 0.180mm. All safety constraints were fulfilled and the algorithm was robust to previously unseen eyes as well as unseen objects in the scene. Live video demonstration is available here: https://youtu.be/n5j5jCCelXk |
2106.05825 | Mohammad Samavatian | Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber, Radu
Teodorescu | HASI: Hardware-Accelerated Stochastic Inference, A Defense Against
Adversarial Machine Learning Attacks | null | Secure and Private Systems for Machine Learning Workshop 2021 | null | null | cs.CR cs.AR cs.LG | http://creativecommons.org/licenses/by/4.0/ | Deep Neural Networks (DNNs) are employed in an increasing number of
applications, some of which are safety critical. Unfortunately, DNNs are known
to be vulnerable to so-called adversarial attacks that manipulate inputs to
cause incorrect results that can be beneficial to an attacker or damaging to
the victim. Multiple defenses have been proposed to increase the robustness of
DNNs. In general, these defenses have high overhead, some require
attack-specific re-training of the model or careful tuning to adapt to
different attacks.
This paper presents HASI, a hardware-accelerated defense that uses a process
we call stochastic inference to detect adversarial inputs. We show that by
carefully injecting noise into the model at inference time, we can
differentiate adversarial inputs from benign ones. HASI uses the output
distribution characteristics of noisy inference compared to a non-noisy
reference to detect adversarial inputs. We show an adversarial detection rate
of 86% when applied to VGG16 and 93% when applied to ResNet50, which exceeds
the detection rate of the state of the art approaches, with a much lower
overhead. We demonstrate two software/hardware-accelerated co-designs, which
reduces the performance impact of stochastic inference to 1.58X-2X relative to
the unprotected baseline, compared to 15X-20X overhead for a software-only GPU
implementation.
| [
{
"created": "Wed, 9 Jun 2021 14:31:28 GMT",
"version": "v1"
},
{
"created": "Thu, 15 Jul 2021 14:01:49 GMT",
"version": "v2"
},
{
"created": "Fri, 6 Aug 2021 16:03:11 GMT",
"version": "v3"
}
] | 2021-09-29 | [
[
"Samavatian",
"Mohammad Hossein",
""
],
[
"Majumdar",
"Saikat",
""
],
[
"Barber",
"Kristin",
""
],
[
"Teodorescu",
"Radu",
""
]
] | Deep Neural Networks (DNNs) are employed in an increasing number of applications, some of which are safety critical. Unfortunately, DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to cause incorrect results that can be beneficial to an attacker or damaging to the victim. Multiple defenses have been proposed to increase the robustness of DNNs. In general, these defenses have high overhead, some require attack-specific re-training of the model or careful tuning to adapt to different attacks. This paper presents HASI, a hardware-accelerated defense that uses a process we call stochastic inference to detect adversarial inputs. We show that by carefully injecting noise into the model at inference time, we can differentiate adversarial inputs from benign ones. HASI uses the output distribution characteristics of noisy inference compared to a non-noisy reference to detect adversarial inputs. We show an adversarial detection rate of 86% when applied to VGG16 and 93% when applied to ResNet50, which exceeds the detection rate of the state of the art approaches, with a much lower overhead. We demonstrate two software/hardware-accelerated co-designs, which reduces the performance impact of stochastic inference to 1.58X-2X relative to the unprotected baseline, compared to 15X-20X overhead for a software-only GPU implementation. |
1303.5723 | Daniel Hunter | Daniel Hunter | Non-monotonic Reasoning and the Reversibility of Belief Change | Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991) | null | null | UAI-P-1991-PG-159-164 | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traditional approaches to non-monotonic reasoning fail to satisfy a number of
plausible axioms for belief revision and suffer from conceptual difficulties as
well. Recent work on ranked preferential models (RPMs) promises to overcome
some of these difficulties. Here we show that RPMs are not adequate to handle
iterated belief change. Specifically, we show that RPMs do not always allow for
the reversibility of belief change. This result indicates the need for
numerical strengths of belief.
| [
{
"created": "Wed, 20 Mar 2013 15:31:06 GMT",
"version": "v1"
}
] | 2013-03-26 | [
[
"Hunter",
"Daniel",
""
]
] | Traditional approaches to non-monotonic reasoning fail to satisfy a number of plausible axioms for belief revision and suffer from conceptual difficulties as well. Recent work on ranked preferential models (RPMs) promises to overcome some of these difficulties. Here we show that RPMs are not adequate to handle iterated belief change. Specifically, we show that RPMs do not always allow for the reversibility of belief change. This result indicates the need for numerical strengths of belief. |
1709.05374 | Frank Ong | Frank Ong, Joseph Cheng, and Michael Lustig | General Phase Regularized Reconstruction using Phase Cycling | null | null | null | null | cs.CV physics.med-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Purpose: To develop a general phase regularized image reconstruction method,
with applications to partial Fourier imaging, water-fat imaging and flow
imaging.
Theory and Methods: The problem of enforcing phase constraints in
reconstruction was studied under a regularized inverse problem framework. A
general phase regularized reconstruction algorithm was proposed to enable
various joint reconstruction of partial Fourier imaging, water-fat imaging and
flow imaging, along with parallel imaging (PI) and compressed sensing (CS).
Since phase regularized reconstruction is inherently non-convex and sensitive
to phase wraps in the initial solution, a reconstruction technique, named phase
cycling, was proposed to render the overall algorithm invariant to phase wraps.
The proposed method was applied to retrospectively under-sampled in vivo
datasets and compared with state of the art reconstruction methods.
Results: Phase cycling reconstructions showed reduction of artifacts compared
to reconstructions with- out phase cycling and achieved similar performances as
state of the art results in partial Fourier, water-fat and divergence-free
regularized flow reconstruction. Joint reconstruction of partial Fourier +
water-fat imaging + PI + CS, and partial Fourier + divergence-free regularized
flow imaging + PI + CS were demonstrated.
Conclusion: The proposed phase cycling reconstruction provides an alternative
way to perform phase regularized reconstruction, without the need to perform
phase unwrapping. It is robust to the choice of initial solutions and
encourages the joint reconstruction of phase imaging applications.
| [
{
"created": "Fri, 15 Sep 2017 19:17:13 GMT",
"version": "v1"
}
] | 2017-09-26 | [
[
"Ong",
"Frank",
""
],
[
"Cheng",
"Joseph",
""
],
[
"Lustig",
"Michael",
""
]
] | Purpose: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging. Theory and Methods: The problem of enforcing phase constraints in reconstruction was studied under a regularized inverse problem framework. A general phase regularized reconstruction algorithm was proposed to enable various joint reconstruction of partial Fourier imaging, water-fat imaging and flow imaging, along with parallel imaging (PI) and compressed sensing (CS). Since phase regularized reconstruction is inherently non-convex and sensitive to phase wraps in the initial solution, a reconstruction technique, named phase cycling, was proposed to render the overall algorithm invariant to phase wraps. The proposed method was applied to retrospectively under-sampled in vivo datasets and compared with state of the art reconstruction methods. Results: Phase cycling reconstructions showed reduction of artifacts compared to reconstructions with- out phase cycling and achieved similar performances as state of the art results in partial Fourier, water-fat and divergence-free regularized flow reconstruction. Joint reconstruction of partial Fourier + water-fat imaging + PI + CS, and partial Fourier + divergence-free regularized flow imaging + PI + CS were demonstrated. Conclusion: The proposed phase cycling reconstruction provides an alternative way to perform phase regularized reconstruction, without the need to perform phase unwrapping. It is robust to the choice of initial solutions and encourages the joint reconstruction of phase imaging applications. |
2012.07175 | Chuqing Hu | Lang Su, Chuqing Hu, Guofa Li, Dongpu Cao | MSAF: Multimodal Split Attention Fusion | null | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal learning mimics the reasoning process of the human multi-sensory
system, which is used to perceive the surrounding world. While making a
prediction, the human brain tends to relate crucial cues from multiple sources
of information. In this work, we propose a novel multimodal fusion module that
learns to emphasize more contributive features across all modalities.
Specifically, the proposed Multimodal Split Attention Fusion (MSAF) module
splits each modality into channel-wise equal feature blocks and creates a joint
representation that is used to generate soft attention for each channel across
the feature blocks. Further, the MSAF module is designed to be compatible with
features of various spatial dimensions and sequence lengths, suitable for both
CNNs and RNNs. Thus, MSAF can be easily added to fuse features of any unimodal
networks and utilize existing pretrained unimodal model weights. To demonstrate
the effectiveness of our fusion module, we design three multimodal networks
with MSAF for emotion recognition, sentiment analysis, and action recognition
tasks. Our approach achieves competitive results in each task and outperforms
other application-specific networks and multimodal fusion benchmarks.
| [
{
"created": "Sun, 13 Dec 2020 22:42:41 GMT",
"version": "v1"
},
{
"created": "Sat, 26 Jun 2021 14:24:23 GMT",
"version": "v2"
}
] | 2021-06-29 | [
[
"Su",
"Lang",
""
],
[
"Hu",
"Chuqing",
""
],
[
"Li",
"Guofa",
""
],
[
"Cao",
"Dongpu",
""
]
] | Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world. While making a prediction, the human brain tends to relate crucial cues from multiple sources of information. In this work, we propose a novel multimodal fusion module that learns to emphasize more contributive features across all modalities. Specifically, the proposed Multimodal Split Attention Fusion (MSAF) module splits each modality into channel-wise equal feature blocks and creates a joint representation that is used to generate soft attention for each channel across the feature blocks. Further, the MSAF module is designed to be compatible with features of various spatial dimensions and sequence lengths, suitable for both CNNs and RNNs. Thus, MSAF can be easily added to fuse features of any unimodal networks and utilize existing pretrained unimodal model weights. To demonstrate the effectiveness of our fusion module, we design three multimodal networks with MSAF for emotion recognition, sentiment analysis, and action recognition tasks. Our approach achieves competitive results in each task and outperforms other application-specific networks and multimodal fusion benchmarks. |
1301.3299 | Wanwei Liu | Wanwei Liu and Rui Wang and Xianjin Fu and Ji Wang and Wei Dong and
Xiaoguang Mao | Counterexample-Preserving Reduction for Symbolic Model Checking | null | null | null | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The cost of LTL model checking is highly sensitive to the length of the
formula under verification. We observe that, under some specific conditions,
the input LTL formula can be reduced to an easier-to-handle one before model
checking. In our reduction, these two formulae need not to be logically
equivalent, but they share the same counterexample set w.r.t the model. In the
case that the model is symbolically represented, the condition enabling such
reduction can be detected with a lightweight effort (e.g., with SAT-solving).
In this paper, we tentatively name such technique "Counterexample-Preserving
Reduction" (CePRe for short), and finally the proposed technquie is
experimentally evaluated by adapting NuSMV.
| [
{
"created": "Tue, 15 Jan 2013 10:53:51 GMT",
"version": "v1"
}
] | 2013-01-16 | [
[
"Liu",
"Wanwei",
""
],
[
"Wang",
"Rui",
""
],
[
"Fu",
"Xianjin",
""
],
[
"Wang",
"Ji",
""
],
[
"Dong",
"Wei",
""
],
[
"Mao",
"Xiaoguang",
""
]
] | The cost of LTL model checking is highly sensitive to the length of the formula under verification. We observe that, under some specific conditions, the input LTL formula can be reduced to an easier-to-handle one before model checking. In our reduction, these two formulae need not to be logically equivalent, but they share the same counterexample set w.r.t the model. In the case that the model is symbolically represented, the condition enabling such reduction can be detected with a lightweight effort (e.g., with SAT-solving). In this paper, we tentatively name such technique "Counterexample-Preserving Reduction" (CePRe for short), and finally the proposed technquie is experimentally evaluated by adapting NuSMV. |
1701.01717 | Joshua Grochow | Joshua A. Grochow and Mrinal Kumar and Michael Saks and Shubhangi
Saraf | Towards an algebraic natural proofs barrier via polynomial identity
testing | null | null | null | null | cs.CC math.AG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We observe that a certain kind of algebraic proof - which covers essentially
all known algebraic circuit lower bounds to date - cannot be used to prove
lower bounds against VP if and only if what we call succinct hitting sets exist
for VP. This is analogous to the Razborov-Rudich natural proofs barrier in
Boolean circuit complexity, in that we rule out a large class of lower bound
techniques under a derandomization assumption. We also discuss connections
between this algebraic natural proofs barrier, geometric complexity theory, and
(algebraic) proof complexity.
| [
{
"created": "Fri, 6 Jan 2017 18:27:48 GMT",
"version": "v1"
}
] | 2017-01-09 | [
[
"Grochow",
"Joshua A.",
""
],
[
"Kumar",
"Mrinal",
""
],
[
"Saks",
"Michael",
""
],
[
"Saraf",
"Shubhangi",
""
]
] | We observe that a certain kind of algebraic proof - which covers essentially all known algebraic circuit lower bounds to date - cannot be used to prove lower bounds against VP if and only if what we call succinct hitting sets exist for VP. This is analogous to the Razborov-Rudich natural proofs barrier in Boolean circuit complexity, in that we rule out a large class of lower bound techniques under a derandomization assumption. We also discuss connections between this algebraic natural proofs barrier, geometric complexity theory, and (algebraic) proof complexity. |
2406.19589 | Luke Dzwonczyk | Luke Dzwonczyk and Carmine Emanuele Cella and David Ban | Network Bending of Diffusion Models for Audio-Visual Generation | 8 pages, 5 figures, to be published in the proceedings of the 27th
International Conference on Digital Audio Effects (DAFx24), for additional
image and video examples see https://dzluke.github.io/DAFX2024/ | null | null | null | cs.SD cs.LG cs.MM eess.AS | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In this paper we present the first steps towards the creation of a tool which
enables artists to create music visualizations using pre-trained, generative,
machine learning models. First, we investigate the application of network
bending, the process of applying transforms within the layers of a generative
network, to image generation diffusion models by utilizing a range of
point-wise, tensor-wise, and morphological operators. We identify a number of
visual effects that result from various operators, including some that are not
easily recreated with standard image editing tools. We find that this process
allows for continuous, fine-grain control of image generation which can be
helpful for creative applications. Next, we generate music-reactive videos
using Stable Diffusion by passing audio features as parameters to network
bending operators. Finally, we comment on certain transforms which radically
shift the image and the possibilities of learning more about the latent space
of Stable Diffusion based on these transforms.
| [
{
"created": "Fri, 28 Jun 2024 00:39:17 GMT",
"version": "v1"
}
] | 2024-07-01 | [
[
"Dzwonczyk",
"Luke",
""
],
[
"Cella",
"Carmine Emanuele",
""
],
[
"Ban",
"David",
""
]
] | In this paper we present the first steps towards the creation of a tool which enables artists to create music visualizations using pre-trained, generative, machine learning models. First, we investigate the application of network bending, the process of applying transforms within the layers of a generative network, to image generation diffusion models by utilizing a range of point-wise, tensor-wise, and morphological operators. We identify a number of visual effects that result from various operators, including some that are not easily recreated with standard image editing tools. We find that this process allows for continuous, fine-grain control of image generation which can be helpful for creative applications. Next, we generate music-reactive videos using Stable Diffusion by passing audio features as parameters to network bending operators. Finally, we comment on certain transforms which radically shift the image and the possibilities of learning more about the latent space of Stable Diffusion based on these transforms. |
2108.09897 | Khoi Nguyen | Khoi Nguyen, Sinisa Todorovic | A Weakly Supervised Amodal Segmenter with Boundary Uncertainty
Estimation | Accepted to ICCV 2021 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | This paper addresses weakly supervised amodal instance segmentation, where
the goal is to segment both visible and occluded (amodal) object parts, while
training provides only ground-truth visible (modal) segmentations. Following
prior work, we use data manipulation to generate occlusions in training images
and thus train a segmenter to predict amodal segmentations of the manipulated
data. The resulting predictions on training images are taken as the
pseudo-ground truth for the standard training of Mask-RCNN, which we use for
amodal instance segmentation of test images. For generating the pseudo-ground
truth, we specify a new Amodal Segmenter based on Boundary Uncertainty
estimation (ASBU) and make two contributions. First, while prior work uses the
occluder's mask, our ASBU uses the occlusion boundary as input. Second, ASBU
estimates an uncertainty map of the prediction. The estimated uncertainty
regularizes learning such that lower segmentation loss is incurred on regions
with high uncertainty. ASBU achieves significant performance improvement
relative to the state of the art on the COCOA and KINS datasets in three tasks:
amodal instance segmentation, amodal completion, and ordering recovery.
| [
{
"created": "Mon, 23 Aug 2021 02:27:29 GMT",
"version": "v1"
},
{
"created": "Mon, 30 Aug 2021 02:17:00 GMT",
"version": "v2"
}
] | 2021-09-01 | [
[
"Nguyen",
"Khoi",
""
],
[
"Todorovic",
"Sinisa",
""
]
] | This paper addresses weakly supervised amodal instance segmentation, where the goal is to segment both visible and occluded (amodal) object parts, while training provides only ground-truth visible (modal) segmentations. Following prior work, we use data manipulation to generate occlusions in training images and thus train a segmenter to predict amodal segmentations of the manipulated data. The resulting predictions on training images are taken as the pseudo-ground truth for the standard training of Mask-RCNN, which we use for amodal instance segmentation of test images. For generating the pseudo-ground truth, we specify a new Amodal Segmenter based on Boundary Uncertainty estimation (ASBU) and make two contributions. First, while prior work uses the occluder's mask, our ASBU uses the occlusion boundary as input. Second, ASBU estimates an uncertainty map of the prediction. The estimated uncertainty regularizes learning such that lower segmentation loss is incurred on regions with high uncertainty. ASBU achieves significant performance improvement relative to the state of the art on the COCOA and KINS datasets in three tasks: amodal instance segmentation, amodal completion, and ordering recovery. |
2211.05627 | Alexander K\"uchler | Alexander K\"uchler and Christian Banse | Representing LLVM-IR in a Code Property Graph | null | Information Security (ISC) 2022 | 10.1007/978-3-031-22390-7_21 | null | cs.SE cs.CR cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the past years, a number of static application security testing tools have
been proposed which make use of so-called code property graphs, a graph model
which keeps rich information about the source code while enabling its user to
write language-agnostic analyses. However, they suffer from several
shortcomings. They work mostly on source code and exclude the analysis of
third-party dependencies if they are only available as compiled binaries.
Furthermore, they are limited in their analysis to whether an individual
programming language is supported or not. While often support for
well-established languages such as C/C++ or Java is included, languages that
are still heavily evolving, such as Rust, are not considered because of the
constant changes in the language design. To overcome these limitations, we
extend an open source implementation of a code property graph to support
LLVM-IR which can be used as output by many compilers and binary lifters. In
this paper, we discuss how we address challenges that arise when mapping
concepts of an intermediate representation to a CPG. At the same time, we
optimize the resulting graph to be minimal and close to the representation of
equivalent source code. Our evaluation indicates that existing analyses can be
reused without modifications and that the performance requirements are
comparable to operating on source code. This makes the approach suitable for an
analysis of large-scale projects.
| [
{
"created": "Wed, 9 Nov 2022 09:37:30 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Dec 2022 07:00:31 GMT",
"version": "v2"
}
] | 2022-12-12 | [
[
"Küchler",
"Alexander",
""
],
[
"Banse",
"Christian",
""
]
] | In the past years, a number of static application security testing tools have been proposed which make use of so-called code property graphs, a graph model which keeps rich information about the source code while enabling its user to write language-agnostic analyses. However, they suffer from several shortcomings. They work mostly on source code and exclude the analysis of third-party dependencies if they are only available as compiled binaries. Furthermore, they are limited in their analysis to whether an individual programming language is supported or not. While often support for well-established languages such as C/C++ or Java is included, languages that are still heavily evolving, such as Rust, are not considered because of the constant changes in the language design. To overcome these limitations, we extend an open source implementation of a code property graph to support LLVM-IR which can be used as output by many compilers and binary lifters. In this paper, we discuss how we address challenges that arise when mapping concepts of an intermediate representation to a CPG. At the same time, we optimize the resulting graph to be minimal and close to the representation of equivalent source code. Our evaluation indicates that existing analyses can be reused without modifications and that the performance requirements are comparable to operating on source code. This makes the approach suitable for an analysis of large-scale projects. |
2302.10257 | Md Ibrahim | Moloy Kumar Ghosh, Milton Kumar Kundu, Md Ibrahim, A. S. M.
Badrudduza, Md. Shamim Anower, Imran Shafique Ansari, Ali A. Shaikhi,
Mohammed A. Mohandes | Secrecy Outage Analysis of Energy Harvesting Relay-based Mixed UOWC-RF
Network with Multiple Eavesdroppers | No | null | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/licenses/by/4.0/ | This work deals with the physical layer security performance of a dual-hop
underwater optical communication (UOWC)-radio frequency (RF) network under the
intruding attempts of multiple eavesdroppers via RF links. The intermediate
decode and forward relay node between the underwater source and the destination
transforms the optical signal into electrical form and re-transmits it to the
destination node with the help of harvested energy by the relay from an
integrated power beacon within the system. The source-to-relay link (UOWC)
follows a mixture exponential generalized Gamma turbulence with pointing error
impairments whereas all the remaining links (RF) undergo $\kappa-\mu$ shadowed
fading. With regards to the types of intruders, herein two scenarios are
considered, i.e., colluding (\textit{Scenario-I}) and non-colluding
(\textit{Scenario-II}) eavesdroppers and the analytical expressions of secure
outage probability, probability of strictly positive secrecy capacity, and
effective secrecy throughput are derived in closed form for each scenario.
Furthermore, the impacts of UOWC and RF channel parameters as well as detection
techniques on secrecy capacity are demonstrated, and following this a
comparison between the two considered scenarios is demonstrated that reveals
the collusion between the eavesdroppers imposes the most harmful threat on
secrecy throughput but a better secrecy level can be attained adopting
diversity at the destination and power beacon nodes along with heterodyne
detection rather than intensity modulation and direct detection technique.
Finally, all the derived expressions are corroborated via Monte Carlo
simulations.
| [
{
"created": "Mon, 20 Feb 2023 19:40:40 GMT",
"version": "v1"
}
] | 2023-02-22 | [
[
"Ghosh",
"Moloy Kumar",
""
],
[
"Kundu",
"Milton Kumar",
""
],
[
"Ibrahim",
"Md",
""
],
[
"Badrudduza",
"A. S. M.",
""
],
[
"Anower",
"Md. Shamim",
""
],
[
"Ansari",
"Imran Shafique",
""
],
[
"Shaikhi",
"Ali A.",
""
],
[
"Mohandes",
"Mohammed A.",
""
]
] | This work deals with the physical layer security performance of a dual-hop underwater optical communication (UOWC)-radio frequency (RF) network under the intruding attempts of multiple eavesdroppers via RF links. The intermediate decode and forward relay node between the underwater source and the destination transforms the optical signal into electrical form and re-transmits it to the destination node with the help of harvested energy by the relay from an integrated power beacon within the system. The source-to-relay link (UOWC) follows a mixture exponential generalized Gamma turbulence with pointing error impairments whereas all the remaining links (RF) undergo $\kappa-\mu$ shadowed fading. With regards to the types of intruders, herein two scenarios are considered, i.e., colluding (\textit{Scenario-I}) and non-colluding (\textit{Scenario-II}) eavesdroppers and the analytical expressions of secure outage probability, probability of strictly positive secrecy capacity, and effective secrecy throughput are derived in closed form for each scenario. Furthermore, the impacts of UOWC and RF channel parameters as well as detection techniques on secrecy capacity are demonstrated, and following this a comparison between the two considered scenarios is demonstrated that reveals the collusion between the eavesdroppers imposes the most harmful threat on secrecy throughput but a better secrecy level can be attained adopting diversity at the destination and power beacon nodes along with heterodyne detection rather than intensity modulation and direct detection technique. Finally, all the derived expressions are corroborated via Monte Carlo simulations. |
2202.12855 | Krishnasuri Narayanam | Krishnasuri Narayanam, Venkatraman Ramakrishna, Dhinakaran
Vinayagamurthy and Sandeep Nishad | Atomic cross-chain exchanges of shared assets | null | null | 10.1145/3558535.3559786 | null | cs.CR cs.DC | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A core enabler for blockchain or DLT interoperability is the ability to
atomically exchange assets held by mutually untrusting owners on different
ledgers. This atomic swap problem has been well-studied, with the Hash Time
Locked Contract (HTLC) emerging as a canonical solution. HTLC ensures atomicity
of exchange, albeit with caveats for node failure and timeliness of claims. But
a bigger limitation of HTLC is that it only applies to a model consisting of
two adversarial parties having sole ownership of a single asset in each ledger.
Realistic extensions of the model in which assets may be jointly owned by
multiple parties, all of whose consents are required for exchanges, or where
multiple assets must be exchanged for one, are susceptible to collusion attacks
and hence cannot be handled by HTLC. In this paper, we generalize the model of
asset exchanges across DLT networks and present a taxonomy of use cases,
describe the threat model, and propose MPHTLC, an augmented HTLC protocol for
atomic multi-owner-and-asset exchanges. We analyze the correctness, safety, and
application scope of MPHTLC. As proof-of-concept, we show how MPHTLC primitives
can be implemented in networks built on Hyperledger Fabric and Corda, and how
MPHTLC can be implemented in the Hyperledger Labs Weaver framework by
augmenting its existing HTLC protocol.
| [
{
"created": "Fri, 25 Feb 2022 18:04:30 GMT",
"version": "v1"
},
{
"created": "Tue, 31 May 2022 12:33:04 GMT",
"version": "v2"
},
{
"created": "Sat, 10 Sep 2022 19:50:03 GMT",
"version": "v3"
}
] | 2022-09-13 | [
[
"Narayanam",
"Krishnasuri",
""
],
[
"Ramakrishna",
"Venkatraman",
""
],
[
"Vinayagamurthy",
"Dhinakaran",
""
],
[
"Nishad",
"Sandeep",
""
]
] | A core enabler for blockchain or DLT interoperability is the ability to atomically exchange assets held by mutually untrusting owners on different ledgers. This atomic swap problem has been well-studied, with the Hash Time Locked Contract (HTLC) emerging as a canonical solution. HTLC ensures atomicity of exchange, albeit with caveats for node failure and timeliness of claims. But a bigger limitation of HTLC is that it only applies to a model consisting of two adversarial parties having sole ownership of a single asset in each ledger. Realistic extensions of the model in which assets may be jointly owned by multiple parties, all of whose consents are required for exchanges, or where multiple assets must be exchanged for one, are susceptible to collusion attacks and hence cannot be handled by HTLC. In this paper, we generalize the model of asset exchanges across DLT networks and present a taxonomy of use cases, describe the threat model, and propose MPHTLC, an augmented HTLC protocol for atomic multi-owner-and-asset exchanges. We analyze the correctness, safety, and application scope of MPHTLC. As proof-of-concept, we show how MPHTLC primitives can be implemented in networks built on Hyperledger Fabric and Corda, and how MPHTLC can be implemented in the Hyperledger Labs Weaver framework by augmenting its existing HTLC protocol. |
1902.06914 | Adil Rajput | Adil E. Rajput, Akila Sarirete and Tamer F. Desouky | Using Crowdsourcing to Identify a Proxy of Socio-Economic status | null | null | null | null | cs.CY | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Social Media provides researchers with an unprecedented opportunity to gain
insight into various facets of human life. Health practitioners put a great
emphasis on pinpointing socioeconomic status (SES) of individuals as they can
use to it to predict certain diseases. Crowdsourcing is a term coined that
entails gathering intelligence from a user community online. In order to group
the users online into communities, researchers have made use of hashtags that
will cull the interest of a community of users. In this paper, we propose a
mechanism to group a certain group of users based on their geographic
background and build a corpus for such users. Specifically, we have looked at
discussion forums for some vehi-cles where the site has established communities
for different areas to air their grievances or sing the praises of the vehicle.
From such a discussion, it was pos-sible to glean the vocabulary that these
group of users adheres to. We compared the corpus of different communities and
noted the difference in the choice of language. This provided us with the
groundwork for predicting the socio-eco-nomic status of such communities that
can be particularly helpful to health prac-titioners and in turn used in smart
cities to provide better services to the commu-nity members. More work is
underway to take words and emojis out of vo-cablary(OOV) and assessing the
average score as special cases.
| [
{
"created": "Tue, 19 Feb 2019 06:25:23 GMT",
"version": "v1"
}
] | 2019-02-20 | [
[
"Rajput",
"Adil E.",
""
],
[
"Sarirete",
"Akila",
""
],
[
"Desouky",
"Tamer F.",
""
]
] | Social Media provides researchers with an unprecedented opportunity to gain insight into various facets of human life. Health practitioners put a great emphasis on pinpointing socioeconomic status (SES) of individuals as they can use to it to predict certain diseases. Crowdsourcing is a term coined that entails gathering intelligence from a user community online. In order to group the users online into communities, researchers have made use of hashtags that will cull the interest of a community of users. In this paper, we propose a mechanism to group a certain group of users based on their geographic background and build a corpus for such users. Specifically, we have looked at discussion forums for some vehi-cles where the site has established communities for different areas to air their grievances or sing the praises of the vehicle. From such a discussion, it was pos-sible to glean the vocabulary that these group of users adheres to. We compared the corpus of different communities and noted the difference in the choice of language. This provided us with the groundwork for predicting the socio-eco-nomic status of such communities that can be particularly helpful to health prac-titioners and in turn used in smart cities to provide better services to the commu-nity members. More work is underway to take words and emojis out of vo-cablary(OOV) and assessing the average score as special cases. |
2407.05461 | Mohamed Elmahallawy | Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong | CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious
Sensors in CAV Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | The adoption of connected and automated vehicles (CAVs) has sparked
considerable interest across diverse industries, including public
transportation, underground mining, and agriculture sectors. However, CAVs'
reliance on sensor readings makes them vulnerable to significant threats.
Manipulating these readings can compromise CAV network security, posing serious
risks for malicious activities. Although several anomaly detection (AD)
approaches for CAV networks are proposed, they often fail to: i) detect
multiple anomalies in specific sensor(s) with high accuracy or F1 score, and
ii) identify the specific sensor being attacked. In response, this paper
proposes a novel framework tailored to CAV networks, called CAV-AD, for
distinguishing abnormal readings amidst multiple anomaly data while identifying
malicious sensors. Specifically, CAV-AD comprises two main components: i) A
novel CNN model architecture called optimized omni-scale CNN (O-OS-CNN), which
optimally selects the time scale by generating all possible kernel sizes for
input time series data; ii) An amplification block to increase the values of
anomaly readings, enhancing sensitivity for detecting anomalies. Not only that,
but CAV-AD integrates the proposed O-OS-CNN with a Kalman filter to instantly
identify the malicious sensors. We extensively train CAV-AD using real-world
datasets containing both instant and constant attacks, evaluating its
performance in detecting intrusions from multiple anomalies, which presents a
more challenging scenario. Our results demonstrate that CAV-AD outperforms
state-of-the-art methods, achieving an average accuracy of 98% and an average
F1 score of 89\%, while accurately identifying the malicious sensors.
| [
{
"created": "Sun, 7 Jul 2024 18:19:03 GMT",
"version": "v1"
}
] | 2024-07-09 | [
[
"Rahman",
"Md Sazedur",
""
],
[
"Elmahallawy",
"Mohamed",
""
],
[
"Madria",
"Sanjay",
""
],
[
"Frimpong",
"Samuel",
""
]
] | The adoption of connected and automated vehicles (CAVs) has sparked considerable interest across diverse industries, including public transportation, underground mining, and agriculture sectors. However, CAVs' reliance on sensor readings makes them vulnerable to significant threats. Manipulating these readings can compromise CAV network security, posing serious risks for malicious activities. Although several anomaly detection (AD) approaches for CAV networks are proposed, they often fail to: i) detect multiple anomalies in specific sensor(s) with high accuracy or F1 score, and ii) identify the specific sensor being attacked. In response, this paper proposes a novel framework tailored to CAV networks, called CAV-AD, for distinguishing abnormal readings amidst multiple anomaly data while identifying malicious sensors. Specifically, CAV-AD comprises two main components: i) A novel CNN model architecture called optimized omni-scale CNN (O-OS-CNN), which optimally selects the time scale by generating all possible kernel sizes for input time series data; ii) An amplification block to increase the values of anomaly readings, enhancing sensitivity for detecting anomalies. Not only that, but CAV-AD integrates the proposed O-OS-CNN with a Kalman filter to instantly identify the malicious sensors. We extensively train CAV-AD using real-world datasets containing both instant and constant attacks, evaluating its performance in detecting intrusions from multiple anomalies, which presents a more challenging scenario. Our results demonstrate that CAV-AD outperforms state-of-the-art methods, achieving an average accuracy of 98% and an average F1 score of 89\%, while accurately identifying the malicious sensors. |
1809.08613 | Namiko Saito | Namiko Saito, Kitae Kim, Shingo Murata, Tetsuya Ogata and Shigeki
Sugano | Detecting Features of Tools, Objects, and Actions from Effects in a
Robot using Deep Learning | 7 pages, 9 figures | null | null | null | cs.RO cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a tool-use model that can detect the features of tools, target
objects, and actions from the provided effects of object manipulation. We
construct a model that enables robots to manipulate objects with tools, using
infant learning as a concept. To realize this, we train sensory-motor data
recorded during a tool-use task performed by a robot with deep learning.
Experiments include four factors: (1) tools, (2) objects, (3) actions, and (4)
effects, which the model considers simultaneously. For evaluation, the robot
generates predicted images and motions given information of the effects of
using unknown tools and objects. We confirm that the robot is capable of
detecting features of tools, objects, and actions by learning the effects and
executing the task.
| [
{
"created": "Sun, 23 Sep 2018 15:24:21 GMT",
"version": "v1"
}
] | 2018-09-25 | [
[
"Saito",
"Namiko",
""
],
[
"Kim",
"Kitae",
""
],
[
"Murata",
"Shingo",
""
],
[
"Ogata",
"Tetsuya",
""
],
[
"Sugano",
"Shigeki",
""
]
] | We propose a tool-use model that can detect the features of tools, target objects, and actions from the provided effects of object manipulation. We construct a model that enables robots to manipulate objects with tools, using infant learning as a concept. To realize this, we train sensory-motor data recorded during a tool-use task performed by a robot with deep learning. Experiments include four factors: (1) tools, (2) objects, (3) actions, and (4) effects, which the model considers simultaneously. For evaluation, the robot generates predicted images and motions given information of the effects of using unknown tools and objects. We confirm that the robot is capable of detecting features of tools, objects, and actions by learning the effects and executing the task. |
2004.12480 | Najma Mathema | Najma Mathema, Michael A. Goodrich, and Jacob W. Crandall | Predicting Plans and Actions in Two-Player Repeated Games | Accepted in The AAAI 2020 Workshop on Plan, Activity, and Intent
Recognition | null | null | null | cs.AI cs.GT cs.HC cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence (AI) agents will need to interact with both other AI
agents and humans. Creating models of associates help to predict the modeled
agents' actions, plans, and intentions. This work introduces algorithms that
predict actions, plans and intentions in repeated play games, with providing an
exploration of algorithms. We form a generative Bayesian approach to model S#.
S# is designed as a robust algorithm that learns to cooperate with its
associate in 2 by 2 matrix games. The actions, plans and intentions associated
with each S# expert are identified from the literature, grouping the S# experts
accordingly, and thus predicting actions, plans, and intentions based on their
state probabilities. Two prediction methods are explored for Prisoners Dilemma:
the Maximum A Posteriori (MAP) and an Aggregation approach. MAP (~89% accuracy)
performed the best for action prediction. Both methods predicted plans of S#
with ~88% accuracy. Paired T-test shows that MAP performs significantly better
than Aggregation for predicting S#'s actions without cheap talk. Intention is
explored based on the goals of the S# experts; results show that goals are
predicted precisely when modeling S#. The obtained results show that the
proposed Bayesian approach is well suited for modeling agents in two-player
repeated games.
| [
{
"created": "Sun, 26 Apr 2020 21:03:28 GMT",
"version": "v1"
}
] | 2020-04-28 | [
[
"Mathema",
"Najma",
""
],
[
"Goodrich",
"Michael A.",
""
],
[
"Crandall",
"Jacob W.",
""
]
] | Artificial intelligence (AI) agents will need to interact with both other AI agents and humans. Creating models of associates help to predict the modeled agents' actions, plans, and intentions. This work introduces algorithms that predict actions, plans and intentions in repeated play games, with providing an exploration of algorithms. We form a generative Bayesian approach to model S#. S# is designed as a robust algorithm that learns to cooperate with its associate in 2 by 2 matrix games. The actions, plans and intentions associated with each S# expert are identified from the literature, grouping the S# experts accordingly, and thus predicting actions, plans, and intentions based on their state probabilities. Two prediction methods are explored for Prisoners Dilemma: the Maximum A Posteriori (MAP) and an Aggregation approach. MAP (~89% accuracy) performed the best for action prediction. Both methods predicted plans of S# with ~88% accuracy. Paired T-test shows that MAP performs significantly better than Aggregation for predicting S#'s actions without cheap talk. Intention is explored based on the goals of the S# experts; results show that goals are predicted precisely when modeling S#. The obtained results show that the proposed Bayesian approach is well suited for modeling agents in two-player repeated games. |
2004.10596 | Amit Saha | Arpita Sanyal (Bhaduri), Amit Saha, Debasri Saha, Banani Saha and
Amlan Chakrabarti | Circuit Design for Clique Problem and Its Implementation on Quantum
Computer | 25 pages, 18 figures. arXiv admin note: text overlap with
arXiv:1805.10224 by other authors | IET Quantum Communication, 2021 | 10.1049/qtc2.12029 | null | cs.DS | http://creativecommons.org/licenses/by/4.0/ | Finding cliques in a graph has several applications for its pattern matching
ability. $k$-clique problem, a special case of clique problem, determines
whether an arbitrary graph contains a clique of size $k$, has already been
addressed in quantum domain. A variant of $k$-clique problem that lists all
cliques of size $k$, has also popular modern-day applications. Albeit, the
implementation of such variant of $k$-clique problem in quantum setting still
remains untouched. In this paper, apart from theoretical solution of such
$k$-clique problem, practical quantum gate-based implementation has been
addressed using Grover's algorithm. This approach is further extended to design
circuit for the maximum clique problem in classical-quantum hybrid
architecture. The algorithm automatically generates the circuit for any given
undirected and unweighted graph and any given $k$, which makes our approach
generalized in nature. The proposed approach of solving $k$-clique problem has
exhibited a reduction of qubit cost and circuit depth as compared to the
state-of-the-art approach, for a small $k$ with respect to a large graph. A
framework that can map the automated generated circuit for clique problem to
quantum devices is also proposed. An analysis of the experimental results is
demonstrated using IBM's Qiskit.
| [
{
"created": "Tue, 10 Mar 2020 04:29:35 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Jan 2021 11:03:36 GMT",
"version": "v2"
},
{
"created": "Wed, 20 Jan 2021 18:20:17 GMT",
"version": "v3"
},
{
"created": "Wed, 7 Jul 2021 18:59:30 GMT",
"version": "v4"
}
] | 2022-02-23 | [
[
"Sanyal",
"Arpita",
"",
"Bhaduri"
],
[
"Saha",
"Amit",
""
],
[
"Saha",
"Debasri",
""
],
[
"Saha",
"Banani",
""
],
[
"Chakrabarti",
"Amlan",
""
]
] | Finding cliques in a graph has several applications for its pattern matching ability. $k$-clique problem, a special case of clique problem, determines whether an arbitrary graph contains a clique of size $k$, has already been addressed in quantum domain. A variant of $k$-clique problem that lists all cliques of size $k$, has also popular modern-day applications. Albeit, the implementation of such variant of $k$-clique problem in quantum setting still remains untouched. In this paper, apart from theoretical solution of such $k$-clique problem, practical quantum gate-based implementation has been addressed using Grover's algorithm. This approach is further extended to design circuit for the maximum clique problem in classical-quantum hybrid architecture. The algorithm automatically generates the circuit for any given undirected and unweighted graph and any given $k$, which makes our approach generalized in nature. The proposed approach of solving $k$-clique problem has exhibited a reduction of qubit cost and circuit depth as compared to the state-of-the-art approach, for a small $k$ with respect to a large graph. A framework that can map the automated generated circuit for clique problem to quantum devices is also proposed. An analysis of the experimental results is demonstrated using IBM's Qiskit. |
2105.14835 | Christoph Hertrich | Christoph Hertrich, Amitabh Basu, Marco Di Summa, Martin Skutella | Towards Lower Bounds on the Depth of ReLU Neural Networks | Authors' accepted manuscript for SIAM Journal on Discrete
Mathematics. A preliminary conference version appeared at NeurIPS 2021 | SIAM Journal on Discrete Mathematics 2023 37:2, 997-1029 | 10.1137/22M1489332 | null | cs.LG cs.DM cs.NE math.CO stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We contribute to a better understanding of the class of functions that can be
represented by a neural network with ReLU activations and a given architecture.
Using techniques from mixed-integer optimization, polyhedral theory, and
tropical geometry, we provide a mathematical counterbalance to the universal
approximation theorems which suggest that a single hidden layer is sufficient
for learning any function. In particular, we investigate whether the class of
exactly representable functions strictly increases by adding more layers (with
no restrictions on size). As a by-product of our investigations, we settle an
old conjecture about piecewise linear functions by Wang and Sun (2005) in the
affirmative. We also present upper bounds on the sizes of neural networks
required to represent functions with logarithmic depth.
| [
{
"created": "Mon, 31 May 2021 09:49:14 GMT",
"version": "v1"
},
{
"created": "Tue, 26 Oct 2021 08:46:28 GMT",
"version": "v2"
},
{
"created": "Fri, 7 Jan 2022 16:15:27 GMT",
"version": "v3"
},
{
"created": "Thu, 16 Mar 2023 16:22:13 GMT",
"version": "v4"
},
{
"created": "Wed, 17 Jul 2024 16:15:49 GMT",
"version": "v5"
}
] | 2024-07-18 | [
[
"Hertrich",
"Christoph",
""
],
[
"Basu",
"Amitabh",
""
],
[
"Di Summa",
"Marco",
""
],
[
"Skutella",
"Martin",
""
]
] | We contribute to a better understanding of the class of functions that can be represented by a neural network with ReLU activations and a given architecture. Using techniques from mixed-integer optimization, polyhedral theory, and tropical geometry, we provide a mathematical counterbalance to the universal approximation theorems which suggest that a single hidden layer is sufficient for learning any function. In particular, we investigate whether the class of exactly representable functions strictly increases by adding more layers (with no restrictions on size). As a by-product of our investigations, we settle an old conjecture about piecewise linear functions by Wang and Sun (2005) in the affirmative. We also present upper bounds on the sizes of neural networks required to represent functions with logarithmic depth. |
2302.02083 | Michal Kosinski | Michal Kosinski | Evaluating Large Language Models in Theory of Mind Tasks | TRY RUNNING ToM EXPERIMENTS ON YOUR OWN: The code and tasks used in
this study are available at Colab
(https://colab.research.google.com/drive/1ZRtmw87CdA4xp24DNS_Ik_uA2ypaRnoU).
Don't worry if you are not an expert coder, you should be able to run this
code with no-to-minimum Python skills. Or copy-paste the tasks to ChatGPT's
web interface | null | null | null | cs.CL cs.CY cs.HC | http://creativecommons.org/licenses/by-sa/4.0/ | Eleven Large Language Models (LLMs) were assessed using a custom-made battery
of false-belief tasks, considered a gold standard in testing Theory of Mind
(ToM) in humans. The battery included 640 prompts spread across 40 diverse
tasks, each one including a false-belief scenario, three closely matched
true-belief control scenarios, and the reversed versions of all four. To solve
a single task, a model needed to correctly answer 16 prompts across all eight
scenarios. Smaller and older models solved no tasks; GPT-3-davinci-003 (from
November 2022) and ChatGPT-3.5-turbo (from March 2023) solved 20% of the tasks;
ChatGPT-4 (from June 2023) solved 75% of the tasks, matching the performance of
six-year-old children observed in past studies. We explore the potential
interpretation of these findings, including the intriguing possibility that
ToM, previously considered exclusive to humans, may have spontaneously emerged
as a byproduct of LLMs' improving language skills.
| [
{
"created": "Sat, 4 Feb 2023 03:50:01 GMT",
"version": "v1"
},
{
"created": "Fri, 10 Feb 2023 19:01:49 GMT",
"version": "v2"
},
{
"created": "Tue, 14 Mar 2023 18:49:26 GMT",
"version": "v3"
},
{
"created": "Tue, 29 Aug 2023 14:55:37 GMT",
"version": "v4"
},
{
"created": "Sat, 11 Nov 2023 23:05:44 GMT",
"version": "v5"
},
{
"created": "Sat, 17 Feb 2024 02:05:32 GMT",
"version": "v6"
}
] | 2024-02-20 | [
[
"Kosinski",
"Michal",
""
]
] | Eleven Large Language Models (LLMs) were assessed using a custom-made battery of false-belief tasks, considered a gold standard in testing Theory of Mind (ToM) in humans. The battery included 640 prompts spread across 40 diverse tasks, each one including a false-belief scenario, three closely matched true-belief control scenarios, and the reversed versions of all four. To solve a single task, a model needed to correctly answer 16 prompts across all eight scenarios. Smaller and older models solved no tasks; GPT-3-davinci-003 (from November 2022) and ChatGPT-3.5-turbo (from March 2023) solved 20% of the tasks; ChatGPT-4 (from June 2023) solved 75% of the tasks, matching the performance of six-year-old children observed in past studies. We explore the potential interpretation of these findings, including the intriguing possibility that ToM, previously considered exclusive to humans, may have spontaneously emerged as a byproduct of LLMs' improving language skills. |
2404.11209 | Hongzhao Li | Hongzhao Li, Hongyu Wang, Xia Sun, Hua He, Jun Feng | Prompt-Guided Generation of Structured Chest X-Ray Report Using a
Pre-trained LLM | Accepted by IEEE Conference on Multimedia Expo 2024 | null | null | null | cs.AI cs.CV cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Medical report generation automates radiology descriptions from images,
easing the burden on physicians and minimizing errors. However, current methods
lack structured outputs and physician interactivity for clear, clinically
relevant reports. Our method introduces a prompt-guided approach to generate
structured chest X-ray reports using a pre-trained large language model (LLM).
First, we identify anatomical regions in chest X-rays to generate focused
sentences that center on key visual elements, thereby establishing a structured
report foundation with anatomy-based sentences. We also convert the detected
anatomy into textual prompts conveying anatomical comprehension to the LLM.
Additionally, the clinical context prompts guide the LLM to emphasize
interactivity and clinical requirements. By integrating anatomy-focused
sentences and anatomy/clinical prompts, the pre-trained LLM can generate
structured chest X-ray reports tailored to prompted anatomical regions and
clinical contexts. We evaluate using language generation and clinical
effectiveness metrics, demonstrating strong performance.
| [
{
"created": "Wed, 17 Apr 2024 09:45:43 GMT",
"version": "v1"
}
] | 2024-04-18 | [
[
"Li",
"Hongzhao",
""
],
[
"Wang",
"Hongyu",
""
],
[
"Sun",
"Xia",
""
],
[
"He",
"Hua",
""
],
[
"Feng",
"Jun",
""
]
] | Medical report generation automates radiology descriptions from images, easing the burden on physicians and minimizing errors. However, current methods lack structured outputs and physician interactivity for clear, clinically relevant reports. Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM). First, we identify anatomical regions in chest X-rays to generate focused sentences that center on key visual elements, thereby establishing a structured report foundation with anatomy-based sentences. We also convert the detected anatomy into textual prompts conveying anatomical comprehension to the LLM. Additionally, the clinical context prompts guide the LLM to emphasize interactivity and clinical requirements. By integrating anatomy-focused sentences and anatomy/clinical prompts, the pre-trained LLM can generate structured chest X-ray reports tailored to prompted anatomical regions and clinical contexts. We evaluate using language generation and clinical effectiveness metrics, demonstrating strong performance. |
2207.00477 | Yang Xing | Karan Kheta, Claire Delgove, Ruolin Liu, Adeola Aderogba, Marc-Olivier
Pokam, Muhammed Mehmet Unal, Yang Xing, Weisi Guo | Vision-based Conflict Detection within Crowds based on High-Resolution
Human Pose Estimation for Smart and Safe Airport | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Future airports are becoming more complex and congested with the increasing
number of travellers. While the airports are more likely to become hotspots for
potential conflicts to break out which can cause serious delays to flights and
several safety issues. An intelligent algorithm which renders security
surveillance more effective in detecting conflicts would bring many benefits to
the passengers in terms of their safety, finance, and travelling efficiency.
This paper details the development of a machine learning model to classify
conflicting behaviour in a crowd. HRNet is used to segment the images and then
two approaches are taken to classify the poses of people in the frame via
multiple classifiers. Among them, it was found that the support vector machine
(SVM) achieved the most performant achieving precision of 94.37%. Where the
model falls short is against ambiguous behaviour such as a hug or losing track
of a subject in the frame. The resulting model has potential for deployment
within an airport if improvements are made to cope with the vast number of
potential passengers in view as well as training against further ambiguous
behaviours which will arise in an airport setting. In turn, will provide the
capability to enhance security surveillance and improve airport safety.
| [
{
"created": "Fri, 1 Jul 2022 14:54:12 GMT",
"version": "v1"
}
] | 2022-07-04 | [
[
"Kheta",
"Karan",
""
],
[
"Delgove",
"Claire",
""
],
[
"Liu",
"Ruolin",
""
],
[
"Aderogba",
"Adeola",
""
],
[
"Pokam",
"Marc-Olivier",
""
],
[
"Unal",
"Muhammed Mehmet",
""
],
[
"Xing",
"Yang",
""
],
[
"Guo",
"Weisi",
""
]
] | Future airports are becoming more complex and congested with the increasing number of travellers. While the airports are more likely to become hotspots for potential conflicts to break out which can cause serious delays to flights and several safety issues. An intelligent algorithm which renders security surveillance more effective in detecting conflicts would bring many benefits to the passengers in terms of their safety, finance, and travelling efficiency. This paper details the development of a machine learning model to classify conflicting behaviour in a crowd. HRNet is used to segment the images and then two approaches are taken to classify the poses of people in the frame via multiple classifiers. Among them, it was found that the support vector machine (SVM) achieved the most performant achieving precision of 94.37%. Where the model falls short is against ambiguous behaviour such as a hug or losing track of a subject in the frame. The resulting model has potential for deployment within an airport if improvements are made to cope with the vast number of potential passengers in view as well as training against further ambiguous behaviours which will arise in an airport setting. In turn, will provide the capability to enhance security surveillance and improve airport safety. |
1808.03387 | Janardan Misra | Janardan Misra | Computational Complexity of Observing Evolution in Artificial-Life Forms | arXiv admin note: substantial text overlap with arXiv:0901.1610 | null | null | null | cs.NE cs.AI cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Observations are an essential component of the simulation based studies on
artificial-evolutionary systems (AES) by which entities are identified and
their behavior is observed to uncover higher-level "emergent" phenomena.
Because of the heterogeneity of AES models and implicit nature of observations,
precise characterization of the observation process, independent of the
underlying micro-level reaction semantics of the model, is a difficult problem.
Building upon the multiset based algebraic framework to characterize
state-space trajectory of AES model simulations, we estimate bounds on
computational resource requirements of the process of automatically discovering
life-like evolutionary behavior in AES models during simulations. For
illustration, we consider the case of Langton's Cellular Automata model and
characterize the worst case computational complexity bounds for identifying
entity and population level reproduction.
| [
{
"created": "Sun, 24 Jun 2018 04:18:55 GMT",
"version": "v1"
}
] | 2018-08-13 | [
[
"Misra",
"Janardan",
""
]
] | Observations are an essential component of the simulation based studies on artificial-evolutionary systems (AES) by which entities are identified and their behavior is observed to uncover higher-level "emergent" phenomena. Because of the heterogeneity of AES models and implicit nature of observations, precise characterization of the observation process, independent of the underlying micro-level reaction semantics of the model, is a difficult problem. Building upon the multiset based algebraic framework to characterize state-space trajectory of AES model simulations, we estimate bounds on computational resource requirements of the process of automatically discovering life-like evolutionary behavior in AES models during simulations. For illustration, we consider the case of Langton's Cellular Automata model and characterize the worst case computational complexity bounds for identifying entity and population level reproduction. |
2404.03088 | Bin Han | Zexin Fang, Bin Han, and Hans D. Schotten | Robust Federated Learning for Wireless Networks: A Demonstration with
Channel Estimation | Submitted to IEEE GLOBECOM 2024 | null | null | null | cs.LG cs.AI cs.NI eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated learning (FL) offers a privacy-preserving collaborative approach
for training models in wireless networks, with channel estimation emerging as a
promising application. Despite extensive studies on FL-empowered channel
estimation, the security concerns associated with FL require meticulous
attention. In a scenario where small base stations (SBSs) serve as local models
trained on cached data, and a macro base station (MBS) functions as the global
model setting, an attacker can exploit the vulnerability of FL, launching
attacks with various adversarial attacks or deployment tactics. In this paper,
we analyze such vulnerabilities, corresponding solutions were brought forth,
and validated through simulation.
| [
{
"created": "Wed, 3 Apr 2024 22:03:28 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Jul 2024 08:19:53 GMT",
"version": "v2"
}
] | 2024-07-31 | [
[
"Fang",
"Zexin",
""
],
[
"Han",
"Bin",
""
],
[
"Schotten",
"Hans D.",
""
]
] | Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation. |
cs/0402008 | Richard McClatchey | Mohammed Odeh, Tamas Hauer, Richard McClatchey & Tony Solomonides | A Use-Case Driven Approach in Requirements Engineering : The Mammogrid
Project | 6 pages, 3 figures. Presented at the 7th IASTED Int Conf on Software
Engineering Applications. Marina del Rey, USA November 2003 | null | null | null | cs.DB cs.SE | null | We report on the application of the use-case modeling technique to identify
and specify the user requirements of the MammoGrid project in an incremental
and controlled iterative approach. Modeling has been carried out in close
collaboration with clinicians and radiologists with no prior experience of use
cases. The study reveals the advantages and limitations of applying this
technique to requirements specification in the domains of breast cancer
screening and mammography research, with implications for medical imaging more
generally. In addition, this research has shown a return on investment in
use-case modeling in shorter gaps between phases of the requirements
engineering process. The qualitative result of this analysis leads us to
propose that a use-case modeling approach may result in reducing the cycle of
the requirements engineering process for medical imaging.
| [
{
"created": "Mon, 2 Feb 2004 20:18:23 GMT",
"version": "v1"
}
] | 2009-09-29 | [
[
"Odeh",
"Mohammed",
""
],
[
"Hauer",
"Tamas",
""
],
[
"McClatchey",
"Richard",
""
],
[
"Solomonides",
"Tony",
""
]
] | We report on the application of the use-case modeling technique to identify and specify the user requirements of the MammoGrid project in an incremental and controlled iterative approach. Modeling has been carried out in close collaboration with clinicians and radiologists with no prior experience of use cases. The study reveals the advantages and limitations of applying this technique to requirements specification in the domains of breast cancer screening and mammography research, with implications for medical imaging more generally. In addition, this research has shown a return on investment in use-case modeling in shorter gaps between phases of the requirements engineering process. The qualitative result of this analysis leads us to propose that a use-case modeling approach may result in reducing the cycle of the requirements engineering process for medical imaging. |
2404.04718 | Prasun Tripathi | Prasun C Tripathi, Sina Tabakhi, Mohammod N I Suvon, Lawrence Sch\"ob,
Samer Alabed, Andrew J Swift, Shuo Zhou, and Haiping Lu | Interpretable Multimodal Learning for Cardiovascular Hemodynamics
Assessment | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular
hemodynamics marker to detect heart failure. In clinical practice, Right Heart
Catheterization is considered a gold standard for assessing cardiac
hemodynamics while non-invasive methods are often needed to screen high-risk
patients from a large population. In this paper, we propose a multimodal
learning pipeline to predict PAWP marker. We utilize complementary information
from Cardiac Magnetic Resonance Imaging (CMR) scans (short-axis and
four-chamber) and Electronic Health Records (EHRs). We extract spatio-temporal
features from CMR scans using tensor-based learning. We propose a graph
attention network to select important EHR features for prediction, where we
model subjects as graph nodes and feature relationships as graph edges using
the attention mechanism. We design four feature fusion strategies: early,
intermediate, late, and hybrid fusion. With a linear classifier and linear
fusion strategies, our pipeline is interpretable. We validate our pipeline on a
large dataset of $2,641$ subjects from our ASPIRE registry. The comparative
study against state-of-the-art methods confirms the superiority of our
pipeline. The decision curve analysis further validates that our pipeline can
be applied to screen a large population. The code is available at
https://github.com/prasunc/hemodynamics.
| [
{
"created": "Sat, 6 Apr 2024 19:42:25 GMT",
"version": "v1"
}
] | 2024-04-09 | [
[
"Tripathi",
"Prasun C",
""
],
[
"Tabakhi",
"Sina",
""
],
[
"Suvon",
"Mohammod N I",
""
],
[
"Schöb",
"Lawrence",
""
],
[
"Alabed",
"Samer",
""
],
[
"Swift",
"Andrew J",
""
],
[
"Zhou",
"Shuo",
""
],
[
"Lu",
"Haiping",
""
]
] | Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered a gold standard for assessing cardiac hemodynamics while non-invasive methods are often needed to screen high-risk patients from a large population. In this paper, we propose a multimodal learning pipeline to predict PAWP marker. We utilize complementary information from Cardiac Magnetic Resonance Imaging (CMR) scans (short-axis and four-chamber) and Electronic Health Records (EHRs). We extract spatio-temporal features from CMR scans using tensor-based learning. We propose a graph attention network to select important EHR features for prediction, where we model subjects as graph nodes and feature relationships as graph edges using the attention mechanism. We design four feature fusion strategies: early, intermediate, late, and hybrid fusion. With a linear classifier and linear fusion strategies, our pipeline is interpretable. We validate our pipeline on a large dataset of $2,641$ subjects from our ASPIRE registry. The comparative study against state-of-the-art methods confirms the superiority of our pipeline. The decision curve analysis further validates that our pipeline can be applied to screen a large population. The code is available at https://github.com/prasunc/hemodynamics. |
1703.06117 | John Prpi\'c | J. Prpi\'c | Unpacking Blockchains | Collective Intelligence 2017. NYU Tandon School of Engineering. June
15-16, 2017 | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Bitcoin digital currency appeared in 2009. Since this time, researchers
and practitioners have looked under the hood of the open source Bitcoin
currency, and discovered that Bitcoins Blockchain software architecture is
useful for non-monetary purposes too. By coalescing the research and practice
on Blockchains, this work begins to unpack Blockchains as a general phenomenon,
therein, arguing that all Blockchain phenomena can be conceived as being
comprised of transaction platforms and digital ledgers, and illustrating where
public key encryption plays a differential role in facilitating these features
of Blockchains.
| [
{
"created": "Mon, 13 Mar 2017 22:03:09 GMT",
"version": "v1"
}
] | 2017-03-20 | [
[
"Prpić",
"J.",
""
]
] | The Bitcoin digital currency appeared in 2009. Since this time, researchers and practitioners have looked under the hood of the open source Bitcoin currency, and discovered that Bitcoins Blockchain software architecture is useful for non-monetary purposes too. By coalescing the research and practice on Blockchains, this work begins to unpack Blockchains as a general phenomenon, therein, arguing that all Blockchain phenomena can be conceived as being comprised of transaction platforms and digital ledgers, and illustrating where public key encryption plays a differential role in facilitating these features of Blockchains. |
2205.13098 | Yifei Wang | Yifei Wang, Peng Chen, Mert Pilanci, Wuchen Li | Optimal Neural Network Approximation of Wasserstein Gradient Direction
via Convex Optimization | null | null | null | null | cs.LG math.OC stat.ML | http://creativecommons.org/licenses/by/4.0/ | The computation of Wasserstein gradient direction is essential for posterior
sampling problems and scientific computing. The approximation of the
Wasserstein gradient with finite samples requires solving a variational
problem. We study the variational problem in the family of two-layer networks
with squared-ReLU activations, towards which we derive a semi-definite
programming (SDP) relaxation. This SDP can be viewed as an approximation of the
Wasserstein gradient in a broader function family including two-layer networks.
By solving the convex SDP, we obtain the optimal approximation of the
Wasserstein gradient direction in this class of functions. Numerical
experiments including PDE-constrained Bayesian inference and parameter
estimation in COVID-19 modeling demonstrate the effectiveness of the proposed
method.
| [
{
"created": "Thu, 26 May 2022 00:51:12 GMT",
"version": "v1"
}
] | 2022-05-27 | [
[
"Wang",
"Yifei",
""
],
[
"Chen",
"Peng",
""
],
[
"Pilanci",
"Mert",
""
],
[
"Li",
"Wuchen",
""
]
] | The computation of Wasserstein gradient direction is essential for posterior sampling problems and scientific computing. The approximation of the Wasserstein gradient with finite samples requires solving a variational problem. We study the variational problem in the family of two-layer networks with squared-ReLU activations, towards which we derive a semi-definite programming (SDP) relaxation. This SDP can be viewed as an approximation of the Wasserstein gradient in a broader function family including two-layer networks. By solving the convex SDP, we obtain the optimal approximation of the Wasserstein gradient direction in this class of functions. Numerical experiments including PDE-constrained Bayesian inference and parameter estimation in COVID-19 modeling demonstrate the effectiveness of the proposed method. |
2304.13664 | Luisa Coheur | Hugo Rodrigues, Eric Nyberg, Luisa Coheur | Using Implicit Feedback to Improve Question Generation | 27 pages, 8 figures | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Question Generation (QG) is a task of Natural Language Processing (NLP) that
aims at automatically generating questions from text. Many applications can
benefit from automatically generated questions, but often it is necessary to
curate those questions, either by selecting or editing them. This task is
informative on its own, but it is typically done post-generation, and, thus,
the effort is wasted. In addition, most existing systems cannot incorporate
this feedback back into them easily. In this work, we present a system, GEN,
that learns from such (implicit) feedback. Following a pattern-based approach,
it takes as input a small set of sentence/question pairs and creates patterns
which are then applied to new unseen sentences. Each generated question, after
being corrected by the user, is used as a new seed in the next iteration, so
more patterns are created each time. We also take advantage of the corrections
made by the user to score the patterns and therefore rank the generated
questions. Results show that GEN is able to improve by learning from both
levels of implicit feedback when compared to the version with no learning,
considering the top 5, 10, and 20 questions. Improvements go up from 10%,
depending on the metric and strategy used.
| [
{
"created": "Wed, 26 Apr 2023 16:37:47 GMT",
"version": "v1"
}
] | 2023-04-27 | [
[
"Rodrigues",
"Hugo",
""
],
[
"Nyberg",
"Eric",
""
],
[
"Coheur",
"Luisa",
""
]
] | Question Generation (QG) is a task of Natural Language Processing (NLP) that aims at automatically generating questions from text. Many applications can benefit from automatically generated questions, but often it is necessary to curate those questions, either by selecting or editing them. This task is informative on its own, but it is typically done post-generation, and, thus, the effort is wasted. In addition, most existing systems cannot incorporate this feedback back into them easily. In this work, we present a system, GEN, that learns from such (implicit) feedback. Following a pattern-based approach, it takes as input a small set of sentence/question pairs and creates patterns which are then applied to new unseen sentences. Each generated question, after being corrected by the user, is used as a new seed in the next iteration, so more patterns are created each time. We also take advantage of the corrections made by the user to score the patterns and therefore rank the generated questions. Results show that GEN is able to improve by learning from both levels of implicit feedback when compared to the version with no learning, considering the top 5, 10, and 20 questions. Improvements go up from 10%, depending on the metric and strategy used. |
1904.10504 | Li Chen | Li Chen | Understanding the efficacy, reliability and resiliency of computer
vision techniques for malware detection and future research directions | Report | null | null | null | cs.CR cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | My research lies in the intersection of security and machine learning. This
overview summarizes one component of my research: combining computer vision
with malware exploit detection for enhanced security solutions. I will present
the perspectives of efficacy, reliability and resiliency to formulate threat
detection as computer vision problems and develop state-of-the-art image-based
malware classification. Representing malware binary as images provides a direct
visualization of data samples, reduces the efforts for feature extraction, and
consumes the whole binary for holistic structural analysis. Employing transfer
learning of deep neural networks effective for large scale image classification
to malware classification demonstrates superior classification efficacy
compared with classical machine learning algorithms. To enhance reliability of
these vision-based malware detectors, interpretation frameworks can be
constructed on the malware visual representations and useful for extracting
faithful explanation, so that security practitioners have confidence in the
model before deployment. In cyber-security applications, we should always
assume that a malware writer constantly modifies code to bypass detection.
Addressing the resiliency of the malware detectors is equivalently important as
efficacy and reliability. Via understanding the attack surfaces of machine
learning models used for malware detection, we can greatly improve the
robustness of the algorithms to combat malware adversaries in the wild. Finally
I will discuss future research directions worth pursuing in this research
community.
| [
{
"created": "Wed, 3 Apr 2019 18:34:20 GMT",
"version": "v1"
}
] | 2019-04-25 | [
[
"Chen",
"Li",
""
]
] | My research lies in the intersection of security and machine learning. This overview summarizes one component of my research: combining computer vision with malware exploit detection for enhanced security solutions. I will present the perspectives of efficacy, reliability and resiliency to formulate threat detection as computer vision problems and develop state-of-the-art image-based malware classification. Representing malware binary as images provides a direct visualization of data samples, reduces the efforts for feature extraction, and consumes the whole binary for holistic structural analysis. Employing transfer learning of deep neural networks effective for large scale image classification to malware classification demonstrates superior classification efficacy compared with classical machine learning algorithms. To enhance reliability of these vision-based malware detectors, interpretation frameworks can be constructed on the malware visual representations and useful for extracting faithful explanation, so that security practitioners have confidence in the model before deployment. In cyber-security applications, we should always assume that a malware writer constantly modifies code to bypass detection. Addressing the resiliency of the malware detectors is equivalently important as efficacy and reliability. Via understanding the attack surfaces of machine learning models used for malware detection, we can greatly improve the robustness of the algorithms to combat malware adversaries in the wild. Finally I will discuss future research directions worth pursuing in this research community. |
1810.13337 | Pengcheng Yin | Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt,
Alexander L. Gaunt | Learning to Represent Edits | ICLR 2019 | null | null | null | cs.LG cs.SE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the problem of learning distributed representations of edits. By
combining a "neural editor" with an "edit encoder", our models learn to
represent the salient information of an edit and can be used to apply edits to
new inputs. We experiment on natural language and source code edit data. Our
evaluation yields promising results that suggest that our neural network models
learn to capture the structure and semantics of edits. We hope that this
interesting task and data source will inspire other researchers to work further
on this problem.
| [
{
"created": "Wed, 31 Oct 2018 15:29:30 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Feb 2019 05:16:03 GMT",
"version": "v2"
}
] | 2019-02-25 | [
[
"Yin",
"Pengcheng",
""
],
[
"Neubig",
"Graham",
""
],
[
"Allamanis",
"Miltiadis",
""
],
[
"Brockschmidt",
"Marc",
""
],
[
"Gaunt",
"Alexander L.",
""
]
] | We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient information of an edit and can be used to apply edits to new inputs. We experiment on natural language and source code edit data. Our evaluation yields promising results that suggest that our neural network models learn to capture the structure and semantics of edits. We hope that this interesting task and data source will inspire other researchers to work further on this problem. |
1312.7645 | Rob van Glabbeek | Ansgar Fehnker, Rob van Glabbeek, Peter H\"ofner, Annabelle McIver,
Marius Portmann and Wee Lum Tan | A Process Algebra for Wireless Mesh Networks used for Modelling,
Verifying and Analysing AODV | null | null | null | Technical Report 5513, NICTA, 2013 | cs.NI cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose AWN (Algebra for Wireless Networks), a process algebra tailored to
the modelling of Mobile Ad hoc Network (MANET) and Wireless Mesh Network (WMN)
protocols. It combines novel treatments of local broadcast, conditional unicast
and data structures.
In this framework we present a rigorous analysis of the Ad hoc On-Demand
Distance Vector (AODV) protocol, a popular routing protocol designed for MANETs
and WMNs, and one of the four protocols currently standardised by the IETF
MANET working group.
We give a complete and unambiguous specification of this protocol, thereby
formalising the RFC of AODV, the de facto standard specification, given in
English prose. In doing so, we had to make non-evident assumptions to resolve
ambiguities occurring in that specification. Our formalisation models the exact
details of the core functionality of AODV, such as route maintenance and error
handling, and only omits timing aspects.
The process algebra allows us to formalise and (dis)prove crucial properties
of mesh network routing protocols such as loop freedom and packet delivery. We
are the first to provide a detailed proof of loop freedom of AODV. In contrast
to evaluations using simulation or model checking, our proof is generic and
holds for any possible network scenario in terms of network topology, node
mobility, etc. Due to ambiguities and contradictions the RFC specification
allows several interpretations; we show for more than 5000 of them whether they
are loop free or not, thereby demonstrating how the reasoning and proofs can
relatively easily be adapted to protocol variants.
Using our formal and unambiguous specification, we find shortcomings of AODV
that affect performance, e.g. the establishment of non-optimal routes, and some
routes not being found at all. We formalise improvements in the same process
algebra; carrying over the proofs is again easy.
| [
{
"created": "Mon, 30 Dec 2013 07:18:04 GMT",
"version": "v1"
}
] | 2013-12-31 | [
[
"Fehnker",
"Ansgar",
""
],
[
"van Glabbeek",
"Rob",
""
],
[
"Höfner",
"Peter",
""
],
[
"McIver",
"Annabelle",
""
],
[
"Portmann",
"Marius",
""
],
[
"Tan",
"Wee Lum",
""
]
] | We propose AWN (Algebra for Wireless Networks), a process algebra tailored to the modelling of Mobile Ad hoc Network (MANET) and Wireless Mesh Network (WMN) protocols. It combines novel treatments of local broadcast, conditional unicast and data structures. In this framework we present a rigorous analysis of the Ad hoc On-Demand Distance Vector (AODV) protocol, a popular routing protocol designed for MANETs and WMNs, and one of the four protocols currently standardised by the IETF MANET working group. We give a complete and unambiguous specification of this protocol, thereby formalising the RFC of AODV, the de facto standard specification, given in English prose. In doing so, we had to make non-evident assumptions to resolve ambiguities occurring in that specification. Our formalisation models the exact details of the core functionality of AODV, such as route maintenance and error handling, and only omits timing aspects. The process algebra allows us to formalise and (dis)prove crucial properties of mesh network routing protocols such as loop freedom and packet delivery. We are the first to provide a detailed proof of loop freedom of AODV. In contrast to evaluations using simulation or model checking, our proof is generic and holds for any possible network scenario in terms of network topology, node mobility, etc. Due to ambiguities and contradictions the RFC specification allows several interpretations; we show for more than 5000 of them whether they are loop free or not, thereby demonstrating how the reasoning and proofs can relatively easily be adapted to protocol variants. Using our formal and unambiguous specification, we find shortcomings of AODV that affect performance, e.g. the establishment of non-optimal routes, and some routes not being found at all. We formalise improvements in the same process algebra; carrying over the proofs is again easy. |
2110.05422 | Rose Wang | Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman | Calibrate your listeners! Robust communication-based training for
pragmatic speakers | Findings of EMNLP 2021 Code:
https://github.com/rosewang2008/calibrate_your_listeners | null | null | null | cs.CL cs.AI cs.LG cs.MA | http://creativecommons.org/licenses/by/4.0/ | To be good conversational partners, natural language processing (NLP) systems
should be trained to produce contextually useful utterances. Prior work has
investigated training NLP systems with communication-based objectives, where a
neural listener stands in as a communication partner. However, these systems
commonly suffer from semantic drift where the learned language diverges
radically from natural language. We propose a method that uses a population of
neural listeners to regularize speaker training. We first show that language
drift originates from the poor uncertainty calibration of a neural listener,
which makes high-certainty predictions on novel sentences. We explore ensemble-
and dropout-based populations of listeners and find that the former results in
better uncertainty quantification. We evaluate both population-based objectives
on reference games, and show that the ensemble method with better calibration
enables the speaker to generate pragmatic utterances while scaling to a large
vocabulary and generalizing to new games and listeners.
| [
{
"created": "Mon, 11 Oct 2021 17:07:38 GMT",
"version": "v1"
}
] | 2021-10-12 | [
[
"Wang",
"Rose E.",
""
],
[
"White",
"Julia",
""
],
[
"Mu",
"Jesse",
""
],
[
"Goodman",
"Noah D.",
""
]
] | To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP systems with communication-based objectives, where a neural listener stands in as a communication partner. However, these systems commonly suffer from semantic drift where the learned language diverges radically from natural language. We propose a method that uses a population of neural listeners to regularize speaker training. We first show that language drift originates from the poor uncertainty calibration of a neural listener, which makes high-certainty predictions on novel sentences. We explore ensemble- and dropout-based populations of listeners and find that the former results in better uncertainty quantification. We evaluate both population-based objectives on reference games, and show that the ensemble method with better calibration enables the speaker to generate pragmatic utterances while scaling to a large vocabulary and generalizing to new games and listeners. |
1306.0865 | Jinkyu Kang | Jinkyu Kang and Osvaldo Simeone and Joonhyuk Kang and Shlomo Shamai
(Shitz) | Joint Signal and Channel State Information Compression for the Backhaul
of Uplink Network MIMO Systems | 34 pages, 6 figures. Submitted to IEEE Transactions on Wireless
Communication | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In network MIMO cellular systems, subsets of base stations (BSs), or remote
radio heads, are connected via backhaul links to central units (CUs) that
perform joint encoding in the downlink and joint decoding in the uplink.
Focusing on the uplink, an effective solution for the communication between BSs
and the corresponding CU on the backhaul links is based on compressing and
forwarding the baseband received signal from each BS. In the presence of
ergodic fading, communicating the channel state information (CSI) from the BSs
to the CU may require a sizable part of the backhaul capacity. In a prior work,
this aspect was studied by assuming a Compress-Forward-Estimate (CFE) approach,
whereby the BSs compress the training signal and CSI estimation takes place at
the CU. In this work, instead, an Estimate-Compress-Forward (ECF) approach is
investigated, whereby the BSs perform CSI estimation and forward a compressed
version of the CSI to the CU. This choice is motivated by the information
theoretic optimality of separate estimation and compression. Various ECF
strategies are proposed that perform either separate or joint compression of
estimated CSI and received signal. Moreover, the proposed strategies are
combined with distributed source coding when considering multiple BSs.
"Semi-coherent" strategies are also proposed that do not convey any CSI or
training information on the backhaul links. Via numerical results, it is shown
that a proper design of ECF strategies based on joint received signal and
estimated CSI compression or of semi-coherent schemes leads to substantial
performance gains compared to more conventional approaches based on
non-coherent transmission or the CFE approach.
| [
{
"created": "Tue, 4 Jun 2013 18:08:13 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Oct 2013 11:01:29 GMT",
"version": "v2"
}
] | 2013-10-24 | [
[
"Kang",
"Jinkyu",
"",
"Shitz"
],
[
"Simeone",
"Osvaldo",
"",
"Shitz"
],
[
"Kang",
"Joonhyuk",
"",
"Shitz"
],
[
"Shamai",
"Shlomo",
"",
"Shitz"
]
] | In network MIMO cellular systems, subsets of base stations (BSs), or remote radio heads, are connected via backhaul links to central units (CUs) that perform joint encoding in the downlink and joint decoding in the uplink. Focusing on the uplink, an effective solution for the communication between BSs and the corresponding CU on the backhaul links is based on compressing and forwarding the baseband received signal from each BS. In the presence of ergodic fading, communicating the channel state information (CSI) from the BSs to the CU may require a sizable part of the backhaul capacity. In a prior work, this aspect was studied by assuming a Compress-Forward-Estimate (CFE) approach, whereby the BSs compress the training signal and CSI estimation takes place at the CU. In this work, instead, an Estimate-Compress-Forward (ECF) approach is investigated, whereby the BSs perform CSI estimation and forward a compressed version of the CSI to the CU. This choice is motivated by the information theoretic optimality of separate estimation and compression. Various ECF strategies are proposed that perform either separate or joint compression of estimated CSI and received signal. Moreover, the proposed strategies are combined with distributed source coding when considering multiple BSs. "Semi-coherent" strategies are also proposed that do not convey any CSI or training information on the backhaul links. Via numerical results, it is shown that a proper design of ECF strategies based on joint received signal and estimated CSI compression or of semi-coherent schemes leads to substantial performance gains compared to more conventional approaches based on non-coherent transmission or the CFE approach. |
0711.0618 | Wim Vanhoof | Jan Wielemaker, Anjo Anjewierden | PIDoc: Wiki style Literate Programming for Prolog | Paper presented at the 17th Workshop on Logic-based Methods in
Programming Environments (WLPE2007) | null | null | null | cs.PL cs.SE | null | This document introduces PlDoc, a literate programming system for Prolog.
Starting point for PlDoc was minimal distraction from the programming task and
maximal immediate reward, attempting to seduce the programmer to use the
system. Minimal distraction is achieved using structured comments that are as
closely as possible related to common Prolog documentation practices. Immediate
reward is provided by a web interface powered from the Prolog development
environment that integrates searching and browsing application and system
documentation. When accessed from localhost, it is possible to go from
documentation shown in a browser to the source code displayed in the user's
editor of choice.
| [
{
"created": "Mon, 5 Nov 2007 12:13:12 GMT",
"version": "v1"
}
] | 2007-11-06 | [
[
"Wielemaker",
"Jan",
""
],
[
"Anjewierden",
"Anjo",
""
]
] | This document introduces PlDoc, a literate programming system for Prolog. Starting point for PlDoc was minimal distraction from the programming task and maximal immediate reward, attempting to seduce the programmer to use the system. Minimal distraction is achieved using structured comments that are as closely as possible related to common Prolog documentation practices. Immediate reward is provided by a web interface powered from the Prolog development environment that integrates searching and browsing application and system documentation. When accessed from localhost, it is possible to go from documentation shown in a browser to the source code displayed in the user's editor of choice. |
2102.10196 | Romain Cosson | Romain Cosson, Devavrat Shah | Quantifying Variational Approximation for the Log-Partition Function | null | null | null | null | cs.DS cs.LG math.ST stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Variational approximation, such as mean-field (MF) and tree-reweighted (TRW),
provide a computationally efficient approximation of the log-partition function
for a generic graphical model. TRW provably provides an upper bound, but the
approximation ratio is generally not quantified.
As the primary contribution of this work, we provide an approach to quantify
the approximation ratio through the property of the underlying graph structure.
Specifically, we argue that (a variant of) TRW produces an estimate that is
within factor $\frac{1}{\sqrt{\kappa(G)}}$ of the true log-partition function
for any discrete pairwise graphical model over graph $G$, where $\kappa(G) \in
(0,1]$ captures how far $G$ is from tree structure with $\kappa(G) = 1$ for
trees and $2/N$ for the complete graph over $N$ vertices. As a consequence, the
approximation ratio is $1$ for trees, $\sqrt{(d+1)/2}$ for any graph with
maximum average degree $d$, and $\stackrel{\beta\to\infty}{\approx}
1+1/(2\beta)$ for graphs with girth (shortest cycle) at least $\beta \log N$.
In general, $\kappa(G)$ is the solution of a max-min problem associated with
$G$ that can be evaluated in polynomial time for any graph.
Using samples from the uniform distribution over the spanning trees of G, we
provide a near linear-time variant that achieves an approximation ratio equal
to the inverse of square-root of minimal (across edges) effective resistance of
the graph. We connect our results to the graph partition-based approximation
method and thus provide a unified perspective.
Keywords: variational inference, log-partition function, spanning tree
polytope, minimum effective resistance, min-max spanning tree, local inference
| [
{
"created": "Fri, 19 Feb 2021 22:57:32 GMT",
"version": "v1"
},
{
"created": "Thu, 19 Aug 2021 22:10:39 GMT",
"version": "v2"
}
] | 2021-08-23 | [
[
"Cosson",
"Romain",
""
],
[
"Shah",
"Devavrat",
""
]
] | Variational approximation, such as mean-field (MF) and tree-reweighted (TRW), provide a computationally efficient approximation of the log-partition function for a generic graphical model. TRW provably provides an upper bound, but the approximation ratio is generally not quantified. As the primary contribution of this work, we provide an approach to quantify the approximation ratio through the property of the underlying graph structure. Specifically, we argue that (a variant of) TRW produces an estimate that is within factor $\frac{1}{\sqrt{\kappa(G)}}$ of the true log-partition function for any discrete pairwise graphical model over graph $G$, where $\kappa(G) \in (0,1]$ captures how far $G$ is from tree structure with $\kappa(G) = 1$ for trees and $2/N$ for the complete graph over $N$ vertices. As a consequence, the approximation ratio is $1$ for trees, $\sqrt{(d+1)/2}$ for any graph with maximum average degree $d$, and $\stackrel{\beta\to\infty}{\approx} 1+1/(2\beta)$ for graphs with girth (shortest cycle) at least $\beta \log N$. In general, $\kappa(G)$ is the solution of a max-min problem associated with $G$ that can be evaluated in polynomial time for any graph. Using samples from the uniform distribution over the spanning trees of G, we provide a near linear-time variant that achieves an approximation ratio equal to the inverse of square-root of minimal (across edges) effective resistance of the graph. We connect our results to the graph partition-based approximation method and thus provide a unified perspective. Keywords: variational inference, log-partition function, spanning tree polytope, minimum effective resistance, min-max spanning tree, local inference |
2205.12701 | Qinyuan Ye | Qinyuan Ye, Juan Zha, Xiang Ren | Eliciting and Understanding Cross-Task Skills with Task-Level
Mixture-of-Experts | Accepted to EMNLP 2022 Findings. Camera-ready version | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Recent works suggest that transformer models are capable of multi-tasking on
diverse NLP tasks and adapting to new tasks efficiently. However, the potential
of these multi-task models may be limited as they use the same set of
parameters for all tasks. In contrast, humans tackle tasks in a more flexible
way, by making proper presumptions on what skills and knowledge are relevant
and executing only the necessary computations. Inspired by this, we propose to
use task-level mixture-of-expert models, which has a collection of transformer
layers (i.e., experts) and a router component that chooses from these experts
dynamically and flexibly. We find that these models help improve the average
performance gain (ARG) metric by 2.6% when adapting to unseen tasks in the
few-shot setting and by 5.6% in the zero-shot generalization setting. Further,
we show that the learned routing decisions partly rediscover human
categorization of NLP tasks -- certain experts are strongly associated with
extractive tasks, some with classification tasks, and some with tasks requiring
world knowledge.
| [
{
"created": "Wed, 25 May 2022 11:59:05 GMT",
"version": "v1"
},
{
"created": "Tue, 22 Nov 2022 00:15:25 GMT",
"version": "v2"
}
] | 2022-11-23 | [
[
"Ye",
"Qinyuan",
""
],
[
"Zha",
"Juan",
""
],
[
"Ren",
"Xiang",
""
]
] | Recent works suggest that transformer models are capable of multi-tasking on diverse NLP tasks and adapting to new tasks efficiently. However, the potential of these multi-task models may be limited as they use the same set of parameters for all tasks. In contrast, humans tackle tasks in a more flexible way, by making proper presumptions on what skills and knowledge are relevant and executing only the necessary computations. Inspired by this, we propose to use task-level mixture-of-expert models, which has a collection of transformer layers (i.e., experts) and a router component that chooses from these experts dynamically and flexibly. We find that these models help improve the average performance gain (ARG) metric by 2.6% when adapting to unseen tasks in the few-shot setting and by 5.6% in the zero-shot generalization setting. Further, we show that the learned routing decisions partly rediscover human categorization of NLP tasks -- certain experts are strongly associated with extractive tasks, some with classification tasks, and some with tasks requiring world knowledge. |
1507.01443 | Erik Ferragut | Erik M. Ferragut, Jason Laska | Nonparametric Bayesian Modeling for Automated Database Schema Matching | null | null | null | null | cs.IR cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of merging databases arises in many government and commercial
applications. Schema matching, a common first step, identifies equivalent
fields between databases. We introduce a schema matching framework that builds
nonparametric Bayesian models for each field and compares them by computing the
probability that a single model could have generated both fields. Our
experiments show that our method is more accurate and faster than the existing
instance-based matching algorithms in part because of the use of nonparametric
Bayesian models.
| [
{
"created": "Mon, 6 Jul 2015 13:26:02 GMT",
"version": "v1"
}
] | 2015-07-07 | [
[
"Ferragut",
"Erik M.",
""
],
[
"Laska",
"Jason",
""
]
] | The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models. |
1902.03683 | Xiaojiang Du | Caidan Zhao, Mingxian Shi, MinMin Huang, Xiaojiang Du | Authentication Scheme Based on Hashchain for Space-Air-Ground Integrated
Network | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the development of artificial intelligence and self-driving, vehicular
ad-hoc network (VANET) has become an irreplaceable part of the Intelligent
Transportation Systems (ITSs). However, the traditional network of the ground
cannot meet the requirements of transmission, processing, and storage among
vehicles. Under this circumstance, integrating space and air nodes into the
whole network can provide comprehensive traffic information and reduce the
transmission delay. The high mobility and low latency in the Space-Air-Ground
Integrated Network (SAGIN) put forward higher requirements for security issues
such as identity authentication, privacy protection, and data security. This
paper simplifies the Blockchain and proposes an identity authentication and
privacy protection scheme based on the Hashchain in the SAGIN. The scheme
focuses on the characteristics of the wireless signal to identify and
authenticate the nodes. The verification and backup of the records on the block
are implemented with the distributed streaming platform, Kafka algorithm,
instead of the consensus. Furthermore, this paper analyzes the security of this
scheme. Afterward, the experimental results reveal the delay brought by the
scheme using the simulation of SUMO, OMNeT++, and Veins.
| [
{
"created": "Sun, 10 Feb 2019 23:22:23 GMT",
"version": "v1"
}
] | 2019-02-12 | [
[
"Zhao",
"Caidan",
""
],
[
"Shi",
"Mingxian",
""
],
[
"Huang",
"MinMin",
""
],
[
"Du",
"Xiaojiang",
""
]
] | With the development of artificial intelligence and self-driving, vehicular ad-hoc network (VANET) has become an irreplaceable part of the Intelligent Transportation Systems (ITSs). However, the traditional network of the ground cannot meet the requirements of transmission, processing, and storage among vehicles. Under this circumstance, integrating space and air nodes into the whole network can provide comprehensive traffic information and reduce the transmission delay. The high mobility and low latency in the Space-Air-Ground Integrated Network (SAGIN) put forward higher requirements for security issues such as identity authentication, privacy protection, and data security. This paper simplifies the Blockchain and proposes an identity authentication and privacy protection scheme based on the Hashchain in the SAGIN. The scheme focuses on the characteristics of the wireless signal to identify and authenticate the nodes. The verification and backup of the records on the block are implemented with the distributed streaming platform, Kafka algorithm, instead of the consensus. Furthermore, this paper analyzes the security of this scheme. Afterward, the experimental results reveal the delay brought by the scheme using the simulation of SUMO, OMNeT++, and Veins. |
2204.01065 | Roni Stern | A.A. Snarskii, I.V. Bezsudnov | Forward and backward mapping of image to 2D vector field using fiber
bundle color space | null | null | null | null | cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce the concept of a fiber bundle color space, which acts according
to the psychophysiological rules of trichromacy perception of colors by a
human. The image resides in the fiber bundle base space and the fiber color
space contains color vectors. Further we propose the decomposition of color
vectors into spectral and achromatic parts. A homomorphism of a color image and
constructed two-dimensional vector field is demonstrated that allows us to
apply well-known advanced methods of vector analysis to a color image, i.e.
ultimately give new numerical characteristics of the image. Appropriate image
to vector field forward mapping is constructed. The proposed backward mapping
algorithm converts a two-dimensional vector field to color image. The type of
image filter is described using sequential forward and backward mapping
algorithms. An example of the color image formation on the base of
two-dimensional magnetic vector field scattered by a typical pipe line defect
is given.
| [
{
"created": "Sun, 3 Apr 2022 12:53:16 GMT",
"version": "v1"
}
] | 2022-04-05 | [
[
"Snarskii",
"A. A.",
""
],
[
"Bezsudnov",
"I. V.",
""
]
] | We introduce the concept of a fiber bundle color space, which acts according to the psychophysiological rules of trichromacy perception of colors by a human. The image resides in the fiber bundle base space and the fiber color space contains color vectors. Further we propose the decomposition of color vectors into spectral and achromatic parts. A homomorphism of a color image and constructed two-dimensional vector field is demonstrated that allows us to apply well-known advanced methods of vector analysis to a color image, i.e. ultimately give new numerical characteristics of the image. Appropriate image to vector field forward mapping is constructed. The proposed backward mapping algorithm converts a two-dimensional vector field to color image. The type of image filter is described using sequential forward and backward mapping algorithms. An example of the color image formation on the base of two-dimensional magnetic vector field scattered by a typical pipe line defect is given. |
2003.07892 | Shrey Desai | Shrey Desai and Greg Durrett | Calibration of Pre-trained Transformers | Accepted to EMNLP 2020 | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pre-trained Transformers are now ubiquitous in natural language processing,
but despite their high end-task performance, little is known empirically about
whether they are calibrated. Specifically, do these models' posterior
probabilities provide an accurate empirical measure of how likely the model is
to be correct on a given example? We focus on BERT and RoBERTa in this work,
and analyze their calibration across three tasks: natural language inference,
paraphrase detection, and commonsense reasoning. For each task, we consider
in-domain as well as challenging out-of-domain settings, where models face more
examples they should be uncertain about. We show that: (1) when used
out-of-the-box, pre-trained models are calibrated in-domain, and compared to
baselines, their calibration error out-of-domain can be as much as 3.5x lower;
(2) temperature scaling is effective at further reducing calibration error
in-domain, and using label smoothing to deliberately increase empirical
uncertainty helps calibrate posteriors out-of-domain.
| [
{
"created": "Tue, 17 Mar 2020 18:58:44 GMT",
"version": "v1"
},
{
"created": "Fri, 20 Mar 2020 21:35:54 GMT",
"version": "v2"
},
{
"created": "Thu, 15 Oct 2020 17:04:21 GMT",
"version": "v3"
}
] | 2020-10-16 | [
[
"Desai",
"Shrey",
""
],
[
"Durrett",
"Greg",
""
]
] | Pre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrated. Specifically, do these models' posterior probabilities provide an accurate empirical measure of how likely the model is to be correct on a given example? We focus on BERT and RoBERTa in this work, and analyze their calibration across three tasks: natural language inference, paraphrase detection, and commonsense reasoning. For each task, we consider in-domain as well as challenging out-of-domain settings, where models face more examples they should be uncertain about. We show that: (1) when used out-of-the-box, pre-trained models are calibrated in-domain, and compared to baselines, their calibration error out-of-domain can be as much as 3.5x lower; (2) temperature scaling is effective at further reducing calibration error in-domain, and using label smoothing to deliberately increase empirical uncertainty helps calibrate posteriors out-of-domain. |
2004.05909 | Tao Zhang | Tao Zhang, Wei Li | kDecay: Just adding k-decay items on Learning-Rate Schedule to improve
Neural Networks | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work has shown that optimizing the Learning Rate (LR) schedule can be
a very accurate and efficient way to train deep neural networks. We observe
that the rate of change (ROC) of LR has correlation with the training process,
but how to use this relationship to control the training to achieve the purpose
of improving accuracy? We propose a new method, k-decay, just add an extra item
to the commonly used and easy LR schedule(exp, cosine and polynomial), is
effectively improves the performance of these schedule, also better than the
state-of-the-art algorithms of LR shcedule such as SGDR, CLR and AutoLRS. In
the k-decay, by adjusting the hyper-parameter \(k\), to generate different LR
schedule, when k increases, the performance is improved. We evaluate the
k-decay method on CIFAR And ImageNet datasets with different neural networks
(ResNet, Wide ResNet). Our experiments show that this method can improve on
most of them. The accuracy has been improved by 1.08\% on the CIFAR-10 dataset
and by 2.07 \% on the CIFAR-100 dataset. On the ImageNet, accuracy is improved
by 1.25\%. Our method is not only a general method to be applied other LR
Shcedule, but also has no additional computational cost.
| [
{
"created": "Mon, 13 Apr 2020 12:58:45 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Apr 2020 06:47:54 GMT",
"version": "v2"
},
{
"created": "Mon, 29 Jun 2020 13:03:36 GMT",
"version": "v3"
},
{
"created": "Fri, 2 Oct 2020 10:17:13 GMT",
"version": "v4"
},
{
"created": "Tue, 22 Mar 2022 02:05:24 GMT",
"version": "v5"
}
] | 2022-03-23 | [
[
"Zhang",
"Tao",
""
],
[
"Li",
"Wei",
""
]
] | Recent work has shown that optimizing the Learning Rate (LR) schedule can be a very accurate and efficient way to train deep neural networks. We observe that the rate of change (ROC) of LR has correlation with the training process, but how to use this relationship to control the training to achieve the purpose of improving accuracy? We propose a new method, k-decay, just add an extra item to the commonly used and easy LR schedule(exp, cosine and polynomial), is effectively improves the performance of these schedule, also better than the state-of-the-art algorithms of LR shcedule such as SGDR, CLR and AutoLRS. In the k-decay, by adjusting the hyper-parameter \(k\), to generate different LR schedule, when k increases, the performance is improved. We evaluate the k-decay method on CIFAR And ImageNet datasets with different neural networks (ResNet, Wide ResNet). Our experiments show that this method can improve on most of them. The accuracy has been improved by 1.08\% on the CIFAR-10 dataset and by 2.07 \% on the CIFAR-100 dataset. On the ImageNet, accuracy is improved by 1.25\%. Our method is not only a general method to be applied other LR Shcedule, but also has no additional computational cost. |
2306.08906 | Lukas Daniel Klausner | Dagmar Gromann, Manuel Lardelli, Katta Spiel, Sabrina Burtscher, Lukas
Daniel Klausner, Arthur Mettinger, Igor Miladinovic, Sigrid Schefer-Wenzl,
Daniela Duh, Katharina B\"uhn | Participatory Research as a Path to Community-Informed, Gender-Fair
Machine Translation | 11 pages, 4 figures | Proceedings of the First Workshop on Gender-Inclusive Translation
Technologies (GITT 2023), 2023, 49-59 | null | null | cs.CL cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent years have seen a strongly increased visibility of non-binary people
in public discourse. Accordingly, considerations of gender-fair language go
beyond a binary conception of male/female. However, language technology,
especially machine translation (MT), still suffers from binary gender bias.
Proposing a solution for gender-fair MT beyond the binary from a purely
technological perspective might fall short to accommodate different target user
groups and in the worst case might lead to misgendering. To address this
challenge, we propose a method and case study building on participatory action
research to include experiential experts, i.e., queer and non-binary people,
translators, and MT experts, in the MT design process. The case study focuses
on German, where central findings are the importance of context dependency to
avoid identity invalidation and a desire for customizable MT solutions.
| [
{
"created": "Thu, 15 Jun 2023 07:20:14 GMT",
"version": "v1"
}
] | 2023-09-12 | [
[
"Gromann",
"Dagmar",
""
],
[
"Lardelli",
"Manuel",
""
],
[
"Spiel",
"Katta",
""
],
[
"Burtscher",
"Sabrina",
""
],
[
"Klausner",
"Lukas Daniel",
""
],
[
"Mettinger",
"Arthur",
""
],
[
"Miladinovic",
"Igor",
""
],
[
"Schefer-Wenzl",
"Sigrid",
""
],
[
"Duh",
"Daniela",
""
],
[
"Bühn",
"Katharina",
""
]
] | Recent years have seen a strongly increased visibility of non-binary people in public discourse. Accordingly, considerations of gender-fair language go beyond a binary conception of male/female. However, language technology, especially machine translation (MT), still suffers from binary gender bias. Proposing a solution for gender-fair MT beyond the binary from a purely technological perspective might fall short to accommodate different target user groups and in the worst case might lead to misgendering. To address this challenge, we propose a method and case study building on participatory action research to include experiential experts, i.e., queer and non-binary people, translators, and MT experts, in the MT design process. The case study focuses on German, where central findings are the importance of context dependency to avoid identity invalidation and a desire for customizable MT solutions. |
2109.00471 | Ruiqi Zhao | Ruiqi Zhao, Tianyi Wu and Guodong Guo | Sparse to Dense Motion Transfer for Face Image Animation | Accepted by ICCV 2021 Advances in Image Manipulation Workshop | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Face image animation from a single image has achieved remarkable progress.
However, it remains challenging when only sparse landmarks are available as the
driving signal. Given a source face image and a sequence of sparse face
landmarks, our goal is to generate a video of the face imitating the motion of
landmarks. We develop an efficient and effective method for motion transfer
from sparse landmarks to the face image. We then combine global and local
motion estimation in a unified model to faithfully transfer the motion. The
model can learn to segment the moving foreground from the background and
generate not only global motion, such as rotation and translation of the face,
but also subtle local motion such as the gaze change. We further improve face
landmark detection on videos. With temporally better aligned landmark sequences
for training, our method can generate temporally coherent videos with higher
visual quality. Experiments suggest we achieve results comparable to the
state-of-the-art image driven method on the same identity testing and better
results on cross identity testing.
| [
{
"created": "Wed, 1 Sep 2021 16:23:57 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Sep 2021 04:05:08 GMT",
"version": "v2"
}
] | 2021-09-06 | [
[
"Zhao",
"Ruiqi",
""
],
[
"Wu",
"Tianyi",
""
],
[
"Guo",
"Guodong",
""
]
] | Face image animation from a single image has achieved remarkable progress. However, it remains challenging when only sparse landmarks are available as the driving signal. Given a source face image and a sequence of sparse face landmarks, our goal is to generate a video of the face imitating the motion of landmarks. We develop an efficient and effective method for motion transfer from sparse landmarks to the face image. We then combine global and local motion estimation in a unified model to faithfully transfer the motion. The model can learn to segment the moving foreground from the background and generate not only global motion, such as rotation and translation of the face, but also subtle local motion such as the gaze change. We further improve face landmark detection on videos. With temporally better aligned landmark sequences for training, our method can generate temporally coherent videos with higher visual quality. Experiments suggest we achieve results comparable to the state-of-the-art image driven method on the same identity testing and better results on cross identity testing. |
2302.07074 | Matthieu Doutreligne Mr. | Matthieu Doutreligne, Adeline Degremont, Pierre-Alain Jachiet, Antoine
Lamer, Xavier Tannier | Good practices for clinical data warehouse implementation: a case study
in France | 16 pages | null | null | null | cs.CY | http://creativecommons.org/licenses/by/4.0/ | Real World Data (RWD) bears great promises to improve the quality of care.
However, specific infrastructures and methodologies are required to derive
robust knowledge and brings innovations to the patient. Drawing upon the
national case study of the 32 French regional and university hospitals
governance, we highlight key aspects of modern Clinical Data Warehouses (CDWs):
governance, transparency, types of data, data reuse, technical tools,
documentation and data quality control processes. Semi-structured interviews as
well as a review of reported studies on French CDWs were conducted in a
semi-structured manner from March to November 2022. Out of 32 regional and
university hospitals in France, 14 have a CDW in production, 5 are
experimenting, 5 have a prospective CDW project, 8 did not have any CDW project
at the time of writing. The implementation of CDW in France dates from 2011 and
accelerated in the late 2020. From this case study, we draw some general
guidelines for CDWs. The actual orientation of CDWs towards research requires
efforts in governance stabilization, standardization of data schema and
development in data quality and data documentation. Particular attention must
be paid to the sustainability of the warehouse teams and to the multi-level
governance. The transparency of the studies and the tools of transformation of
the data must improve to allow successful multi-centric data reuses as well as
innovations in routine care.
| [
{
"created": "Mon, 6 Feb 2023 13:38:12 GMT",
"version": "v1"
},
{
"created": "Tue, 7 Mar 2023 08:35:36 GMT",
"version": "v2"
}
] | 2023-03-08 | [
[
"Doutreligne",
"Matthieu",
""
],
[
"Degremont",
"Adeline",
""
],
[
"Jachiet",
"Pierre-Alain",
""
],
[
"Lamer",
"Antoine",
""
],
[
"Tannier",
"Xavier",
""
]
] | Real World Data (RWD) bears great promises to improve the quality of care. However, specific infrastructures and methodologies are required to derive robust knowledge and brings innovations to the patient. Drawing upon the national case study of the 32 French regional and university hospitals governance, we highlight key aspects of modern Clinical Data Warehouses (CDWs): governance, transparency, types of data, data reuse, technical tools, documentation and data quality control processes. Semi-structured interviews as well as a review of reported studies on French CDWs were conducted in a semi-structured manner from March to November 2022. Out of 32 regional and university hospitals in France, 14 have a CDW in production, 5 are experimenting, 5 have a prospective CDW project, 8 did not have any CDW project at the time of writing. The implementation of CDW in France dates from 2011 and accelerated in the late 2020. From this case study, we draw some general guidelines for CDWs. The actual orientation of CDWs towards research requires efforts in governance stabilization, standardization of data schema and development in data quality and data documentation. Particular attention must be paid to the sustainability of the warehouse teams and to the multi-level governance. The transparency of the studies and the tools of transformation of the data must improve to allow successful multi-centric data reuses as well as innovations in routine care. |
2308.07473 | Neel Patel | Ramiro Deo-Campo Vuong and Shaddin Dughmi and Neel Patel and Aditya
Prasad | On Supermodular Contracts and Dense Subgraphs | 31 pages, 2 figures | null | null | null | cs.GT | http://creativecommons.org/licenses/by/4.0/ | We study the combinatorial contract design problem, introduced and studied by
Dutting et. al. (2021, 2022), in both the single and multi-agent settings.
Prior work has examined the problem when the principal's utility function is
submodular in the actions chosen by the agent(s).
We complement this emerging literature with an examination of the problem
when the principal's utility is supermodular.
In the single-agent setting, we obtain a strongly polynomial time algorithm
for the optimal contract.
This stands in contrast to the NP-hardness of the problem with submodular
principal utility due to Dutting et. al. (2021).
This result has two technical components, the first of which applies beyond
supermodular or submodular utilities.
This result strengthens and simplifies analogous enumeration algorithms from
Dutting et. al. (2021), and applies to any nondecreasing valuation function for
the principal.
Second, we show that supermodular valuations lead to a polynomial number of
breakpoints, analogous to a similar result by Dutting et. al. (2021) for gross
substitutes valuations.
In the multi-agent setting, we obtain a mixed bag of positive and negative
results.
First, we show that it is NP-hard to obtain any finite multiplicative
approximation, or an additive FPTAS.
This stands in contrast to the submodular case, where efficient computation
of approximately optimal contracts was shown by Dutting et. al. (2022).
Second, we derive an additive PTAS for the problem in the instructive special
case of graph-based supermodular valuations, and equal costs.
En-route to this result, we discover an intimate connection between the
multi-agent contract problem and the notorious k-densest subgraph problem.
We build on and combine techniques from the literature on dense subgraph
problems to obtain our additive PTAS.
| [
{
"created": "Mon, 14 Aug 2023 21:57:25 GMT",
"version": "v1"
}
] | 2023-08-16 | [
[
"Vuong",
"Ramiro Deo-Campo",
""
],
[
"Dughmi",
"Shaddin",
""
],
[
"Patel",
"Neel",
""
],
[
"Prasad",
"Aditya",
""
]
] | We study the combinatorial contract design problem, introduced and studied by Dutting et. al. (2021, 2022), in both the single and multi-agent settings. Prior work has examined the problem when the principal's utility function is submodular in the actions chosen by the agent(s). We complement this emerging literature with an examination of the problem when the principal's utility is supermodular. In the single-agent setting, we obtain a strongly polynomial time algorithm for the optimal contract. This stands in contrast to the NP-hardness of the problem with submodular principal utility due to Dutting et. al. (2021). This result has two technical components, the first of which applies beyond supermodular or submodular utilities. This result strengthens and simplifies analogous enumeration algorithms from Dutting et. al. (2021), and applies to any nondecreasing valuation function for the principal. Second, we show that supermodular valuations lead to a polynomial number of breakpoints, analogous to a similar result by Dutting et. al. (2021) for gross substitutes valuations. In the multi-agent setting, we obtain a mixed bag of positive and negative results. First, we show that it is NP-hard to obtain any finite multiplicative approximation, or an additive FPTAS. This stands in contrast to the submodular case, where efficient computation of approximately optimal contracts was shown by Dutting et. al. (2022). Second, we derive an additive PTAS for the problem in the instructive special case of graph-based supermodular valuations, and equal costs. En-route to this result, we discover an intimate connection between the multi-agent contract problem and the notorious k-densest subgraph problem. We build on and combine techniques from the literature on dense subgraph problems to obtain our additive PTAS. |
2403.20132 | Michael F\"arber | Michael F\"arber | A formal specification of the jq language | null | null | null | null | cs.LO cs.PL | http://creativecommons.org/licenses/by/4.0/ | jq is a widely used tool that provides a programming language to manipulate
JSON data. However, the jq language is currently only specified by its
implementation, making it difficult to reason about its behaviour. To this end,
we provide a formal syntax and denotational semantics for a large subset of the
jq language. Our most significant contribution is to provide a new way to
interpret updates that allows for more predictable and performant execution.
| [
{
"created": "Fri, 29 Mar 2024 11:49:42 GMT",
"version": "v1"
}
] | 2024-04-01 | [
[
"Färber",
"Michael",
""
]
] | jq is a widely used tool that provides a programming language to manipulate JSON data. However, the jq language is currently only specified by its implementation, making it difficult to reason about its behaviour. To this end, we provide a formal syntax and denotational semantics for a large subset of the jq language. Our most significant contribution is to provide a new way to interpret updates that allows for more predictable and performant execution. |
1510.04440 | Silvia Crafa | Silvia Crafa | Modelling the Evolution of Programming Languages | null | null | null | null | cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Programming languages are engineered languages that allow to instruct a
machine and share algorithmic information; they have a great influence on the
society since they underlie almost every information technology artefact, and
they are at the core of the current explosion of software technology. The
history of programming languages is marked by innovations, diversifications,
lateral transfers and social influences; moreover, it represents an
intermediate case study between the evolution of human languages and the
evolution of technology. In this paper we study the application of the
Darwinian explanation to the programming languages evolution by discussing to
what extent the evolutionary mechanisms distinctive of biology can be applied
to this area. We show that a number of evolutionary building blocks can be
recognised in the realm of computer languages, but we also identify critical
issues. Far from being crystal clear, this fine-grained study shows to be a
useful tool to assess recent results about programming languages phylogenies.
Finally, we show that rich evolutionary patterns, such as co-evolution,
macro-evolutionary trends, niche construction and exaptation, can be
effectively applied to programming languages and provide for interesting
explanatory tools.
| [
{
"created": "Thu, 15 Oct 2015 08:18:54 GMT",
"version": "v1"
}
] | 2015-10-16 | [
[
"Crafa",
"Silvia",
""
]
] | Programming languages are engineered languages that allow to instruct a machine and share algorithmic information; they have a great influence on the society since they underlie almost every information technology artefact, and they are at the core of the current explosion of software technology. The history of programming languages is marked by innovations, diversifications, lateral transfers and social influences; moreover, it represents an intermediate case study between the evolution of human languages and the evolution of technology. In this paper we study the application of the Darwinian explanation to the programming languages evolution by discussing to what extent the evolutionary mechanisms distinctive of biology can be applied to this area. We show that a number of evolutionary building blocks can be recognised in the realm of computer languages, but we also identify critical issues. Far from being crystal clear, this fine-grained study shows to be a useful tool to assess recent results about programming languages phylogenies. Finally, we show that rich evolutionary patterns, such as co-evolution, macro-evolutionary trends, niche construction and exaptation, can be effectively applied to programming languages and provide for interesting explanatory tools. |
2012.06780 | Hao Zhang | Fuzhao Xue, Aixin Sun, Hao Zhang, Eng Siong Chng | GDPNet: Refining Latent Multi-View Graph for Relation Extraction | To appear at AAAI 2021 | null | 10.1609/aaai.v35i16.17670 | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Relation Extraction (RE) is to predict the relation type of two entities that
are mentioned in a piece of text, e.g., a sentence or a dialogue. When the
given text is long, it is challenging to identify indicative words for the
relation prediction. Recent advances on RE task are from BERT-based sequence
modeling and graph-based modeling of relationships among the tokens in the
sequence. In this paper, we propose to construct a latent multi-view graph to
capture various possible relationships among tokens. We then refine this graph
to select important words for relation prediction. Finally, the representation
of the refined graph and the BERT-based sequence representation are
concatenated for relation extraction. Specifically, in our proposed GDPNet
(Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph
Generator (GGG) to generate edges of the multi-view graph. The graph is then
refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we
show that GDPNet achieves the best performance on dialogue-level RE, and
comparable performance with the state-of-the-arts on sentence-level RE.
| [
{
"created": "Sat, 12 Dec 2020 10:43:41 GMT",
"version": "v1"
}
] | 2023-04-26 | [
[
"Xue",
"Fuzhao",
""
],
[
"Sun",
"Aixin",
""
],
[
"Zhang",
"Hao",
""
],
[
"Chng",
"Eng Siong",
""
]
] | Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence. In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. We then refine this graph to select important words for relation prediction. Finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction. Specifically, in our proposed GDPNet (Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph Generator (GGG) to generate edges of the multi-view graph. The graph is then refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE. |
1404.4785 | Olegs Verhodubs | Olegs Verhodubs | Ontology as a Source for Rule Generation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discloses the potential of OWL (Web Ontology Language) ontologies
for generation of rules. The main purpose of this paper is to identify new
types of rules, which may be generated from OWL ontologies. Rules, generated
from OWL ontologies, are necessary for the functioning of the Semantic Web
Expert System. It is expected that the Semantic Web Expert System (SWES) will
be able to process ontologies from the Web with the purpose to supplement or
even to develop its knowledge base.
| [
{
"created": "Fri, 18 Apr 2014 13:36:17 GMT",
"version": "v1"
}
] | 2014-04-21 | [
[
"Verhodubs",
"Olegs",
""
]
] | This paper discloses the potential of OWL (Web Ontology Language) ontologies for generation of rules. The main purpose of this paper is to identify new types of rules, which may be generated from OWL ontologies. Rules, generated from OWL ontologies, are necessary for the functioning of the Semantic Web Expert System. It is expected that the Semantic Web Expert System (SWES) will be able to process ontologies from the Web with the purpose to supplement or even to develop its knowledge base. |
2303.06149 | Marcel Matha | Marcel Matha and Christian Morsbach | Improved self-consistency of the Reynolds stress tensor eigenspace
perturbation for uncertainty quantification | This article may be downloaded for personal use only. Any other use
requires prior permission of the author and AIP Publishing. This article
appeared in Physics of Fluids (Vol.35, Issue 6) and may be found at
https://doi.org/10.1063/5.0149747 | Physics of Fluids, Vol.35, Issue 6, 2023 | 10.1063/5.0149747 | null | cs.CE physics.flu-dyn | http://creativecommons.org/licenses/by/4.0/ | The limitations of turbulence closure models in the context of
Reynolds-averaged NavierStokes (RANS) simulations play a significant part in
contributing to the uncertainty of Computational Fluid Dynamics (CFD).
Perturbing the spectral representation of the Reynolds stress tensor within
physical limits is common practice in several commercial and open-source CFD
solvers, in order to obtain estimates for the epistemic uncertainties of RANS
turbulence models. Recent research revealed, that there is a need for
moderating the amount of perturbed Reynolds stress tensor tensor to be
considered due to upcoming stability issues of the solver. In this paper we
point out that the consequent common implementation can lead to unintended
states of the resulting perturbed Reynolds stress tensor. The combination of
eigenvector perturbation and moderation factor may actually result in moderated
eigenvalues, which are not linearly dependent on the originally unperturbed and
fully perturbed eigenvalues anymore. Hence, the computational implementation is
no longer in accordance with the conceptual idea of the Eigenspace Perturbation
Framework. We verify the implementation of the conceptual description with
respect to its self-consistency. Adequately representing the basic concept
results in formulating a computational implementation to improve
self-consistency of the Reynolds stress tensor perturbation
| [
{
"created": "Wed, 8 Mar 2023 13:02:26 GMT",
"version": "v1"
},
{
"created": "Wed, 3 May 2023 07:35:52 GMT",
"version": "v2"
},
{
"created": "Fri, 26 May 2023 13:42:20 GMT",
"version": "v3"
},
{
"created": "Tue, 30 May 2023 06:35:01 GMT",
"version": "v4"
},
{
"created": "Tue, 20 Jun 2023 15:00:02 GMT",
"version": "v5"
}
] | 2023-06-21 | [
[
"Matha",
"Marcel",
""
],
[
"Morsbach",
"Christian",
""
]
] | The limitations of turbulence closure models in the context of Reynolds-averaged NavierStokes (RANS) simulations play a significant part in contributing to the uncertainty of Computational Fluid Dynamics (CFD). Perturbing the spectral representation of the Reynolds stress tensor within physical limits is common practice in several commercial and open-source CFD solvers, in order to obtain estimates for the epistemic uncertainties of RANS turbulence models. Recent research revealed, that there is a need for moderating the amount of perturbed Reynolds stress tensor tensor to be considered due to upcoming stability issues of the solver. In this paper we point out that the consequent common implementation can lead to unintended states of the resulting perturbed Reynolds stress tensor. The combination of eigenvector perturbation and moderation factor may actually result in moderated eigenvalues, which are not linearly dependent on the originally unperturbed and fully perturbed eigenvalues anymore. Hence, the computational implementation is no longer in accordance with the conceptual idea of the Eigenspace Perturbation Framework. We verify the implementation of the conceptual description with respect to its self-consistency. Adequately representing the basic concept results in formulating a computational implementation to improve self-consistency of the Reynolds stress tensor perturbation |
2306.14708 | Mingyu Jin | Mingyu Jin, Chong Zhang, Qinkai Yu, Haochen Xue, Xiaobo Jin, Xi Yang | A Simple and Effective Baseline for Attentional Generative Adversarial
Networks | 12 pages, 3 figures | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Synthesising a text-to-image model of high-quality images by guiding the
generative model through the Text description is an innovative and challenging
task. In recent years, AttnGAN based on the Attention mechanism to guide GAN
training has been proposed, SD-GAN, which adopts a self-distillation technique
to improve the performance of the generator and the quality of image
generation, and Stack-GAN++, which gradually improves the details and quality
of the image by stacking multiple generators and discriminators. However, this
series of improvements to GAN all have redundancy to a certain extent, which
affects the generation performance and complexity to a certain extent. We use
the popular simple and effective idea (1) to remove redundancy structure and
improve the backbone network of AttnGAN. (2) to integrate and reconstruct
multiple losses of DAMSM. Our improvements have significantly improved the
model size and training efficiency while ensuring that the model's performance
is unchanged and finally proposed our SEAttnGAN. Code is avalilable at
https://github.com/jmyissb/SEAttnGAN.
| [
{
"created": "Mon, 26 Jun 2023 13:55:57 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Jul 2023 14:07:35 GMT",
"version": "v2"
}
] | 2023-07-07 | [
[
"Jin",
"Mingyu",
""
],
[
"Zhang",
"Chong",
""
],
[
"Yu",
"Qinkai",
""
],
[
"Xue",
"Haochen",
""
],
[
"Jin",
"Xiaobo",
""
],
[
"Yang",
"Xi",
""
]
] | Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnGAN based on the Attention mechanism to guide GAN training has been proposed, SD-GAN, which adopts a self-distillation technique to improve the performance of the generator and the quality of image generation, and Stack-GAN++, which gradually improves the details and quality of the image by stacking multiple generators and discriminators. However, this series of improvements to GAN all have redundancy to a certain extent, which affects the generation performance and complexity to a certain extent. We use the popular simple and effective idea (1) to remove redundancy structure and improve the backbone network of AttnGAN. (2) to integrate and reconstruct multiple losses of DAMSM. Our improvements have significantly improved the model size and training efficiency while ensuring that the model's performance is unchanged and finally proposed our SEAttnGAN. Code is avalilable at https://github.com/jmyissb/SEAttnGAN. |
2408.03397 | Pratyush Dhingra | Pratyush Dhingra, Janardhan Rao Doppa, and Partha Pratim Pande | HeTraX: Energy Efficient 3D Heterogeneous Manycore Architecture for
Transformer Acceleration | Presented at ACM/IEEE International Symposium on Low Power
Electronics and Design (ISLPED-24) | null | null | null | cs.AR cs.LG | http://creativecommons.org/licenses/by/4.0/ | Transformers have revolutionized deep learning and generative modeling to
enable unprecedented advancements in natural language processing tasks and
beyond. However, designing hardware accelerators for executing transformer
models is challenging due to the wide variety of computing kernels involved in
the transformer architecture. Existing accelerators are either inadequate to
accelerate end-to-end transformer models or suffer notable thermal limitations.
In this paper, we propose the design of a three-dimensional heterogeneous
architecture referred to as HeTraX specifically optimized to accelerate
end-to-end transformer models. HeTraX employs hardware resources aligned with
the computational kernels of transformers and optimizes both performance and
energy. Experimental results show that HeTraX outperforms existing
state-of-the-art by up to 5.6x in speedup and improves EDP by 14.5x while
ensuring thermally feasibility.
| [
{
"created": "Tue, 6 Aug 2024 18:48:01 GMT",
"version": "v1"
}
] | 2024-08-08 | [
[
"Dhingra",
"Pratyush",
""
],
[
"Doppa",
"Janardhan Rao",
""
],
[
"Pande",
"Partha Pratim",
""
]
] | Transformers have revolutionized deep learning and generative modeling to enable unprecedented advancements in natural language processing tasks and beyond. However, designing hardware accelerators for executing transformer models is challenging due to the wide variety of computing kernels involved in the transformer architecture. Existing accelerators are either inadequate to accelerate end-to-end transformer models or suffer notable thermal limitations. In this paper, we propose the design of a three-dimensional heterogeneous architecture referred to as HeTraX specifically optimized to accelerate end-to-end transformer models. HeTraX employs hardware resources aligned with the computational kernels of transformers and optimizes both performance and energy. Experimental results show that HeTraX outperforms existing state-of-the-art by up to 5.6x in speedup and improves EDP by 14.5x while ensuring thermally feasibility. |
2401.05971 | Rouwan Wu | Rouwan Wu, Xiaoya Cheng, Juelin Zhu, Xuxiang Liu, Maojun Zhang, Shen
Yan | UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization | null | 3DV 2024 | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite significant progress in global localization of Unmanned Aerial
Vehicles (UAVs) in GPS-denied environments, existing methods remain constrained
by the availability of datasets. Current datasets often focus on small-scale
scenes and lack viewpoint variability, accurate ground truth (GT) pose, and UAV
build-in sensor data. To address these limitations, we introduce a large-scale
6-DoF UAV dataset for localization (UAVD4L) and develop a two-stage 6-DoF
localization pipeline (UAVLoc), which consists of offline synthetic data
generation and online visual localization. Additionally, based on the 6-DoF
estimator, we design a hierarchical system for tracking ground target in 3D
space. Experimental results on the new dataset demonstrate the effectiveness of
the proposed approach. Code and dataset are available at
https://github.com/RingoWRW/UAVD4L
| [
{
"created": "Thu, 11 Jan 2024 15:19:21 GMT",
"version": "v1"
}
] | 2024-01-12 | [
[
"Wu",
"Rouwan",
""
],
[
"Cheng",
"Xiaoya",
""
],
[
"Zhu",
"Juelin",
""
],
[
"Liu",
"Xuxiang",
""
],
[
"Zhang",
"Maojun",
""
],
[
"Yan",
"Shen",
""
]
] | Despite significant progress in global localization of Unmanned Aerial Vehicles (UAVs) in GPS-denied environments, existing methods remain constrained by the availability of datasets. Current datasets often focus on small-scale scenes and lack viewpoint variability, accurate ground truth (GT) pose, and UAV build-in sensor data. To address these limitations, we introduce a large-scale 6-DoF UAV dataset for localization (UAVD4L) and develop a two-stage 6-DoF localization pipeline (UAVLoc), which consists of offline synthetic data generation and online visual localization. Additionally, based on the 6-DoF estimator, we design a hierarchical system for tracking ground target in 3D space. Experimental results on the new dataset demonstrate the effectiveness of the proposed approach. Code and dataset are available at https://github.com/RingoWRW/UAVD4L |
2207.05696 | Prateek Chhikara | Prateek Chhikara, Anil Goyal, Chirag Sharma | RE-Tagger: A light-weight Real-Estate Image Classifier | European Conference on Machine Learning and Principles and Practice
of Knowledge Discovery in Databases (DEMO TRACK) | null | 10.1007/978-3-031-26422-1_44 | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-estate image tagging is one of the essential use-cases to save efforts
involved in manual annotation and enhance the user experience. This paper
proposes an end-to-end pipeline (referred to as RE-Tagger) for the real-estate
image classification problem. We present a two-stage transfer learning approach
using custom InceptionV3 architecture to classify images into different
categories (i.e., bedroom, bathroom, kitchen, balcony, hall, and others).
Finally, we released the application as REST API hosted as a web application
running on 2 cores machine with 2 GB RAM. The demo video is available here.
| [
{
"created": "Tue, 12 Jul 2022 17:16:06 GMT",
"version": "v1"
}
] | 2023-06-06 | [
[
"Chhikara",
"Prateek",
""
],
[
"Goyal",
"Anil",
""
],
[
"Sharma",
"Chirag",
""
]
] | Real-estate image tagging is one of the essential use-cases to save efforts involved in manual annotation and enhance the user experience. This paper proposes an end-to-end pipeline (referred to as RE-Tagger) for the real-estate image classification problem. We present a two-stage transfer learning approach using custom InceptionV3 architecture to classify images into different categories (i.e., bedroom, bathroom, kitchen, balcony, hall, and others). Finally, we released the application as REST API hosted as a web application running on 2 cores machine with 2 GB RAM. The demo video is available here. |
2008.09105 | Mingkui Tan | Deng Huang, Peihao Chen, Runhao Zeng, Qing Du, Mingkui Tan, Chuang Gan | Location-aware Graph Convolutional Networks for Video Question Answering | null | null | null | null | cs.CV cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We addressed the challenging task of video question answering, which requires
machines to answer questions about videos in a natural language form. Previous
state-of-the-art methods attempt to apply spatio-temporal attention mechanism
on video frame features without explicitly modeling the location and relations
among object interaction occurred in videos. However, the relations between
object interaction and their location information are very critical for both
action recognition and question reasoning. In this work, we propose to
represent the contents in the video as a location-aware graph by incorporating
the location information of an object into the graph construction. Here, each
node is associated with an object represented by its appearance and location
features. Based on the constructed graph, we propose to use graph convolution
to infer both the category and temporal locations of an action. As the graph is
built on objects, our method is able to focus on the foreground action contents
for better video question answering. Lastly, we leverage an attention mechanism
to combine the output of graph convolution and encoded question features for
final answer reasoning. Extensive experiments demonstrate the effectiveness of
the proposed methods. Specifically, our method significantly outperforms
state-of-the-art methods on TGIF-QA, Youtube2Text-QA, and MSVD-QA datasets.
Code and pre-trained models are publicly available at:
https://github.com/SunDoge/L-GCN
| [
{
"created": "Fri, 7 Aug 2020 02:12:56 GMT",
"version": "v1"
}
] | 2020-08-21 | [
[
"Huang",
"Deng",
""
],
[
"Chen",
"Peihao",
""
],
[
"Zeng",
"Runhao",
""
],
[
"Du",
"Qing",
""
],
[
"Tan",
"Mingkui",
""
],
[
"Gan",
"Chuang",
""
]
] | We addressed the challenging task of video question answering, which requires machines to answer questions about videos in a natural language form. Previous state-of-the-art methods attempt to apply spatio-temporal attention mechanism on video frame features without explicitly modeling the location and relations among object interaction occurred in videos. However, the relations between object interaction and their location information are very critical for both action recognition and question reasoning. In this work, we propose to represent the contents in the video as a location-aware graph by incorporating the location information of an object into the graph construction. Here, each node is associated with an object represented by its appearance and location features. Based on the constructed graph, we propose to use graph convolution to infer both the category and temporal locations of an action. As the graph is built on objects, our method is able to focus on the foreground action contents for better video question answering. Lastly, we leverage an attention mechanism to combine the output of graph convolution and encoded question features for final answer reasoning. Extensive experiments demonstrate the effectiveness of the proposed methods. Specifically, our method significantly outperforms state-of-the-art methods on TGIF-QA, Youtube2Text-QA, and MSVD-QA datasets. Code and pre-trained models are publicly available at: https://github.com/SunDoge/L-GCN |
2103.10357 | Vincent Vajnovszki | Phan Thuan Do, Thi Thu Huong Tran, Vincent Vajnovszki | The equidistribution of some Mahonian statistics over permutations
avoiding a pattern of length three | null | null | null | null | cs.DM math.CO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We prove the equidistribution of several multistatistics over some classes of
permutations avoiding a $3$-length pattern. We deduce the equidistribution, on
the one hand of inv and foze" statistics, and on the other hand that of maj and
makl statistics, over these classes of pattern avoiding permutations. Here inv
and maj are the celebrated Mahonian statistics, foze" is one of the statistics
defined in terms of generalized patterns in the 2000 pioneering paper of Babson
and Steingr\'imsson, and makl is one of the statistics defined by Clarke,
Steingr\'imsson and Zeng in 1997. These results solve several conjectures posed
by Amini in 2018.
| [
{
"created": "Thu, 18 Mar 2021 16:25:36 GMT",
"version": "v1"
},
{
"created": "Wed, 11 Aug 2021 15:04:40 GMT",
"version": "v2"
}
] | 2021-08-12 | [
[
"Do",
"Phan Thuan",
""
],
[
"Tran",
"Thi Thu Huong",
""
],
[
"Vajnovszki",
"Vincent",
""
]
] | We prove the equidistribution of several multistatistics over some classes of permutations avoiding a $3$-length pattern. We deduce the equidistribution, on the one hand of inv and foze" statistics, and on the other hand that of maj and makl statistics, over these classes of pattern avoiding permutations. Here inv and maj are the celebrated Mahonian statistics, foze" is one of the statistics defined in terms of generalized patterns in the 2000 pioneering paper of Babson and Steingr\'imsson, and makl is one of the statistics defined by Clarke, Steingr\'imsson and Zeng in 1997. These results solve several conjectures posed by Amini in 2018. |
1811.05010 | Long Nguyen Msc | Long Nguyen, Zhou Yang, Jiazhen Zhu, Jia Li, Fang Jin | Coordinating Disaster Emergency Response with Heuristic Reinforcement
Learning | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A crucial and time-sensitive task when any disaster occurs is to rescue
victims and distribute resources to the right groups and locations. This task
is challenging in populated urban areas, due to the huge burst of help requests
generated in a very short period. To improve the efficiency of the emergency
response in the immediate aftermath of a disaster, we propose a heuristic
multi-agent reinforcement learning scheduling algorithm, named as ResQ, which
can effectively schedule the rapid deployment of volunteers to rescue victims
in dynamic settings. The core concept is to quickly identify victims and
volunteers from social network data and then schedule rescue parties with an
adaptive learning algorithm. This framework performs two key functions: 1)
identify trapped victims and rescue volunteers, and 2) optimize the volunteers'
rescue strategy in a complex time-sensitive environment. The proposed ResQ
algorithm can speed up the training processes through a heuristic function
which reduces the state-action space by identifying the set of particular
actions over others. Experimental results showed that the proposed heuristic
multi-agent reinforcement learning based scheduling outperforms several
state-of-art methods, in terms of both reward rate and response times.
| [
{
"created": "Mon, 12 Nov 2018 21:39:07 GMT",
"version": "v1"
}
] | 2018-11-14 | [
[
"Nguyen",
"Long",
""
],
[
"Yang",
"Zhou",
""
],
[
"Zhu",
"Jiazhen",
""
],
[
"Li",
"Jia",
""
],
[
"Jin",
"Fang",
""
]
] | A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations. This task is challenging in populated urban areas, due to the huge burst of help requests generated in a very short period. To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule the rapid deployment of volunteers to rescue victims in dynamic settings. The core concept is to quickly identify victims and volunteers from social network data and then schedule rescue parties with an adaptive learning algorithm. This framework performs two key functions: 1) identify trapped victims and rescue volunteers, and 2) optimize the volunteers' rescue strategy in a complex time-sensitive environment. The proposed ResQ algorithm can speed up the training processes through a heuristic function which reduces the state-action space by identifying the set of particular actions over others. Experimental results showed that the proposed heuristic multi-agent reinforcement learning based scheduling outperforms several state-of-art methods, in terms of both reward rate and response times. |
2302.11296 | Mashaan Alshammari Dr. | Mashaan Alshammari, John Stavrakakis, Masahiro Takatsuka | Refining a $k$-nearest neighbor graph for a computationally efficient
spectral clustering | null | Pattern Recognition, Volume 114, 2021 | 10.1016/j.patcog.2021.107869 | null | cs.LG cs.AI cs.IR cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spectral clustering became a popular choice for data clustering for its
ability of uncovering clusters of different shapes. However, it is not always
preferable over other clustering methods due to its computational demands. One
of the effective ways to bypass these computational demands is to perform
spectral clustering on a subset of points (data representatives) then
generalize the clustering outcome, this is known as approximate spectral
clustering (ASC). ASC uses sampling or quantization to select data
representatives. This makes it vulnerable to 1) performance inconsistency
(since these methods have a random step either in initialization or training),
2) local statistics loss (because the pairwise similarities are extracted from
data representatives instead of data points). We proposed a refined version of
$k$-nearest neighbor graph, in which we keep data points and aggressively
reduce number of edges for computational efficiency. Local statistics were
exploited to keep the edges that do not violate the intra-cluster distances and
nullify all other edges in the $k$-nearest neighbor graph. We also introduced
an optional step to automatically select the number of clusters $C$. The
proposed method was tested on synthetic and real datasets. Compared to ASC
methods, the proposed method delivered a consistent performance despite
significant reduction of edges.
| [
{
"created": "Wed, 22 Feb 2023 11:31:32 GMT",
"version": "v1"
}
] | 2023-02-23 | [
[
"Alshammari",
"Mashaan",
""
],
[
"Stavrakakis",
"John",
""
],
[
"Takatsuka",
"Masahiro",
""
]
] | Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clustering methods due to its computational demands. One of the effective ways to bypass these computational demands is to perform spectral clustering on a subset of points (data representatives) then generalize the clustering outcome, this is known as approximate spectral clustering (ASC). ASC uses sampling or quantization to select data representatives. This makes it vulnerable to 1) performance inconsistency (since these methods have a random step either in initialization or training), 2) local statistics loss (because the pairwise similarities are extracted from data representatives instead of data points). We proposed a refined version of $k$-nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency. Local statistics were exploited to keep the edges that do not violate the intra-cluster distances and nullify all other edges in the $k$-nearest neighbor graph. We also introduced an optional step to automatically select the number of clusters $C$. The proposed method was tested on synthetic and real datasets. Compared to ASC methods, the proposed method delivered a consistent performance despite significant reduction of edges. |
2010.01753 | Rodrigo Toro Icarte | Rodrigo Toro Icarte, Richard Valenzano, Toryn Q. Klassen, Phillip
Christoffersen, Amir-massoud Farahmand, Sheila A. McIlraith | The act of remembering: a study in partially observable reinforcement
learning | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement Learning (RL) agents typically learn memoryless
policies---policies that only consider the last observation when selecting
actions. Learning memoryless policies is efficient and optimal in fully
observable environments. However, some form of memory is necessary when RL
agents are faced with partial observability. In this paper, we study a
lightweight approach to tackle partial observability in RL. We provide the
agent with an external memory and additional actions to control what, if
anything, is written to the memory. At every step, the current memory state is
part of the agent's observation, and the agent selects a tuple of actions: one
action that modifies the environment and another that modifies the memory. When
the external memory is sufficiently expressive, optimal memoryless policies
yield globally optimal solutions. Unfortunately, previous attempts to use
external memory in the form of binary memory have produced poor results in
practice. Here, we investigate alternative forms of memory in support of
learning effective memoryless policies. Our novel forms of memory outperform
binary and LSTM-based memory in well-established partially observable domains.
| [
{
"created": "Mon, 5 Oct 2020 02:56:43 GMT",
"version": "v1"
}
] | 2020-10-06 | [
[
"Icarte",
"Rodrigo Toro",
""
],
[
"Valenzano",
"Richard",
""
],
[
"Klassen",
"Toryn Q.",
""
],
[
"Christoffersen",
"Phillip",
""
],
[
"Farahmand",
"Amir-massoud",
""
],
[
"McIlraith",
"Sheila A.",
""
]
] | Reinforcement Learning (RL) agents typically learn memoryless policies---policies that only consider the last observation when selecting actions. Learning memoryless policies is efficient and optimal in fully observable environments. However, some form of memory is necessary when RL agents are faced with partial observability. In this paper, we study a lightweight approach to tackle partial observability in RL. We provide the agent with an external memory and additional actions to control what, if anything, is written to the memory. At every step, the current memory state is part of the agent's observation, and the agent selects a tuple of actions: one action that modifies the environment and another that modifies the memory. When the external memory is sufficiently expressive, optimal memoryless policies yield globally optimal solutions. Unfortunately, previous attempts to use external memory in the form of binary memory have produced poor results in practice. Here, we investigate alternative forms of memory in support of learning effective memoryless policies. Our novel forms of memory outperform binary and LSTM-based memory in well-established partially observable domains. |
1909.00508 | Nathan Dahlin | Nathan Dahlin and Rahul Jain | Two-Stage Electricity Markets with Renewable Energy Integration: Market
Mechanisms and Equilibrium Analysis | null | null | null | null | cs.GT cs.SY econ.GN eess.SY q-fin.EC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a two-stage market mechanism for trading electricity including
renewable generation as an alternative to the widely used multi-settlement
market structure. The two-stage market structure allows for recourse decisions
by the market operator, which are not possible in today's markets. We allow for
different conventional generation cost curves in the forward and the real-time
stages. We have considered costs of demand response programs and black outs,
and adopt a DC power flow model to account for network constraints. Our first
result is to show existence (by construction) of a sequential competitive
equilibrium (SCEq) in such a two-stage market. We argue social welfare
properties of such an SCEq, and then design a market mechanism that achieves
social welfare maximization when the market participants are non-strategic. We
also show that under either a congestion-free or a monopoly-free condition, an
efficient Nash equilibrium exists.
| [
{
"created": "Mon, 2 Sep 2019 01:46:35 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Jun 2021 00:40:53 GMT",
"version": "v2"
}
] | 2021-06-16 | [
[
"Dahlin",
"Nathan",
""
],
[
"Jain",
"Rahul",
""
]
] | We consider a two-stage market mechanism for trading electricity including renewable generation as an alternative to the widely used multi-settlement market structure. The two-stage market structure allows for recourse decisions by the market operator, which are not possible in today's markets. We allow for different conventional generation cost curves in the forward and the real-time stages. We have considered costs of demand response programs and black outs, and adopt a DC power flow model to account for network constraints. Our first result is to show existence (by construction) of a sequential competitive equilibrium (SCEq) in such a two-stage market. We argue social welfare properties of such an SCEq, and then design a market mechanism that achieves social welfare maximization when the market participants are non-strategic. We also show that under either a congestion-free or a monopoly-free condition, an efficient Nash equilibrium exists. |
2009.00099 | Behrooz Omidvar-Tehrani | Behrooz Omidvar-Tehrani, Sruthi Viswanathan, Jean-Michel Renders | Interactive and Explainable Point-of-Interest Recommendation using
Look-alike Groups | null | null | null | null | cs.DB | http://creativecommons.org/licenses/by/4.0/ | Recommending Points-of-Interest (POIs) is surfacing in many location-based
applications. The literature contains personalized and socialized POI
recommendation approaches which employ historical check-ins and social links to
make recommendations. However these systems still lack customizability
(incorporating session-based user interactions with the system) and
contextuality (incorporating the situational context of the user), particularly
in cold start situations, where nearly no user information is available. In
this paper, we propose LikeMind, a POI recommendation system which tackles the
challenges of cold start, customizability, contextuality, and explainability by
exploiting look-alike groups mined in public POI datasets. LikeMind
reformulates the problem of POI recommendation, as recommending explainable
look-alike groups (and their POIs) which are in line with user's interests.
LikeMind frames the task of POI recommendation as an exploratory process where
users interact with the system by expressing their favorite POIs, and their
interactions impact the way look-alike groups are selected out. Moreover,
LikeMind employs "mindsets", which capture actual situation and intent of the
user, and enforce the semantics of POI interestingness. In an extensive set of
experiments, we show the quality of our approach in recommending relevant
look-alike groups and their POIs, in terms of efficiency and effectiveness.
| [
{
"created": "Mon, 31 Aug 2020 21:05:21 GMT",
"version": "v1"
}
] | 2020-09-02 | [
[
"Omidvar-Tehrani",
"Behrooz",
""
],
[
"Viswanathan",
"Sruthi",
""
],
[
"Renders",
"Jean-Michel",
""
]
] | Recommending Points-of-Interest (POIs) is surfacing in many location-based applications. The literature contains personalized and socialized POI recommendation approaches which employ historical check-ins and social links to make recommendations. However these systems still lack customizability (incorporating session-based user interactions with the system) and contextuality (incorporating the situational context of the user), particularly in cold start situations, where nearly no user information is available. In this paper, we propose LikeMind, a POI recommendation system which tackles the challenges of cold start, customizability, contextuality, and explainability by exploiting look-alike groups mined in public POI datasets. LikeMind reformulates the problem of POI recommendation, as recommending explainable look-alike groups (and their POIs) which are in line with user's interests. LikeMind frames the task of POI recommendation as an exploratory process where users interact with the system by expressing their favorite POIs, and their interactions impact the way look-alike groups are selected out. Moreover, LikeMind employs "mindsets", which capture actual situation and intent of the user, and enforce the semantics of POI interestingness. In an extensive set of experiments, we show the quality of our approach in recommending relevant look-alike groups and their POIs, in terms of efficiency and effectiveness. |
1810.09807 | Hong Chen | Hong Chen, Zhenhua Fan, Hao Lu, Alan L. Yuille and Shu Rong | PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference
Resolution | EMNLP 2018 | null | null | null | cs.CL cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce PreCo, a large-scale English dataset for coreference resolution.
The dataset is designed to embody the core challenges in coreference, such as
entity representation, by alleviating the challenge of low overlap between
training and test sets and enabling separated analysis of mention detection and
mention clustering. To strengthen the training-test overlap, we collect a large
corpus of about 38K documents and 12.4M words which are mostly from the
vocabulary of English-speaking preschoolers. Experiments show that with higher
training-test overlap, error analysis on PreCo is more efficient than the one
on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton
mentions making it possible for the first time to quantify the influence that a
mention detector makes on coreference resolution performance. The dataset is
freely available at https://preschool-lab.github.io/PreCo/.
| [
{
"created": "Tue, 23 Oct 2018 12:09:37 GMT",
"version": "v1"
}
] | 2018-10-24 | [
[
"Chen",
"Hong",
""
],
[
"Fan",
"Zhenhua",
""
],
[
"Lu",
"Hao",
""
],
[
"Yuille",
"Alan L.",
""
],
[
"Rong",
"Shu",
""
]
] | We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of about 38K documents and 12.4M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available at https://preschool-lab.github.io/PreCo/. |
2210.01234 | Rafid Mahmood | Rafid Mahmood, James Lucas, Jose M. Alvarez, Sanja Fidler, Marc T. Law | Optimizing Data Collection for Machine Learning | Accepted to NeurIPS 2022 | null | null | null | cs.LG cs.AI cs.CV | http://creativecommons.org/licenses/by/4.0/ | Modern deep learning systems require huge data sets to achieve impressive
performance, but there is little guidance on how much or what kind of data to
collect. Over-collecting data incurs unnecessary present costs, while
under-collecting may incur future costs and delay workflows. We propose a new
paradigm for modeling the data collection workflow as a formal optimal data
collection problem that allows designers to specify performance targets,
collection costs, a time horizon, and penalties for failing to meet the
targets. Additionally, this formulation generalizes to tasks requiring multiple
data sources, such as labeled and unlabeled data used in semi-supervised
learning. To solve our problem, we develop Learn-Optimize-Collect (LOC), which
minimizes expected future collection costs. Finally, we numerically compare our
framework to the conventional baseline of estimating data requirements by
extrapolating from neural scaling laws. We significantly reduce the risks of
failing to meet desired performance targets on several classification,
segmentation, and detection tasks, while maintaining low total collection
costs.
| [
{
"created": "Mon, 3 Oct 2022 21:19:05 GMT",
"version": "v1"
}
] | 2022-10-05 | [
[
"Mahmood",
"Rafid",
""
],
[
"Lucas",
"James",
""
],
[
"Alvarez",
"Jose M.",
""
],
[
"Fidler",
"Sanja",
""
],
[
"Law",
"Marc T.",
""
]
] | Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect. Over-collecting data incurs unnecessary present costs, while under-collecting may incur future costs and delay workflows. We propose a new paradigm for modeling the data collection workflow as a formal optimal data collection problem that allows designers to specify performance targets, collection costs, a time horizon, and penalties for failing to meet the targets. Additionally, this formulation generalizes to tasks requiring multiple data sources, such as labeled and unlabeled data used in semi-supervised learning. To solve our problem, we develop Learn-Optimize-Collect (LOC), which minimizes expected future collection costs. Finally, we numerically compare our framework to the conventional baseline of estimating data requirements by extrapolating from neural scaling laws. We significantly reduce the risks of failing to meet desired performance targets on several classification, segmentation, and detection tasks, while maintaining low total collection costs. |
1903.10404 | Bharat Prakash | Bharat Prakash, Mark Horton, Nicholas R. Waytowich, William David
Hairston, Tim Oates, Tinoosh Mohsenin | On the use of Deep Autoencoders for Efficient Embedded Reinforcement
Learning | null | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In autonomous embedded systems, it is often vital to reduce the amount of
actions taken in the real world and energy required to learn a policy. Training
reinforcement learning agents from high dimensional image representations can
be very expensive and time consuming. Autoencoders are deep neural network used
to compress high dimensional data such as pixelated images into small latent
representations. This compression model is vital to efficiently learn policies,
especially when learning on embedded systems. We have implemented this model on
the NVIDIA Jetson TX2 embedded GPU, and evaluated the power consumption,
throughput, and energy consumption of the autoencoders for various CPU/GPU core
combinations, frequencies, and model parameters. Additionally, we have shown
the reconstructions generated by the autoencoder to analyze the quality of the
generated compressed representation and also the performance of the
reinforcement learning agent. Finally, we have presented an assessment of the
viability of training these models on embedded systems and their usefulness in
developing autonomous policies. Using autoencoders, we were able to achieve 4-5
$\times$ improved performance compared to a baseline RL agent with a
convolutional feature extractor, while using less than 2W of power.
| [
{
"created": "Mon, 25 Mar 2019 15:38:37 GMT",
"version": "v1"
}
] | 2019-03-26 | [
[
"Prakash",
"Bharat",
""
],
[
"Horton",
"Mark",
""
],
[
"Waytowich",
"Nicholas R.",
""
],
[
"Hairston",
"William David",
""
],
[
"Oates",
"Tim",
""
],
[
"Mohsenin",
"Tinoosh",
""
]
] | In autonomous embedded systems, it is often vital to reduce the amount of actions taken in the real world and energy required to learn a policy. Training reinforcement learning agents from high dimensional image representations can be very expensive and time consuming. Autoencoders are deep neural network used to compress high dimensional data such as pixelated images into small latent representations. This compression model is vital to efficiently learn policies, especially when learning on embedded systems. We have implemented this model on the NVIDIA Jetson TX2 embedded GPU, and evaluated the power consumption, throughput, and energy consumption of the autoencoders for various CPU/GPU core combinations, frequencies, and model parameters. Additionally, we have shown the reconstructions generated by the autoencoder to analyze the quality of the generated compressed representation and also the performance of the reinforcement learning agent. Finally, we have presented an assessment of the viability of training these models on embedded systems and their usefulness in developing autonomous policies. Using autoencoders, we were able to achieve 4-5 $\times$ improved performance compared to a baseline RL agent with a convolutional feature extractor, while using less than 2W of power. |
2307.07935 | Jinlong Li | Jinlong Li, Runsheng Xu, Xinyu Liu, Baolu Li, Qin Zou, Jiaqi Ma,
Hongkai Yu | S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from
Simulation to Reality | submit the latest one, accepted by the 2024 IEEE International
Conference on Robotics and Automation (ICRA) | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Due to the lack of enough real multi-agent data and time-consuming of
labeling, existing multi-agent cooperative perception algorithms usually select
the simulated sensor data for training and validating. However, the perception
performance is degraded when these simulation-trained models are deployed to
the real world, due to the significant domain gap between the simulated and
real data. In this paper, we propose the first Simulation-to-Reality transfer
learning framework for multi-agent cooperative perception using a novel Vision
Transformer, named as S2R-ViT, which considers both the Deployment Gap and
Feature Gap between simulated and real data. We investigate the effects of
these two types of domain gaps and propose a novel uncertainty-aware vision
transformer to effectively relief the Deployment Gap and an agent-based feature
adaptation module with inter-agent and ego-agent discriminators to reduce the
Feature Gap. Our intensive experiments on the public multi-agent cooperative
perception datasets OPV2V and V2V4Real demonstrate that the proposed S2R-ViT
can effectively bridge the gap from simulation to reality and outperform other
methods significantly for point cloud-based 3D object detection.
| [
{
"created": "Sun, 16 Jul 2023 03:54:10 GMT",
"version": "v1"
},
{
"created": "Tue, 18 Jul 2023 22:33:55 GMT",
"version": "v2"
},
{
"created": "Tue, 26 Sep 2023 18:01:44 GMT",
"version": "v3"
},
{
"created": "Tue, 20 Feb 2024 20:50:55 GMT",
"version": "v4"
}
] | 2024-02-22 | [
[
"Li",
"Jinlong",
""
],
[
"Xu",
"Runsheng",
""
],
[
"Liu",
"Xinyu",
""
],
[
"Li",
"Baolu",
""
],
[
"Zou",
"Qin",
""
],
[
"Ma",
"Jiaqi",
""
],
[
"Yu",
"Hongkai",
""
]
] | Due to the lack of enough real multi-agent data and time-consuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for training and validating. However, the perception performance is degraded when these simulation-trained models are deployed to the real world, due to the significant domain gap between the simulated and real data. In this paper, we propose the first Simulation-to-Reality transfer learning framework for multi-agent cooperative perception using a novel Vision Transformer, named as S2R-ViT, which considers both the Deployment Gap and Feature Gap between simulated and real data. We investigate the effects of these two types of domain gaps and propose a novel uncertainty-aware vision transformer to effectively relief the Deployment Gap and an agent-based feature adaptation module with inter-agent and ego-agent discriminators to reduce the Feature Gap. Our intensive experiments on the public multi-agent cooperative perception datasets OPV2V and V2V4Real demonstrate that the proposed S2R-ViT can effectively bridge the gap from simulation to reality and outperform other methods significantly for point cloud-based 3D object detection. |
1811.12099 | Felix Rath | Felix Rath, Daniel Schemmel, Klaus Wehrle | Interoperability-Guided Testing of QUIC Implementations using Symbolic
Execution | 6 pages | null | 10.1145/3284850.3284853 | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The main reason for the standardization of network protocols, like QUIC, is
to ensure interoperability between implementations, which poses a challenging
task. Manual tests are currently used to test the different existing
implementations for interoperability, but given the complex nature of network
protocols, it is hard to cover all possible edge cases.
State-of-the-art automated software testing techniques, such as Symbolic
Execution (SymEx), have proven themselves capable of analyzing complex
real-world software and finding hard to detect bugs. We present a SymEx-based
method for finding interoperability issues in QUIC implementations, and explore
its merit in a case study that analyzes the interoperability of picoquic and
QUANT. We find that, while SymEx is able to analyze deep interactions between
different implementations and uncovers several bugs, in order to enable
efficient interoperability testing, implementations need to provide additional
information about their current protocol state.
| [
{
"created": "Thu, 29 Nov 2018 12:41:38 GMT",
"version": "v1"
}
] | 2018-11-30 | [
[
"Rath",
"Felix",
""
],
[
"Schemmel",
"Daniel",
""
],
[
"Wehrle",
"Klaus",
""
]
] | The main reason for the standardization of network protocols, like QUIC, is to ensure interoperability between implementations, which poses a challenging task. Manual tests are currently used to test the different existing implementations for interoperability, but given the complex nature of network protocols, it is hard to cover all possible edge cases. State-of-the-art automated software testing techniques, such as Symbolic Execution (SymEx), have proven themselves capable of analyzing complex real-world software and finding hard to detect bugs. We present a SymEx-based method for finding interoperability issues in QUIC implementations, and explore its merit in a case study that analyzes the interoperability of picoquic and QUANT. We find that, while SymEx is able to analyze deep interactions between different implementations and uncovers several bugs, in order to enable efficient interoperability testing, implementations need to provide additional information about their current protocol state. |
2107.11208 | Simon Burton | Simon Burton | Entropy, Derivation Operators and Huffman Trees | null | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | We build a theory of binary trees on finite multisets that categorifies, or
operationalizes, the entropy of a finite probability distribution. Multisets
operationalize probabilities as the event outcomes of an experiment. Huffman
trees operationalize the entropy of the distribution of these events. We show
how the derivation property of the entropy of a joint distribution lifts to
Huffman trees.
| [
{
"created": "Fri, 23 Jul 2021 13:08:37 GMT",
"version": "v1"
}
] | 2021-07-26 | [
[
"Burton",
"Simon",
""
]
] | We build a theory of binary trees on finite multisets that categorifies, or operationalizes, the entropy of a finite probability distribution. Multisets operationalize probabilities as the event outcomes of an experiment. Huffman trees operationalize the entropy of the distribution of these events. We show how the derivation property of the entropy of a joint distribution lifts to Huffman trees. |
1802.02178 | Ruizhou Ding | Ruizhou Ding, Zeye Liu, Rongye Shi, Diana Marculescu, and R. D.
Blanton | LightNN: Filling the Gap between Conventional Deep Neural Networks and
Binarized Networks | null | null | null | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Application-specific integrated circuit (ASIC) implementations for Deep
Neural Networks (DNNs) have been adopted in many systems because of their
higher classification speed. However, although they may be characterized by
better accuracy, larger DNNs require significant energy and area, thereby
limiting their wide adoption. The energy consumption of DNNs is driven by both
memory accesses and computation. Binarized Neural Networks (BNNs), as a
trade-off between accuracy and energy consumption, can achieve great energy
reduction, and have good accuracy for large DNNs due to its regularization
effect. However, BNNs show poor accuracy when a smaller DNN configuration is
adopted. In this paper, we propose a new DNN model, LightNN, which replaces the
multiplications to one shift or a constrained number of shifts and adds. For a
fixed DNN configuration, LightNNs have better accuracy at a slight energy
increase than BNNs, yet are more energy efficient with only slightly less
accuracy than conventional DNNs. Therefore, LightNNs provide more options for
hardware designers to make trade-offs between accuracy and energy. Moreover,
for large DNN configurations, LightNNs have a regularization effect, making
them better in accuracy than conventional DNNs. These conclusions are verified
by experiment using the MNIST and CIFAR-10 datasets for different DNN
configurations.
| [
{
"created": "Sat, 2 Dec 2017 21:34:39 GMT",
"version": "v1"
}
] | 2018-02-08 | [
[
"Ding",
"Ruizhou",
""
],
[
"Liu",
"Zeye",
""
],
[
"Shi",
"Rongye",
""
],
[
"Marculescu",
"Diana",
""
],
[
"Blanton",
"R. D.",
""
]
] | Application-specific integrated circuit (ASIC) implementations for Deep Neural Networks (DNNs) have been adopted in many systems because of their higher classification speed. However, although they may be characterized by better accuracy, larger DNNs require significant energy and area, thereby limiting their wide adoption. The energy consumption of DNNs is driven by both memory accesses and computation. Binarized Neural Networks (BNNs), as a trade-off between accuracy and energy consumption, can achieve great energy reduction, and have good accuracy for large DNNs due to its regularization effect. However, BNNs show poor accuracy when a smaller DNN configuration is adopted. In this paper, we propose a new DNN model, LightNN, which replaces the multiplications to one shift or a constrained number of shifts and adds. For a fixed DNN configuration, LightNNs have better accuracy at a slight energy increase than BNNs, yet are more energy efficient with only slightly less accuracy than conventional DNNs. Therefore, LightNNs provide more options for hardware designers to make trade-offs between accuracy and energy. Moreover, for large DNN configurations, LightNNs have a regularization effect, making them better in accuracy than conventional DNNs. These conclusions are verified by experiment using the MNIST and CIFAR-10 datasets for different DNN configurations. |
2303.15671 | Suncheng Xiang | Qingzhong Chen, Shilun Cai, Crystal Cai, Zefang Yu, Dahong Qian,
Suncheng Xiang | Colo-SCRL: Self-Supervised Contrastive Representation Learning for
Colonoscopic Video Retrieval | Accepted by ICME 2023 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Colonoscopic video retrieval, which is a critical part of polyp treatment,
has great clinical significance for the prevention and treatment of colorectal
cancer. However, retrieval models trained on action recognition datasets
usually produce unsatisfactory retrieval results on colonoscopic datasets due
to the large domain gap between them. To seek a solution to this problem, we
construct a large-scale colonoscopic dataset named Colo-Pair for medical
practice. Based on this dataset, a simple yet effective training method called
Colo-SCRL is proposed for more robust representation learning. It aims to
refine general knowledge from colonoscopies through masked autoencoder-based
reconstruction and momentum contrast to improve retrieval performance. To the
best of our knowledge, this is the first attempt to employ the contrastive
learning paradigm for medical video retrieval. Empirical results show that our
method significantly outperforms current state-of-the-art methods in the
colonoscopic video retrieval task.
| [
{
"created": "Tue, 28 Mar 2023 01:27:23 GMT",
"version": "v1"
}
] | 2023-03-29 | [
[
"Chen",
"Qingzhong",
""
],
[
"Cai",
"Shilun",
""
],
[
"Cai",
"Crystal",
""
],
[
"Yu",
"Zefang",
""
],
[
"Qian",
"Dahong",
""
],
[
"Xiang",
"Suncheng",
""
]
] | Colonoscopic video retrieval, which is a critical part of polyp treatment, has great clinical significance for the prevention and treatment of colorectal cancer. However, retrieval models trained on action recognition datasets usually produce unsatisfactory retrieval results on colonoscopic datasets due to the large domain gap between them. To seek a solution to this problem, we construct a large-scale colonoscopic dataset named Colo-Pair for medical practice. Based on this dataset, a simple yet effective training method called Colo-SCRL is proposed for more robust representation learning. It aims to refine general knowledge from colonoscopies through masked autoencoder-based reconstruction and momentum contrast to improve retrieval performance. To the best of our knowledge, this is the first attempt to employ the contrastive learning paradigm for medical video retrieval. Empirical results show that our method significantly outperforms current state-of-the-art methods in the colonoscopic video retrieval task. |
2006.07486 | Zihan Tan | Julia Chuzhoy, Merav Parter, Zihan Tan | On Packing Low-Diameter Spanning Trees | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Edge connectivity of a graph is one of the most fundamental graph-theoretic
concepts. The celebrated tree packing theorem of Tutte and Nash-Williams from
1961 states that every $k$-edge connected graph $G$ contains a collection
$\cal{T}$ of $\lfloor k/2 \rfloor$ edge-disjoint spanning trees, that we refer
to as a tree packing; the diameter of the tree packing $\cal{T}$ is the largest
diameter of any tree in $\cal{T}$. A desirable property of a tree packing, that
is both sufficient and necessary for leveraging the high connectivity of a
graph in distributed communication, is that its diameter is low. Yet, despite
extensive research in this area, it is still unclear how to compute a tree
packing, whose diameter is sublinear in $|V(G)|$, in a low-diameter graph $G$,
or alternatively how to show that such a packing does not exist.
In this paper we provide first non-trivial upper and lower bounds on the
diameter of tree packing. First, we show that, for every $k$-edge connected
$n$-vertex graph $G$ of diameter $D$, there is a tree packing $\cal{T}$ of size
$\Omega(k)$, diameter $O((101k\log n)^D)$, that causes edge-congestion at most
$2$. Second, we show that for every $k$-edge connected $n$-vertex graph $G$ of
diameter $D$, the diameter of $G[p]$ is $O(k^{D(D+1)/2})$ with high
probability, where $G[p]$ is obtained by sampling each edge of $G$
independently with probability $p=\Theta(\log n/k)$. This provides a packing of
$\Omega(k/\log n)$ edge-disjoint trees of diameter at most $O(k^{(D(D+1)/2)})$
each. We then prove that these two results are nearly tight. Lastly, we show
that if every pair of vertices in a graph has $k$ edge-disjoint paths of length
at most $D$ connecting them, then there is a tree packing of size $k$, diameter
$O(D\log n)$, causing edge-congestion $O(\log n)$. We also provide several
applications of low-diameter tree packing in distributed computation.
| [
{
"created": "Fri, 12 Jun 2020 21:54:03 GMT",
"version": "v1"
}
] | 2020-06-16 | [
[
"Chuzhoy",
"Julia",
""
],
[
"Parter",
"Merav",
""
],
[
"Tan",
"Zihan",
""
]
] | Edge connectivity of a graph is one of the most fundamental graph-theoretic concepts. The celebrated tree packing theorem of Tutte and Nash-Williams from 1961 states that every $k$-edge connected graph $G$ contains a collection $\cal{T}$ of $\lfloor k/2 \rfloor$ edge-disjoint spanning trees, that we refer to as a tree packing; the diameter of the tree packing $\cal{T}$ is the largest diameter of any tree in $\cal{T}$. A desirable property of a tree packing, that is both sufficient and necessary for leveraging the high connectivity of a graph in distributed communication, is that its diameter is low. Yet, despite extensive research in this area, it is still unclear how to compute a tree packing, whose diameter is sublinear in $|V(G)|$, in a low-diameter graph $G$, or alternatively how to show that such a packing does not exist. In this paper we provide first non-trivial upper and lower bounds on the diameter of tree packing. First, we show that, for every $k$-edge connected $n$-vertex graph $G$ of diameter $D$, there is a tree packing $\cal{T}$ of size $\Omega(k)$, diameter $O((101k\log n)^D)$, that causes edge-congestion at most $2$. Second, we show that for every $k$-edge connected $n$-vertex graph $G$ of diameter $D$, the diameter of $G[p]$ is $O(k^{D(D+1)/2})$ with high probability, where $G[p]$ is obtained by sampling each edge of $G$ independently with probability $p=\Theta(\log n/k)$. This provides a packing of $\Omega(k/\log n)$ edge-disjoint trees of diameter at most $O(k^{(D(D+1)/2)})$ each. We then prove that these two results are nearly tight. Lastly, we show that if every pair of vertices in a graph has $k$ edge-disjoint paths of length at most $D$ connecting them, then there is a tree packing of size $k$, diameter $O(D\log n)$, causing edge-congestion $O(\log n)$. We also provide several applications of low-diameter tree packing in distributed computation. |
2107.09862 | Bruno Benedetti | Bruno Benedetti, Crystal Lai, Davide Lofano, and Frank H. Lutz | Random Simple-Homotopy Theory | 23 pages, 6 figures, 5 tables | null | null | null | cs.CG math.AT math.CO math.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We implement an algorithm RSHT (Random Simple-Homotopy) to study the
simple-homotopy types of simplicial complexes, with a particular focus on
contractible spaces and on finding substructures in higher-dimensional
complexes. The algorithm combines elementary simplicial collapses with pure
elementary expansions. For triangulated d-manifolds with d < 7, we show that
RSHT reduces to (random) bistellar flips.
Among the many examples on which we test RSHT, we describe an explicit
15-vertex triangulation of the Abalone, and more generally, (14k+1)-vertex
triangulations of Bing's houses with k rooms, which all can be deformed to a
point using only six pure elementary expansions.
| [
{
"created": "Wed, 21 Jul 2021 03:05:11 GMT",
"version": "v1"
},
{
"created": "Sun, 26 Sep 2021 13:48:55 GMT",
"version": "v2"
}
] | 2021-09-28 | [
[
"Benedetti",
"Bruno",
""
],
[
"Lai",
"Crystal",
""
],
[
"Lofano",
"Davide",
""
],
[
"Lutz",
"Frank H.",
""
]
] | We implement an algorithm RSHT (Random Simple-Homotopy) to study the simple-homotopy types of simplicial complexes, with a particular focus on contractible spaces and on finding substructures in higher-dimensional complexes. The algorithm combines elementary simplicial collapses with pure elementary expansions. For triangulated d-manifolds with d < 7, we show that RSHT reduces to (random) bistellar flips. Among the many examples on which we test RSHT, we describe an explicit 15-vertex triangulation of the Abalone, and more generally, (14k+1)-vertex triangulations of Bing's houses with k rooms, which all can be deformed to a point using only six pure elementary expansions. |
1709.10177 | Maria-Laura Torrente | Maria-Laura Torrente, Silvia Biasotti, Bianca Falcidieno | Recognition of feature curves on 3D shapes using an algebraic approach
to Hough transforms | null | Pattern Recognition, Volume 73, Pages 1-288 (January 2018) | 10.1016/j.patcog.2017.08.008 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Feature curves are largely adopted to highlight shape features, such as sharp
lines, or to divide surfaces into meaningful segments, like convex or concave
regions. Extracting these curves is not sufficient to convey prominent and
meaningful information about a shape. We have first to separate the curves
belonging to features from those caused by noise and then to select the lines,
which describe non-trivial portions of a surface. The automatic detection of
such features is crucial for the identification and/or annotation of relevant
parts of a given shape. To do this, the Hough transform (HT) is a feature
extraction technique widely used in image analysis, computer vision and digital
image processing, while, for 3D shapes, the extraction of salient feature
curves is still an open problem.
Thanks to algebraic geometry concepts, the HT technique has been recently
extended to include a vast class of algebraic curves, thus proving to be a
competitive tool for yielding an explicit representation of the diverse feature
lines equations. In the paper, for the first time we apply this novel extension
of the HT technique to the realm of 3D shapes in order to identify and localize
semantic features like patterns, decorations or anatomical details on 3D
objects (both complete and fragments), even in the case of features partially
damaged or incomplete. The method recognizes various features, possibly
compound, and it selects the most suitable feature profiles among families of
algebraic curves.
| [
{
"created": "Thu, 28 Sep 2017 21:36:05 GMT",
"version": "v1"
}
] | 2017-10-02 | [
[
"Torrente",
"Maria-Laura",
""
],
[
"Biasotti",
"Silvia",
""
],
[
"Falcidieno",
"Bianca",
""
]
] | Feature curves are largely adopted to highlight shape features, such as sharp lines, or to divide surfaces into meaningful segments, like convex or concave regions. Extracting these curves is not sufficient to convey prominent and meaningful information about a shape. We have first to separate the curves belonging to features from those caused by noise and then to select the lines, which describe non-trivial portions of a surface. The automatic detection of such features is crucial for the identification and/or annotation of relevant parts of a given shape. To do this, the Hough transform (HT) is a feature extraction technique widely used in image analysis, computer vision and digital image processing, while, for 3D shapes, the extraction of salient feature curves is still an open problem. Thanks to algebraic geometry concepts, the HT technique has been recently extended to include a vast class of algebraic curves, thus proving to be a competitive tool for yielding an explicit representation of the diverse feature lines equations. In the paper, for the first time we apply this novel extension of the HT technique to the realm of 3D shapes in order to identify and localize semantic features like patterns, decorations or anatomical details on 3D objects (both complete and fragments), even in the case of features partially damaged or incomplete. The method recognizes various features, possibly compound, and it selects the most suitable feature profiles among families of algebraic curves. |
1901.03396 | Ryan Webster | Ryan Webster, Julien Rabin, Loic Simon, Frederic Jurie | Detecting Overfitting of Deep Generative Networks via Latent Recovery | null | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | State of the art deep generative networks are capable of producing images
with such incredible realism that they can be suspected of memorizing training
images. It is why it is not uncommon to include visualizations of training set
nearest neighbors, to suggest generated images are not simply memorized. We
demonstrate this is not sufficient and motivates the need to study
memorization/overfitting of deep generators with more scrutiny. This paper
addresses this question by i) showing how simple losses are highly effective at
reconstructing images for deep generators ii) analyzing the statistics of
reconstruction errors when reconstructing training and validation images, which
is the standard way to analyze overfitting in machine learning. Using this
methodology, this paper shows that overfitting is not detectable in the pure
GAN models proposed in the literature, in contrast with those using hybrid
adversarial losses, which are amongst the most widely applied generative
methods. The paper also shows that standard GAN evaluation metrics fail to
capture memorization for some deep generators. Finally, the paper also shows
how off-the-shelf GAN generators can be successfully applied to face inpainting
and face super-resolution using the proposed reconstruction method, without
hybrid adversarial losses.
| [
{
"created": "Wed, 9 Jan 2019 16:29:05 GMT",
"version": "v1"
}
] | 2019-01-14 | [
[
"Webster",
"Ryan",
""
],
[
"Rabin",
"Julien",
""
],
[
"Simon",
"Loic",
""
],
[
"Jurie",
"Frederic",
""
]
] | State of the art deep generative networks are capable of producing images with such incredible realism that they can be suspected of memorizing training images. It is why it is not uncommon to include visualizations of training set nearest neighbors, to suggest generated images are not simply memorized. We demonstrate this is not sufficient and motivates the need to study memorization/overfitting of deep generators with more scrutiny. This paper addresses this question by i) showing how simple losses are highly effective at reconstructing images for deep generators ii) analyzing the statistics of reconstruction errors when reconstructing training and validation images, which is the standard way to analyze overfitting in machine learning. Using this methodology, this paper shows that overfitting is not detectable in the pure GAN models proposed in the literature, in contrast with those using hybrid adversarial losses, which are amongst the most widely applied generative methods. The paper also shows that standard GAN evaluation metrics fail to capture memorization for some deep generators. Finally, the paper also shows how off-the-shelf GAN generators can be successfully applied to face inpainting and face super-resolution using the proposed reconstruction method, without hybrid adversarial losses. |
2406.12975 | Xiaoze Liu | Xiaoze Liu, Ting Sun, Tianyang Xu, Feijie Wu, Cunxiang Wang, Xiaoqian
Wang, Jing Gao | SHIELD: Evaluation and Defense Strategies for Copyright Compliance in
LLM Text Generation | null | null | null | null | cs.CL cs.AI cs.CY | http://creativecommons.org/licenses/by/4.0/ | Large Language Models (LLMs) have transformed machine learning but raised
significant legal concerns due to their potential to produce text that
infringes on copyrights, resulting in several high-profile lawsuits. The legal
landscape is struggling to keep pace with these rapid advancements, with
ongoing debates about whether generated text might plagiarize copyrighted
materials. Current LLMs may infringe on copyrights or overly restrict
non-copyrighted texts, leading to these challenges: (i) the need for a
comprehensive evaluation benchmark to assess copyright compliance from multiple
aspects; (ii) evaluating robustness against safeguard bypassing attacks; and
(iii) developing effective defenses targeted against the generation of
copyrighted text. To tackle these challenges, we introduce a curated dataset to
evaluate methods, test attack strategies, and propose lightweight, real-time
defenses to prevent the generation of copyrighted text, ensuring the safe and
lawful use of LLMs. Our experiments demonstrate that current LLMs frequently
output copyrighted text, and that jailbreaking attacks can significantly
increase the volume of copyrighted output. Our proposed defense mechanisms
significantly reduce the volume of copyrighted text generated by LLMs by
effectively refusing malicious requests. Code is publicly available at
https://github.com/xz-liu/SHIELD
| [
{
"created": "Tue, 18 Jun 2024 18:00:03 GMT",
"version": "v1"
}
] | 2024-06-21 | [
[
"Liu",
"Xiaoze",
""
],
[
"Sun",
"Ting",
""
],
[
"Xu",
"Tianyang",
""
],
[
"Wu",
"Feijie",
""
],
[
"Wang",
"Cunxiang",
""
],
[
"Wang",
"Xiaoqian",
""
],
[
"Gao",
"Jing",
""
]
] | Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns due to their potential to produce text that infringes on copyrights, resulting in several high-profile lawsuits. The legal landscape is struggling to keep pace with these rapid advancements, with ongoing debates about whether generated text might plagiarize copyrighted materials. Current LLMs may infringe on copyrights or overly restrict non-copyrighted texts, leading to these challenges: (i) the need for a comprehensive evaluation benchmark to assess copyright compliance from multiple aspects; (ii) evaluating robustness against safeguard bypassing attacks; and (iii) developing effective defenses targeted against the generation of copyrighted text. To tackle these challenges, we introduce a curated dataset to evaluate methods, test attack strategies, and propose lightweight, real-time defenses to prevent the generation of copyrighted text, ensuring the safe and lawful use of LLMs. Our experiments demonstrate that current LLMs frequently output copyrighted text, and that jailbreaking attacks can significantly increase the volume of copyrighted output. Our proposed defense mechanisms significantly reduce the volume of copyrighted text generated by LLMs by effectively refusing malicious requests. Code is publicly available at https://github.com/xz-liu/SHIELD |
2104.14234 | Jannis Clausius | Jannis Clausius, Sebastian D\"orner, Sebastian Cammerer, Stephan ten
Brink | Serial vs. Parallel Turbo-Autoencoders and Accelerated Training for
Learned Channel Codes | Submitted to ISTC 2021 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Attracted by its scalability towards practical codeword lengths, we revisit
the idea of Turbo-autoencoders for end-to-end learning of PHY-Layer
communications. For this, we study the existing concepts of Turbo-autoencoders
from the literature and compare the concept with state-of-the-art classical
coding schemes. We propose a new component-wise training algorithm based on the
idea of Gaussian a priori distributions that reduces the overall training time
by almost a magnitude. Further, we propose a new serial architecture inspired
by classical serially concatenated Turbo code structures and show that a
carefully optimized interface between the two component autoencoders is
required. To the best of our knowledge, these serial Turbo autoencoder
structures are the best known neural network based learned sequences that can
be trained from scratch without any required expert knowledge in the domain of
channel codes.
| [
{
"created": "Thu, 29 Apr 2021 09:54:22 GMT",
"version": "v1"
},
{
"created": "Thu, 22 Jul 2021 08:04:45 GMT",
"version": "v2"
}
] | 2021-07-23 | [
[
"Clausius",
"Jannis",
""
],
[
"Dörner",
"Sebastian",
""
],
[
"Cammerer",
"Sebastian",
""
],
[
"Brink",
"Stephan ten",
""
]
] | Attracted by its scalability towards practical codeword lengths, we revisit the idea of Turbo-autoencoders for end-to-end learning of PHY-Layer communications. For this, we study the existing concepts of Turbo-autoencoders from the literature and compare the concept with state-of-the-art classical coding schemes. We propose a new component-wise training algorithm based on the idea of Gaussian a priori distributions that reduces the overall training time by almost a magnitude. Further, we propose a new serial architecture inspired by classical serially concatenated Turbo code structures and show that a carefully optimized interface between the two component autoencoders is required. To the best of our knowledge, these serial Turbo autoencoder structures are the best known neural network based learned sequences that can be trained from scratch without any required expert knowledge in the domain of channel codes. |
2312.11556 | Juan A. Rodriguez | Juan A. Rodriguez, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez,
David Vazquez, Christopher Pal, and Marco Pedersoli | StarVector: Generating Scalable Vector Graphics Code from Images | null | null | null | null | cs.CV cs.AI cs.CL | http://creativecommons.org/licenses/by/4.0/ | Scalable Vector Graphics (SVGs) have become integral in modern image
rendering applications due to their infinite scalability in resolution,
versatile usability, and editing capabilities. SVGs are particularly popular in
the fields of web development and graphic design. Existing approaches for SVG
modeling using deep learning often struggle with generating complex SVGs and
are restricted to simpler ones that require extensive processing and
simplification. This paper introduces StarVector, a multimodal SVG generation
model that effectively integrates Code Generation Large Language Models
(CodeLLMs) and vision models. Our approach utilizes a CLIP image encoder to
extract visual representations from pixel-based images, which are then
transformed into visual tokens via an adapter module. These visual tokens are
pre-pended to the SVG token embeddings, and the sequence is modeled by the
StarCoder model using next-token prediction, effectively learning to align the
visual and code tokens. This enables StarVector to generate unrestricted SVGs
that accurately represent pixel images. To evaluate StarVector's performance,
we present SVG-Bench, a comprehensive benchmark for evaluating SVG methods
across multiple datasets and relevant metrics. Within this benchmark, we
introduce novel datasets including SVG-Stack, a large-scale dataset of
real-world SVG examples, and use it to pre-train StarVector as a large
foundation model for SVGs. Our results demonstrate significant enhancements in
visual quality and complexity handling over current methods, marking a notable
advancement in SVG generation technology. Code and models:
https://github.com/joanrod/star-vector
| [
{
"created": "Sun, 17 Dec 2023 08:07:32 GMT",
"version": "v1"
}
] | 2023-12-20 | [
[
"Rodriguez",
"Juan A.",
""
],
[
"Agarwal",
"Shubham",
""
],
[
"Laradji",
"Issam H.",
""
],
[
"Rodriguez",
"Pau",
""
],
[
"Vazquez",
"David",
""
],
[
"Pal",
"Christopher",
""
],
[
"Pedersoli",
"Marco",
""
]
] | Scalable Vector Graphics (SVGs) have become integral in modern image rendering applications due to their infinite scalability in resolution, versatile usability, and editing capabilities. SVGs are particularly popular in the fields of web development and graphic design. Existing approaches for SVG modeling using deep learning often struggle with generating complex SVGs and are restricted to simpler ones that require extensive processing and simplification. This paper introduces StarVector, a multimodal SVG generation model that effectively integrates Code Generation Large Language Models (CodeLLMs) and vision models. Our approach utilizes a CLIP image encoder to extract visual representations from pixel-based images, which are then transformed into visual tokens via an adapter module. These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens. This enables StarVector to generate unrestricted SVGs that accurately represent pixel images. To evaluate StarVector's performance, we present SVG-Bench, a comprehensive benchmark for evaluating SVG methods across multiple datasets and relevant metrics. Within this benchmark, we introduce novel datasets including SVG-Stack, a large-scale dataset of real-world SVG examples, and use it to pre-train StarVector as a large foundation model for SVGs. Our results demonstrate significant enhancements in visual quality and complexity handling over current methods, marking a notable advancement in SVG generation technology. Code and models: https://github.com/joanrod/star-vector |
2011.06680 | Pouya Agheli | Pouya Agheli, Mohammad Javad Emadi, Hamzeh Beyranvand | Cognitive RF-FSO Fronthaul Assignment in Cell-Free and User-Centric
mMIMO Networks | 14 pages, 10 figures, This work has been submitted for possible
publication | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | Cell-free massive MIMO (CF-mMIMO) network and its user-centric (UC) version
are considered as promising techniques for the next generations of wireless
networks. However, fronthaul and backhaul assignments are challenging issues in
these networks. In this paper, energy efficiencies of uplink transmission for
the CF- and UC-mMIMO networks are studied, wherein access points (APs) are
connected to aggregation nodes (ANs) through radio frequency (RF) and/or
free-space optic (FSO) fronthauls, and the ANs are connected to a central
processing unit via fiber backhauls. The achievable data rates are derived by
taking into account the effects of hardware non-ideality at the APs and ANs,
FSO alignment and weather conditions. To have a robust and energy-efficient
network, especially in the presence of FSO misalignment and adverse weather
conditions, firstly, a cognitive RF--FSO fronthaul assignment algorithm is
proposed at the cost of sharing the available RF bandwidth between the access
and fronthaul links. Then, optimal power allocations at the users and APs are
investigated, and two analytical approaches are proposed to solve the
non-convex optimization problem. Through numerical results, we have discussed
how utilizing the cognitive RF--FSO fronthaul assignment achieves higher energy
efficiency compared to that of FSO-only, RF-only, or simultaneously using RF
and FSO fronthaul links, e.g., achieving up to $198\%$ higher energy efficiency
under unfavorable conditions. Moreover, the effects of FSO misalignment,
weather conditions, and power allocations on the performances of the CF- and
UC-mMIMO networks are discussed.
| [
{
"created": "Thu, 12 Nov 2020 22:56:42 GMT",
"version": "v1"
},
{
"created": "Wed, 18 Nov 2020 10:50:01 GMT",
"version": "v2"
},
{
"created": "Tue, 9 Mar 2021 10:37:16 GMT",
"version": "v3"
}
] | 2021-03-10 | [
[
"Agheli",
"Pouya",
""
],
[
"Emadi",
"Mohammad Javad",
""
],
[
"Beyranvand",
"Hamzeh",
""
]
] | Cell-free massive MIMO (CF-mMIMO) network and its user-centric (UC) version are considered as promising techniques for the next generations of wireless networks. However, fronthaul and backhaul assignments are challenging issues in these networks. In this paper, energy efficiencies of uplink transmission for the CF- and UC-mMIMO networks are studied, wherein access points (APs) are connected to aggregation nodes (ANs) through radio frequency (RF) and/or free-space optic (FSO) fronthauls, and the ANs are connected to a central processing unit via fiber backhauls. The achievable data rates are derived by taking into account the effects of hardware non-ideality at the APs and ANs, FSO alignment and weather conditions. To have a robust and energy-efficient network, especially in the presence of FSO misalignment and adverse weather conditions, firstly, a cognitive RF--FSO fronthaul assignment algorithm is proposed at the cost of sharing the available RF bandwidth between the access and fronthaul links. Then, optimal power allocations at the users and APs are investigated, and two analytical approaches are proposed to solve the non-convex optimization problem. Through numerical results, we have discussed how utilizing the cognitive RF--FSO fronthaul assignment achieves higher energy efficiency compared to that of FSO-only, RF-only, or simultaneously using RF and FSO fronthaul links, e.g., achieving up to $198\%$ higher energy efficiency under unfavorable conditions. Moreover, the effects of FSO misalignment, weather conditions, and power allocations on the performances of the CF- and UC-mMIMO networks are discussed. |
2104.03631 | Daniel Reti | Daniel Reti, Daniel Fraunholz, Janis Zemitis, Daniel Schneider, Hans
Dieter Schotten | Deep Down the Rabbit Hole: On References in Networks of Decoy Elements | null | 2020 International Conference on Cyber Security and Protection of
Digital Services (Cyber Security) | 10.1109/CyberSecurity49315.2020.9138850 | null | cs.CR cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deception technology has proven to be a sound approach against threats to
information systems. Aside from well-established honeypots, decoy elements,
also known as honeytokens, are an excellent method to address various types of
threats. Decoy elements are causing distraction and uncertainty to an attacker
and help detecting malicious activity. Deception is meant to be complementing
firewalls and intrusion detection systems. Particularly insider threats may be
mitigated with deception methods. While current approaches consider the use of
multiple decoy elements as well as context-sensitivity, they do not
sufficiently describe a relationship between individual elements. In this work,
inter-referencing decoy elements are introduced as a plausible extension to
existing deception frameworks, leading attackers along a path of decoy
elements. A theoretical foundation is introduced, as well as a stochastic model
and a reference implementation. It was found that the proposed system is
suitable to enhance current decoy frameworks by adding a further dimension of
inter-connectivity and therefore improve intrusion detection and prevention.
| [
{
"created": "Thu, 8 Apr 2021 09:34:05 GMT",
"version": "v1"
}
] | 2021-04-09 | [
[
"Reti",
"Daniel",
""
],
[
"Fraunholz",
"Daniel",
""
],
[
"Zemitis",
"Janis",
""
],
[
"Schneider",
"Daniel",
""
],
[
"Schotten",
"Hans Dieter",
""
]
] | Deception technology has proven to be a sound approach against threats to information systems. Aside from well-established honeypots, decoy elements, also known as honeytokens, are an excellent method to address various types of threats. Decoy elements are causing distraction and uncertainty to an attacker and help detecting malicious activity. Deception is meant to be complementing firewalls and intrusion detection systems. Particularly insider threats may be mitigated with deception methods. While current approaches consider the use of multiple decoy elements as well as context-sensitivity, they do not sufficiently describe a relationship between individual elements. In this work, inter-referencing decoy elements are introduced as a plausible extension to existing deception frameworks, leading attackers along a path of decoy elements. A theoretical foundation is introduced, as well as a stochastic model and a reference implementation. It was found that the proposed system is suitable to enhance current decoy frameworks by adding a further dimension of inter-connectivity and therefore improve intrusion detection and prevention. |
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