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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1607.08659 | Helge Rhodin | Helge Rhodin, Nadia Robertini, Dan Casas, Christian Richardt,
Hans-Peter Seidel, Christian Theobalt | General Automatic Human Shape and Motion Capture Using Volumetric
Contour Cues | Accepted to ECCV 2016, added additional references | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Markerless motion capture algorithms require a 3D body with properly
personalized skeleton dimension and/or body shape and appearance to
successfully track a person. Unfortunately, many tracking methods consider
model personalization a different problem and use manual or semi-automatic
model initialization, which greatly reduces applicability. In this paper, we
propose a fully automatic algorithm that jointly creates a rigged actor model
commonly used for animation - skeleton, volumetric shape, appearance, and
optionally a body surface - and estimates the actor's motion from multi-view
video input only. The approach is rigorously designed to work on footage of
general outdoor scenes recorded with very few cameras and without background
subtraction. Our method uses a new image formation model with analytic
visibility and analytically differentiable alignment energy. For
reconstruction, 3D body shape is approximated as Gaussian density field. For
pose and shape estimation, we minimize a new edge-based alignment energy
inspired by volume raycasting in an absorbing medium. We further propose a new
statistical human body model that represents the body surface, volumetric
Gaussian density, as well as variability in skeleton shape. Given any
multi-view sequence, our method jointly optimizes the pose and shape parameters
of this model fully automatically in a spatiotemporal way.
| [
{
"created": "Thu, 28 Jul 2016 22:59:55 GMT",
"version": "v1"
},
{
"created": "Fri, 21 Oct 2016 11:23:31 GMT",
"version": "v2"
}
] | 2016-10-24 | [
[
"Rhodin",
"Helge",
""
],
[
"Robertini",
"Nadia",
""
],
[
"Casas",
"Dan",
""
],
[
"Richardt",
"Christian",
""
],
[
"Seidel",
"Hans-Peter",
""
],
[
"Theobalt",
"Christian",
""
]
] | Markerless motion capture algorithms require a 3D body with properly personalized skeleton dimension and/or body shape and appearance to successfully track a person. Unfortunately, many tracking methods consider model personalization a different problem and use manual or semi-automatic model initialization, which greatly reduces applicability. In this paper, we propose a fully automatic algorithm that jointly creates a rigged actor model commonly used for animation - skeleton, volumetric shape, appearance, and optionally a body surface - and estimates the actor's motion from multi-view video input only. The approach is rigorously designed to work on footage of general outdoor scenes recorded with very few cameras and without background subtraction. Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy. For reconstruction, 3D body shape is approximated as Gaussian density field. For pose and shape estimation, we minimize a new edge-based alignment energy inspired by volume raycasting in an absorbing medium. We further propose a new statistical human body model that represents the body surface, volumetric Gaussian density, as well as variability in skeleton shape. Given any multi-view sequence, our method jointly optimizes the pose and shape parameters of this model fully automatically in a spatiotemporal way. |
1712.08263 | Arnold Wiliem | Siqi Yang, Arnold Wiliem, Shaokang Chen, Brian C. Lovell | Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks | to appear ECCV 2018 (accepted version) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work shows that it is possible to fool/attack recent state-of-the-art
face detectors which are based on the single-stage networks. Successfully
attacking face detectors could be a serious malware vulnerability when
deploying a smart surveillance system utilizing face detectors. We show that
existing adversarial perturbation methods are not effective to perform such an
attack, especially when there are multiple faces in the input image. This is
because the adversarial perturbation specifically generated for one face may
disrupt the adversarial perturbation for another face. In this paper, we call
this problem the Instance Perturbation Interference (IPI) problem. This IPI
problem is addressed by studying the relationship between the deep neural
network receptive field and the adversarial perturbation. As such, we propose
the Localized Instance Perturbation (LIP) that uses adversarial perturbation
constrained to the Effective Receptive Field (ERF) of a target to perform the
attack. Experiment results show the LIP method massively outperforms existing
adversarial perturbation generation methods -- often by a factor of 2 to 10.
| [
{
"created": "Fri, 22 Dec 2017 00:42:42 GMT",
"version": "v1"
},
{
"created": "Thu, 5 Jul 2018 01:23:11 GMT",
"version": "v2"
}
] | 2018-07-06 | [
[
"Yang",
"Siqi",
""
],
[
"Wiliem",
"Arnold",
""
],
[
"Chen",
"Shaokang",
""
],
[
"Lovell",
"Brian C.",
""
]
] | This work shows that it is possible to fool/attack recent state-of-the-art face detectors which are based on the single-stage networks. Successfully attacking face detectors could be a serious malware vulnerability when deploying a smart surveillance system utilizing face detectors. We show that existing adversarial perturbation methods are not effective to perform such an attack, especially when there are multiple faces in the input image. This is because the adversarial perturbation specifically generated for one face may disrupt the adversarial perturbation for another face. In this paper, we call this problem the Instance Perturbation Interference (IPI) problem. This IPI problem is addressed by studying the relationship between the deep neural network receptive field and the adversarial perturbation. As such, we propose the Localized Instance Perturbation (LIP) that uses adversarial perturbation constrained to the Effective Receptive Field (ERF) of a target to perform the attack. Experiment results show the LIP method massively outperforms existing adversarial perturbation generation methods -- often by a factor of 2 to 10. |
1012.0557 | Andrey Rumyantsev | Andrey Rumyantsev | Infinite computable version of Lovasz Local Lemma | null | null | null | null | cs.DS cs.DM math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Lov\'asz Local Lemma (LLL) is a probabilistic tool that allows us to prove
the existence of combinatorial objects in the cases when standard probabilistic
argument does not work (there are many partly independent conditions).
LLL can be also used to prove the consistency of an infinite set of
conditions, using standard compactness argument (if an infinite set of
conditions is inconsistent, then some finite part of it is inconsistent, too,
which contradicts LLL). In this way we show that objects satisfying all the
conditions do exist (though the probability of this event equals~$0$). However,
if we are interested in finding a computable solution that satisfies all the
constraints, compactness arguments do not work anymore.
Moser and Tardos recently gave a nice constructive proof of LLL. Lance
Fortnow asked whether one can apply Moser--Tardos technique to prove the
existence of a computable solution. We show that this is indeed possible (under
almost the same conditions as used in the non-constructive version).
| [
{
"created": "Thu, 2 Dec 2010 20:11:02 GMT",
"version": "v1"
}
] | 2010-12-03 | [
[
"Rumyantsev",
"Andrey",
""
]
] | Lov\'asz Local Lemma (LLL) is a probabilistic tool that allows us to prove the existence of combinatorial objects in the cases when standard probabilistic argument does not work (there are many partly independent conditions). LLL can be also used to prove the consistency of an infinite set of conditions, using standard compactness argument (if an infinite set of conditions is inconsistent, then some finite part of it is inconsistent, too, which contradicts LLL). In this way we show that objects satisfying all the conditions do exist (though the probability of this event equals~$0$). However, if we are interested in finding a computable solution that satisfies all the constraints, compactness arguments do not work anymore. Moser and Tardos recently gave a nice constructive proof of LLL. Lance Fortnow asked whether one can apply Moser--Tardos technique to prove the existence of a computable solution. We show that this is indeed possible (under almost the same conditions as used in the non-constructive version). |
2211.13818 | Mushu Li | Mushu Li, Jie Gao, Conghao Zhou, Xuemin (Sherman) Shen and Weihua
Zhuang | Digital Twin-Driven Computing Resource Management for Vehicular Networks | 6 pages, 4 figures, accepted by 2022 IEEE GLOBECOM | null | null | null | cs.NI cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a novel approach for computing resource management of
edge servers in vehicular networks based on digital twins and artificial
intelligence (AI). Specifically, we construct two-tier digital twins tailored
for vehicular networks to capture networking-related features of vehicles and
edge servers. By exploiting such features, we propose a two-stage computing
resource allocation scheme. First, the central controller periodically
generates reference policies for real-time computing resource allocation
according to the network dynamics and service demands captured by digital twins
of edge servers. Second, computing resources of the edge servers are allocated
in real time to individual vehicles via low-complexity matching-based
allocation that complies with the reference policies. By leveraging digital
twins, the proposed scheme can adapt to dynamic service demands and vehicle
mobility in a scalable manner. Simulation results demonstrate that the proposed
digital twin-driven scheme enables the vehicular network to support more
computing tasks than benchmark schemes.
| [
{
"created": "Thu, 24 Nov 2022 23:06:52 GMT",
"version": "v1"
}
] | 2022-11-28 | [
[
"Li",
"Mushu",
"",
"Sherman"
],
[
"Gao",
"Jie",
"",
"Sherman"
],
[
"Zhou",
"Conghao",
"",
"Sherman"
],
[
"Xuemin",
"",
"",
"Sherman"
],
[
"Shen",
"",
""
],
[
"Zhuang",
"Weihua",
""
]
] | This paper presents a novel approach for computing resource management of edge servers in vehicular networks based on digital twins and artificial intelligence (AI). Specifically, we construct two-tier digital twins tailored for vehicular networks to capture networking-related features of vehicles and edge servers. By exploiting such features, we propose a two-stage computing resource allocation scheme. First, the central controller periodically generates reference policies for real-time computing resource allocation according to the network dynamics and service demands captured by digital twins of edge servers. Second, computing resources of the edge servers are allocated in real time to individual vehicles via low-complexity matching-based allocation that complies with the reference policies. By leveraging digital twins, the proposed scheme can adapt to dynamic service demands and vehicle mobility in a scalable manner. Simulation results demonstrate that the proposed digital twin-driven scheme enables the vehicular network to support more computing tasks than benchmark schemes. |
2101.09818 | Ali Rasteh | Ali Rasteh, Florian Delpech, Carlos Aguilar-Melchor, Romain Zimmer,
Saeed Bagheri Shouraki and Timoth\'ee Masquelier | Encrypted Internet traffic classification using a supervised Spiking
Neural Network | 22 pages, 8 figures. Neurocomputing (2022) | Neurocomputing (2022) | 10.1016/j.neucom.2022.06.055 | null | cs.LG cs.NI | http://creativecommons.org/licenses/by/4.0/ | Internet traffic recognition is an essential tool for access providers since
recognizing traffic categories related to different data packets transmitted on
a network help them define adapted priorities. That means, for instance, high
priority requirements for an audio conference and low ones for a file transfer,
to enhance user experience. As internet traffic becomes increasingly encrypted,
the mainstream classic traffic recognition technique, payload inspection, is
rendered ineffective. This paper uses machine learning techniques for encrypted
traffic classification, looking only at packet size and time of arrival.
Spiking neural networks (SNN), largely inspired by how biological neurons
operate, were used for two reasons. Firstly, they are able to recognize
time-related data packet features. Secondly, they can be implemented
efficiently on neuromorphic hardware with a low energy footprint. Here we used
a very simple feedforward SNN, with only one fully-connected hidden layer, and
trained in a supervised manner using the newly introduced method known as
Surrogate Gradient Learning. Surprisingly, such a simple SNN reached an
accuracy of 95.9% on ISCX datasets, outperforming previous approaches. Besides
better accuracy, there is also a very significant improvement on simplicity:
input size, number of neurons, trainable parameters are all reduced by one to
four orders of magnitude. Next, we analyzed the reasons for this good accuracy.
It turns out that, beyond spatial (i.e. packet size) features, the SNN also
exploits temporal ones, mostly the nearly synchronous (within a 200ms range)
arrival times of packets with certain sizes. Taken together, these results show
that SNNs are an excellent fit for encrypted internet traffic classification:
they can be more accurate than conventional artificial neural networks (ANN),
and they could be implemented efficiently on low power embedded systems.
| [
{
"created": "Sun, 24 Jan 2021 22:46:08 GMT",
"version": "v1"
},
{
"created": "Thu, 21 Jul 2022 13:06:28 GMT",
"version": "v2"
}
] | 2022-07-25 | [
[
"Rasteh",
"Ali",
""
],
[
"Delpech",
"Florian",
""
],
[
"Aguilar-Melchor",
"Carlos",
""
],
[
"Zimmer",
"Romain",
""
],
[
"Shouraki",
"Saeed Bagheri",
""
],
[
"Masquelier",
"Timothée",
""
]
] | Internet traffic recognition is an essential tool for access providers since recognizing traffic categories related to different data packets transmitted on a network help them define adapted priorities. That means, for instance, high priority requirements for an audio conference and low ones for a file transfer, to enhance user experience. As internet traffic becomes increasingly encrypted, the mainstream classic traffic recognition technique, payload inspection, is rendered ineffective. This paper uses machine learning techniques for encrypted traffic classification, looking only at packet size and time of arrival. Spiking neural networks (SNN), largely inspired by how biological neurons operate, were used for two reasons. Firstly, they are able to recognize time-related data packet features. Secondly, they can be implemented efficiently on neuromorphic hardware with a low energy footprint. Here we used a very simple feedforward SNN, with only one fully-connected hidden layer, and trained in a supervised manner using the newly introduced method known as Surrogate Gradient Learning. Surprisingly, such a simple SNN reached an accuracy of 95.9% on ISCX datasets, outperforming previous approaches. Besides better accuracy, there is also a very significant improvement on simplicity: input size, number of neurons, trainable parameters are all reduced by one to four orders of magnitude. Next, we analyzed the reasons for this good accuracy. It turns out that, beyond spatial (i.e. packet size) features, the SNN also exploits temporal ones, mostly the nearly synchronous (within a 200ms range) arrival times of packets with certain sizes. Taken together, these results show that SNNs are an excellent fit for encrypted internet traffic classification: they can be more accurate than conventional artificial neural networks (ANN), and they could be implemented efficiently on low power embedded systems. |
1709.09250 | Omar Al-Harbi Mohammad | Omar Al-Harbi, Shaidah Jusoh, Norita Md Norwawi | Lexical Disambiguation in Natural Language Questions (NLQs) | 8 pages, 4 figures | IJCSI International Journal of Computer Science Issues, Vol. 8,
Issue 4, No 2, July 2011 (143-150) | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Question processing is a fundamental step in a question answering (QA)
application, and its quality impacts the performance of QA application. The
major challenging issue in processing question is how to extract semantic of
natural language questions (NLQs). A human language is ambiguous. Ambiguity may
occur at two levels; lexical and syntactic. In this paper, we propose a new
approach for resolving lexical ambiguity problem by integrating context
knowledge and concepts knowledge of a domain, into shallow natural language
processing (SNLP) techniques. Concepts knowledge is modeled using ontology,
while context knowledge is obtained from WordNet, and it is determined based on
neighborhood words in a question. The approach will be applied to a university
QA system.
| [
{
"created": "Tue, 26 Sep 2017 20:24:10 GMT",
"version": "v1"
}
] | 2017-09-28 | [
[
"Al-Harbi",
"Omar",
""
],
[
"Jusoh",
"Shaidah",
""
],
[
"Norwawi",
"Norita Md",
""
]
] | Question processing is a fundamental step in a question answering (QA) application, and its quality impacts the performance of QA application. The major challenging issue in processing question is how to extract semantic of natural language questions (NLQs). A human language is ambiguous. Ambiguity may occur at two levels; lexical and syntactic. In this paper, we propose a new approach for resolving lexical ambiguity problem by integrating context knowledge and concepts knowledge of a domain, into shallow natural language processing (SNLP) techniques. Concepts knowledge is modeled using ontology, while context knowledge is obtained from WordNet, and it is determined based on neighborhood words in a question. The approach will be applied to a university QA system. |
2404.09473 | Dwaipayan Roy | Aman Sinha, Priyanshu Raj Mall, and Dwaipayan Roy | Exploring the Nexus Between Retrievability and Query Generation
Strategies | Accepted at ECIR 2024 | null | null | null | cs.IR | http://creativecommons.org/licenses/by/4.0/ | Quantifying bias in retrieval functions through document retrievability
scores is vital for assessing recall-oriented retrieval systems. However, many
studies investigating retrieval model bias lack validation of their query
generation methods as accurate representations of retrievability for real users
and their queries. This limitation results from the absence of established
criteria for query generation in retrievability assessments. Typically,
researchers resort to using frequent collocations from document corpora when no
query log is available. In this study, we address the issue of reproducibility
and seek to validate query generation methods by comparing retrievability
scores generated from artificially generated queries to those derived from
query logs. Our findings demonstrate a minimal or negligible correlation
between retrievability scores from artificial queries and those from query
logs. This suggests that artificially generated queries may not accurately
reflect retrievability scores as derived from query logs. We further explore
alternative query generation techniques, uncovering a variation that exhibits
the highest correlation. This alternative approach holds promise for improving
reproducibility when query logs are unavailable.
| [
{
"created": "Mon, 15 Apr 2024 05:56:13 GMT",
"version": "v1"
}
] | 2024-04-16 | [
[
"Sinha",
"Aman",
""
],
[
"Mall",
"Priyanshu Raj",
""
],
[
"Roy",
"Dwaipayan",
""
]
] | Quantifying bias in retrieval functions through document retrievability scores is vital for assessing recall-oriented retrieval systems. However, many studies investigating retrieval model bias lack validation of their query generation methods as accurate representations of retrievability for real users and their queries. This limitation results from the absence of established criteria for query generation in retrievability assessments. Typically, researchers resort to using frequent collocations from document corpora when no query log is available. In this study, we address the issue of reproducibility and seek to validate query generation methods by comparing retrievability scores generated from artificially generated queries to those derived from query logs. Our findings demonstrate a minimal or negligible correlation between retrievability scores from artificial queries and those from query logs. This suggests that artificially generated queries may not accurately reflect retrievability scores as derived from query logs. We further explore alternative query generation techniques, uncovering a variation that exhibits the highest correlation. This alternative approach holds promise for improving reproducibility when query logs are unavailable. |
1911.04866 | Alexandros Milolidakis | Alexandros Milolidakis, Romain Fontugne, Xenofontas Dimitropoulos | Detecting Network Disruptions At Colocation Facilities | 10 pages, IEEE INFOCOM 2019-IEEE Conference on Computer
Communications | In IEEE INFOCOM 2019-IEEE Conference on Computer Communications
(pp. 2161-2169). IEEE (2019) | 10.1109/INFOCOM.2019.8737615 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Colocation facilities and Internet eXchange Points (IXPs) provide neutral
places for concurrent networks to daily exchange terabytes of data traffic.
Although very reliable, these facilities are not immune to failure and may
experience difficulties that can have significant impacts on exchanged traffic.
In this paper we devise a methodology to identify collocation facilities in
traceroute data and to monitor delay and routing patterns between facilities.
We also present an anomaly detection technique to report abnormal traffic
changes usually due to facilities outages. We evaluate this method with eight
months of traceroute data from the RIPE Atlas measurement platform and manually
inspect the most prominent events, that are: an IXP outage, a DDoS attack, and
a power failure in a facility. These case studies validate the benefits of the
proposed system to detect real world outages from traceroute data. We also
investigate the impact of anomalies at the metropolitan-level and identify
outages that span across up to eight facilities.
| [
{
"created": "Tue, 12 Nov 2019 14:07:25 GMT",
"version": "v1"
}
] | 2019-11-13 | [
[
"Milolidakis",
"Alexandros",
""
],
[
"Fontugne",
"Romain",
""
],
[
"Dimitropoulos",
"Xenofontas",
""
]
] | Colocation facilities and Internet eXchange Points (IXPs) provide neutral places for concurrent networks to daily exchange terabytes of data traffic. Although very reliable, these facilities are not immune to failure and may experience difficulties that can have significant impacts on exchanged traffic. In this paper we devise a methodology to identify collocation facilities in traceroute data and to monitor delay and routing patterns between facilities. We also present an anomaly detection technique to report abnormal traffic changes usually due to facilities outages. We evaluate this method with eight months of traceroute data from the RIPE Atlas measurement platform and manually inspect the most prominent events, that are: an IXP outage, a DDoS attack, and a power failure in a facility. These case studies validate the benefits of the proposed system to detect real world outages from traceroute data. We also investigate the impact of anomalies at the metropolitan-level and identify outages that span across up to eight facilities. |
1502.07591 | Cristopher Moore | Cristopher Moore | The phase transition in random regular exact cover | Added sentence pointing out that the threshold is never an integer | null | null | null | cs.CC cond-mat.stat-mech math.CO math.PR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A $k$-uniform, $d$-regular instance of Exact Cover is a family of $m$ sets
$F_{n,d,k} = \{ S_j \subseteq \{1,...,n\} \}$, where each subset has size $k$
and each $1 \le i \le n$ is contained in $d$ of the $S_j$. It is satisfiable if
there is a subset $T \subseteq \{1,...,n\}$ such that $|T \cap S_j|=1$ for all
$j$. Alternately, we can consider it a $d$-regular instance of Positive
1-in-$k$ SAT, i.e., a Boolean formula with $m$ clauses and $n$ variables where
each clause contains $k$ variables and demands that exactly one of them is
true. We determine the satisfiability threshold for random instances of this
type with $k > 2$. Letting $d^\star = \frac{\ln k}{(k-1)(- \ln (1-1/k))} + 1$,
we show that $F_{n,d,k}$ is satisfiable with high probability if $d < d^\star$
and unsatisfiable with high probability if $d > d^\star$. We do this with a
simple application of the first and second moment methods, boosting the
probability of satisfiability below $d^\star$ to $1-o(1)$ using the small
subgraph conditioning method.
| [
{
"created": "Thu, 26 Feb 2015 15:22:02 GMT",
"version": "v1"
},
{
"created": "Fri, 27 Feb 2015 01:45:31 GMT",
"version": "v2"
},
{
"created": "Wed, 4 Mar 2015 17:49:19 GMT",
"version": "v3"
}
] | 2015-03-05 | [
[
"Moore",
"Cristopher",
""
]
] | A $k$-uniform, $d$-regular instance of Exact Cover is a family of $m$ sets $F_{n,d,k} = \{ S_j \subseteq \{1,...,n\} \}$, where each subset has size $k$ and each $1 \le i \le n$ is contained in $d$ of the $S_j$. It is satisfiable if there is a subset $T \subseteq \{1,...,n\}$ such that $|T \cap S_j|=1$ for all $j$. Alternately, we can consider it a $d$-regular instance of Positive 1-in-$k$ SAT, i.e., a Boolean formula with $m$ clauses and $n$ variables where each clause contains $k$ variables and demands that exactly one of them is true. We determine the satisfiability threshold for random instances of this type with $k > 2$. Letting $d^\star = \frac{\ln k}{(k-1)(- \ln (1-1/k))} + 1$, we show that $F_{n,d,k}$ is satisfiable with high probability if $d < d^\star$ and unsatisfiable with high probability if $d > d^\star$. We do this with a simple application of the first and second moment methods, boosting the probability of satisfiability below $d^\star$ to $1-o(1)$ using the small subgraph conditioning method. |
2308.08956 | Emma Nilsson | Emma Nilsson, Jonas Lukasczyk, Talha Bin Masood, Christoph Garth,
Ingrid Hotz | Probabilistic Gradient-Based Extrema Tracking | null | null | null | null | cs.GR | http://creativecommons.org/licenses/by/4.0/ | Feature tracking is a common task in visualization applications, where
methods based on topological data analysis (TDA) have successfully been applied
in the past for feature definition as well as tracking. In this work, we focus
on tracking extrema of temporal scalar fields. A family of TDA approaches
address this task by establishing one-to-one correspondences between extrema
based on discrete gradient vector fields. More specifically, two extrema of
subsequent time steps are matched if they fall into their respective ascending
and descending manifolds. However, due to this one-to-one assignment, these
approaches are prone to fail where, e.g., extrema are located in regions with
low gradient magnitude, or are located close to boundaries of the manifolds.
Therefore, we propose a probabilistic matching that captures a larger set of
possible correspondences via neighborhood sampling, or by computing the overlap
of the manifolds. We illustrate the usefulness of the approach with two
application cases.
| [
{
"created": "Thu, 17 Aug 2023 12:55:38 GMT",
"version": "v1"
}
] | 2023-08-21 | [
[
"Nilsson",
"Emma",
""
],
[
"Lukasczyk",
"Jonas",
""
],
[
"Masood",
"Talha Bin",
""
],
[
"Garth",
"Christoph",
""
],
[
"Hotz",
"Ingrid",
""
]
] | Feature tracking is a common task in visualization applications, where methods based on topological data analysis (TDA) have successfully been applied in the past for feature definition as well as tracking. In this work, we focus on tracking extrema of temporal scalar fields. A family of TDA approaches address this task by establishing one-to-one correspondences between extrema based on discrete gradient vector fields. More specifically, two extrema of subsequent time steps are matched if they fall into their respective ascending and descending manifolds. However, due to this one-to-one assignment, these approaches are prone to fail where, e.g., extrema are located in regions with low gradient magnitude, or are located close to boundaries of the manifolds. Therefore, we propose a probabilistic matching that captures a larger set of possible correspondences via neighborhood sampling, or by computing the overlap of the manifolds. We illustrate the usefulness of the approach with two application cases. |
1504.04339 | Tamara Bonaci | Tamara Bonaci, Jeffrey Herron, Tariq Yusuf, Junjie Yan, Tadayoshi
Kohno and Howard Jay Chizeck | To Make a Robot Secure: An Experimental Analysis of Cyber Security
Threats Against Teleoperated Surgical Robots | null | null | null | null | cs.RO cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Teleoperated robots are playing an increasingly important role in military
actions and medical services. In the future, remotely operated surgical robots
will likely be used in more scenarios such as battlefields and emergency
response. But rapidly growing applications of teleoperated surgery raise the
question; what if the computer systems for these robots are attacked, taken
over and even turned into weapons? Our work seeks to answer this question by
systematically analyzing possible cyber security attacks against Raven II, an
advanced teleoperated robotic surgery system. We identify a slew of possible
cyber security threats, and experimentally evaluate their scopes and impacts.
We demonstrate the ability to maliciously control a wide range of robots
functions, and even to completely ignore or override command inputs from the
surgeon. We further find that it is possible to abuse the robot's existing
emergency stop (E-stop) mechanism to execute efficient (single packet) attacks.
We then consider steps to mitigate these identified attacks, and experimentally
evaluate the feasibility of applying the existing security solutions against
these threats. The broader goal of our paper, however, is to raise awareness
and increase understanding of these emerging threats. We anticipate that the
majority of attacks against telerobotic surgery will also be relevant to other
teleoperated robotic and co-robotic systems.
| [
{
"created": "Thu, 16 Apr 2015 19:01:28 GMT",
"version": "v1"
},
{
"created": "Tue, 12 May 2015 17:55:38 GMT",
"version": "v2"
}
] | 2015-05-13 | [
[
"Bonaci",
"Tamara",
""
],
[
"Herron",
"Jeffrey",
""
],
[
"Yusuf",
"Tariq",
""
],
[
"Yan",
"Junjie",
""
],
[
"Kohno",
"Tadayoshi",
""
],
[
"Chizeck",
"Howard Jay",
""
]
] | Teleoperated robots are playing an increasingly important role in military actions and medical services. In the future, remotely operated surgical robots will likely be used in more scenarios such as battlefields and emergency response. But rapidly growing applications of teleoperated surgery raise the question; what if the computer systems for these robots are attacked, taken over and even turned into weapons? Our work seeks to answer this question by systematically analyzing possible cyber security attacks against Raven II, an advanced teleoperated robotic surgery system. We identify a slew of possible cyber security threats, and experimentally evaluate their scopes and impacts. We demonstrate the ability to maliciously control a wide range of robots functions, and even to completely ignore or override command inputs from the surgeon. We further find that it is possible to abuse the robot's existing emergency stop (E-stop) mechanism to execute efficient (single packet) attacks. We then consider steps to mitigate these identified attacks, and experimentally evaluate the feasibility of applying the existing security solutions against these threats. The broader goal of our paper, however, is to raise awareness and increase understanding of these emerging threats. We anticipate that the majority of attacks against telerobotic surgery will also be relevant to other teleoperated robotic and co-robotic systems. |
2203.04698 | Dwaraknath Gnaneshwar Mr | Dwaraknath Gnaneshwar, Bharath Ramsundar, Dhairya Gandhi, Rachel
Kurchin, Venkatasubramanian Viswanathan | Score-Based Generative Models for Molecule Generation | null | null | null | null | cs.LG q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | Recent advances in generative models have made exploring design spaces easier
for de novo molecule generation. However, popular generative models like GANs
and normalizing flows face challenges such as training instabilities due to
adversarial training and architectural constraints, respectively. Score-based
generative models sidestep these challenges by modelling the gradient of the
log probability density using a score function approximation, as opposed to
modelling the density function directly, and sampling from it using annealed
Langevin Dynamics. We believe that score-based generative models could open up
new opportunities in molecule generation due to their architectural
flexibility, such as replacing the score function with an SE(3) equivariant
model. In this work, we lay the foundations by testing the efficacy of
score-based models for molecule generation. We train a Transformer-based score
function on Self-Referencing Embedded Strings (SELFIES) representations of 1.5
million samples from the ZINC dataset and use the Moses benchmarking framework
to evaluate the generated samples on a suite of metrics.
| [
{
"created": "Mon, 7 Mar 2022 13:46:02 GMT",
"version": "v1"
}
] | 2022-03-10 | [
[
"Gnaneshwar",
"Dwaraknath",
""
],
[
"Ramsundar",
"Bharath",
""
],
[
"Gandhi",
"Dhairya",
""
],
[
"Kurchin",
"Rachel",
""
],
[
"Viswanathan",
"Venkatasubramanian",
""
]
] | Recent advances in generative models have made exploring design spaces easier for de novo molecule generation. However, popular generative models like GANs and normalizing flows face challenges such as training instabilities due to adversarial training and architectural constraints, respectively. Score-based generative models sidestep these challenges by modelling the gradient of the log probability density using a score function approximation, as opposed to modelling the density function directly, and sampling from it using annealed Langevin Dynamics. We believe that score-based generative models could open up new opportunities in molecule generation due to their architectural flexibility, such as replacing the score function with an SE(3) equivariant model. In this work, we lay the foundations by testing the efficacy of score-based models for molecule generation. We train a Transformer-based score function on Self-Referencing Embedded Strings (SELFIES) representations of 1.5 million samples from the ZINC dataset and use the Moses benchmarking framework to evaluate the generated samples on a suite of metrics. |
1504.01380 | Maitham Alhubail | Maitham Makki Alhubail and Qiqi Wang | The swept rule for breaking the latency barrier in time advancing PDEs | 30 pages | Journal of Computational Physics (2016), pp. 110-121 | 10.1016/j.jcp.2015.11.026 | null | cs.CE cs.MS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article investigates the swept rule of space-time domain decomposition,
an idea to break the latency barrier via communicating less often when
explicitly solving time-dependent PDEs. The swept rule decomposes space and
time among computing nodes in ways that exploit the domains of influence and
the domain of dependency, making it possible to communicate once per many
timesteps without redundant computation. The article presents simple
theoretical analysis to the performance of the swept rule which then was shown
to be accurate by conducting numerical experiments.
| [
{
"created": "Mon, 6 Apr 2015 16:00:32 GMT",
"version": "v1"
},
{
"created": "Sat, 14 Nov 2015 16:26:05 GMT",
"version": "v2"
}
] | 2015-12-10 | [
[
"Alhubail",
"Maitham Makki",
""
],
[
"Wang",
"Qiqi",
""
]
] | This article investigates the swept rule of space-time domain decomposition, an idea to break the latency barrier via communicating less often when explicitly solving time-dependent PDEs. The swept rule decomposes space and time among computing nodes in ways that exploit the domains of influence and the domain of dependency, making it possible to communicate once per many timesteps without redundant computation. The article presents simple theoretical analysis to the performance of the swept rule which then was shown to be accurate by conducting numerical experiments. |
cs/0008001 | Randal E. Bryant | Randal E. Bryant, Miroslav N. Velev | Boolean Satisfiability with Transitivity Constraints | Submitted to ACM Transactions on Computational Logic | null | null | null | cs.LO | null | We consider a variant of the Boolean satisfiability problem where a subset E
of the propositional variables appearing in formula Fsat encode a symmetric,
transitive, binary relation over N elements. Each of these relational
variables, e[i,j], for 1 <= i < j <= N, expresses whether or not the relation
holds between elements i and j. The task is to either find a satisfying
assignment to Fsat that also satisfies all transitivity constraints over the
relational variables (e.g., e[1,2] & e[2,3] ==> e[1,3]), or to prove that no
such assignment exists. Solving this satisfiability problem is the final and
most difficult step in our decision procedure for a logic of equality with
uninterpreted functions. This procedure forms the core of our tool for
verifying pipelined microprocessors.
To use a conventional Boolean satisfiability checker, we augment the set of
clauses expressing Fsat with clauses expressing the transitivity constraints.
We consider methods to reduce the number of such clauses based on the sparse
structure of the relational variables.
To use Ordered Binary Decision Diagrams (OBDDs), we show that for some sets
E, the OBDD representation of the transitivity constraints has exponential size
for all possible variable orderings. By considering only those relational
variables that occur in the OBDD representation of Fsat, our experiments show
that we can readily construct an OBDD representation of the relevant
transitivity constraints and thus solve the constrained satisfiability problem.
| [
{
"created": "Tue, 1 Aug 2000 13:51:56 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Bryant",
"Randal E.",
""
],
[
"Velev",
"Miroslav N.",
""
]
] | We consider a variant of the Boolean satisfiability problem where a subset E of the propositional variables appearing in formula Fsat encode a symmetric, transitive, binary relation over N elements. Each of these relational variables, e[i,j], for 1 <= i < j <= N, expresses whether or not the relation holds between elements i and j. The task is to either find a satisfying assignment to Fsat that also satisfies all transitivity constraints over the relational variables (e.g., e[1,2] & e[2,3] ==> e[1,3]), or to prove that no such assignment exists. Solving this satisfiability problem is the final and most difficult step in our decision procedure for a logic of equality with uninterpreted functions. This procedure forms the core of our tool for verifying pipelined microprocessors. To use a conventional Boolean satisfiability checker, we augment the set of clauses expressing Fsat with clauses expressing the transitivity constraints. We consider methods to reduce the number of such clauses based on the sparse structure of the relational variables. To use Ordered Binary Decision Diagrams (OBDDs), we show that for some sets E, the OBDD representation of the transitivity constraints has exponential size for all possible variable orderings. By considering only those relational variables that occur in the OBDD representation of Fsat, our experiments show that we can readily construct an OBDD representation of the relevant transitivity constraints and thus solve the constrained satisfiability problem. |
1307.4264 | Rong Zheng | Huy Nguyen and Rong Zheng | A Data-driven Study of Influences in Twitter Communities | 11 pages | null | null | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a quantitative study of Twitter, one of the most popular
micro-blogging services, from the perspective of user influence. We crawl
several datasets from the most active communities on Twitter and obtain 20.5
million user profiles, along with 420.2 million directed relations and 105
million tweets among the users. User influence scores are obtained from
influence measurement services, Klout and PeerIndex. Our analysis reveals
interesting findings, including non-power-law influence distribution, strong
reciprocity among users in a community, the existence of homophily and
hierarchical relationships in social influences. Most importantly, we observe
that whether a user retweets a message is strongly influenced by the first of
his followees who posted that message. To capture such an effect, we propose
the first influencer (FI) information diffusion model and show through
extensive evaluation that compared to the widely adopted independent cascade
model, the FI model is more stable and more accurate in predicting influence
spreads in Twitter communities.
| [
{
"created": "Tue, 16 Jul 2013 13:07:24 GMT",
"version": "v1"
}
] | 2013-07-17 | [
[
"Nguyen",
"Huy",
""
],
[
"Zheng",
"Rong",
""
]
] | This paper presents a quantitative study of Twitter, one of the most popular micro-blogging services, from the perspective of user influence. We crawl several datasets from the most active communities on Twitter and obtain 20.5 million user profiles, along with 420.2 million directed relations and 105 million tweets among the users. User influence scores are obtained from influence measurement services, Klout and PeerIndex. Our analysis reveals interesting findings, including non-power-law influence distribution, strong reciprocity among users in a community, the existence of homophily and hierarchical relationships in social influences. Most importantly, we observe that whether a user retweets a message is strongly influenced by the first of his followees who posted that message. To capture such an effect, we propose the first influencer (FI) information diffusion model and show through extensive evaluation that compared to the widely adopted independent cascade model, the FI model is more stable and more accurate in predicting influence spreads in Twitter communities. |
1912.09816 | Petr Chunaev | Petr Chunaev | Community detection in node-attributed social networks: a survey | This is an essentially revised version of the manuscript | null | 10.1016/j.cosrev.2020.100286 | null | cs.SI cs.LG cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Community detection is a fundamental problem in social network analysis
consisting in unsupervised dividing social actors (nodes in a social graph)
with certain social connections (edges in a social graph) into densely knitted
and highly related groups with each group well separated from the others.
Classical approaches for community detection usually deal only with network
structure and ignore features of its nodes (called node attributes), although
many real-world social networks provide additional actors' information such as
interests. It is believed that the attributes may clarify and enrich the
knowledge about the actors and give sense to the communities. This belief has
motivated the progress in developing community detection methods that use both
the structure and the attributes of network (i.e. deal with a node-attributed
graph) to yield more informative and qualitative results.
During the last decade many such methods based on different ideas have
appeared. Although there exist partial overviews of them, a recent survey is a
necessity as the growing number of the methods may cause repetitions in
methodology and uncertainty in practice.
In this paper we aim at describing and clarifying the overall situation in
the field of community detection in node-attributed social networks. Namely, we
perform an exhaustive search of known methods and propose a classification of
them based on when and how structure and attributes are fused. We not only give
a description of each class but also provide general technical ideas behind
each method in the class. Furthermore, we pay attention to available
information which methods outperform others and which datasets and quality
measures are used for their evaluation. Basing on the information collected, we
make conclusions on the current state of the field and disclose several
problems that seem important to be resolved in future.
| [
{
"created": "Fri, 20 Dec 2019 13:35:32 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Jun 2020 11:01:39 GMT",
"version": "v2"
}
] | 2022-01-14 | [
[
"Chunaev",
"Petr",
""
]
] | Community detection is a fundamental problem in social network analysis consisting in unsupervised dividing social actors (nodes in a social graph) with certain social connections (edges in a social graph) into densely knitted and highly related groups with each group well separated from the others. Classical approaches for community detection usually deal only with network structure and ignore features of its nodes (called node attributes), although many real-world social networks provide additional actors' information such as interests. It is believed that the attributes may clarify and enrich the knowledge about the actors and give sense to the communities. This belief has motivated the progress in developing community detection methods that use both the structure and the attributes of network (i.e. deal with a node-attributed graph) to yield more informative and qualitative results. During the last decade many such methods based on different ideas have appeared. Although there exist partial overviews of them, a recent survey is a necessity as the growing number of the methods may cause repetitions in methodology and uncertainty in practice. In this paper we aim at describing and clarifying the overall situation in the field of community detection in node-attributed social networks. Namely, we perform an exhaustive search of known methods and propose a classification of them based on when and how structure and attributes are fused. We not only give a description of each class but also provide general technical ideas behind each method in the class. Furthermore, we pay attention to available information which methods outperform others and which datasets and quality measures are used for their evaluation. Basing on the information collected, we make conclusions on the current state of the field and disclose several problems that seem important to be resolved in future. |
1207.3208 | Brian Huffman | Brian Huffman | Formal Verification of Monad Transformers | ICFP 2012: The 17th ACM SIGPLAN International Conference on
Functional Programming, 12 pages | null | null | null | cs.LO | http://creativecommons.org/licenses/publicdomain/ | We present techniques for reasoning about constructor classes that (like the
monad class) fix polymorphic operations and assert polymorphic axioms. We do
not require a logic with first-class type constructors, first-class
polymorphism, or type quantification; instead, we rely on a domain-theoretic
model of the type system in a universal domain to provide these features.
These ideas are implemented in the Tycon library for the Isabelle theorem
prover, which builds on the HOLCF library of domain theory. The Tycon library
provides various axiomatic type constructor classes, including functors and
monads. It also provides automation for instantiating those classes, and for
defining further subclasses.
We use the Tycon library to formalize three Haskell monad transformers: the
error transformer, the writer transformer, and the resumption transformer. The
error and writer transformers do not universally preserve the monad laws;
however, we establish datatype invariants for each, showing that they are valid
monads when viewed as abstract datatypes.
| [
{
"created": "Fri, 13 Jul 2012 11:53:44 GMT",
"version": "v1"
}
] | 2012-07-16 | [
[
"Huffman",
"Brian",
""
]
] | We present techniques for reasoning about constructor classes that (like the monad class) fix polymorphic operations and assert polymorphic axioms. We do not require a logic with first-class type constructors, first-class polymorphism, or type quantification; instead, we rely on a domain-theoretic model of the type system in a universal domain to provide these features. These ideas are implemented in the Tycon library for the Isabelle theorem prover, which builds on the HOLCF library of domain theory. The Tycon library provides various axiomatic type constructor classes, including functors and monads. It also provides automation for instantiating those classes, and for defining further subclasses. We use the Tycon library to formalize three Haskell monad transformers: the error transformer, the writer transformer, and the resumption transformer. The error and writer transformers do not universally preserve the monad laws; however, we establish datatype invariants for each, showing that they are valid monads when viewed as abstract datatypes. |
1503.04377 | EPTCS | Jakob Rehof (TU-Dortmund) | Proceedings Seventh Workshop on Intersection Types and Related Systems | null | EPTCS 177, 2015 | 10.4204/EPTCS.177 | null | cs.LO cs.PL cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This volume contains a final and revised selection of papers presented at the
Seventh Workshop on Intersection Types and Related Systems (ITRS 2014), held in
Vienna (Austria) on July 18th, affiliated with TLCA 2014, Typed Lambda Calculi
and Applications (held jointly with RTA, Rewriting Techniques and Applications)
as part of FLoC and the Vienna Summer of Logic (VSL) 2014. Intersection types
have been introduced in the late 1970s as a language for describing properties
of lambda calculus which were not captured by all previous type systems. They
provided the first characterisation of strongly normalising lambda terms and
have become a powerful syntactic and semantic tool for analysing various
normalisation properties as well as lambda models. Over the years the scope of
research on intersection types has broadened. Recently, there have been a
number of breakthroughs in the use of intersection types and similar technology
for practical purposes such as program analysis, verification and concurrency,
and program synthesis. The aim of the ITRS workshop series is to bring together
researchers working on both the theory and practical applications of systems
based on intersection types and related approaches (e.g., union types,
refinement types, behavioral types).
| [
{
"created": "Sun, 15 Mar 2015 02:58:54 GMT",
"version": "v1"
}
] | 2015-03-17 | [
[
"Rehof",
"Jakob",
"",
"TU-Dortmund"
]
] | This volume contains a final and revised selection of papers presented at the Seventh Workshop on Intersection Types and Related Systems (ITRS 2014), held in Vienna (Austria) on July 18th, affiliated with TLCA 2014, Typed Lambda Calculi and Applications (held jointly with RTA, Rewriting Techniques and Applications) as part of FLoC and the Vienna Summer of Logic (VSL) 2014. Intersection types have been introduced in the late 1970s as a language for describing properties of lambda calculus which were not captured by all previous type systems. They provided the first characterisation of strongly normalising lambda terms and have become a powerful syntactic and semantic tool for analysing various normalisation properties as well as lambda models. Over the years the scope of research on intersection types has broadened. Recently, there have been a number of breakthroughs in the use of intersection types and similar technology for practical purposes such as program analysis, verification and concurrency, and program synthesis. The aim of the ITRS workshop series is to bring together researchers working on both the theory and practical applications of systems based on intersection types and related approaches (e.g., union types, refinement types, behavioral types). |
2406.10842 | Zhuoxu Duan | Zhuoxu Duan, Zhengye Yang, Samuel Westby, Christoph Riedl, Brooke
Foucault Welles, Richard J. Radke | Large Language Models for Automatic Milestone Detection in Group
Discussions | null | null | null | null | cs.CL cs.AI cs.HC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Large language models like GPT have proven widely successful on natural
language understanding tasks based on written text documents. In this paper, we
investigate an LLM's performance on recordings of a group oral communication
task in which utterances are often truncated or not well-formed. We propose a
new group task experiment involving a puzzle with several milestones that can
be achieved in any order. We investigate methods for processing transcripts to
detect if, when, and by whom a milestone has been completed. We demonstrate
that iteratively prompting GPT with transcription chunks outperforms semantic
similarity search methods using text embeddings, and further discuss the
quality and randomness of GPT responses under different context window sizes.
| [
{
"created": "Sun, 16 Jun 2024 08:32:22 GMT",
"version": "v1"
}
] | 2024-06-18 | [
[
"Duan",
"Zhuoxu",
""
],
[
"Yang",
"Zhengye",
""
],
[
"Westby",
"Samuel",
""
],
[
"Riedl",
"Christoph",
""
],
[
"Welles",
"Brooke Foucault",
""
],
[
"Radke",
"Richard J.",
""
]
] | Large language models like GPT have proven widely successful on natural language understanding tasks based on written text documents. In this paper, we investigate an LLM's performance on recordings of a group oral communication task in which utterances are often truncated or not well-formed. We propose a new group task experiment involving a puzzle with several milestones that can be achieved in any order. We investigate methods for processing transcripts to detect if, when, and by whom a milestone has been completed. We demonstrate that iteratively prompting GPT with transcription chunks outperforms semantic similarity search methods using text embeddings, and further discuss the quality and randomness of GPT responses under different context window sizes. |
2309.14788 | Gabriel Bathie | Gabriel Bathie, Tomasz Kociumaka and Tatiana Starikovskaya | Small-Space Algorithms for the Online Language Distance Problem for
Palindromes and Squares | Accepted to ISAAC'23 | null | null | null | cs.DS | http://creativecommons.org/licenses/by/4.0/ | We study the online variant of the language distance problem for two
classical formal languages, the language of palindromes and the language of
squares, and for the two most fundamental distances, the Hamming distance and
the edit (Levenshtein) distance. In this problem, defined for a fixed formal
language $L$, we are given a string $T$ of length $n$, and the task is to
compute the minimal distance to $L$ from every prefix of $T$. We focus on the
low-distance regime, where one must compute only the distances smaller than a
given threshold $k$. In this work, our contribution is twofold:
- First, we show streaming algorithms, which access the input string $T$ only
through a single left-to-right scan. Both for palindromes and squares, our
algorithms use $O(k \cdot\mathrm{poly}~\log n)$ space and time per character in
the Hamming-distance case and $O(k^2 \cdot\mathrm{poly}~\log n)$ space and time
per character in the edit-distance case. These algorithms are randomised by
necessity, and they err with probability inverse-polynomial in $n$.
- Second, we show deterministic read-only online algorithms, which are also
provided with read-only random access to the already processed characters of
$T$. Both for palindromes and squares, our algorithms use $O(k
\cdot\mathrm{poly}~\log n)$ space and time per character in the
Hamming-distance case and $O(k^4 \cdot\mathrm{poly}~\log n)$ space and
amortised time per character in the edit-distance case.
| [
{
"created": "Tue, 26 Sep 2023 09:36:24 GMT",
"version": "v1"
},
{
"created": "Tue, 30 Apr 2024 13:18:33 GMT",
"version": "v2"
}
] | 2024-05-01 | [
[
"Bathie",
"Gabriel",
""
],
[
"Kociumaka",
"Tomasz",
""
],
[
"Starikovskaya",
"Tatiana",
""
]
] | We study the online variant of the language distance problem for two classical formal languages, the language of palindromes and the language of squares, and for the two most fundamental distances, the Hamming distance and the edit (Levenshtein) distance. In this problem, defined for a fixed formal language $L$, we are given a string $T$ of length $n$, and the task is to compute the minimal distance to $L$ from every prefix of $T$. We focus on the low-distance regime, where one must compute only the distances smaller than a given threshold $k$. In this work, our contribution is twofold: - First, we show streaming algorithms, which access the input string $T$ only through a single left-to-right scan. Both for palindromes and squares, our algorithms use $O(k \cdot\mathrm{poly}~\log n)$ space and time per character in the Hamming-distance case and $O(k^2 \cdot\mathrm{poly}~\log n)$ space and time per character in the edit-distance case. These algorithms are randomised by necessity, and they err with probability inverse-polynomial in $n$. - Second, we show deterministic read-only online algorithms, which are also provided with read-only random access to the already processed characters of $T$. Both for palindromes and squares, our algorithms use $O(k \cdot\mathrm{poly}~\log n)$ space and time per character in the Hamming-distance case and $O(k^4 \cdot\mathrm{poly}~\log n)$ space and amortised time per character in the edit-distance case. |
2309.02561 | Jensen Gao | Jensen Gao, Bidipta Sarkar, Fei Xia, Ted Xiao, Jiajun Wu, Brian
Ichter, Anirudha Majumdar, Dorsa Sadigh | Physically Grounded Vision-Language Models for Robotic Manipulation | Updated version for ICRA 2024 | null | null | null | cs.RO cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent advances in vision-language models (VLMs) have led to improved
performance on tasks such as visual question answering and image captioning.
Consequently, these models are now well-positioned to reason about the physical
world, particularly within domains such as robotic manipulation. However,
current VLMs are limited in their understanding of the physical concepts (e.g.,
material, fragility) of common objects, which restricts their usefulness for
robotic manipulation tasks that involve interaction and physical reasoning
about such objects. To address this limitation, we propose PhysObjects, an
object-centric dataset of 39.6K crowd-sourced and 417K automated physical
concept annotations of common household objects. We demonstrate that
fine-tuning a VLM on PhysObjects improves its understanding of physical object
concepts, including generalization to held-out concepts, by capturing human
priors of these concepts from visual appearance. We incorporate this physically
grounded VLM in an interactive framework with a large language model-based
robotic planner, and show improved planning performance on tasks that require
reasoning about physical object concepts, compared to baselines that do not
leverage physically grounded VLMs. We additionally illustrate the benefits of
our physically grounded VLM on a real robot, where it improves task success
rates. We release our dataset and provide further details and visualizations of
our results at https://iliad.stanford.edu/pg-vlm/.
| [
{
"created": "Tue, 5 Sep 2023 20:21:03 GMT",
"version": "v1"
},
{
"created": "Wed, 13 Sep 2023 21:40:56 GMT",
"version": "v2"
},
{
"created": "Thu, 29 Feb 2024 08:44:12 GMT",
"version": "v3"
},
{
"created": "Sun, 3 Mar 2024 08:12:36 GMT",
"version": "v4"
}
] | 2024-03-05 | [
[
"Gao",
"Jensen",
""
],
[
"Sarkar",
"Bidipta",
""
],
[
"Xia",
"Fei",
""
],
[
"Xiao",
"Ted",
""
],
[
"Wu",
"Jiajun",
""
],
[
"Ichter",
"Brian",
""
],
[
"Majumdar",
"Anirudha",
""
],
[
"Sadigh",
"Dorsa",
""
]
] | Recent advances in vision-language models (VLMs) have led to improved performance on tasks such as visual question answering and image captioning. Consequently, these models are now well-positioned to reason about the physical world, particularly within domains such as robotic manipulation. However, current VLMs are limited in their understanding of the physical concepts (e.g., material, fragility) of common objects, which restricts their usefulness for robotic manipulation tasks that involve interaction and physical reasoning about such objects. To address this limitation, we propose PhysObjects, an object-centric dataset of 39.6K crowd-sourced and 417K automated physical concept annotations of common household objects. We demonstrate that fine-tuning a VLM on PhysObjects improves its understanding of physical object concepts, including generalization to held-out concepts, by capturing human priors of these concepts from visual appearance. We incorporate this physically grounded VLM in an interactive framework with a large language model-based robotic planner, and show improved planning performance on tasks that require reasoning about physical object concepts, compared to baselines that do not leverage physically grounded VLMs. We additionally illustrate the benefits of our physically grounded VLM on a real robot, where it improves task success rates. We release our dataset and provide further details and visualizations of our results at https://iliad.stanford.edu/pg-vlm/. |
1307.7790 | Kester Quist-Aphetsi | Quist-Aphetsi Kester | Using SOA with Web Services for effective Integration of Hospital
Information Systems via an Enterprise Service Bus | 6 pages. International Journal of Research in Engineering & Advanced
Technology (IJREAT), 2013. arXiv admin note: text overlap with
arXiv:1204.0179 by other authors without attribution | International Journal of Research in Engineering & Advanced
Technology (IJREAT).pp: 1-6.1.2.(2013) | null | null | cs.SE | http://creativecommons.org/licenses/by-nc-sa/3.0/ | Hospitals are distributed across geographical areas and it is important for
all hospitals to share information as well as integrate their systems for
effective researching and health delivery. Health personals and institutions in
need of information from hospitals with respect to geographical areas can
easily do researches on patients, treatments, disease outbreaks, and effects of
drugs. This research work is aimed at integrating of database systems of
hospital across geographical areas via a service bus. A centralized service bus
was used to facilitate interoperability of applications across platforms and
enhance communication within the hospital infrastructure as well as creating
enabling environment for new layer of abstractions to be added without
modification of the entire system. Concept of Service Oriented Architecture
with web services was used for rapid integration solution in solving the
challenges faced during integration of multiple incompatible applications.
| [
{
"created": "Tue, 30 Jul 2013 02:46:25 GMT",
"version": "v1"
}
] | 2013-07-31 | [
[
"Kester",
"Quist-Aphetsi",
""
]
] | Hospitals are distributed across geographical areas and it is important for all hospitals to share information as well as integrate their systems for effective researching and health delivery. Health personals and institutions in need of information from hospitals with respect to geographical areas can easily do researches on patients, treatments, disease outbreaks, and effects of drugs. This research work is aimed at integrating of database systems of hospital across geographical areas via a service bus. A centralized service bus was used to facilitate interoperability of applications across platforms and enhance communication within the hospital infrastructure as well as creating enabling environment for new layer of abstractions to be added without modification of the entire system. Concept of Service Oriented Architecture with web services was used for rapid integration solution in solving the challenges faced during integration of multiple incompatible applications. |
2202.11295 | Jingxin Zhang | Jingxin Zhang, Donghua Zhou, Maoyin Chen, Xia Hong | Continual learning-based probabilistic slow feature analysis for
multimode dynamic process monitoring | This paper has been submitted to IEEE Transactions on Automation
Science and Engineering for potential publication | null | null | null | cs.LG eess.SP | http://creativecommons.org/licenses/by/4.0/ | In this paper, a novel multimode dynamic process monitoring approach is
proposed by extending elastic weight consolidation (EWC) to probabilistic slow
feature analysis (PSFA) in order to extract multimode slow features for online
monitoring. EWC was originally introduced in the setting of machine learning of
sequential multi-tasks with the aim of avoiding catastrophic forgetting issue,
which equally poses as a major challenge in multimode dynamic process
monitoring. When a new mode arrives, a set of data should be collected so that
this mode can be identified by PSFA and prior knowledge. Then, a regularization
term is introduced to prevent new data from significantly interfering with the
learned knowledge, where the parameter importance measures are estimated. The
proposed method is denoted as PSFA-EWC, which is updated continually and
capable of achieving excellent performance for successive modes. Different from
traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and
forward transfer ability. The significant features of previous modes are
retained while consolidating new information, which may contribute to learning
new relevant modes. Compared with several known methods, the effectiveness of
the proposed method is demonstrated via a continuous stirred tank heater and a
practical coal pulverizing system.
| [
{
"created": "Wed, 23 Feb 2022 03:57:59 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Apr 2022 14:44:55 GMT",
"version": "v2"
}
] | 2022-04-29 | [
[
"Zhang",
"Jingxin",
""
],
[
"Zhou",
"Donghua",
""
],
[
"Chen",
"Maoyin",
""
],
[
"Hong",
"Xia",
""
]
] | In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode dynamic process monitoring. When a new mode arrives, a set of data should be collected so that this mode can be identified by PSFA and prior knowledge. Then, a regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is denoted as PSFA-EWC, which is updated continually and capable of achieving excellent performance for successive modes. Different from traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and forward transfer ability. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. Compared with several known methods, the effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system. |
1212.0892 | Vasiliy Tereshkov | Vasiliy M. Tereshkov | An Intuitive Approach to Inertial Sensor Bias Estimation | 6 pages, 7 figures | null | null | null | cs.SY math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A simple approach to gyro and accelerometer bias estimation is proposed. It
does not involve Kalman filtering or similar formal techniques. Instead, it is
based on physical intuition and exploits a duality between gimbaled and
strapdown inertial systems. The estimation problem is decoupled into two
separate stages. At the first stage, inertial system attitude errors are
corrected by means of a feedback from an external aid. In the presence of
uncompensated biases, the steady-state feedback rebalances those biases and can
be used to estimate them. At the second stage, the desired bias estimates are
expressed in a closed form in terms of the feedback signal. The estimator has
only three tunable parameters and is easy to implement and use. The tests
proved the feasibility of the proposed approach for the estimation of low-cost
MEMS inertial sensor biases on a moving land vehicle.
| [
{
"created": "Tue, 4 Dec 2012 22:11:10 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jun 2013 09:12:46 GMT",
"version": "v2"
},
{
"created": "Mon, 1 Jul 2013 13:04:41 GMT",
"version": "v3"
}
] | 2013-07-02 | [
[
"Tereshkov",
"Vasiliy M.",
""
]
] | A simple approach to gyro and accelerometer bias estimation is proposed. It does not involve Kalman filtering or similar formal techniques. Instead, it is based on physical intuition and exploits a duality between gimbaled and strapdown inertial systems. The estimation problem is decoupled into two separate stages. At the first stage, inertial system attitude errors are corrected by means of a feedback from an external aid. In the presence of uncompensated biases, the steady-state feedback rebalances those biases and can be used to estimate them. At the second stage, the desired bias estimates are expressed in a closed form in terms of the feedback signal. The estimator has only three tunable parameters and is easy to implement and use. The tests proved the feasibility of the proposed approach for the estimation of low-cost MEMS inertial sensor biases on a moving land vehicle. |
1903.02508 | Oded Lachish Dr | Nikola K. Blanchard and Eldar Fischer and Oded Lachish and Felix Reidl | Longest paths in 2-edge-connected cubic graphs | null | null | null | null | cs.DM math.CO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We prove almost tight bounds on the length of paths in $2$-edge-connected
cubic graphs. Concretely, we show that (i) every $2$-edge-connected cubic graph
of size $n$ has a path of length
$\Omega\left(\frac{\log^2{n}}{\log{\log{n}}}\right)$, and (ii) there exists a
$2$-edge-connected cubic graph, such that every path in the graph has length
$O(\log^2{n})$.
| [
{
"created": "Wed, 6 Mar 2019 17:32:56 GMT",
"version": "v1"
}
] | 2019-03-07 | [
[
"Blanchard",
"Nikola K.",
""
],
[
"Fischer",
"Eldar",
""
],
[
"Lachish",
"Oded",
""
],
[
"Reidl",
"Felix",
""
]
] | We prove almost tight bounds on the length of paths in $2$-edge-connected cubic graphs. Concretely, we show that (i) every $2$-edge-connected cubic graph of size $n$ has a path of length $\Omega\left(\frac{\log^2{n}}{\log{\log{n}}}\right)$, and (ii) there exists a $2$-edge-connected cubic graph, such that every path in the graph has length $O(\log^2{n})$. |
1512.04150 | Bolei Zhou | Bolei Zhou and Aditya Khosla and Agata Lapedriza and Aude Oliva and
Antonio Torralba | Learning Deep Features for Discriminative Localization | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we revisit the global average pooling layer proposed in [13],
and shed light on how it explicitly enables the convolutional neural network to
have remarkable localization ability despite being trained on image-level
labels. While this technique was previously proposed as a means for
regularizing training, we find that it actually builds a generic localizable
deep representation that can be applied to a variety of tasks. Despite the
apparent simplicity of global average pooling, we are able to achieve 37.1%
top-5 error for object localization on ILSVRC 2014, which is remarkably close
to the 34.2% top-5 error achieved by a fully supervised CNN approach. We
demonstrate that our network is able to localize the discriminative image
regions on a variety of tasks despite not being trained for them
| [
{
"created": "Mon, 14 Dec 2015 01:32:33 GMT",
"version": "v1"
}
] | 2015-12-15 | [
[
"Zhou",
"Bolei",
""
],
[
"Khosla",
"Aditya",
""
],
[
"Lapedriza",
"Agata",
""
],
[
"Oliva",
"Aude",
""
],
[
"Torralba",
"Antonio",
""
]
] | In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels. While this technique was previously proposed as a means for regularizing training, we find that it actually builds a generic localizable deep representation that can be applied to a variety of tasks. Despite the apparent simplicity of global average pooling, we are able to achieve 37.1% top-5 error for object localization on ILSVRC 2014, which is remarkably close to the 34.2% top-5 error achieved by a fully supervised CNN approach. We demonstrate that our network is able to localize the discriminative image regions on a variety of tasks despite not being trained for them |
2306.02413 | Sam Powers | Sam Powers, Abhinav Gupta, Chris Paxton | Evaluating Continual Learning on a Home Robot | null | null | null | null | cs.RO cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robots in home environments need to be able to learn new skills continuously
as data becomes available, becoming ever more capable over time while using as
little real-world data as possible. However, traditional robot learning
approaches typically assume large amounts of iid data, which is inconsistent
with this goal. In contrast, continual learning methods like CLEAR and SANE
allow autonomous agents to learn off of a stream of non-iid samples; they,
however, have not previously been demonstrated on real robotics platforms. In
this work, we show how continual learning methods can be adapted for use on a
real, low-cost home robot, and in particular look at the case where we have
extremely small numbers of examples, in a task-id-free setting. Specifically,
we propose SANER, a method for continuously learning a library of skills, and
ABIP (Attention-Based Interaction Policies) as the backbone to support it. We
learn four sequential kitchen tasks on a low-cost home robot, using only a
handful of demonstrations per task.
| [
{
"created": "Sun, 4 Jun 2023 17:14:49 GMT",
"version": "v1"
}
] | 2023-06-06 | [
[
"Powers",
"Sam",
""
],
[
"Gupta",
"Abhinav",
""
],
[
"Paxton",
"Chris",
""
]
] | Robots in home environments need to be able to learn new skills continuously as data becomes available, becoming ever more capable over time while using as little real-world data as possible. However, traditional robot learning approaches typically assume large amounts of iid data, which is inconsistent with this goal. In contrast, continual learning methods like CLEAR and SANE allow autonomous agents to learn off of a stream of non-iid samples; they, however, have not previously been demonstrated on real robotics platforms. In this work, we show how continual learning methods can be adapted for use on a real, low-cost home robot, and in particular look at the case where we have extremely small numbers of examples, in a task-id-free setting. Specifically, we propose SANER, a method for continuously learning a library of skills, and ABIP (Attention-Based Interaction Policies) as the backbone to support it. We learn four sequential kitchen tasks on a low-cost home robot, using only a handful of demonstrations per task. |
2201.09574 | Jian-Wei Liu | Ze-yu Liu, Jian-wei Liu, Xin Zuo, Ming-fei Hu | Multi-Scale Iterative Refinement Network for RGB-D Salient Object
Detection | 40 pages | Engineering Applications of Artificial Intelligence(2021) | 10.1016/j.engappai.2021.104473 | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | The extensive research leveraging RGB-D information has been exploited in
salient object detection. However, salient visual cues appear in various scales
and resolutions of RGB images due to semantic gaps at different feature levels.
Meanwhile, similar salient patterns are available in cross-modal depth images
as well as multi-scale versions. Cross-modal fusion and multi-scale refinement
are still an open problem in RGB-D salient object detection task. In this
paper, we begin by introducing top-down and bottom-up iterative refinement
architecture to leverage multi-scale features, and then devise attention based
fusion module (ABF) to address on cross-modal correlation. We conduct extensive
experiments on seven public datasets. The experimental results show the
effectiveness of our devised method
| [
{
"created": "Mon, 24 Jan 2022 10:33:00 GMT",
"version": "v1"
}
] | 2022-01-25 | [
[
"Liu",
"Ze-yu",
""
],
[
"Liu",
"Jian-wei",
""
],
[
"Zuo",
"Xin",
""
],
[
"Hu",
"Ming-fei",
""
]
] | The extensive research leveraging RGB-D information has been exploited in salient object detection. However, salient visual cues appear in various scales and resolutions of RGB images due to semantic gaps at different feature levels. Meanwhile, similar salient patterns are available in cross-modal depth images as well as multi-scale versions. Cross-modal fusion and multi-scale refinement are still an open problem in RGB-D salient object detection task. In this paper, we begin by introducing top-down and bottom-up iterative refinement architecture to leverage multi-scale features, and then devise attention based fusion module (ABF) to address on cross-modal correlation. We conduct extensive experiments on seven public datasets. The experimental results show the effectiveness of our devised method |
2212.12070 | Miquel Ferriol-Galm\'es | Miquel Ferriol-Galm\'es, Jordi Paillisse, Jos\'e Su\'arez-Varela,
Krzysztof Rusek, Shihan Xiao, Xiang Shi, Xiangle Cheng, Pere Barlet-Ros,
Albert Cabellos-Aparicio | RouteNet-Fermi: Network Modeling with Graph Neural Networks | This paper has been accepted for publication at IEEE/ACM Transactions
on Networking 2023 (DOI: 10.1109/TNET.2023.3269983). \copyright 2023 IEEE.
Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses | null | 10.1109/TNET.2023.3269983 | null | cs.NI cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Network models are an essential block of modern networks. For example, they
are widely used in network planning and optimization. However, as networks
increase in scale and complexity, some models present limitations, such as the
assumption of Markovian traffic in queuing theory models, or the high
computational cost of network simulators. Recent advances in machine learning,
such as Graph Neural Networks (GNN), are enabling a new generation of network
models that are data-driven and can learn complex non-linear behaviors. In this
paper, we present RouteNet-Fermi, a custom GNN model that shares the same goals
as Queuing Theory, while being considerably more accurate in the presence of
realistic traffic models. The proposed model predicts accurately the delay,
jitter, and packet loss of a network. We have tested RouteNet-Fermi in networks
of increasing size (up to 300 nodes), including samples with mixed traffic
profiles -- e.g., with complex non-Markovian models -- and arbitrary routing
and queue scheduling configurations. Our experimental results show that
RouteNet-Fermi achieves similar accuracy as computationally-expensive
packet-level simulators and scales accurately to larger networks. Our model
produces delay estimates with a mean relative error of 6.24% when applied to a
test dataset of 1,000 samples, including network topologies one order of
magnitude larger than those seen during training. Finally, we have also
evaluated RouteNet-Fermi with measurements from a physical testbed and packet
traces from a real-life network.
| [
{
"created": "Thu, 22 Dec 2022 23:02:40 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Sep 2023 16:47:16 GMT",
"version": "v2"
},
{
"created": "Wed, 20 Sep 2023 07:42:10 GMT",
"version": "v3"
}
] | 2023-09-21 | [
[
"Ferriol-Galmés",
"Miquel",
""
],
[
"Paillisse",
"Jordi",
""
],
[
"Suárez-Varela",
"José",
""
],
[
"Rusek",
"Krzysztof",
""
],
[
"Xiao",
"Shihan",
""
],
[
"Shi",
"Xiang",
""
],
[
"Cheng",
"Xiangle",
""
],
[
"Barlet-Ros",
"Pere",
""
],
[
"Cabellos-Aparicio",
"Albert",
""
]
] | Network models are an essential block of modern networks. For example, they are widely used in network planning and optimization. However, as networks increase in scale and complexity, some models present limitations, such as the assumption of Markovian traffic in queuing theory models, or the high computational cost of network simulators. Recent advances in machine learning, such as Graph Neural Networks (GNN), are enabling a new generation of network models that are data-driven and can learn complex non-linear behaviors. In this paper, we present RouteNet-Fermi, a custom GNN model that shares the same goals as Queuing Theory, while being considerably more accurate in the presence of realistic traffic models. The proposed model predicts accurately the delay, jitter, and packet loss of a network. We have tested RouteNet-Fermi in networks of increasing size (up to 300 nodes), including samples with mixed traffic profiles -- e.g., with complex non-Markovian models -- and arbitrary routing and queue scheduling configurations. Our experimental results show that RouteNet-Fermi achieves similar accuracy as computationally-expensive packet-level simulators and scales accurately to larger networks. Our model produces delay estimates with a mean relative error of 6.24% when applied to a test dataset of 1,000 samples, including network topologies one order of magnitude larger than those seen during training. Finally, we have also evaluated RouteNet-Fermi with measurements from a physical testbed and packet traces from a real-life network. |
2201.03115 | Rohitash Chandra | Rohitash Chandra, Venkatesh Kulkarni | Semantic and sentiment analysis of selected Bhagavad Gita translations
using BERT-based language framework | null | IEEE Access, 2022 | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | It is well known that translations of songs and poems not only break rhythm
and rhyming patterns, but can also result in loss of semantic information. The
Bhagavad Gita is an ancient Hindu philosophical text originally written in
Sanskrit that features a conversation between Lord Krishna and Arjuna prior to
the Mahabharata war. The Bhagavad Gita is also one of the key sacred texts in
Hinduism and is known as the forefront of the Vedic corpus of Hinduism. In the
last two centuries, there has been a lot of interest in Hindu philosophy from
western scholars; hence, the Bhagavad Gita has been translated in a number of
languages. However, there is not much work that validates the quality of the
English translations. Recent progress of language models powered by deep
learning has enabled not only translations but a better understanding of
language and texts with semantic and sentiment analysis. Our work is motivated
by the recent progress of language models powered by deep learning methods. In
this paper, we present a framework that compares selected translations (from
Sanskrit to English) of the Bhagavad Gita using semantic and sentiment
analyses. We use hand-labelled sentiment dataset for tuning state-of-art deep
learning-based language model known as bidirectional encoder representations
from transformers (BERT). We provide sentiment and semantic analysis for
selected chapters and verses across translations. Our results show that
although the style and vocabulary in the respective translations vary widely,
the sentiment analysis and semantic similarity shows that the message conveyed
are mostly similar.
| [
{
"created": "Sun, 9 Jan 2022 23:59:11 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Feb 2022 10:22:32 GMT",
"version": "v2"
}
] | 2022-02-16 | [
[
"Chandra",
"Rohitash",
""
],
[
"Kulkarni",
"Venkatesh",
""
]
] | It is well known that translations of songs and poems not only break rhythm and rhyming patterns, but can also result in loss of semantic information. The Bhagavad Gita is an ancient Hindu philosophical text originally written in Sanskrit that features a conversation between Lord Krishna and Arjuna prior to the Mahabharata war. The Bhagavad Gita is also one of the key sacred texts in Hinduism and is known as the forefront of the Vedic corpus of Hinduism. In the last two centuries, there has been a lot of interest in Hindu philosophy from western scholars; hence, the Bhagavad Gita has been translated in a number of languages. However, there is not much work that validates the quality of the English translations. Recent progress of language models powered by deep learning has enabled not only translations but a better understanding of language and texts with semantic and sentiment analysis. Our work is motivated by the recent progress of language models powered by deep learning methods. In this paper, we present a framework that compares selected translations (from Sanskrit to English) of the Bhagavad Gita using semantic and sentiment analyses. We use hand-labelled sentiment dataset for tuning state-of-art deep learning-based language model known as bidirectional encoder representations from transformers (BERT). We provide sentiment and semantic analysis for selected chapters and verses across translations. Our results show that although the style and vocabulary in the respective translations vary widely, the sentiment analysis and semantic similarity shows that the message conveyed are mostly similar. |
2009.10569 | Ozan Unal | Ozan Unal, Luc Van Gool, Dengxin Dai | Improving Point Cloud Semantic Segmentation by Learning 3D Object
Detection | Accepted at IEEE Winter Conference on Applications of Computer Vision
2021 (WACV'21) | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Point cloud semantic segmentation plays an essential role in autonomous
driving, providing vital information about drivable surfaces and nearby objects
that can aid higher level tasks such as path planning and collision avoidance.
While current 3D semantic segmentation networks focus on convolutional
architectures that perform great for well represented classes, they show a
significant drop in performance for underrepresented classes that share similar
geometric features. We propose a novel Detection Aware 3D Semantic Segmentation
(DASS) framework that explicitly leverages localization features from an
auxiliary 3D object detection task. By utilizing multitask training, the shared
feature representation of the network is guided to be aware of per class
detection features that aid tackling the differentiation of geometrically
similar classes. We additionally provide a pipeline that uses DASS to generate
high recall proposals for existing 2-stage detectors and demonstrate that the
added supervisory signal can be used to improve 3D orientation estimation
capabilities. Extensive experiments on both the SemanticKITTI and KITTI object
datasets show that DASS can improve 3D semantic segmentation results of
geometrically similar classes up to 37.8% IoU in image FOV while maintaining
high precision bird's-eye view (BEV) detection results.
| [
{
"created": "Tue, 22 Sep 2020 14:17:40 GMT",
"version": "v1"
},
{
"created": "Wed, 23 Sep 2020 08:18:00 GMT",
"version": "v2"
},
{
"created": "Sat, 7 Nov 2020 15:58:19 GMT",
"version": "v3"
}
] | 2020-11-10 | [
[
"Unal",
"Ozan",
""
],
[
"Van Gool",
"Luc",
""
],
[
"Dai",
"Dengxin",
""
]
] | Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level tasks such as path planning and collision avoidance. While current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes, they show a significant drop in performance for underrepresented classes that share similar geometric features. We propose a novel Detection Aware 3D Semantic Segmentation (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task. By utilizing multitask training, the shared feature representation of the network is guided to be aware of per class detection features that aid tackling the differentiation of geometrically similar classes. We additionally provide a pipeline that uses DASS to generate high recall proposals for existing 2-stage detectors and demonstrate that the added supervisory signal can be used to improve 3D orientation estimation capabilities. Extensive experiments on both the SemanticKITTI and KITTI object datasets show that DASS can improve 3D semantic segmentation results of geometrically similar classes up to 37.8% IoU in image FOV while maintaining high precision bird's-eye view (BEV) detection results. |
1903.10152 | Xiaowei Hu | Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Tianyu Wang, Pheng-Ann Heng | SAC-Net: Spatial Attenuation Context for Salient Object Detection | null | null | 10.1109/TCSVT.2020.2995220 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a new deep neural network design for salient object
detection by maximizing the integration of local and global image context
within, around, and beyond the salient objects. Our key idea is to adaptively
propagate and aggregate the image context features with variable attenuation
over the entire feature maps. To achieve this, we design the spatial
attenuation context (SAC) module to recurrently translate and aggregate the
context features independently with different attenuation factors and then to
attentively learn the weights to adaptively integrate the aggregated context
features. By further embedding the module to process individual layers in a
deep network, namely SAC-Net, we can train the network end-to-end and optimize
the context features for detecting salient objects. Compared with 29
state-of-the-art methods, experimental results show that our method performs
favorably over all the others on six common benchmark data, both quantitatively
and visually.
| [
{
"created": "Mon, 25 Mar 2019 06:56:15 GMT",
"version": "v1"
},
{
"created": "Tue, 9 Jul 2019 01:34:49 GMT",
"version": "v2"
},
{
"created": "Tue, 12 May 2020 12:45:17 GMT",
"version": "v3"
}
] | 2020-05-21 | [
[
"Hu",
"Xiaowei",
""
],
[
"Fu",
"Chi-Wing",
""
],
[
"Zhu",
"Lei",
""
],
[
"Wang",
"Tianyu",
""
],
[
"Heng",
"Pheng-Ann",
""
]
] | This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects. Our key idea is to adaptively propagate and aggregate the image context features with variable attenuation over the entire feature maps. To achieve this, we design the spatial attenuation context (SAC) module to recurrently translate and aggregate the context features independently with different attenuation factors and then to attentively learn the weights to adaptively integrate the aggregated context features. By further embedding the module to process individual layers in a deep network, namely SAC-Net, we can train the network end-to-end and optimize the context features for detecting salient objects. Compared with 29 state-of-the-art methods, experimental results show that our method performs favorably over all the others on six common benchmark data, both quantitatively and visually. |
2103.07854 | Jianhua Sun | Jianhua Sun, Yuxuan Li, Hao-Shu Fang, Cewu Lu | Three Steps to Multimodal Trajectory Prediction: Modality Clustering,
Classification and Synthesis | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal prediction results are essential for trajectory prediction task as
there is no single correct answer for the future. Previous frameworks can be
divided into three categories: regression, generation and classification
frameworks. However, these frameworks have weaknesses in different aspects so
that they cannot model the multimodal prediction task comprehensively. In this
paper, we present a novel insight along with a brand-new prediction framework
by formulating multimodal prediction into three steps: modality clustering,
classification and synthesis, and address the shortcomings of earlier
frameworks. Exhaustive experiments on popular benchmarks have demonstrated that
our proposed method surpasses state-of-the-art works even without introducing
social and map information. Specifically, we achieve 19.2% and 20.8%
improvement on ADE and FDE respectively on ETH/UCY dataset. Our code will be
made publicly availabe.
| [
{
"created": "Sun, 14 Mar 2021 06:21:03 GMT",
"version": "v1"
},
{
"created": "Mon, 22 Mar 2021 15:22:04 GMT",
"version": "v2"
}
] | 2021-03-23 | [
[
"Sun",
"Jianhua",
""
],
[
"Li",
"Yuxuan",
""
],
[
"Fang",
"Hao-Shu",
""
],
[
"Lu",
"Cewu",
""
]
] | Multimodal prediction results are essential for trajectory prediction task as there is no single correct answer for the future. Previous frameworks can be divided into three categories: regression, generation and classification frameworks. However, these frameworks have weaknesses in different aspects so that they cannot model the multimodal prediction task comprehensively. In this paper, we present a novel insight along with a brand-new prediction framework by formulating multimodal prediction into three steps: modality clustering, classification and synthesis, and address the shortcomings of earlier frameworks. Exhaustive experiments on popular benchmarks have demonstrated that our proposed method surpasses state-of-the-art works even without introducing social and map information. Specifically, we achieve 19.2% and 20.8% improvement on ADE and FDE respectively on ETH/UCY dataset. Our code will be made publicly availabe. |
2202.07137 | Wanming Hao | Wanming Hao, Fuhui Zhou, Ming Zeng, Octavia A. Dobre, Naofal Al-Dhahir | Ultra Wide Band THz IRS Communications: Applications, Challenges, Key
Techniques, and Research Opportunities | null | IEEE Network,2022 | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/licenses/by/4.0/ | Terahertz (THz) communication is a promising technology for future wireless
networks due to its ultra-wide bandwidth. However, THz signals suffer from
severe attenuation and poor diffraction capability, making it vulnerable to
blocking obstacles. To compensate for these two shortcomings and improve the
system performance, an intelligent reflecting surface (IRS) can be exploited to
change the propagation direction and enhance the signal strength. In this
article, we investigate this promising ultra wide band (UWB) THz IRS
communication paradigm. We start by motivating our research and describing
several potential application scenarios. Then, we identify major challenges
faced by UWB THz IRS communications. To overcome these challenges, several
effective key techniques are developed, i.e., the time delayer-based sparse
radio frequency antenna structure, delay hybrid precoding and IRS deployment.
Simulation results are also presented to compare the system performance for
these proposed techniques, thus demonstrating their effectiveness. Finally, we
highlight several open issues and research opportunities for UWB THz IRS
communications.
| [
{
"created": "Tue, 15 Feb 2022 02:15:50 GMT",
"version": "v1"
}
] | 2022-02-16 | [
[
"Hao",
"Wanming",
""
],
[
"Zhou",
"Fuhui",
""
],
[
"Zeng",
"Ming",
""
],
[
"Dobre",
"Octavia A.",
""
],
[
"Al-Dhahir",
"Naofal",
""
]
] | Terahertz (THz) communication is a promising technology for future wireless networks due to its ultra-wide bandwidth. However, THz signals suffer from severe attenuation and poor diffraction capability, making it vulnerable to blocking obstacles. To compensate for these two shortcomings and improve the system performance, an intelligent reflecting surface (IRS) can be exploited to change the propagation direction and enhance the signal strength. In this article, we investigate this promising ultra wide band (UWB) THz IRS communication paradigm. We start by motivating our research and describing several potential application scenarios. Then, we identify major challenges faced by UWB THz IRS communications. To overcome these challenges, several effective key techniques are developed, i.e., the time delayer-based sparse radio frequency antenna structure, delay hybrid precoding and IRS deployment. Simulation results are also presented to compare the system performance for these proposed techniques, thus demonstrating their effectiveness. Finally, we highlight several open issues and research opportunities for UWB THz IRS communications. |
1909.00384 | Hyunjung Kwak | Gloria Hyunjung Kwak and Pan Hui | DeepHealth: Review and challenges of artificial intelligence in health
informatics | 42 pages, 19 figures, under review | null | null | null | cs.LG cs.CV eess.IV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence has provided us with an exploration of a whole new
research era. As more data and better computational power become available, the
approach is being implemented in various fields. The demand for it in health
informatics is also increasing, and we can expect to see the potential benefits
of its applications in healthcare. It can help clinicians diagnose disease,
identify drug effects for each patient, understand the relationship between
genotypes and phenotypes, explore new phenotypes or treatment recommendations,
and predict infectious disease outbreaks with high accuracy. In contrast to
traditional models, recent artificial intelligence approaches do not require
domain-specific data pre-processing, and it is expected that it will ultimately
change life in the future. Despite its notable advantages, there are some key
challenges on data (high dimensionality, heterogeneity, time dependency,
sparsity, irregularity, lack of label, bias) and model (reliability,
interpretability, feasibility, security, scalability) for practical use. This
article presents a comprehensive review of research applying artificial
intelligence in health informatics, focusing on the last seven years in the
fields of medical imaging, electronic health records, genomics, sensing, and
online communication health, as well as challenges and promising directions for
future research. We highlight ongoing popular approaches' research and identify
several challenges in building models.
| [
{
"created": "Sun, 1 Sep 2019 11:54:38 GMT",
"version": "v1"
},
{
"created": "Sat, 8 Aug 2020 05:54:41 GMT",
"version": "v2"
}
] | 2020-08-11 | [
[
"Kwak",
"Gloria Hyunjung",
""
],
[
"Hui",
"Pan",
""
]
] | Artificial intelligence has provided us with an exploration of a whole new research era. As more data and better computational power become available, the approach is being implemented in various fields. The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare. It can help clinicians diagnose disease, identify drug effects for each patient, understand the relationship between genotypes and phenotypes, explore new phenotypes or treatment recommendations, and predict infectious disease outbreaks with high accuracy. In contrast to traditional models, recent artificial intelligence approaches do not require domain-specific data pre-processing, and it is expected that it will ultimately change life in the future. Despite its notable advantages, there are some key challenges on data (high dimensionality, heterogeneity, time dependency, sparsity, irregularity, lack of label, bias) and model (reliability, interpretability, feasibility, security, scalability) for practical use. This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records, genomics, sensing, and online communication health, as well as challenges and promising directions for future research. We highlight ongoing popular approaches' research and identify several challenges in building models. |
1909.05363 | Armins Stepanjans | Armins Stepanjans and Andr\'e Freitas | Identifying and Explaining Discriminative Attributes | EMNLP-IJCNLP 2019, source code available at
https://github.com/ab-10/hawk | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Identifying what is at the center of the meaning of a word and what
discriminates it from other words is a fundamental natural language inference
task. This paper describes an explicit word vector representation model (WVM)
to support the identification of discriminative attributes. A core contribution
of the paper is a quantitative and qualitative comparative analysis of
different types of data sources and Knowledge Bases in the construction of
explainable and explicit WVMs: (i) knowledge graphs built from dictionary
definitions, (ii) entity-attribute-relationships graphs derived from images and
(iii) commonsense knowledge graphs. Using a detailed quantitative and
qualitative analysis, we demonstrate that these data sources have complementary
semantic aspects, supporting the creation of explicit semantic vector spaces.
The explicit vector spaces are evaluated using the task of discriminative
attribute identification, showing comparable performance to the
state-of-the-art systems in the task (F1-score = 0.69), while delivering full
model transparency and explainability.
| [
{
"created": "Thu, 5 Sep 2019 01:13:41 GMT",
"version": "v1"
}
] | 2019-09-13 | [
[
"Stepanjans",
"Armins",
""
],
[
"Freitas",
"André",
""
]
] | Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an explicit word vector representation model (WVM) to support the identification of discriminative attributes. A core contribution of the paper is a quantitative and qualitative comparative analysis of different types of data sources and Knowledge Bases in the construction of explainable and explicit WVMs: (i) knowledge graphs built from dictionary definitions, (ii) entity-attribute-relationships graphs derived from images and (iii) commonsense knowledge graphs. Using a detailed quantitative and qualitative analysis, we demonstrate that these data sources have complementary semantic aspects, supporting the creation of explicit semantic vector spaces. The explicit vector spaces are evaluated using the task of discriminative attribute identification, showing comparable performance to the state-of-the-art systems in the task (F1-score = 0.69), while delivering full model transparency and explainability. |
1204.5952 | Sergei Kozyrev | S. Albeverio, S.V. Kozyrev | Clustering by hypergraphs and dimensionality of cluster systems | 15 pages | p-Adic Numbers, Ultrametric Analysis and Applications, 4 (2012)
no. 3, 167--178 | null | null | cs.DS q-bio.PE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the present paper we discuss the clustering procedure in the case where
instead of a single metric we have a family of metrics. In this case we can
obtain a partially ordered graph of clusters which is not necessarily a tree.
We discuss a structure of a hypergraph above this graph. We propose two
definitions of dimension for hyperedges of this hypergraph and show that for
the multidimensional p-adic case both dimensions are reduced to the number of
p-adic parameters.
We discuss the application of the hypergraph clustering procedure to the
construction of phylogenetic graphs in biology. In this case the dimension of a
hyperedge will describe the number of sources of genetic diversity.
| [
{
"created": "Thu, 26 Apr 2012 14:57:59 GMT",
"version": "v1"
}
] | 2012-08-01 | [
[
"Albeverio",
"S.",
""
],
[
"Kozyrev",
"S. V.",
""
]
] | In the present paper we discuss the clustering procedure in the case where instead of a single metric we have a family of metrics. In this case we can obtain a partially ordered graph of clusters which is not necessarily a tree. We discuss a structure of a hypergraph above this graph. We propose two definitions of dimension for hyperedges of this hypergraph and show that for the multidimensional p-adic case both dimensions are reduced to the number of p-adic parameters. We discuss the application of the hypergraph clustering procedure to the construction of phylogenetic graphs in biology. In this case the dimension of a hyperedge will describe the number of sources of genetic diversity. |
1608.05444 | Alan Jeffrey | Connor G. Brewster and Alan Jeffrey | A Model of Navigation History | null | null | null | null | cs.SE | http://creativecommons.org/licenses/by/4.0/ | Navigation has been a core component of the web since its inception: users
and scripts can follow hyperlinks, and can go back or forwards through the
navigation history. In this paper, we present a formal model aligned with the
WHATWG specification of navigation history, and investigate its properties. The
fundamental property of navigation history is that traversing the history by
delta then by delta' should be the same as traversing by delta+delta'. In
particular, traversing by +1 (forward) then by -1 (back) is the same as
traversing by 0 (doing nothing). We show that the specification-aligned model
does not satisfy this property, by exhibiting a series of counter-examples,
which motivate four patches to the model. We present a series of experiments,
showing that browsers are inconsistent in their implementation of navigation
history, but that their behaviour is closer to the patched model than to the
specification-aligned model. We propose patches to the specification to align
it with the patched model.
| [
{
"created": "Thu, 18 Aug 2016 22:35:40 GMT",
"version": "v1"
}
] | 2016-08-22 | [
[
"Brewster",
"Connor G.",
""
],
[
"Jeffrey",
"Alan",
""
]
] | Navigation has been a core component of the web since its inception: users and scripts can follow hyperlinks, and can go back or forwards through the navigation history. In this paper, we present a formal model aligned with the WHATWG specification of navigation history, and investigate its properties. The fundamental property of navigation history is that traversing the history by delta then by delta' should be the same as traversing by delta+delta'. In particular, traversing by +1 (forward) then by -1 (back) is the same as traversing by 0 (doing nothing). We show that the specification-aligned model does not satisfy this property, by exhibiting a series of counter-examples, which motivate four patches to the model. We present a series of experiments, showing that browsers are inconsistent in their implementation of navigation history, but that their behaviour is closer to the patched model than to the specification-aligned model. We propose patches to the specification to align it with the patched model. |
2405.03971 | Zhang Bozhen | Zhiwei Li, Bozhen Zhang, Lei Yang, Tianyu Shen, Nuo Xu, Ruosen Hao,
Weiting Li, Tao Yan, Huaping Liu | Unified End-to-End V2X Cooperative Autonomous Driving | null | null | null | null | cs.CV cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | V2X cooperation, through the integration of sensor data from both vehicles
and infrastructure, is considered a pivotal approach to advancing autonomous
driving technology. Current research primarily focuses on enhancing perception
accuracy, often overlooking the systematic improvement of accident prediction
accuracy through end-to-end learning, leading to insufficient attention to the
safety issues of autonomous driving. To address this challenge, this paper
introduces the UniE2EV2X framework, a V2X-integrated end-to-end autonomous
driving system that consolidates key driving modules within a unified network.
The framework employs a deformable attention-based data fusion strategy,
effectively facilitating cooperation between vehicles and infrastructure. The
main advantages include: 1) significantly enhancing agents' perception and
motion prediction capabilities, thereby improving the accuracy of accident
predictions; 2) ensuring high reliability in the data fusion process; 3)
superior end-to-end perception compared to modular approaches. Furthermore, We
implement the UniE2EV2X framework on the challenging DeepAccident, a simulation
dataset designed for V2X cooperative driving.
| [
{
"created": "Tue, 7 May 2024 03:01:40 GMT",
"version": "v1"
}
] | 2024-05-08 | [
[
"Li",
"Zhiwei",
""
],
[
"Zhang",
"Bozhen",
""
],
[
"Yang",
"Lei",
""
],
[
"Shen",
"Tianyu",
""
],
[
"Xu",
"Nuo",
""
],
[
"Hao",
"Ruosen",
""
],
[
"Li",
"Weiting",
""
],
[
"Yan",
"Tao",
""
],
[
"Liu",
"Huaping",
""
]
] | V2X cooperation, through the integration of sensor data from both vehicles and infrastructure, is considered a pivotal approach to advancing autonomous driving technology. Current research primarily focuses on enhancing perception accuracy, often overlooking the systematic improvement of accident prediction accuracy through end-to-end learning, leading to insufficient attention to the safety issues of autonomous driving. To address this challenge, this paper introduces the UniE2EV2X framework, a V2X-integrated end-to-end autonomous driving system that consolidates key driving modules within a unified network. The framework employs a deformable attention-based data fusion strategy, effectively facilitating cooperation between vehicles and infrastructure. The main advantages include: 1) significantly enhancing agents' perception and motion prediction capabilities, thereby improving the accuracy of accident predictions; 2) ensuring high reliability in the data fusion process; 3) superior end-to-end perception compared to modular approaches. Furthermore, We implement the UniE2EV2X framework on the challenging DeepAccident, a simulation dataset designed for V2X cooperative driving. |
2407.10026 | Shubhransh Singhvi | Shubhransh Singhvi, Omer Sabary, Daniella Bar-Lev and Eitan Yaakobi | Conditional Entropies of k-Deletion/Insertion Channels | arXiv admin note: substantial text overlap with arXiv:2202.03024 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The channel output entropy of a transmitted sequence is the entropy of the
possible channel outputs and similarly the channel input entropy of a received
sequence is the entropy of all possible transmitted sequences. The goal of this
work is to study these entropy values for the k-deletion, k-insertion channels,
where exactly k symbols are deleted, inserted in the transmitted sequence,
respectively. If all possible sequences are transmitted with the same
probability then studying the input and output entropies is equivalent. For
both the 1-deletion and 1-insertion channels, it is proved that among all
sequences with a fixed number of runs, the input entropy is minimized for
sequences with a skewed distribution of their run lengths and it is maximized
for sequences with a balanced distribution of their run lengths. Among our
results, we establish a conjecture by Atashpendar et al. which claims that for
the 1-deletion channel, the input entropy is maximized by the alternating
sequences over all binary sequences. This conjecture is also verified for the
2-deletion channel, where it is proved that constant sequences with a single
run minimize the input entropy.
| [
{
"created": "Sat, 13 Jul 2024 23:04:56 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Singhvi",
"Shubhransh",
""
],
[
"Sabary",
"Omer",
""
],
[
"Bar-Lev",
"Daniella",
""
],
[
"Yaakobi",
"Eitan",
""
]
] | The channel output entropy of a transmitted sequence is the entropy of the possible channel outputs and similarly the channel input entropy of a received sequence is the entropy of all possible transmitted sequences. The goal of this work is to study these entropy values for the k-deletion, k-insertion channels, where exactly k symbols are deleted, inserted in the transmitted sequence, respectively. If all possible sequences are transmitted with the same probability then studying the input and output entropies is equivalent. For both the 1-deletion and 1-insertion channels, it is proved that among all sequences with a fixed number of runs, the input entropy is minimized for sequences with a skewed distribution of their run lengths and it is maximized for sequences with a balanced distribution of their run lengths. Among our results, we establish a conjecture by Atashpendar et al. which claims that for the 1-deletion channel, the input entropy is maximized by the alternating sequences over all binary sequences. This conjecture is also verified for the 2-deletion channel, where it is proved that constant sequences with a single run minimize the input entropy. |
1412.0348 | Arturs Backurs | Arturs Backurs, Piotr Indyk | Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless
SETH is false) | STOC'15 | null | null | null | cs.CC cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The edit distance (a.k.a. the Levenshtein distance) between two strings is
defined as the minimum number of insertions, deletions or substitutions of
symbols needed to transform one string into another. The problem of computing
the edit distance between two strings is a classical computational task, with a
well-known algorithm based on dynamic programming. Unfortunately, all known
algorithms for this problem run in nearly quadratic time.
In this paper we provide evidence that the near-quadratic running time bounds
known for the problem of computing edit distance might be tight. Specifically,
we show that, if the edit distance can be computed in time $O(n^{2-\delta})$
for some constant $\delta>0$, then the satisfiability of conjunctive normal
form formulas with $N$ variables and $M$ clauses can be solved in time
$M^{O(1)} 2^{(1-\epsilon)N}$ for a constant $\epsilon>0$. The latter result
would violate the Strong Exponential Time Hypothesis, which postulates that
such algorithms do not exist.
| [
{
"created": "Mon, 1 Dec 2014 04:57:06 GMT",
"version": "v1"
},
{
"created": "Mon, 13 Apr 2015 21:13:21 GMT",
"version": "v2"
},
{
"created": "Mon, 3 Apr 2017 17:11:08 GMT",
"version": "v3"
},
{
"created": "Tue, 15 Aug 2017 18:01:17 GMT",
"version": "v4"
}
] | 2017-08-17 | [
[
"Backurs",
"Arturs",
""
],
[
"Indyk",
"Piotr",
""
]
] | The edit distance (a.k.a. the Levenshtein distance) between two strings is defined as the minimum number of insertions, deletions or substitutions of symbols needed to transform one string into another. The problem of computing the edit distance between two strings is a classical computational task, with a well-known algorithm based on dynamic programming. Unfortunately, all known algorithms for this problem run in nearly quadratic time. In this paper we provide evidence that the near-quadratic running time bounds known for the problem of computing edit distance might be tight. Specifically, we show that, if the edit distance can be computed in time $O(n^{2-\delta})$ for some constant $\delta>0$, then the satisfiability of conjunctive normal form formulas with $N$ variables and $M$ clauses can be solved in time $M^{O(1)} 2^{(1-\epsilon)N}$ for a constant $\epsilon>0$. The latter result would violate the Strong Exponential Time Hypothesis, which postulates that such algorithms do not exist. |
2103.02015 | Nati Daniel | Nati Daniel, Ariel Larey, Eliel Aknin, Garrett A. Osswald, Julie M.
Caldwell, Mark Rochman, Margaret H. Collins, Guang-Yu Yang, Nicoleta C. Arva,
Kelley E. Capocelli, Marc E. Rothenberg, Yonatan Savir | PECNet: A Deep Multi-Label Segmentation Network for Eosinophilic
Esophagitis Biopsy Diagnostics | null | null | null | null | cs.CV cs.LG eess.IV q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background. Eosinophilic esophagitis (EoE) is an allergic inflammatory
condition of the esophagus associated with elevated numbers of eosinophils.
Disease diagnosis and monitoring requires determining the concentration of
eosinophils in esophageal biopsies, a time-consuming, tedious and somewhat
subjective task currently performed by pathologists. Methods. Herein, we aimed
to use machine learning to identify, quantitate and diagnose EoE. We labeled
more than 100M pixels of 4345 images obtained by scanning whole slides of
H&E-stained sections of esophageal biopsies derived from 23 EoE patients. We
used this dataset to train a multi-label segmentation deep network. To validate
the network, we examined a replication cohort of 1089 whole slide images from
419 patients derived from multiple institutions. Findings. PECNet segmented
both intact and not-intact eosinophils with a mean intersection over union
(mIoU) of 0.93. This segmentation was able to quantitate intact eosinophils
with a mean absolute error of 0.611 eosinophils and classify EoE disease
activity with an accuracy of 98.5%. Using whole slide images from the
validation cohort, PECNet achieved an accuracy of 94.8%, sensitivity of 94.3%,
and specificity of 95.14% in reporting EoE disease activity. Interpretation. We
have developed a deep learning multi-label semantic segmentation network that
successfully addresses two of the main challenges in EoE diagnostics and
digital pathology, the need to detect several types of small features
simultaneously and the ability to analyze whole slides efficiently. Our results
pave the way for an automated diagnosis of EoE and can be utilized for other
conditions with similar challenges.
| [
{
"created": "Tue, 2 Mar 2021 20:37:57 GMT",
"version": "v1"
}
] | 2021-03-04 | [
[
"Daniel",
"Nati",
""
],
[
"Larey",
"Ariel",
""
],
[
"Aknin",
"Eliel",
""
],
[
"Osswald",
"Garrett A.",
""
],
[
"Caldwell",
"Julie M.",
""
],
[
"Rochman",
"Mark",
""
],
[
"Collins",
"Margaret H.",
""
],
[
"Yang",
"Guang-Yu",
""
],
[
"Arva",
"Nicoleta C.",
""
],
[
"Capocelli",
"Kelley E.",
""
],
[
"Rothenberg",
"Marc E.",
""
],
[
"Savir",
"Yonatan",
""
]
] | Background. Eosinophilic esophagitis (EoE) is an allergic inflammatory condition of the esophagus associated with elevated numbers of eosinophils. Disease diagnosis and monitoring requires determining the concentration of eosinophils in esophageal biopsies, a time-consuming, tedious and somewhat subjective task currently performed by pathologists. Methods. Herein, we aimed to use machine learning to identify, quantitate and diagnose EoE. We labeled more than 100M pixels of 4345 images obtained by scanning whole slides of H&E-stained sections of esophageal biopsies derived from 23 EoE patients. We used this dataset to train a multi-label segmentation deep network. To validate the network, we examined a replication cohort of 1089 whole slide images from 419 patients derived from multiple institutions. Findings. PECNet segmented both intact and not-intact eosinophils with a mean intersection over union (mIoU) of 0.93. This segmentation was able to quantitate intact eosinophils with a mean absolute error of 0.611 eosinophils and classify EoE disease activity with an accuracy of 98.5%. Using whole slide images from the validation cohort, PECNet achieved an accuracy of 94.8%, sensitivity of 94.3%, and specificity of 95.14% in reporting EoE disease activity. Interpretation. We have developed a deep learning multi-label semantic segmentation network that successfully addresses two of the main challenges in EoE diagnostics and digital pathology, the need to detect several types of small features simultaneously and the ability to analyze whole slides efficiently. Our results pave the way for an automated diagnosis of EoE and can be utilized for other conditions with similar challenges. |
2404.02053 | Enmin Zhu | Enmin Zhu, Jerome Yen | BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights | null | null | null | null | cs.CL cs.CE q-fin.ST | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper explores the intersection of Natural Language Processing (NLP) and
financial analysis, focusing on the impact of sentiment analysis in stock price
prediction. We employ BERTopic, an advanced NLP technique, to analyze the
sentiment of topics derived from stock market comments. Our methodology
integrates this sentiment analysis with various deep learning models, renowned
for their effectiveness in time series and stock prediction tasks. Through
comprehensive experiments, we demonstrate that incorporating topic sentiment
notably enhances the performance of these models. The results indicate that
topics in stock market comments provide implicit, valuable insights into stock
market volatility and price trends. This study contributes to the field by
showcasing the potential of NLP in enriching financial analysis and opens up
avenues for further research into real-time sentiment analysis and the
exploration of emotional and contextual aspects of market sentiment. The
integration of advanced NLP techniques like BERTopic with traditional financial
analysis methods marks a step forward in developing more sophisticated tools
for understanding and predicting market behaviors.
| [
{
"created": "Tue, 2 Apr 2024 15:50:10 GMT",
"version": "v1"
},
{
"created": "Thu, 4 Apr 2024 08:05:37 GMT",
"version": "v2"
}
] | 2024-04-05 | [
[
"Zhu",
"Enmin",
""
],
[
"Yen",
"Jerome",
""
]
] | This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction. We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep learning models, renowned for their effectiveness in time series and stock prediction tasks. Through comprehensive experiments, we demonstrate that incorporating topic sentiment notably enhances the performance of these models. The results indicate that topics in stock market comments provide implicit, valuable insights into stock market volatility and price trends. This study contributes to the field by showcasing the potential of NLP in enriching financial analysis and opens up avenues for further research into real-time sentiment analysis and the exploration of emotional and contextual aspects of market sentiment. The integration of advanced NLP techniques like BERTopic with traditional financial analysis methods marks a step forward in developing more sophisticated tools for understanding and predicting market behaviors. |
2111.04261 | Fei Cheng | Fei Cheng, Shuntaro Yada, Ribeka Tanaka, Eiji Aramaki, Sadao Kurohashi | JaMIE: A Pipeline Japanese Medical Information Extraction System | 8 pages | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present an open-access natural language processing toolkit for Japanese
medical information extraction. We first propose a novel relation annotation
schema for investigating the medical and temporal relations between medical
entities in Japanese medical reports. We experiment with the practical
annotation scenarios by separately annotating two different types of reports.
We design a pipeline system with three components for recognizing medical
entities, classifying entity modalities, and extracting relations. The
empirical results show accurate analyzing performance and suggest the
satisfactory annotation quality, the effective annotation strategy for
targeting report types, and the superiority of the latest contextual embedding
models.
| [
{
"created": "Mon, 8 Nov 2021 03:54:09 GMT",
"version": "v1"
}
] | 2021-11-09 | [
[
"Cheng",
"Fei",
""
],
[
"Yada",
"Shuntaro",
""
],
[
"Tanaka",
"Ribeka",
""
],
[
"Aramaki",
"Eiji",
""
],
[
"Kurohashi",
"Sadao",
""
]
] | We present an open-access natural language processing toolkit for Japanese medical information extraction. We first propose a novel relation annotation schema for investigating the medical and temporal relations between medical entities in Japanese medical reports. We experiment with the practical annotation scenarios by separately annotating two different types of reports. We design a pipeline system with three components for recognizing medical entities, classifying entity modalities, and extracting relations. The empirical results show accurate analyzing performance and suggest the satisfactory annotation quality, the effective annotation strategy for targeting report types, and the superiority of the latest contextual embedding models. |
2102.02608 | Mira Gonen | Mira Gonen, Michael Langberg, Alex Sprintson | Minimizing the alphabet size in codes with restricted error sets | null | null | null | null | cs.IT math.IT | http://creativecommons.org/licenses/by/4.0/ | This paper focuses on error-correcting codes that can handle a predefined set
of specific error patterns. The need for such codes arises in many settings of
practical interest, including wireless communication and flash memory systems.
In many such settings, a smaller field size is achievable than that offered by
MDS and other standard codes. We establish a connection between the minimum
alphabet size for this generalized setting and the combinatorial properties of
a hypergraph that represents the prespecified collection of error patterns. We
also show a connection between error and erasure correcting codes in this
specialized setting. This allows us to establish bounds on the minimum alphabet
size and show an advantage of non-linear codes over linear codes in a
generalized setting. We also consider a variation of the problem which allows a
small probability of decoding error and relate it to an approximate version of
hypergraph coloring.
| [
{
"created": "Thu, 4 Feb 2021 13:41:24 GMT",
"version": "v1"
}
] | 2021-02-05 | [
[
"Gonen",
"Mira",
""
],
[
"Langberg",
"Michael",
""
],
[
"Sprintson",
"Alex",
""
]
] | This paper focuses on error-correcting codes that can handle a predefined set of specific error patterns. The need for such codes arises in many settings of practical interest, including wireless communication and flash memory systems. In many such settings, a smaller field size is achievable than that offered by MDS and other standard codes. We establish a connection between the minimum alphabet size for this generalized setting and the combinatorial properties of a hypergraph that represents the prespecified collection of error patterns. We also show a connection between error and erasure correcting codes in this specialized setting. This allows us to establish bounds on the minimum alphabet size and show an advantage of non-linear codes over linear codes in a generalized setting. We also consider a variation of the problem which allows a small probability of decoding error and relate it to an approximate version of hypergraph coloring. |
1712.07863 | Bernhard C. Geiger | Bernhard C. Geiger and Tobias Koch | On the Information Dimension of Multivariate Gaussian Processes | This work will be presented in part at the 2018 International Zurich
Seminar on Information and Communication | IEEE Trans. on Information Theory 65(10):6496-6518. (C) IEEE 2019 | 10.1109/TIT.2019.2922186 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The authors have recently defined the R\'enyi information dimension rate
$d(\{X_t\})$ of a stationary stochastic process $\{X_t,\,t\in\mathbb{Z}\}$ as
the entropy rate of the uniformly-quantized process divided by minus the
logarithm of the quantizer step size $1/m$ in the limit as $m\to\infty$ (B.
Geiger and T. Koch, "On the information dimension rate of stochastic
processes," in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Aachen, Germany, June
2017). For Gaussian processes with a given spectral distribution function
$F_X$, they showed that the information dimension rate equals the Lebesgue
measure of the set of harmonics where the derivative of $F_X$ is positive. This
paper extends this result to multivariate Gaussian processes with a given
matrix-valued spectral distribution function $F_{\mathbf{X}}$. It is
demonstrated that the information dimension rate equals the average rank of the
derivative of $F_{\mathbf{X}}$. As a side result, it is shown that the scale
and translation invariance of information dimension carries over from random
variables to stochastic processes.
| [
{
"created": "Thu, 21 Dec 2017 10:36:44 GMT",
"version": "v1"
}
] | 2019-10-11 | [
[
"Geiger",
"Bernhard C.",
""
],
[
"Koch",
"Tobias",
""
]
] | The authors have recently defined the R\'enyi information dimension rate $d(\{X_t\})$ of a stationary stochastic process $\{X_t,\,t\in\mathbb{Z}\}$ as the entropy rate of the uniformly-quantized process divided by minus the logarithm of the quantizer step size $1/m$ in the limit as $m\to\infty$ (B. Geiger and T. Koch, "On the information dimension rate of stochastic processes," in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Aachen, Germany, June 2017). For Gaussian processes with a given spectral distribution function $F_X$, they showed that the information dimension rate equals the Lebesgue measure of the set of harmonics where the derivative of $F_X$ is positive. This paper extends this result to multivariate Gaussian processes with a given matrix-valued spectral distribution function $F_{\mathbf{X}}$. It is demonstrated that the information dimension rate equals the average rank of the derivative of $F_{\mathbf{X}}$. As a side result, it is shown that the scale and translation invariance of information dimension carries over from random variables to stochastic processes. |
2401.09678 | Simon Chu | Simon Chu, Justin Koe, David Garlan, and Eunsuk Kang | Integrating Graceful Degradation and Recovery through Requirement-driven
Adaptation | Pre-print for the SEAMS '24 conference (Software Engineering for
Adaptive and Self-Managing Systems Conference) | null | null | null | cs.SE cs.FL cs.LO cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | Cyber-physical systems (CPS) are subject to environmental uncertainties such
as adverse operating conditions, malicious attacks, and hardware degradation.
These uncertainties may lead to failures that put the system in a sub-optimal
or unsafe state. Systems that are resilient to such uncertainties rely on two
types of operations: (1) graceful degradation, to ensure that the system
maintains an acceptable level of safety during unexpected environmental
conditions and (2) recovery, to facilitate the resumption of normal system
functions. Typically, mechanisms for degradation and recovery are developed
independently from each other, and later integrated into a system, requiring
the designer to develop an additional, ad-hoc logic for activating and
coordinating between the two operations. In this paper, we propose a
self-adaptation approach for improving system resiliency through automated
triggering and coordination of graceful degradation and recovery. The key idea
behind our approach is to treat degradation and recovery as requirement-driven
adaptation tasks: Degradation can be thought of as temporarily weakening
original (i.e., ideal) system requirements to be achieved by the system, and
recovery as strengthening the weakened requirements when the environment
returns within an expected operating boundary. Furthermore, by treating
weakening and strengthening as dual operations, we argue that a single
requirement-based adaptation method is sufficient to enable coordination
between degradation and recovery. Given system requirements specified in signal
temporal logic (STL), we propose a run-time adaptation framework that performs
degradation and recovery in response to environmental changes. We describe a
prototype implementation of our framework and demonstrate the feasibility of
the proposed approach using a case study in unmanned underwater vehicles.
| [
{
"created": "Thu, 18 Jan 2024 02:04:37 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Apr 2024 16:44:50 GMT",
"version": "v2"
}
] | 2024-04-09 | [
[
"Chu",
"Simon",
""
],
[
"Koe",
"Justin",
""
],
[
"Garlan",
"David",
""
],
[
"Kang",
"Eunsuk",
""
]
] | Cyber-physical systems (CPS) are subject to environmental uncertainties such as adverse operating conditions, malicious attacks, and hardware degradation. These uncertainties may lead to failures that put the system in a sub-optimal or unsafe state. Systems that are resilient to such uncertainties rely on two types of operations: (1) graceful degradation, to ensure that the system maintains an acceptable level of safety during unexpected environmental conditions and (2) recovery, to facilitate the resumption of normal system functions. Typically, mechanisms for degradation and recovery are developed independently from each other, and later integrated into a system, requiring the designer to develop an additional, ad-hoc logic for activating and coordinating between the two operations. In this paper, we propose a self-adaptation approach for improving system resiliency through automated triggering and coordination of graceful degradation and recovery. The key idea behind our approach is to treat degradation and recovery as requirement-driven adaptation tasks: Degradation can be thought of as temporarily weakening original (i.e., ideal) system requirements to be achieved by the system, and recovery as strengthening the weakened requirements when the environment returns within an expected operating boundary. Furthermore, by treating weakening and strengthening as dual operations, we argue that a single requirement-based adaptation method is sufficient to enable coordination between degradation and recovery. Given system requirements specified in signal temporal logic (STL), we propose a run-time adaptation framework that performs degradation and recovery in response to environmental changes. We describe a prototype implementation of our framework and demonstrate the feasibility of the proposed approach using a case study in unmanned underwater vehicles. |
2002.09571 | Nicholas Cheney | Shawn Beaulieu, Lapo Frati, Thomas Miconi, Joel Lehman, Kenneth O.
Stanley, Jeff Clune, Nick Cheney | Learning to Continually Learn | null | null | null | null | cs.LG cs.CV cs.NE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Continual lifelong learning requires an agent or model to learn many
sequentially ordered tasks, building on previous knowledge without
catastrophically forgetting it. Much work has gone towards preventing the
default tendency of machine learning models to catastrophically forget, yet
virtually all such work involves manually-designed solutions to the problem. We
instead advocate meta-learning a solution to catastrophic forgetting, allowing
AI to learn to continually learn. Inspired by neuromodulatory processes in the
brain, we propose A Neuromodulated Meta-Learning Algorithm (ANML). It
differentiates through a sequential learning process to meta-learn an
activation-gating function that enables context-dependent selective activation
within a deep neural network. Specifically, a neuromodulatory (NM) neural
network gates the forward pass of another (otherwise normal) neural network
called the prediction learning network (PLN). The NM network also thus
indirectly controls selective plasticity (i.e. the backward pass of) the PLN.
ANML enables continual learning without catastrophic forgetting at scale: it
produces state-of-the-art continual learning performance, sequentially learning
as many as 600 classes (over 9,000 SGD updates).
| [
{
"created": "Fri, 21 Feb 2020 22:52:00 GMT",
"version": "v1"
},
{
"created": "Wed, 4 Mar 2020 03:22:48 GMT",
"version": "v2"
}
] | 2020-03-05 | [
[
"Beaulieu",
"Shawn",
""
],
[
"Frati",
"Lapo",
""
],
[
"Miconi",
"Thomas",
""
],
[
"Lehman",
"Joel",
""
],
[
"Stanley",
"Kenneth O.",
""
],
[
"Clune",
"Jeff",
""
],
[
"Cheney",
"Nick",
""
]
] | Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it. Much work has gone towards preventing the default tendency of machine learning models to catastrophically forget, yet virtually all such work involves manually-designed solutions to the problem. We instead advocate meta-learning a solution to catastrophic forgetting, allowing AI to learn to continually learn. Inspired by neuromodulatory processes in the brain, we propose A Neuromodulated Meta-Learning Algorithm (ANML). It differentiates through a sequential learning process to meta-learn an activation-gating function that enables context-dependent selective activation within a deep neural network. Specifically, a neuromodulatory (NM) neural network gates the forward pass of another (otherwise normal) neural network called the prediction learning network (PLN). The NM network also thus indirectly controls selective plasticity (i.e. the backward pass of) the PLN. ANML enables continual learning without catastrophic forgetting at scale: it produces state-of-the-art continual learning performance, sequentially learning as many as 600 classes (over 9,000 SGD updates). |
2309.08622 | Yijia Dai | Yijia Dai, Wen Sun | Representation Learning in Low-rank Slate-based Recommender Systems | in MFPL, ICML 2023 | null | null | null | cs.IR cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Reinforcement learning (RL) in recommendation systems offers the potential to
optimize recommendations for long-term user engagement. However, the
environment often involves large state and action spaces, which makes it hard
to efficiently learn and explore. In this work, we propose a sample-efficient
representation learning algorithm, using the standard slate recommendation
setup, to treat this as an online RL problem with low-rank Markov decision
processes (MDPs). We also construct the recommender simulation environment with
the proposed setup and sampling method.
| [
{
"created": "Sun, 10 Sep 2023 21:40:51 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Sep 2023 03:05:32 GMT",
"version": "v2"
}
] | 2023-09-20 | [
[
"Dai",
"Yijia",
""
],
[
"Sun",
"Wen",
""
]
] | Reinforcement learning (RL) in recommendation systems offers the potential to optimize recommendations for long-term user engagement. However, the environment often involves large state and action spaces, which makes it hard to efficiently learn and explore. In this work, we propose a sample-efficient representation learning algorithm, using the standard slate recommendation setup, to treat this as an online RL problem with low-rank Markov decision processes (MDPs). We also construct the recommender simulation environment with the proposed setup and sampling method. |
2312.14544 | Hongliu Cao | Hongliu Cao, Minh Nhat Do, Alexis Ravanel, Eoin Thomas | Inclusive normalization of face images to passport format | null | null | 10.1109/IJCNN54540.2023.10191995 | null | cs.CV cs.AI | http://creativecommons.org/licenses/by/4.0/ | Face recognition has been used more and more in real world applications in
recent years. However, when the skin color bias is coupled with intra-personal
variations like harsh illumination, the face recognition task is more likely to
fail, even during human inspection. Face normalization methods try to deal with
such challenges by removing intra-personal variations from an input image while
keeping the identity the same. However, most face normalization methods can
only remove one or two variations and ignore dataset biases such as skin color
bias. The outputs of many face normalization methods are also not realistic to
human observers. In this work, a style based face normalization model
(StyleFNM) is proposed to remove most intra-personal variations including large
changes in pose, bad or harsh illumination, low resolution, blur, facial
expressions, and accessories like sunglasses among others. The dataset bias is
also dealt with in this paper by controlling a pretrained GAN to generate a
balanced dataset of passport-like images. The experimental results show that
StyleFNM can generate more realistic outputs and can improve significantly the
accuracy and fairness of face recognition systems.
| [
{
"created": "Fri, 22 Dec 2023 09:15:33 GMT",
"version": "v1"
}
] | 2023-12-25 | [
[
"Cao",
"Hongliu",
""
],
[
"Do",
"Minh Nhat",
""
],
[
"Ravanel",
"Alexis",
""
],
[
"Thomas",
"Eoin",
""
]
] | Face recognition has been used more and more in real world applications in recent years. However, when the skin color bias is coupled with intra-personal variations like harsh illumination, the face recognition task is more likely to fail, even during human inspection. Face normalization methods try to deal with such challenges by removing intra-personal variations from an input image while keeping the identity the same. However, most face normalization methods can only remove one or two variations and ignore dataset biases such as skin color bias. The outputs of many face normalization methods are also not realistic to human observers. In this work, a style based face normalization model (StyleFNM) is proposed to remove most intra-personal variations including large changes in pose, bad or harsh illumination, low resolution, blur, facial expressions, and accessories like sunglasses among others. The dataset bias is also dealt with in this paper by controlling a pretrained GAN to generate a balanced dataset of passport-like images. The experimental results show that StyleFNM can generate more realistic outputs and can improve significantly the accuracy and fairness of face recognition systems. |
2109.07196 | Jiajun Wang | Jiajun Wang, Gang Han, Xiaozhu Ju and Mingguo Zhao | Whole-Body Control with Motion/Force Transmissibility for
Parallel-Legged Robot | 6 pages, 7 figures, submitted to IROS 2022 | null | null | null | cs.RO cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For achieving kinematically suitable configurations and highly dynamic task
execution, an efficient way is to consider robot performance indices in the
whole-body control (WBC) of robots. However, current WBC methods have not
considered the intrinsic features of parallel robots, especially motion/force
transmissibility (MFT). This paper proposes an MFT-enhanced WBC scheme for
parallel-legged robots. Introducing the performance indices of MFT into a WBC
is challenging due to the nonlinear relationship between MFT indices and the
robot configuration. To overcome this challenge, we establish the MFT
preferable space of the robot offline and formulate it as a polyhedron in the
joint space at the acceleration level. Then, the WBC employs the polyhedron as
a soft constraint. As a result, the robot possesses high-speed and
high-acceleration capabilities by satisfying this constraint. The offline
preprocessing relieves the online computation burden and helps the WBC achieve
a 1kHz servo rate. Finally, we validate the performance and robustness of the
proposed method via simulations and experiments on a parallel-legged bipedal
robot.
| [
{
"created": "Wed, 15 Sep 2021 10:27:57 GMT",
"version": "v1"
},
{
"created": "Tue, 1 Mar 2022 11:07:11 GMT",
"version": "v2"
}
] | 2022-03-02 | [
[
"Wang",
"Jiajun",
""
],
[
"Han",
"Gang",
""
],
[
"Ju",
"Xiaozhu",
""
],
[
"Zhao",
"Mingguo",
""
]
] | For achieving kinematically suitable configurations and highly dynamic task execution, an efficient way is to consider robot performance indices in the whole-body control (WBC) of robots. However, current WBC methods have not considered the intrinsic features of parallel robots, especially motion/force transmissibility (MFT). This paper proposes an MFT-enhanced WBC scheme for parallel-legged robots. Introducing the performance indices of MFT into a WBC is challenging due to the nonlinear relationship between MFT indices and the robot configuration. To overcome this challenge, we establish the MFT preferable space of the robot offline and formulate it as a polyhedron in the joint space at the acceleration level. Then, the WBC employs the polyhedron as a soft constraint. As a result, the robot possesses high-speed and high-acceleration capabilities by satisfying this constraint. The offline preprocessing relieves the online computation burden and helps the WBC achieve a 1kHz servo rate. Finally, we validate the performance and robustness of the proposed method via simulations and experiments on a parallel-legged bipedal robot. |
1604.07973 | Andr\'es Silva | Andr\'es Silva | Reference and Structure of Software Engineering Theories | Position paper, 4 pages | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper tries to contribute towards the solution of an important question
raised in the SE literature: What is a Software Engineering (SE) specific
theory? Which are the main features of a theory that is endemic to SE? In this
paper we will use 'theory' as the term is used in traditional sciences. Other
uses of the term 'theory' are discussed. Finally, we propose to focus on the
reference class and on the structuring of SE theories as a basis for further
progress.
| [
{
"created": "Wed, 27 Apr 2016 08:35:46 GMT",
"version": "v1"
}
] | 2016-04-28 | [
[
"Silva",
"Andrés",
""
]
] | This paper tries to contribute towards the solution of an important question raised in the SE literature: What is a Software Engineering (SE) specific theory? Which are the main features of a theory that is endemic to SE? In this paper we will use 'theory' as the term is used in traditional sciences. Other uses of the term 'theory' are discussed. Finally, we propose to focus on the reference class and on the structuring of SE theories as a basis for further progress. |
1901.08537 | Guillaume Sartoretti | Guillaume Sartoretti and William Paivine and Yunfei Shi and Yue Wu and
Howie Choset | Distributed Learning of Decentralized Control Policies for Articulated
Mobile Robots | \c{opyright} 20XX IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other works | null | 10.1109/TRO.2019.2922493 | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | State-of-the-art distributed algorithms for reinforcement learning rely on
multiple independent agents, which simultaneously learn in parallel
environments while asynchronously updating a common, shared policy. Moreover,
decentralized control architectures (e.g., CPGs) can coordinate spatially
distributed portions of an articulated robot to achieve system-level
objectives. In this work, we investigate the relationship between distributed
learning and decentralized control by learning decentralized control policies
for the locomotion of articulated robots in challenging environments. To this
end, we present an approach that leverages the structure of the asynchronous
advantage actor-critic (A3C) algorithm to provide a natural means of learning
decentralized control policies on a single articulated robot. Our primary
contribution shows individual agents in the A3C algorithm can be defined by
independently controlled portions of the robot's body, thus enabling
distributed learning on a single robot for efficient hardware implementation.
We present results of closed-loop locomotion in unstructured terrains on a
snake and a hexapod robot, using decentralized controllers learned offline and
online respectively.
Preprint of the paper submitted to the IEEE Transactions in Robotics (T-RO)
journal in October 2018, and accepted for publication as a regular paper in May
2019.
| [
{
"created": "Thu, 24 Jan 2019 17:59:58 GMT",
"version": "v1"
},
{
"created": "Sun, 9 Jun 2019 17:49:16 GMT",
"version": "v2"
}
] | 2021-02-02 | [
[
"Sartoretti",
"Guillaume",
""
],
[
"Paivine",
"William",
""
],
[
"Shi",
"Yunfei",
""
],
[
"Wu",
"Yue",
""
],
[
"Choset",
"Howie",
""
]
] | State-of-the-art distributed algorithms for reinforcement learning rely on multiple independent agents, which simultaneously learn in parallel environments while asynchronously updating a common, shared policy. Moreover, decentralized control architectures (e.g., CPGs) can coordinate spatially distributed portions of an articulated robot to achieve system-level objectives. In this work, we investigate the relationship between distributed learning and decentralized control by learning decentralized control policies for the locomotion of articulated robots in challenging environments. To this end, we present an approach that leverages the structure of the asynchronous advantage actor-critic (A3C) algorithm to provide a natural means of learning decentralized control policies on a single articulated robot. Our primary contribution shows individual agents in the A3C algorithm can be defined by independently controlled portions of the robot's body, thus enabling distributed learning on a single robot for efficient hardware implementation. We present results of closed-loop locomotion in unstructured terrains on a snake and a hexapod robot, using decentralized controllers learned offline and online respectively. Preprint of the paper submitted to the IEEE Transactions in Robotics (T-RO) journal in October 2018, and accepted for publication as a regular paper in May 2019. |
1912.07172 | Shuyan Zhang | Yuming Li, Rong Zhang, Yuchen Li, Ke Shu, Shuyan Zhang, Aoying Zhou | Lauca: Generating Application-Oriented Synthetic Workloads | null | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The synthetic workload is essential and critical to the performance
evaluation of database systems. When evaluating the database performance for a
specific application, the similarity between synthetic workload and real
application workload determines the credibility of evaluation results. However,
the workload currently used for performance evaluation is difficult to have the
same workload characteristics as the target application, which leads to
inaccurate evaluation results. To address this problem, we propose a workload
duplicator (Lauca) that can generate synthetic workloads with highly similar
performance metrics for specific applications. To the best of our knowledge,
Lauca is the first application-oriented transactional workload generator. By
carefully studying the application-oriented synthetic workload generation
problem, we present the key workload characteristics (transaction logic and
data access distribution) of online transaction processing (OLTP) applications,
and propose novel workload characterization and generation algorithms, which
guarantee the high fidelity of synthetic workloads. We conduct extensive
experiments using workloads from TPC-C, SmallBank and micro benchmarks on both
MySQL and PostgreSQL databases, and experimental results show that Lauca
consistently generates high-quality synthetic workloads.
| [
{
"created": "Mon, 16 Dec 2019 03:13:17 GMT",
"version": "v1"
}
] | 2019-12-17 | [
[
"Li",
"Yuming",
""
],
[
"Zhang",
"Rong",
""
],
[
"Li",
"Yuchen",
""
],
[
"Shu",
"Ke",
""
],
[
"Zhang",
"Shuyan",
""
],
[
"Zhou",
"Aoying",
""
]
] | The synthetic workload is essential and critical to the performance evaluation of database systems. When evaluating the database performance for a specific application, the similarity between synthetic workload and real application workload determines the credibility of evaluation results. However, the workload currently used for performance evaluation is difficult to have the same workload characteristics as the target application, which leads to inaccurate evaluation results. To address this problem, we propose a workload duplicator (Lauca) that can generate synthetic workloads with highly similar performance metrics for specific applications. To the best of our knowledge, Lauca is the first application-oriented transactional workload generator. By carefully studying the application-oriented synthetic workload generation problem, we present the key workload characteristics (transaction logic and data access distribution) of online transaction processing (OLTP) applications, and propose novel workload characterization and generation algorithms, which guarantee the high fidelity of synthetic workloads. We conduct extensive experiments using workloads from TPC-C, SmallBank and micro benchmarks on both MySQL and PostgreSQL databases, and experimental results show that Lauca consistently generates high-quality synthetic workloads. |
2003.11303 | Sunghun Joung | Sunghun Joung, Seungryong Kim, Hanjae Kim, Minsu Kim, Ig-Jae Kim,
Junghyun Cho, Kwanghoon Sohn | Cylindrical Convolutional Networks for Joint Object Detection and
Viewpoint Estimation | CVPR 2020 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing techniques to encode spatial invariance within deep convolutional
neural networks only model 2D transformation fields. This does not account for
the fact that objects in a 2D space are a projection of 3D ones, and thus they
have limited ability to severe object viewpoint changes. To overcome this
limitation, we introduce a learnable module, cylindrical convolutional networks
(CCNs), that exploit cylindrical representation of a convolutional kernel
defined in the 3D space. CCNs extract a view-specific feature through a
view-specific convolutional kernel to predict object category scores at each
viewpoint. With the view-specific feature, we simultaneously determine
objective category and viewpoints using the proposed sinusoidal soft-argmax
module. Our experiments demonstrate the effectiveness of the cylindrical
convolutional networks on joint object detection and viewpoint estimation.
| [
{
"created": "Wed, 25 Mar 2020 10:24:58 GMT",
"version": "v1"
}
] | 2020-03-26 | [
[
"Joung",
"Sunghun",
""
],
[
"Kim",
"Seungryong",
""
],
[
"Kim",
"Hanjae",
""
],
[
"Kim",
"Minsu",
""
],
[
"Kim",
"Ig-Jae",
""
],
[
"Cho",
"Junghyun",
""
],
[
"Sohn",
"Kwanghoon",
""
]
] | Existing techniques to encode spatial invariance within deep convolutional neural networks only model 2D transformation fields. This does not account for the fact that objects in a 2D space are a projection of 3D ones, and thus they have limited ability to severe object viewpoint changes. To overcome this limitation, we introduce a learnable module, cylindrical convolutional networks (CCNs), that exploit cylindrical representation of a convolutional kernel defined in the 3D space. CCNs extract a view-specific feature through a view-specific convolutional kernel to predict object category scores at each viewpoint. With the view-specific feature, we simultaneously determine objective category and viewpoints using the proposed sinusoidal soft-argmax module. Our experiments demonstrate the effectiveness of the cylindrical convolutional networks on joint object detection and viewpoint estimation. |
2408.05829 | Katherine Dearstyne | Katherine R. Dearstyne, Alberto D. Rodriguez, Jane Cleland-Huang | Supporting Software Maintenance with Dynamically Generated Document
Hierarchies | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Software documentation supports a broad set of software maintenance tasks;
however, creating and maintaining high-quality, multi-level software
documentation can be incredibly time-consuming and therefore many code bases
suffer from a lack of adequate documentation. We address this problem through
presenting HGEN, a fully automated pipeline that leverages LLMs to transform
source code through a series of six stages into a well-organized hierarchy of
formatted documents. We evaluate HGEN both quantitatively and qualitatively.
First, we use it to generate documentation for three diverse projects, and
engage key developers in comparing the quality of the generated documentation
against their own previously produced manually-crafted documentation. We then
pilot HGEN in nine different industrial projects using diverse datasets
provided by each project. We collect feedback from project stakeholders, and
analyze it using an inductive approach to identify recurring themes. Results
show that HGEN produces artifact hierarchies similar in quality to manually
constructed documentation, with much higher coverage of the core concepts than
the baseline approach. Stakeholder feedback highlights HGEN's commercial impact
potential as a tool for accelerating code comprehension and maintenance tasks.
Results and associated supplemental materials can be found at
https://zenodo.org/records/11403244
| [
{
"created": "Sun, 11 Aug 2024 17:11:14 GMT",
"version": "v1"
}
] | 2024-08-13 | [
[
"Dearstyne",
"Katherine R.",
""
],
[
"Rodriguez",
"Alberto D.",
""
],
[
"Cleland-Huang",
"Jane",
""
]
] | Software documentation supports a broad set of software maintenance tasks; however, creating and maintaining high-quality, multi-level software documentation can be incredibly time-consuming and therefore many code bases suffer from a lack of adequate documentation. We address this problem through presenting HGEN, a fully automated pipeline that leverages LLMs to transform source code through a series of six stages into a well-organized hierarchy of formatted documents. We evaluate HGEN both quantitatively and qualitatively. First, we use it to generate documentation for three diverse projects, and engage key developers in comparing the quality of the generated documentation against their own previously produced manually-crafted documentation. We then pilot HGEN in nine different industrial projects using diverse datasets provided by each project. We collect feedback from project stakeholders, and analyze it using an inductive approach to identify recurring themes. Results show that HGEN produces artifact hierarchies similar in quality to manually constructed documentation, with much higher coverage of the core concepts than the baseline approach. Stakeholder feedback highlights HGEN's commercial impact potential as a tool for accelerating code comprehension and maintenance tasks. Results and associated supplemental materials can be found at https://zenodo.org/records/11403244 |
2012.07464 | Alejandro Su\'arez Hern\'andez | Alejandro Su\'arez-Hern\'andez and Javier Segovia-Aguas and Carme
Torras and Guillem Aleny\`a | Online Action Recognition | Accepted version in AAAI 21:
https://ojs.aaai.org/index.php/AAAI/article/view/17423 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recognition in planning seeks to find agent intentions, goals or activities
given a set of observations and a knowledge library (e.g. goal states, plans or
domain theories). In this work we introduce the problem of Online Action
Recognition. It consists in recognizing, in an open world, the planning action
that best explains a partially observable state transition from a knowledge
library of first-order STRIPS actions, which is initially empty. We frame this
as an optimization problem, and propose two algorithms to address it: Action
Unification (AU) and Online Action Recognition through Unification (OARU). The
former builds on logic unification and generalizes two input actions using
weighted partial MaxSAT. The latter looks for an action within the library that
explains an observed transition. If there is such action, it generalizes it
making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts
a Trivial Grounded Action (TGA) in the library that explains just that
transition. We report results on benchmarks from the International Planning
Competition and PDDLGym, where OARU recognizes actions accurately with respect
to expert knowledge, and shows real-time performance.
| [
{
"created": "Mon, 14 Dec 2020 12:37:20 GMT",
"version": "v1"
},
{
"created": "Tue, 3 Aug 2021 14:38:17 GMT",
"version": "v2"
}
] | 2021-08-04 | [
[
"Suárez-Hernández",
"Alejandro",
""
],
[
"Segovia-Aguas",
"Javier",
""
],
[
"Torras",
"Carme",
""
],
[
"Alenyà",
"Guillem",
""
]
] | Recognition in planning seeks to find agent intentions, goals or activities given a set of observations and a knowledge library (e.g. goal states, plans or domain theories). In this work we introduce the problem of Online Action Recognition. It consists in recognizing, in an open world, the planning action that best explains a partially observable state transition from a knowledge library of first-order STRIPS actions, which is initially empty. We frame this as an optimization problem, and propose two algorithms to address it: Action Unification (AU) and Online Action Recognition through Unification (OARU). The former builds on logic unification and generalizes two input actions using weighted partial MaxSAT. The latter looks for an action within the library that explains an observed transition. If there is such action, it generalizes it making use of AU, building in this way an AU hierarchy. Otherwise, OARU inserts a Trivial Grounded Action (TGA) in the library that explains just that transition. We report results on benchmarks from the International Planning Competition and PDDLGym, where OARU recognizes actions accurately with respect to expert knowledge, and shows real-time performance. |
2006.08767 | Borja Gonzalez Le\'on | Borja G. Le\'on, Murray Shanahan, Francesco Belardinelli | Systematic Generalisation through Task Temporal Logic and Deep
Reinforcement Learning | null | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work introduces a neuro-symbolic agent that combines deep reinforcement
learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i.e.,
never-seen-before, generalisation of formally specified instructions. In
particular, we present a neuro-symbolic framework where a symbolic module
transforms TL specifications into a form that helps the training of a DRL agent
targeting generalisation, while a neural module learns systematically to solve
the given tasks. We study the emergence of systematic learning in different
settings and find that the architecture of the convolutional layers is key when
generalising to new instructions. We also provide evidence that systematic
learning can emerge with abstract operators such as negation when learning from
a few training examples, which previous research have struggled with.
| [
{
"created": "Fri, 12 Jun 2020 09:02:40 GMT",
"version": "v1"
},
{
"created": "Wed, 30 Sep 2020 19:28:18 GMT",
"version": "v2"
},
{
"created": "Mon, 13 Sep 2021 13:12:32 GMT",
"version": "v3"
}
] | 2021-09-14 | [
[
"León",
"Borja G.",
""
],
[
"Shanahan",
"Murray",
""
],
[
"Belardinelli",
"Francesco",
""
]
] | This work introduces a neuro-symbolic agent that combines deep reinforcement learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i.e., never-seen-before, generalisation of formally specified instructions. In particular, we present a neuro-symbolic framework where a symbolic module transforms TL specifications into a form that helps the training of a DRL agent targeting generalisation, while a neural module learns systematically to solve the given tasks. We study the emergence of systematic learning in different settings and find that the architecture of the convolutional layers is key when generalising to new instructions. We also provide evidence that systematic learning can emerge with abstract operators such as negation when learning from a few training examples, which previous research have struggled with. |
2201.06386 | Alex B\"auerle | Alex B\"auerle, Aybuke Gul Turker, Ken Burke, Osman Aka, Timo
Ropinski, Christina Greer, and Mani Varadarajan | Visual Identification of Problematic Bias in Large Label Spaces | null | null | null | null | cs.AI cs.HC | http://creativecommons.org/licenses/by/4.0/ | While the need for well-trained, fair ML systems is increasing ever more,
measuring fairness for modern models and datasets is becoming increasingly
difficult as they grow at an unprecedented pace. One key challenge in scaling
common fairness metrics to such models and datasets is the requirement of
exhaustive ground truth labeling, which cannot always be done. Indeed, this
often rules out the application of traditional analysis metrics and systems. At
the same time, ML-fairness assessments cannot be made algorithmically, as
fairness is a highly subjective matter. Thus, domain experts need to be able to
extract and reason about bias throughout models and datasets to make informed
decisions. While visual analysis tools are of great help when investigating
potential bias in DL models, none of the existing approaches have been designed
for the specific tasks and challenges that arise in large label spaces.
Addressing the lack of visualization work in this area, we propose guidelines
for designing visualizations for such large label spaces, considering both
technical and ethical issues. Our proposed visualization approach can be
integrated into classical model and data pipelines, and we provide an
implementation of our techniques open-sourced as a TensorBoard plug-in. With
our approach, different models and datasets for large label spaces can be
systematically and visually analyzed and compared to make informed fairness
assessments tackling problematic bias.
| [
{
"created": "Mon, 17 Jan 2022 12:51:08 GMT",
"version": "v1"
}
] | 2022-01-19 | [
[
"Bäuerle",
"Alex",
""
],
[
"Turker",
"Aybuke Gul",
""
],
[
"Burke",
"Ken",
""
],
[
"Aka",
"Osman",
""
],
[
"Ropinski",
"Timo",
""
],
[
"Greer",
"Christina",
""
],
[
"Varadarajan",
"Mani",
""
]
] | While the need for well-trained, fair ML systems is increasing ever more, measuring fairness for modern models and datasets is becoming increasingly difficult as they grow at an unprecedented pace. One key challenge in scaling common fairness metrics to such models and datasets is the requirement of exhaustive ground truth labeling, which cannot always be done. Indeed, this often rules out the application of traditional analysis metrics and systems. At the same time, ML-fairness assessments cannot be made algorithmically, as fairness is a highly subjective matter. Thus, domain experts need to be able to extract and reason about bias throughout models and datasets to make informed decisions. While visual analysis tools are of great help when investigating potential bias in DL models, none of the existing approaches have been designed for the specific tasks and challenges that arise in large label spaces. Addressing the lack of visualization work in this area, we propose guidelines for designing visualizations for such large label spaces, considering both technical and ethical issues. Our proposed visualization approach can be integrated into classical model and data pipelines, and we provide an implementation of our techniques open-sourced as a TensorBoard plug-in. With our approach, different models and datasets for large label spaces can be systematically and visually analyzed and compared to make informed fairness assessments tackling problematic bias. |
2003.08288 | Siu-Wing Cheng | Siu-Wing Cheng and Man-Kit Lau | Dynamic Distribution-Sensitive Point Location | To appear in Proceedings of the International Symposium of
Computational Geometry, 2020 | null | null | null | cs.CG cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a dynamic data structure for the distribution-sensitive point
location problem. Suppose that there is a fixed query distribution in
$\mathbb{R}^2$, and we are given an oracle that can return in $O(1)$ time the
probability of a query point falling into a polygonal region of constant
complexity. We can maintain a convex subdivision $\cal S$ with $n$ vertices
such that each query is answered in $O(\mathrm{OPT})$ expected time, where OPT
is the minimum expected time of the best linear decision tree for point
location in $\cal S$. The space and construction time are $O(n\log^2 n)$. An
update of $\cal S$ as a mixed sequence of $k$ edge insertions and deletions
takes $O(k\log^5 n)$ amortized time. As a corollary, the randomized incremental
construction of the Voronoi diagram of $n$ sites can be performed in $O(n\log^5
n)$ expected time so that, during the incremental construction, a nearest
neighbor query at any time can be answered optimally with respect to the
intermediate Voronoi diagram at that time.
| [
{
"created": "Wed, 18 Mar 2020 15:51:52 GMT",
"version": "v1"
},
{
"created": "Sat, 28 Mar 2020 03:01:49 GMT",
"version": "v2"
},
{
"created": "Wed, 1 Apr 2020 04:37:46 GMT",
"version": "v3"
},
{
"created": "Sat, 25 Apr 2020 06:38:38 GMT",
"version": "v4"
}
] | 2020-04-28 | [
[
"Cheng",
"Siu-Wing",
""
],
[
"Lau",
"Man-Kit",
""
]
] | We propose a dynamic data structure for the distribution-sensitive point location problem. Suppose that there is a fixed query distribution in $\mathbb{R}^2$, and we are given an oracle that can return in $O(1)$ time the probability of a query point falling into a polygonal region of constant complexity. We can maintain a convex subdivision $\cal S$ with $n$ vertices such that each query is answered in $O(\mathrm{OPT})$ expected time, where OPT is the minimum expected time of the best linear decision tree for point location in $\cal S$. The space and construction time are $O(n\log^2 n)$. An update of $\cal S$ as a mixed sequence of $k$ edge insertions and deletions takes $O(k\log^5 n)$ amortized time. As a corollary, the randomized incremental construction of the Voronoi diagram of $n$ sites can be performed in $O(n\log^5 n)$ expected time so that, during the incremental construction, a nearest neighbor query at any time can be answered optimally with respect to the intermediate Voronoi diagram at that time. |
2104.02939 | Shu Kong | Shu Kong, Deva Ramanan | OpenGAN: Open-Set Recognition via Open Data Generation | ICCV 2021 Best Paper Honorable Mention | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real-world machine learning systems need to analyze test data that may differ
from training data. In K-way classification, this is crisply formulated as
open-set recognition, core to which is the ability to discriminate open-set
data outside the K closed-set classes. Two conceptually elegant ideas for
open-set discrimination are: 1) discriminatively learning an open-vs-closed
binary discriminator by exploiting some outlier data as the open-set, and 2)
unsupervised learning the closed-set data distribution with a GAN, using its
discriminator as the open-set likelihood function. However, the former
generalizes poorly to diverse open test data due to overfitting to the training
outliers, which are unlikely to exhaustively span the open-world. The latter
does not work well, presumably due to the instable training of GANs. Motivated
by the above, we propose OpenGAN, which addresses the limitation of each
approach by combining them with several technical insights. First, we show that
a carefully selected GAN-discriminator on some real outlier data already
achieves the state-of-the-art. Second, we augment the available set of real
open training examples with adversarially synthesized "fake" data. Third and
most importantly, we build the discriminator over the features computed by the
closed-world K-way networks. This allows OpenGAN to be implemented via a
lightweight discriminator head built on top of an existing K-way network.
Extensive experiments show that OpenGAN significantly outperforms prior
open-set methods.
| [
{
"created": "Wed, 7 Apr 2021 06:19:24 GMT",
"version": "v1"
},
{
"created": "Fri, 9 Apr 2021 02:55:27 GMT",
"version": "v2"
},
{
"created": "Wed, 13 Oct 2021 05:23:31 GMT",
"version": "v3"
}
] | 2021-10-14 | [
[
"Kong",
"Shu",
""
],
[
"Ramanan",
"Deva",
""
]
] | Real-world machine learning systems need to analyze test data that may differ from training data. In K-way classification, this is crisply formulated as open-set recognition, core to which is the ability to discriminate open-set data outside the K closed-set classes. Two conceptually elegant ideas for open-set discrimination are: 1) discriminatively learning an open-vs-closed binary discriminator by exploiting some outlier data as the open-set, and 2) unsupervised learning the closed-set data distribution with a GAN, using its discriminator as the open-set likelihood function. However, the former generalizes poorly to diverse open test data due to overfitting to the training outliers, which are unlikely to exhaustively span the open-world. The latter does not work well, presumably due to the instable training of GANs. Motivated by the above, we propose OpenGAN, which addresses the limitation of each approach by combining them with several technical insights. First, we show that a carefully selected GAN-discriminator on some real outlier data already achieves the state-of-the-art. Second, we augment the available set of real open training examples with adversarially synthesized "fake" data. Third and most importantly, we build the discriminator over the features computed by the closed-world K-way networks. This allows OpenGAN to be implemented via a lightweight discriminator head built on top of an existing K-way network. Extensive experiments show that OpenGAN significantly outperforms prior open-set methods. |
1607.04648 | Subarna Tripathi | Subarna Tripathi and Zachary C. Lipton and Serge Belongie and Truong
Nguyen | Context Matters: Refining Object Detection in Video with Recurrent
Neural Networks | To appear in BMVC 2016 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Given the vast amounts of video available online, and recent breakthroughs in
object detection with static images, object detection in video offers a
promising new frontier. However, motion blur and compression artifacts cause
substantial frame-level variability, even in videos that appear smooth to the
eye. Additionally, video datasets tend to have sparsely annotated frames. We
present a new framework for improving object detection in videos that captures
temporal context and encourages consistency of predictions. First, we train a
pseudo-labeler, that is, a domain-adapted convolutional neural network for
object detection. The pseudo-labeler is first trained individually on the
subset of labeled frames, and then subsequently applied to all frames. Then we
train a recurrent neural network that takes as input sequences of
pseudo-labeled frames and optimizes an objective that encourages both accuracy
on the target frame and consistency across consecutive frames. The approach
incorporates strong supervision of target frames, weak-supervision on context
frames, and regularization via a smoothness penalty. Our approach achieves mean
Average Precision (mAP) of 68.73, an improvement of 7.1 over the strongest
image-based baselines for the Youtube-Video Objects dataset. Our experiments
demonstrate that neighboring frames can provide valuable information, even
absent labels.
| [
{
"created": "Fri, 15 Jul 2016 20:02:25 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Jul 2016 03:00:35 GMT",
"version": "v2"
}
] | 2016-07-20 | [
[
"Tripathi",
"Subarna",
""
],
[
"Lipton",
"Zachary C.",
""
],
[
"Belongie",
"Serge",
""
],
[
"Nguyen",
"Truong",
""
]
] | Given the vast amounts of video available online, and recent breakthroughs in object detection with static images, object detection in video offers a promising new frontier. However, motion blur and compression artifacts cause substantial frame-level variability, even in videos that appear smooth to the eye. Additionally, video datasets tend to have sparsely annotated frames. We present a new framework for improving object detection in videos that captures temporal context and encourages consistency of predictions. First, we train a pseudo-labeler, that is, a domain-adapted convolutional neural network for object detection. The pseudo-labeler is first trained individually on the subset of labeled frames, and then subsequently applied to all frames. Then we train a recurrent neural network that takes as input sequences of pseudo-labeled frames and optimizes an objective that encourages both accuracy on the target frame and consistency across consecutive frames. The approach incorporates strong supervision of target frames, weak-supervision on context frames, and regularization via a smoothness penalty. Our approach achieves mean Average Precision (mAP) of 68.73, an improvement of 7.1 over the strongest image-based baselines for the Youtube-Video Objects dataset. Our experiments demonstrate that neighboring frames can provide valuable information, even absent labels. |
2308.16122 | Yuta Sato | Yuta Sato, Pak Hei Lam, Shruti Gupta, Fareesah Hussain | Spatial Graph Coarsening: Weather and Weekday Prediction with London's
Bike-Sharing Service using GNN | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | This study introduced the use of Graph Neural Network (GNN) for predicting
the weather and weekday of a day in London, from the dataset of Santander
Cycles bike-sharing system as a graph classification task. The proposed GNN
models newly introduced (i) a concatenation operator of graph features with
trained node embeddings and (ii) a graph coarsening operator based on
geographical contiguity, namely "Spatial Graph Coarsening". With the node
features of land-use characteristics and number of households around the bike
stations and graph features of temperatures in the city, our proposed models
outperformed the baseline model in cross-entropy loss and accuracy of the
validation dataset.
| [
{
"created": "Wed, 30 Aug 2023 16:21:02 GMT",
"version": "v1"
}
] | 2023-08-31 | [
[
"Sato",
"Yuta",
""
],
[
"Lam",
"Pak Hei",
""
],
[
"Gupta",
"Shruti",
""
],
[
"Hussain",
"Fareesah",
""
]
] | This study introduced the use of Graph Neural Network (GNN) for predicting the weather and weekday of a day in London, from the dataset of Santander Cycles bike-sharing system as a graph classification task. The proposed GNN models newly introduced (i) a concatenation operator of graph features with trained node embeddings and (ii) a graph coarsening operator based on geographical contiguity, namely "Spatial Graph Coarsening". With the node features of land-use characteristics and number of households around the bike stations and graph features of temperatures in the city, our proposed models outperformed the baseline model in cross-entropy loss and accuracy of the validation dataset. |
2010.02428 | Tao Li | Tao Li, Tushar Khot, Daniel Khashabi, Ashish Sabharwal, Vivek Srikumar | UnQovering Stereotyping Biases via Underspecified Questions | Accepted at Findings of EMNLP 2020 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While language embeddings have been shown to have stereotyping biases, how
these biases affect downstream question answering (QA) models remains
unexplored. We present UNQOVER, a general framework to probe and quantify
biases through underspecified questions. We show that a naive use of model
scores can lead to incorrect bias estimates due to two forms of reasoning
errors: positional dependence and question independence. We design a formalism
that isolates the aforementioned errors. As case studies, we use this metric to
analyze four important classes of stereotypes: gender, nationality, ethnicity,
and religion. We probe five transformer-based QA models trained on two QA
datasets, along with their underlying language models. Our broad study reveals
that (1) all these models, with and without fine-tuning, have notable
stereotyping biases in these classes; (2) larger models often have higher bias;
and (3) the effect of fine-tuning on bias varies strongly with the dataset and
the model size.
| [
{
"created": "Tue, 6 Oct 2020 01:49:52 GMT",
"version": "v1"
},
{
"created": "Wed, 7 Oct 2020 04:51:22 GMT",
"version": "v2"
},
{
"created": "Sat, 10 Oct 2020 01:48:31 GMT",
"version": "v3"
}
] | 2020-10-13 | [
[
"Li",
"Tao",
""
],
[
"Khot",
"Tushar",
""
],
[
"Khashabi",
"Daniel",
""
],
[
"Sabharwal",
"Ashish",
""
],
[
"Srikumar",
"Vivek",
""
]
] | While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a general framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question independence. We design a formalism that isolates the aforementioned errors. As case studies, we use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion. We probe five transformer-based QA models trained on two QA datasets, along with their underlying language models. Our broad study reveals that (1) all these models, with and without fine-tuning, have notable stereotyping biases in these classes; (2) larger models often have higher bias; and (3) the effect of fine-tuning on bias varies strongly with the dataset and the model size. |
1808.08268 | Alexander Broad | Alexander Broad, Todd Murphey, Brenna Argall | Learning Models for Shared Control of Human-Machine Systems with Unknown
Dynamics | Robotics: Science and Systems Proceedings, 2017 | null | null | null | cs.RO cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel approach to shared control of human-machine systems. Our
method assumes no a priori knowledge of the system dynamics. Instead, we learn
both the dynamics and information about the user's interaction from observation
through the use of the Koopman operator. Using the learned model, we define an
optimization problem to compute the optimal policy for a given task, and
compare the user input to the optimal input. We demonstrate the efficacy of our
approach with a user study. We also analyze the individual nature of the
learned models by comparing the effectiveness of our approach when the
demonstration data comes from a user's own interactions, from the interactions
of a group of users and from a domain expert. Positive results include
statistically significant improvements on task metrics when comparing a
user-only control paradigm with our shared control paradigm. Surprising results
include findings that suggest that individualizing the model based on a user's
own data does not effect the ability to learn a useful dynamic system. We
explore this tension as it relates to developing human-in-the-loop systems
further in the discussion.
| [
{
"created": "Fri, 24 Aug 2018 19:07:10 GMT",
"version": "v1"
}
] | 2018-08-28 | [
[
"Broad",
"Alexander",
""
],
[
"Murphey",
"Todd",
""
],
[
"Argall",
"Brenna",
""
]
] | We present a novel approach to shared control of human-machine systems. Our method assumes no a priori knowledge of the system dynamics. Instead, we learn both the dynamics and information about the user's interaction from observation through the use of the Koopman operator. Using the learned model, we define an optimization problem to compute the optimal policy for a given task, and compare the user input to the optimal input. We demonstrate the efficacy of our approach with a user study. We also analyze the individual nature of the learned models by comparing the effectiveness of our approach when the demonstration data comes from a user's own interactions, from the interactions of a group of users and from a domain expert. Positive results include statistically significant improvements on task metrics when comparing a user-only control paradigm with our shared control paradigm. Surprising results include findings that suggest that individualizing the model based on a user's own data does not effect the ability to learn a useful dynamic system. We explore this tension as it relates to developing human-in-the-loop systems further in the discussion. |
1812.10378 | Polina Lemenkova | Polina Lemenkova | Urban-Rural Environmental Gradient in a Developing City: Testing ENVI
GIS Functionality | 5 pages, 2 figures, 1 table | Conference Proceedings 'Abishevskie Readings. Innovation in the
Complex Processing of Mineral Raw Materials', 21-22 Jan 2016 | 10.6084/m9.figshare.7210286 | null | cs.CY cs.CV | http://creativecommons.org/publicdomain/zero/1.0/ | The research performs urban ecosystem analysis supported by ENVI GIS by
integrated studies on land cover types and geospatial modeling of Taipei city.
The paper deals with the role of anthropogenic pressure on the structure of the
landscape and change of land cover types. Methods included assessment of the
impact from anthropogenic activities on the natural ecosystems, evaluation of
the rate and scale of landscape dynamics using remote sensing data and GIS. The
research aims to assist environmentalists and city planners to evaluate
strategies for specific objectives of urban development in Taiwan, China.
| [
{
"created": "Thu, 6 Dec 2018 02:10:53 GMT",
"version": "v1"
}
] | 2018-12-27 | [
[
"Lemenkova",
"Polina",
""
]
] | The research performs urban ecosystem analysis supported by ENVI GIS by integrated studies on land cover types and geospatial modeling of Taipei city. The paper deals with the role of anthropogenic pressure on the structure of the landscape and change of land cover types. Methods included assessment of the impact from anthropogenic activities on the natural ecosystems, evaluation of the rate and scale of landscape dynamics using remote sensing data and GIS. The research aims to assist environmentalists and city planners to evaluate strategies for specific objectives of urban development in Taiwan, China. |
2102.12844 | Walter Bennette | Walter Bennette, Sally Dufek, Karsten Maurer, Sean Sisti, Bunyod
Tusmatov | Generalized Adversarial Distances to Efficiently Discover Classifier
Errors | 8 pages, 5 figures, International Conference of Machine Learning and
Applications 2020 | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Given a black-box classification model and an unlabeled evaluation dataset
from some application domain, efficient strategies need to be developed to
evaluate the model. Random sampling allows a user to estimate metrics like
accuracy, precision, and recall, but may not provide insight to high-confidence
errors. High-confidence errors are rare events for which the model is highly
confident in its prediction, but is wrong. Such errors can represent costly
mistakes and should be explicitly searched for. In this paper we propose a
generalization to the Adversarial Distance search that leverages concepts from
adversarial machine learning to identify predictions for which a classifier may
be overly confident. These predictions are useful instances to sample when
looking for high-confidence errors because they are prone to a higher rate of
error than expected. Our generalization allows Adversarial Distance to be
applied to any classifier or data domain. Experimental results show that the
generalized method finds errors at rates greater than expected given the
confidence of the sampled predictions, and outperforms competing methods.
| [
{
"created": "Thu, 25 Feb 2021 13:31:21 GMT",
"version": "v1"
}
] | 2021-02-26 | [
[
"Bennette",
"Walter",
""
],
[
"Dufek",
"Sally",
""
],
[
"Maurer",
"Karsten",
""
],
[
"Sisti",
"Sean",
""
],
[
"Tusmatov",
"Bunyod",
""
]
] | Given a black-box classification model and an unlabeled evaluation dataset from some application domain, efficient strategies need to be developed to evaluate the model. Random sampling allows a user to estimate metrics like accuracy, precision, and recall, but may not provide insight to high-confidence errors. High-confidence errors are rare events for which the model is highly confident in its prediction, but is wrong. Such errors can represent costly mistakes and should be explicitly searched for. In this paper we propose a generalization to the Adversarial Distance search that leverages concepts from adversarial machine learning to identify predictions for which a classifier may be overly confident. These predictions are useful instances to sample when looking for high-confidence errors because they are prone to a higher rate of error than expected. Our generalization allows Adversarial Distance to be applied to any classifier or data domain. Experimental results show that the generalized method finds errors at rates greater than expected given the confidence of the sampled predictions, and outperforms competing methods. |
2405.02791 | Mengxian Hu | Mengxian Hu, Minghao Zhu, Xun Zhou, Qingqing Yan, Shu Li, Chengju Liu,
Qijun Chen | Efficient Text-driven Motion Generation via Latent Consistency Training | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Motion diffusion models excel at text-driven motion generation but struggle
with real-time inference since motion sequences are time-axis redundant and
solving reverse diffusion trajectory involves tens or hundreds of sequential
iterations. In this paper, we propose a Motion Latent Consistency Training
(MLCT) framework, which allows for large-scale skip sampling of compact motion
latent representation by constraining the consistency of the outputs of
adjacent perturbed states on the precomputed trajectory. In particular, we
design a flexible motion autoencoder with quantization constraints to guarantee
the low-dimensionality, succinctness, and boundednes of the motion embedding
space. We further present a conditionally guided consistency training framework
based on conditional trajectory simulation without additional pre-training
diffusion model, which significantly improves the conditional generation
performance with minimal training cost. Experiments on two benchmarks
demonstrate our model's state-of-the-art performance with an 80\% inference
cost saving and around 14 ms on a single RTX 4090 GPU.
| [
{
"created": "Sun, 5 May 2024 02:11:57 GMT",
"version": "v1"
},
{
"created": "Sat, 25 May 2024 05:01:20 GMT",
"version": "v2"
}
] | 2024-05-28 | [
[
"Hu",
"Mengxian",
""
],
[
"Zhu",
"Minghao",
""
],
[
"Zhou",
"Xun",
""
],
[
"Yan",
"Qingqing",
""
],
[
"Li",
"Shu",
""
],
[
"Liu",
"Chengju",
""
],
[
"Chen",
"Qijun",
""
]
] | Motion diffusion models excel at text-driven motion generation but struggle with real-time inference since motion sequences are time-axis redundant and solving reverse diffusion trajectory involves tens or hundreds of sequential iterations. In this paper, we propose a Motion Latent Consistency Training (MLCT) framework, which allows for large-scale skip sampling of compact motion latent representation by constraining the consistency of the outputs of adjacent perturbed states on the precomputed trajectory. In particular, we design a flexible motion autoencoder with quantization constraints to guarantee the low-dimensionality, succinctness, and boundednes of the motion embedding space. We further present a conditionally guided consistency training framework based on conditional trajectory simulation without additional pre-training diffusion model, which significantly improves the conditional generation performance with minimal training cost. Experiments on two benchmarks demonstrate our model's state-of-the-art performance with an 80\% inference cost saving and around 14 ms on a single RTX 4090 GPU. |
2003.00899 | George Cevora | Kate Wilkinson, George Cevora | Demonstrating Rosa: the fairness solution for any Data Analytic pipeline | corrected typo in fig 8 caption | null | null | null | cs.LG stat.AP | http://creativecommons.org/licenses/by-sa/4.0/ | Most datasets of interest to the analytics industry are impacted by various
forms of human bias. The outcomes of Data Analytics [DA] or Machine Learning
[ML] on such data are therefore prone to replicating the bias. As a result, a
large number of biased decision-making systems based on DA/ML have recently
attracted attention. In this paper we introduce Rosa, a free, web-based tool to
easily de-bias datasets with respect to a chosen characteristic. Rosa is based
on the principles of Fair Adversarial Networks, developed by illumr Ltd., and
can therefore remove interactive, non-linear, and non-binary bias. Rosa is
stand-alone pre-processing step / API, meaning it can be used easily with any
DA/ML pipeline. We test the efficacy of Rosa in removing bias from data-driven
decision making systems by performing standard DA tasks on five real-world
datasets, selected for their relevance to current DA problems, and also their
high potential for bias. We use simple ML models to model a characteristic of
analytical interest, and compare the level of bias in the model output both
with and without Rosa as a pre-processing step. We find that in all cases there
is a substantial decrease in bias of the data-driven decision making systems
when the data is pre-processed with Rosa.
| [
{
"created": "Fri, 28 Feb 2020 10:02:58 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Mar 2021 15:59:13 GMT",
"version": "v2"
}
] | 2021-03-08 | [
[
"Wilkinson",
"Kate",
""
],
[
"Cevora",
"George",
""
]
] | Most datasets of interest to the analytics industry are impacted by various forms of human bias. The outcomes of Data Analytics [DA] or Machine Learning [ML] on such data are therefore prone to replicating the bias. As a result, a large number of biased decision-making systems based on DA/ML have recently attracted attention. In this paper we introduce Rosa, a free, web-based tool to easily de-bias datasets with respect to a chosen characteristic. Rosa is based on the principles of Fair Adversarial Networks, developed by illumr Ltd., and can therefore remove interactive, non-linear, and non-binary bias. Rosa is stand-alone pre-processing step / API, meaning it can be used easily with any DA/ML pipeline. We test the efficacy of Rosa in removing bias from data-driven decision making systems by performing standard DA tasks on five real-world datasets, selected for their relevance to current DA problems, and also their high potential for bias. We use simple ML models to model a characteristic of analytical interest, and compare the level of bias in the model output both with and without Rosa as a pre-processing step. We find that in all cases there is a substantial decrease in bias of the data-driven decision making systems when the data is pre-processed with Rosa. |
1911.00493 | Veit Elser | Veit Elser | Learning Without Loss | 52 pages, 24 figures, 1 table | null | null | null | cs.LG math.ST stat.ML stat.TH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We explore a new approach for training neural networks where all loss
functions are replaced by hard constraints. The same approach is very
successful in phase retrieval, where signals are reconstructed from magnitude
constraints and general characteristics (sparsity, support, etc.). Instead of
taking gradient steps, the optimizer in the constraint based approach, called
relaxed-reflect-reflect (RRR), derives its steps from projections to local
constraints. In neural networks one such projection makes the minimal
modification to the inputs $x$, the associated weights $w$, and the
pre-activation value $y$ at each neuron, to satisfy the equation $x\cdot w=y$.
These projections, along with a host of other local projections (constraining
pre- and post-activations, etc.) can be partitioned into two sets such that all
the projections in each set can be applied concurrently, across the network and
across all data in the training batch. This partitioning into two sets is
analogous to the situation in phase retrieval and the setting for which the
general purpose RRR optimizer was designed. Owing to the novelty of the method,
this paper also serves as a self-contained tutorial. Starting with a
single-layer network that performs non-negative matrix factorization, and
concluding with a generative model comprising an autoencoder and classifier,
all applications and their implementations by projections are described in
complete detail. Although the new approach has the potential to extend the
scope of neural networks (e.g. by defining activation not through functions but
constraint sets), most of the featured models are standard to allow comparison
with stochastic gradient descent.
| [
{
"created": "Tue, 29 Oct 2019 19:20:08 GMT",
"version": "v1"
}
] | 2019-11-04 | [
[
"Elser",
"Veit",
""
]
] | We explore a new approach for training neural networks where all loss functions are replaced by hard constraints. The same approach is very successful in phase retrieval, where signals are reconstructed from magnitude constraints and general characteristics (sparsity, support, etc.). Instead of taking gradient steps, the optimizer in the constraint based approach, called relaxed-reflect-reflect (RRR), derives its steps from projections to local constraints. In neural networks one such projection makes the minimal modification to the inputs $x$, the associated weights $w$, and the pre-activation value $y$ at each neuron, to satisfy the equation $x\cdot w=y$. These projections, along with a host of other local projections (constraining pre- and post-activations, etc.) can be partitioned into two sets such that all the projections in each set can be applied concurrently, across the network and across all data in the training batch. This partitioning into two sets is analogous to the situation in phase retrieval and the setting for which the general purpose RRR optimizer was designed. Owing to the novelty of the method, this paper also serves as a self-contained tutorial. Starting with a single-layer network that performs non-negative matrix factorization, and concluding with a generative model comprising an autoencoder and classifier, all applications and their implementations by projections are described in complete detail. Although the new approach has the potential to extend the scope of neural networks (e.g. by defining activation not through functions but constraint sets), most of the featured models are standard to allow comparison with stochastic gradient descent. |
2302.05960 | Paul Gutkovich | Paul Gutkovich, Zi Song Yeoh | Computing Truncated Metric Dimension of Trees | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Let $G=(V,E)$ be a simple, unweighted, connected graph. Let $d(u,v)$ denote
the distance between vertices $u,v$. A resolving set of $G$ is a subset $S$ of
$V$ such that knowing the distance from a vertex $v$ to every vertex in $S$
uniquely identifies $v$. The metric dimension of $G$ is defined as the size of
the smallest resolving set of $G$. We define the $k$-truncated resolving set
and $k$-truncated metric dimension of a graph similarly, but with the notion of
distance replaced with $d_k(u,v) := \min(d(u,v),k+1)$.
In this paper, we demonstrate that computing $k$-truncated dimension of trees
is NP-Hard for general $k$. We then present a polynomial-time algorithm to
compute $k$-truncated dimension of trees when $k$ is a fixed constant.
| [
{
"created": "Sun, 12 Feb 2023 17:18:14 GMT",
"version": "v1"
}
] | 2023-02-14 | [
[
"Gutkovich",
"Paul",
""
],
[
"Yeoh",
"Zi Song",
""
]
] | Let $G=(V,E)$ be a simple, unweighted, connected graph. Let $d(u,v)$ denote the distance between vertices $u,v$. A resolving set of $G$ is a subset $S$ of $V$ such that knowing the distance from a vertex $v$ to every vertex in $S$ uniquely identifies $v$. The metric dimension of $G$ is defined as the size of the smallest resolving set of $G$. We define the $k$-truncated resolving set and $k$-truncated metric dimension of a graph similarly, but with the notion of distance replaced with $d_k(u,v) := \min(d(u,v),k+1)$. In this paper, we demonstrate that computing $k$-truncated dimension of trees is NP-Hard for general $k$. We then present a polynomial-time algorithm to compute $k$-truncated dimension of trees when $k$ is a fixed constant. |
1102.2566 | Morgan Barbier | Morgan Barbier (INRIA Saclay - Ile de France), Barreto S. L. M. Paulo
(IME/USP) | Key Reduction of McEliece's Cryptosystem Using List Decoding | null | International Symposium of Information Theory (ISIT) (2011)
2657-2661 | null | null | cs.CR cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Different variants of the code-based McEliece cryptosystem were pro- posed to
reduce the size of the public key. All these variants use very structured
codes, which open the door to new attacks exploiting the underlying structure.
In this paper, we show that the dyadic variant can be designed to resist all
known attacks. In light of a new study on list decoding algorithms for binary
Goppa codes, we explain how to increase the security level for given public
keysizes. Using the state-of-the-art list decoding algorithm instead of unique
decoding, we exhibit a keysize gain of about 4% for the standard McEliece
cryptosystem and up to 21% for the adjusted dyadic variant.
| [
{
"created": "Sun, 13 Feb 2011 07:26:03 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Nov 2011 09:37:57 GMT",
"version": "v2"
}
] | 2011-11-17 | [
[
"Barbier",
"Morgan",
"",
"INRIA Saclay - Ile de France"
],
[
"Paulo",
"Barreto S. L. M.",
"",
"IME/USP"
]
] | Different variants of the code-based McEliece cryptosystem were pro- posed to reduce the size of the public key. All these variants use very structured codes, which open the door to new attacks exploiting the underlying structure. In this paper, we show that the dyadic variant can be designed to resist all known attacks. In light of a new study on list decoding algorithms for binary Goppa codes, we explain how to increase the security level for given public keysizes. Using the state-of-the-art list decoding algorithm instead of unique decoding, we exhibit a keysize gain of about 4% for the standard McEliece cryptosystem and up to 21% for the adjusted dyadic variant. |
1607.01993 | Nikos Gorogiannis | James Brotherston, Nikos Gorogiannis and Max Kanovich | Biabduction (and Related Problems) in Array Separation Logic | null | null | null | null | cs.LO cs.DS cs.PL math.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We investigate array separation logic (ASL), a variant of symbolic-heap
separation logic in which the data structures are either pointers or arrays,
i.e., contiguous blocks of allocated memory. This logic provides a language for
compositional memory safety proofs of imperative array programs.
We focus on the biabduction problem for this logic, which has been
established as the key to automatic specification inference at the industrial
scale. We present an NP decision procedure for biabduction in ASL that produces
solutions of reasonable quality, and we also show that the problem of finding a
consistent solution is NP-hard.
Along the way, we study satisfiability and entailment in our logic, giving
decision procedures and complexity bounds for both problems. We show
satisfiability to be NP-complete, and entailment to be decidable with high
complexity. The somewhat surprising fact that biabduction is much simpler than
entailment is explained by the fact that, as we show, the element of choice
over biabduction solutions enables us to dramatically reduce the search space.
| [
{
"created": "Thu, 7 Jul 2016 12:49:04 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Nov 2016 21:44:27 GMT",
"version": "v2"
},
{
"created": "Fri, 18 Nov 2016 11:20:20 GMT",
"version": "v3"
}
] | 2016-11-21 | [
[
"Brotherston",
"James",
""
],
[
"Gorogiannis",
"Nikos",
""
],
[
"Kanovich",
"Max",
""
]
] | We investigate array separation logic (ASL), a variant of symbolic-heap separation logic in which the data structures are either pointers or arrays, i.e., contiguous blocks of allocated memory. This logic provides a language for compositional memory safety proofs of imperative array programs. We focus on the biabduction problem for this logic, which has been established as the key to automatic specification inference at the industrial scale. We present an NP decision procedure for biabduction in ASL that produces solutions of reasonable quality, and we also show that the problem of finding a consistent solution is NP-hard. Along the way, we study satisfiability and entailment in our logic, giving decision procedures and complexity bounds for both problems. We show satisfiability to be NP-complete, and entailment to be decidable with high complexity. The somewhat surprising fact that biabduction is much simpler than entailment is explained by the fact that, as we show, the element of choice over biabduction solutions enables us to dramatically reduce the search space. |
2302.12813 | Michel Galley | Baolin Peng and Michel Galley and Pengcheng He and Hao Cheng and Yujia
Xie and Yu Hu and Qiuyuan Huang and Lars Liden and Zhou Yu and Weizhu Chen
and Jianfeng Gao | Check Your Facts and Try Again: Improving Large Language Models with
External Knowledge and Automated Feedback | 15 pages | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Large language models (LLMs), such as ChatGPT, are able to generate
human-like, fluent responses for many downstream tasks, e.g., task-oriented
dialog and question answering. However, applying LLMs to real-world,
mission-critical applications remains challenging mainly due to their tendency
to generate hallucinations and their inability to use external knowledge. This
paper proposes a LLM-Augmenter system, which augments a black-box LLM with a
set of plug-and-play modules. Our system makes the LLM generate responses
grounded in external knowledge, e.g., stored in task-specific databases. It
also iteratively revises LLM prompts to improve model responses using feedback
generated by utility functions, e.g., the factuality score of a LLM-generated
response. The effectiveness of LLM-Augmenter is empirically validated on two
types of scenarios, task-oriented dialog and open-domain question answering.
LLM-Augmenter significantly reduces ChatGPT's hallucinations without
sacrificing the fluency and informativeness of its responses. We make the
source code and models publicly available.
| [
{
"created": "Fri, 24 Feb 2023 18:48:43 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Mar 2023 17:21:48 GMT",
"version": "v2"
},
{
"created": "Wed, 8 Mar 2023 23:41:49 GMT",
"version": "v3"
}
] | 2023-03-10 | [
[
"Peng",
"Baolin",
""
],
[
"Galley",
"Michel",
""
],
[
"He",
"Pengcheng",
""
],
[
"Cheng",
"Hao",
""
],
[
"Xie",
"Yujia",
""
],
[
"Hu",
"Yu",
""
],
[
"Huang",
"Qiuyuan",
""
],
[
"Liden",
"Lars",
""
],
[
"Yu",
"Zhou",
""
],
[
"Chen",
"Weizhu",
""
],
[
"Gao",
"Jianfeng",
""
]
] | Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. However, applying LLMs to real-world, mission-critical applications remains challenging mainly due to their tendency to generate hallucinations and their inability to use external knowledge. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules. Our system makes the LLM generate responses grounded in external knowledge, e.g., stored in task-specific databases. It also iteratively revises LLM prompts to improve model responses using feedback generated by utility functions, e.g., the factuality score of a LLM-generated response. The effectiveness of LLM-Augmenter is empirically validated on two types of scenarios, task-oriented dialog and open-domain question answering. LLM-Augmenter significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses. We make the source code and models publicly available. |
1408.5512 | Manuel Kauers | Shaoshi Chen, Manuel Kauers, Michael F. Singer | Desingularization of Ore Operators | null | null | null | null | cs.SC math.AC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show that Ore operators can be desingularized by calculating a least
common left multiple with a random operator of appropriate order. Our result
generalizes a classical result about apparent singularities of linear
differential equations, and it gives rise to a surprisingly simple
desingularization algorithm.
| [
{
"created": "Sat, 23 Aug 2014 16:52:18 GMT",
"version": "v1"
}
] | 2014-08-26 | [
[
"Chen",
"Shaoshi",
""
],
[
"Kauers",
"Manuel",
""
],
[
"Singer",
"Michael F.",
""
]
] | We show that Ore operators can be desingularized by calculating a least common left multiple with a random operator of appropriate order. Our result generalizes a classical result about apparent singularities of linear differential equations, and it gives rise to a surprisingly simple desingularization algorithm. |
2108.07955 | Jiang Yu | Yu Jiang, Lei Hu, Yongmei Zhang, and Xin Yang | WRICNet:A Weighted Rich-scale Inception Coder Network for
Multi-Resolution Remote Sensing Image Change Detection | null | null | 10.1109/TGRS.2022.3145652 | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Majority models of remote sensing image changing detection can only get great
effect in a specific resolution data set. With the purpose of improving change
detection effectiveness of the model in the multi-resolution data set, a
weighted rich-scale inception coder network (WRICNet) is proposed in this
article, which can make a great fusion of shallow multi-scale features, and
deep multi-scale features. The weighted rich-scale inception module of the
proposed can obtain shallow multi-scale features, the weighted rich-scale coder
module can obtain deep multi-scale features. The weighted scale block assigns
appropriate weights to features of different scales, which can strengthen
expressive ability of the edge of the changing area. The performance
experiments on the multi-resolution data set demonstrate that, compared to the
comparative methods, the proposed can further reduce the false alarm outside
the change area, and the missed alarm in the change area, besides, the edge of
the change area is more accurate. The ablation study of the proposed shows that
the training strategy, and improvements of this article can improve the
effectiveness of change detection.
| [
{
"created": "Wed, 18 Aug 2021 02:56:11 GMT",
"version": "v1"
}
] | 2022-05-04 | [
[
"Jiang",
"Yu",
""
],
[
"Hu",
"Lei",
""
],
[
"Zhang",
"Yongmei",
""
],
[
"Yang",
"Xin",
""
]
] | Majority models of remote sensing image changing detection can only get great effect in a specific resolution data set. With the purpose of improving change detection effectiveness of the model in the multi-resolution data set, a weighted rich-scale inception coder network (WRICNet) is proposed in this article, which can make a great fusion of shallow multi-scale features, and deep multi-scale features. The weighted rich-scale inception module of the proposed can obtain shallow multi-scale features, the weighted rich-scale coder module can obtain deep multi-scale features. The weighted scale block assigns appropriate weights to features of different scales, which can strengthen expressive ability of the edge of the changing area. The performance experiments on the multi-resolution data set demonstrate that, compared to the comparative methods, the proposed can further reduce the false alarm outside the change area, and the missed alarm in the change area, besides, the edge of the change area is more accurate. The ablation study of the proposed shows that the training strategy, and improvements of this article can improve the effectiveness of change detection. |
2210.09628 | Fachrina Dewi Puspitasari | Fachrina Dewi Puspitasari and Lik-Hang Lee | Review of Persuasive User Interface as Strategy for Technology Addiction
in Virtual Environments | null | null | null | null | cs.HC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In the era of virtuality, the increasingly ubiquitous technology bears the
challenge of excessive user dependency, also known as user addiction. Augmented
reality (AR) and virtual reality (VR) have become increasingly integrated into
daily life. Although discussions about the drawbacks of these technologies are
abundant, their exploration for solutions is still rare. Thus, using the PRISMA
methodology, this paper reviewed the literature on technology addiction and
persuasive technology. After describing the key research trends, the paper
summed up nine persuasive elements of user interfaces (UIs) that AR and VR
developers could add to their apps to make them less addictive. Furthermore,
this review paper encourages more research into a persuasive strategy for
controlling user dependency in virtual-physical blended cyberspace.
| [
{
"created": "Tue, 18 Oct 2022 06:54:06 GMT",
"version": "v1"
}
] | 2022-10-19 | [
[
"Puspitasari",
"Fachrina Dewi",
""
],
[
"Lee",
"Lik-Hang",
""
]
] | In the era of virtuality, the increasingly ubiquitous technology bears the challenge of excessive user dependency, also known as user addiction. Augmented reality (AR) and virtual reality (VR) have become increasingly integrated into daily life. Although discussions about the drawbacks of these technologies are abundant, their exploration for solutions is still rare. Thus, using the PRISMA methodology, this paper reviewed the literature on technology addiction and persuasive technology. After describing the key research trends, the paper summed up nine persuasive elements of user interfaces (UIs) that AR and VR developers could add to their apps to make them less addictive. Furthermore, this review paper encourages more research into a persuasive strategy for controlling user dependency in virtual-physical blended cyberspace. |
1208.2205 | Mohammad Havaei | Sanaz Moshirian, Soheil Ghadami, Mohammad Havaei | Blind Channel Equalization | null | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Future services demand high data rate and quality. Thus, it is necessary to
define new and robust algorithms to equalize channels and reduce noise in
communications. Nowadays, new equalization algorithms are being developed to
optimize the channel bandwidth and reduce noise, namely, Blind Channel
Equalization. Conventional equalizations minimizing mean-square error generally
require a training sequence accompanying the data sequence. In this study, the
result of Least Mean Square (LMS) algorithm applied on two given communication
channels is analyzed. Considering the fact that blind equalizers do not require
pilot signals to recover the transmitted data, implementation of four types of
Constant Modulus Algorithm (CMA) for blind equalization of the channels are
shown. Finally, a comparison of the simulation results of LMS and CMA for the
test channels is provided.
| [
{
"created": "Fri, 10 Aug 2012 15:35:01 GMT",
"version": "v1"
}
] | 2012-08-13 | [
[
"Moshirian",
"Sanaz",
""
],
[
"Ghadami",
"Soheil",
""
],
[
"Havaei",
"Mohammad",
""
]
] | Future services demand high data rate and quality. Thus, it is necessary to define new and robust algorithms to equalize channels and reduce noise in communications. Nowadays, new equalization algorithms are being developed to optimize the channel bandwidth and reduce noise, namely, Blind Channel Equalization. Conventional equalizations minimizing mean-square error generally require a training sequence accompanying the data sequence. In this study, the result of Least Mean Square (LMS) algorithm applied on two given communication channels is analyzed. Considering the fact that blind equalizers do not require pilot signals to recover the transmitted data, implementation of four types of Constant Modulus Algorithm (CMA) for blind equalization of the channels are shown. Finally, a comparison of the simulation results of LMS and CMA for the test channels is provided. |
2401.10480 | Yiwei Li | Yiwei Li, Peiwen Yuan, Shaoxiong Feng, Boyuan Pan, Xinglin Wang, Bin
Sun, Heda Wang, Kan Li | Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step
Reasoning | ICLR 2024 | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Self-consistency (SC) has been a widely used decoding strategy for
chain-of-thought reasoning. Despite bringing significant performance
improvements across a variety of multi-step reasoning tasks, it is a high-cost
method that requires multiple sampling with the preset size. In this paper, we
propose a simple and scalable sampling process, \textbf{E}arly-Stopping
\textbf{S}elf-\textbf{C}onsistency (ESC), to greatly reduce the cost of SC
without sacrificing performance. On this basis, one control scheme for ESC is
further derivated to dynamically choose the performance-cost balance for
different tasks and models. To demonstrate ESC's effectiveness, we conducted
extensive experiments on three popular categories of reasoning tasks:
arithmetic, commonsense and symbolic reasoning over language models with
varying scales. The empirical results show that ESC reduces the average number
of sampling of chain-of-thought reasoning by a significant margin on six
benchmarks, including MATH (-33.8%), GSM8K (-80.1%), StrategyQA (-76.8%),
CommonsenseQA (-78.5%), Coin Flip (-84.2%) and Last Letters (-67.4%), while
attaining comparable performances.
| [
{
"created": "Fri, 19 Jan 2024 04:03:59 GMT",
"version": "v1"
}
] | 2024-01-22 | [
[
"Li",
"Yiwei",
""
],
[
"Yuan",
"Peiwen",
""
],
[
"Feng",
"Shaoxiong",
""
],
[
"Pan",
"Boyuan",
""
],
[
"Wang",
"Xinglin",
""
],
[
"Sun",
"Bin",
""
],
[
"Wang",
"Heda",
""
],
[
"Li",
"Kan",
""
]
] | Self-consistency (SC) has been a widely used decoding strategy for chain-of-thought reasoning. Despite bringing significant performance improvements across a variety of multi-step reasoning tasks, it is a high-cost method that requires multiple sampling with the preset size. In this paper, we propose a simple and scalable sampling process, \textbf{E}arly-Stopping \textbf{S}elf-\textbf{C}onsistency (ESC), to greatly reduce the cost of SC without sacrificing performance. On this basis, one control scheme for ESC is further derivated to dynamically choose the performance-cost balance for different tasks and models. To demonstrate ESC's effectiveness, we conducted extensive experiments on three popular categories of reasoning tasks: arithmetic, commonsense and symbolic reasoning over language models with varying scales. The empirical results show that ESC reduces the average number of sampling of chain-of-thought reasoning by a significant margin on six benchmarks, including MATH (-33.8%), GSM8K (-80.1%), StrategyQA (-76.8%), CommonsenseQA (-78.5%), Coin Flip (-84.2%) and Last Letters (-67.4%), while attaining comparable performances. |
1106.5928 | Gabriel Cristobal | Salvador Gabarda and Gabriel Cristobal | Image denoising assessment using anisotropic stack filtering | 13 pages, 8 figures | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we propose a measure of anisotropy as a quality parameter to
estimate the amount of noise in noisy images. The anisotropy of an image can be
determined through a directional measure, using an appropriate statistical
distribution of the information contained in the image. This new measure is
achieved through a stack filtering paradigm. First, we define a local
directional entropy, based on the distribution of 0's and 1's in the
neigborhood of every pixel location of each stack level. Then the entropy
variation of this directional entropy is used to define an anisotropic measure.
The empirical results have shown that this measure can be regarded as an
excellent image noise indicator, which is particularly relevant for quality
assessment of denoising algorithms. The method has been evaluated with
artificial and real-world degraded images.
| [
{
"created": "Wed, 29 Jun 2011 13:12:56 GMT",
"version": "v1"
}
] | 2011-06-30 | [
[
"Gabarda",
"Salvador",
""
],
[
"Cristobal",
"Gabriel",
""
]
] | In this paper we propose a measure of anisotropy as a quality parameter to estimate the amount of noise in noisy images. The anisotropy of an image can be determined through a directional measure, using an appropriate statistical distribution of the information contained in the image. This new measure is achieved through a stack filtering paradigm. First, we define a local directional entropy, based on the distribution of 0's and 1's in the neigborhood of every pixel location of each stack level. Then the entropy variation of this directional entropy is used to define an anisotropic measure. The empirical results have shown that this measure can be regarded as an excellent image noise indicator, which is particularly relevant for quality assessment of denoising algorithms. The method has been evaluated with artificial and real-world degraded images. |
2402.00334 | Zhongxia Yan | Zhongxia Yan, Han Zheng, Cathy Wu | Multi-agent Path Finding for Cooperative Autonomous Driving | 7 pages, 3 figures, IEEE International Conference on Robotics and
Automation (ICRA), 2024 | null | null | null | cs.MA cs.AI cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anticipating possible future deployment of connected and automated vehicles
(CAVs), cooperative autonomous driving at intersections has been studied by
many works in control theory and intelligent transportation across decades.
Simultaneously, recent parallel works in robotics have devised efficient
algorithms for multi-agent path finding (MAPF), though often in environments
with simplified kinematics. In this work, we hybridize insights and algorithms
from MAPF with the structure and heuristics of optimizing the crossing order of
CAVs at signal-free intersections. We devise an optimal and complete algorithm,
Order-based Search with Kinematics Arrival Time Scheduling (OBS-KATS), which
significantly outperforms existing algorithms, fixed heuristics, and
prioritized planning with KATS. The performance is maintained under different
vehicle arrival rates, lane lengths, crossing speeds, and control horizon.
Through ablations and dissections, we offer insight on the contributing factors
to OBS-KATS's performance. Our work is directly applicable to many similarly
scaled traffic and multi-robot scenarios with directed lanes.
| [
{
"created": "Thu, 1 Feb 2024 04:39:15 GMT",
"version": "v1"
}
] | 2024-02-02 | [
[
"Yan",
"Zhongxia",
""
],
[
"Zheng",
"Han",
""
],
[
"Wu",
"Cathy",
""
]
] | Anticipating possible future deployment of connected and automated vehicles (CAVs), cooperative autonomous driving at intersections has been studied by many works in control theory and intelligent transportation across decades. Simultaneously, recent parallel works in robotics have devised efficient algorithms for multi-agent path finding (MAPF), though often in environments with simplified kinematics. In this work, we hybridize insights and algorithms from MAPF with the structure and heuristics of optimizing the crossing order of CAVs at signal-free intersections. We devise an optimal and complete algorithm, Order-based Search with Kinematics Arrival Time Scheduling (OBS-KATS), which significantly outperforms existing algorithms, fixed heuristics, and prioritized planning with KATS. The performance is maintained under different vehicle arrival rates, lane lengths, crossing speeds, and control horizon. Through ablations and dissections, we offer insight on the contributing factors to OBS-KATS's performance. Our work is directly applicable to many similarly scaled traffic and multi-robot scenarios with directed lanes. |
2402.02554 | Alon Zolfi | Oryan Yehezkel, Alon Zolfi, Amit Baras, Yuval Elovici, Asaf Shabtai | DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms
in Vision Transformers | 12 pages, 5 figures | null | null | null | cs.CV cs.CR cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vision transformers have contributed greatly to advancements in the computer
vision domain, demonstrating state-of-the-art performance in diverse tasks
(e.g., image classification, object detection). However, their high
computational requirements grow quadratically with the number of tokens used.
Token sparsification techniques have been proposed to address this issue. These
techniques employ an input-dependent strategy, in which uninformative tokens
are discarded from the computation pipeline, improving the model's efficiency.
However, their dynamism and average-case assumption makes them vulnerable to a
new threat vector - carefully crafted adversarial examples capable of fooling
the sparsification mechanism, resulting in worst-case performance. In this
paper, we present DeSparsify, an attack targeting the availability of vision
transformers that use token sparsification mechanisms. The attack aims to
exhaust the operating system's resources, while maintaining its stealthiness.
Our evaluation demonstrates the attack's effectiveness on three token
sparsification techniques and examines the attack's transferability between
them and its effect on the GPU resources. To mitigate the impact of the attack,
we propose various countermeasures.
| [
{
"created": "Sun, 4 Feb 2024 15:59:35 GMT",
"version": "v1"
}
] | 2024-02-07 | [
[
"Yehezkel",
"Oryan",
""
],
[
"Zolfi",
"Alon",
""
],
[
"Baras",
"Amit",
""
],
[
"Elovici",
"Yuval",
""
],
[
"Shabtai",
"Asaf",
""
]
] | Vision transformers have contributed greatly to advancements in the computer vision domain, demonstrating state-of-the-art performance in diverse tasks (e.g., image classification, object detection). However, their high computational requirements grow quadratically with the number of tokens used. Token sparsification techniques have been proposed to address this issue. These techniques employ an input-dependent strategy, in which uninformative tokens are discarded from the computation pipeline, improving the model's efficiency. However, their dynamism and average-case assumption makes them vulnerable to a new threat vector - carefully crafted adversarial examples capable of fooling the sparsification mechanism, resulting in worst-case performance. In this paper, we present DeSparsify, an attack targeting the availability of vision transformers that use token sparsification mechanisms. The attack aims to exhaust the operating system's resources, while maintaining its stealthiness. Our evaluation demonstrates the attack's effectiveness on three token sparsification techniques and examines the attack's transferability between them and its effect on the GPU resources. To mitigate the impact of the attack, we propose various countermeasures. |
1705.07834 | Debadeepta Dey | Sanjiban Choudhury, Ashish Kapoor, Gireeja Ranade, Sebastian Scherer,
Debadeepta Dey | Adaptive Information Gathering via Imitation Learning | Robotics Science and Systems, 2017 | null | null | null | cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the adaptive information gathering problem, a policy is required to select
an informative sensing location using the history of measurements acquired thus
far. While there is an extensive amount of prior work investigating effective
practical approximations using variants of Shannon's entropy, the efficacy of
such policies heavily depends on the geometric distribution of objects in the
world. On the other hand, the principled approach of employing online POMDP
solvers is rendered impractical by the need to explicitly sample online from a
posterior distribution of world maps.
We present a novel data-driven imitation learning framework to efficiently
train information gathering policies. The policy imitates a clairvoyant oracle
- an oracle that at train time has full knowledge about the world map and can
compute maximally informative sensing locations. We analyze the learnt policy
by showing that offline imitation of a clairvoyant oracle is implicitly
equivalent to online oracle execution in conjunction with posterior sampling.
This observation allows us to obtain powerful near-optimality guarantees for
information gathering problems possessing an adaptive sub-modularity property.
As demonstrated on a spectrum of 2D and 3D exploration problems, the trained
policies enjoy the best of both worlds - they adapt to different world map
distributions while being computationally inexpensive to evaluate.
| [
{
"created": "Mon, 22 May 2017 16:28:55 GMT",
"version": "v1"
}
] | 2017-05-23 | [
[
"Choudhury",
"Sanjiban",
""
],
[
"Kapoor",
"Ashish",
""
],
[
"Ranade",
"Gireeja",
""
],
[
"Scherer",
"Sebastian",
""
],
[
"Dey",
"Debadeepta",
""
]
] | In the adaptive information gathering problem, a policy is required to select an informative sensing location using the history of measurements acquired thus far. While there is an extensive amount of prior work investigating effective practical approximations using variants of Shannon's entropy, the efficacy of such policies heavily depends on the geometric distribution of objects in the world. On the other hand, the principled approach of employing online POMDP solvers is rendered impractical by the need to explicitly sample online from a posterior distribution of world maps. We present a novel data-driven imitation learning framework to efficiently train information gathering policies. The policy imitates a clairvoyant oracle - an oracle that at train time has full knowledge about the world map and can compute maximally informative sensing locations. We analyze the learnt policy by showing that offline imitation of a clairvoyant oracle is implicitly equivalent to online oracle execution in conjunction with posterior sampling. This observation allows us to obtain powerful near-optimality guarantees for information gathering problems possessing an adaptive sub-modularity property. As demonstrated on a spectrum of 2D and 3D exploration problems, the trained policies enjoy the best of both worlds - they adapt to different world map distributions while being computationally inexpensive to evaluate. |
1311.3336 | Alexander Sprintson | C. Jasson Casey, Andrew Sutton, Gabriel Dos Reis, and Alex Sprintson | Eliminating Network Protocol Vulnerabilities Through Abstraction and
Systems Language Design | null | null | null | null | cs.NI cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Incorrect implementations of network protocol message specifications affect
the stability, security, and cost of network system development. Most
implementation defects fall into one of three categories of well defined
message constraints. However, the general process of constructing network
protocol stacks and systems does not capture these categorical con- straints.
We introduce a systems programming language with new abstractions that capture
these constraints. Safe and efficient implementations of standard message
handling operations are synthesized by our compiler, and whole-program analysis
is used to ensure constraints are never violated. We present language examples
using the OpenFlow protocol.
| [
{
"created": "Wed, 13 Nov 2013 23:08:12 GMT",
"version": "v1"
}
] | 2013-11-15 | [
[
"Casey",
"C. Jasson",
""
],
[
"Sutton",
"Andrew",
""
],
[
"Reis",
"Gabriel Dos",
""
],
[
"Sprintson",
"Alex",
""
]
] | Incorrect implementations of network protocol message specifications affect the stability, security, and cost of network system development. Most implementation defects fall into one of three categories of well defined message constraints. However, the general process of constructing network protocol stacks and systems does not capture these categorical con- straints. We introduce a systems programming language with new abstractions that capture these constraints. Safe and efficient implementations of standard message handling operations are synthesized by our compiler, and whole-program analysis is used to ensure constraints are never violated. We present language examples using the OpenFlow protocol. |
2007.09202 | Gopinath Mishra | Arijit Bishnu, Arijit Ghosh, Gopinath Mishra and Manaswi Paraashar | Query Complexity of Global Minimum Cut | 15 pages | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we resolve the query complexity of global minimum cut problem
for a graph by designing a randomized algorithm for approximating the size of
minimum cut in a graph, where the graph can be accessed through local queries
like {\sc Degree}, {\sc Neighbor}, and {\sc Adjacency} queries.
Given $\epsilon \in (0,1)$, the algorithm with high probability outputs an
estimate $\hat{t}$ satisfying the following $(1-\epsilon) t \leq \hat{t} \leq
(1+\epsilon) t$, where $m$ is the number of edges in the graph and $t$ is the
size of minimum cut in the graph. The expected number of local queries used by
our algorithm is $\min\left\{m+n,\frac{m}{t}\right\}\mbox{poly}\left(\log
n,\frac{1}{\epsilon}\right)$ where $n$ is the number of vertices in the graph.
Eden and Rosenbaum showed that $\Omega(m/t)$ many local queries are required
for approximating the size of minimum cut in graphs. These two results together
resolve the query complexity of the problem of estimating the size of minimum
cut in graphs using local queries.
Building on the lower bound of Eden and Rosenbaum, we show that, for all $t
\in \mathbb{N}$, $\Omega(m)$ local queries are required to decide if the size
of the minimum cut in the graph is $t$ or $t-2$. Also, we show that, for any $t
\in \mathbb{N}$, $\Omega(m)$ local queries are required to find all the minimum
cut edges even if it is promised that the input graph has a minimum cut of size
$t$. Both of our lower bound results are randomized, and hold even if we can
make {\sc Random Edge} query apart from local queries.
| [
{
"created": "Fri, 17 Jul 2020 19:37:28 GMT",
"version": "v1"
},
{
"created": "Tue, 11 Aug 2020 09:59:49 GMT",
"version": "v2"
}
] | 2020-08-12 | [
[
"Bishnu",
"Arijit",
""
],
[
"Ghosh",
"Arijit",
""
],
[
"Mishra",
"Gopinath",
""
],
[
"Paraashar",
"Manaswi",
""
]
] | In this work, we resolve the query complexity of global minimum cut problem for a graph by designing a randomized algorithm for approximating the size of minimum cut in a graph, where the graph can be accessed through local queries like {\sc Degree}, {\sc Neighbor}, and {\sc Adjacency} queries. Given $\epsilon \in (0,1)$, the algorithm with high probability outputs an estimate $\hat{t}$ satisfying the following $(1-\epsilon) t \leq \hat{t} \leq (1+\epsilon) t$, where $m$ is the number of edges in the graph and $t$ is the size of minimum cut in the graph. The expected number of local queries used by our algorithm is $\min\left\{m+n,\frac{m}{t}\right\}\mbox{poly}\left(\log n,\frac{1}{\epsilon}\right)$ where $n$ is the number of vertices in the graph. Eden and Rosenbaum showed that $\Omega(m/t)$ many local queries are required for approximating the size of minimum cut in graphs. These two results together resolve the query complexity of the problem of estimating the size of minimum cut in graphs using local queries. Building on the lower bound of Eden and Rosenbaum, we show that, for all $t \in \mathbb{N}$, $\Omega(m)$ local queries are required to decide if the size of the minimum cut in the graph is $t$ or $t-2$. Also, we show that, for any $t \in \mathbb{N}$, $\Omega(m)$ local queries are required to find all the minimum cut edges even if it is promised that the input graph has a minimum cut of size $t$. Both of our lower bound results are randomized, and hold even if we can make {\sc Random Edge} query apart from local queries. |
2008.12709 | Roman Shapovalov | David Novotny, Roman Shapovalov, Andrea Vedaldi | Canonical 3D Deformer Maps: Unifying parametric and non-parametric
methods for dense weakly-supervised category reconstruction | Published at NeurIPS 2020 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose the Canonical 3D Deformer Map, a new representation of the 3D
shape of common object categories that can be learned from a collection of 2D
images of independent objects. Our method builds in a novel way on concepts
from parametric deformation models, non-parametric 3D reconstruction, and
canonical embeddings, combining their individual advantages. In particular, it
learns to associate each image pixel with a deformation model of the
corresponding 3D object point which is canonical, i.e. intrinsic to the
identity of the point and shared across objects of the category. The result is
a method that, given only sparse 2D supervision at training time, can, at test
time, reconstruct the 3D shape and texture of objects from single views, while
establishing meaningful dense correspondences between object instances. It also
achieves state-of-the-art results in dense 3D reconstruction on public
in-the-wild datasets of faces, cars, and birds.
| [
{
"created": "Fri, 28 Aug 2020 15:44:05 GMT",
"version": "v1"
},
{
"created": "Sun, 6 Dec 2020 11:59:06 GMT",
"version": "v2"
}
] | 2020-12-08 | [
[
"Novotny",
"David",
""
],
[
"Shapovalov",
"Roman",
""
],
[
"Vedaldi",
"Andrea",
""
]
] | We propose the Canonical 3D Deformer Map, a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects. Our method builds in a novel way on concepts from parametric deformation models, non-parametric 3D reconstruction, and canonical embeddings, combining their individual advantages. In particular, it learns to associate each image pixel with a deformation model of the corresponding 3D object point which is canonical, i.e. intrinsic to the identity of the point and shared across objects of the category. The result is a method that, given only sparse 2D supervision at training time, can, at test time, reconstruct the 3D shape and texture of objects from single views, while establishing meaningful dense correspondences between object instances. It also achieves state-of-the-art results in dense 3D reconstruction on public in-the-wild datasets of faces, cars, and birds. |
2104.11706 | Max Mowbray Mr | Max Mowbray, Panagiotis Petsagkourakis, Ehecatl Antonio del R\'io
Chanona, Dongda Zhang | Safe Chance Constrained Reinforcement Learning for Batch Process Control | null | null | null | null | cs.LG cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | Reinforcement Learning (RL) controllers have generated excitement within the
control community. The primary advantage of RL controllers relative to existing
methods is their ability to optimize uncertain systems independently of
explicit assumption of process uncertainty. Recent focus on engineering
applications has been directed towards the development of safe RL controllers.
Previous works have proposed approaches to account for constraint satisfaction
through constraint tightening from the domain of stochastic model predictive
control. Here, we extend these approaches to account for plant-model mismatch.
Specifically, we propose a data-driven approach that utilizes Gaussian
processes for the offline simulation model and use the associated posterior
uncertainty prediction to account for joint chance constraints and plant-model
mismatch. The method is benchmarked against nonlinear model predictive control
via case studies. The results demonstrate the ability of the methodology to
account for process uncertainty, enabling satisfaction of joint chance
constraints even in the presence of plant-model mismatch.
| [
{
"created": "Fri, 23 Apr 2021 16:48:46 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Dec 2021 11:29:43 GMT",
"version": "v2"
}
] | 2021-12-07 | [
[
"Mowbray",
"Max",
""
],
[
"Petsagkourakis",
"Panagiotis",
""
],
[
"Chanona",
"Ehecatl Antonio del Río",
""
],
[
"Zhang",
"Dongda",
""
]
] | Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their ability to optimize uncertain systems independently of explicit assumption of process uncertainty. Recent focus on engineering applications has been directed towards the development of safe RL controllers. Previous works have proposed approaches to account for constraint satisfaction through constraint tightening from the domain of stochastic model predictive control. Here, we extend these approaches to account for plant-model mismatch. Specifically, we propose a data-driven approach that utilizes Gaussian processes for the offline simulation model and use the associated posterior uncertainty prediction to account for joint chance constraints and plant-model mismatch. The method is benchmarked against nonlinear model predictive control via case studies. The results demonstrate the ability of the methodology to account for process uncertainty, enabling satisfaction of joint chance constraints even in the presence of plant-model mismatch. |
1708.05448 | Philip Thomas | Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, and Emma
Brunskill | On Ensuring that Intelligent Machines Are Well-Behaved | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Machine learning algorithms are everywhere, ranging from simple data analysis
and pattern recognition tools used across the sciences to complex systems that
achieve super-human performance on various tasks. Ensuring that they are
well-behaved---that they do not, for example, cause harm to humans or act in a
racist or sexist way---is therefore not a hypothetical problem to be dealt with
in the future, but a pressing one that we address here. We propose a new
framework for designing machine learning algorithms that simplifies the problem
of specifying and regulating undesirable behaviors. To show the viability of
this new framework, we use it to create new machine learning algorithms that
preclude the sexist and harmful behaviors exhibited by standard machine
learning algorithms in our experiments. Our framework for designing machine
learning algorithms simplifies the safe and responsible application of machine
learning.
| [
{
"created": "Thu, 17 Aug 2017 21:53:47 GMT",
"version": "v1"
}
] | 2017-08-21 | [
[
"Thomas",
"Philip S.",
""
],
[
"da Silva",
"Bruno Castro",
""
],
[
"Barto",
"Andrew G.",
""
],
[
"Brunskill",
"Emma",
""
]
] | Machine learning algorithms are everywhere, ranging from simple data analysis and pattern recognition tools used across the sciences to complex systems that achieve super-human performance on various tasks. Ensuring that they are well-behaved---that they do not, for example, cause harm to humans or act in a racist or sexist way---is therefore not a hypothetical problem to be dealt with in the future, but a pressing one that we address here. We propose a new framework for designing machine learning algorithms that simplifies the problem of specifying and regulating undesirable behaviors. To show the viability of this new framework, we use it to create new machine learning algorithms that preclude the sexist and harmful behaviors exhibited by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning. |
2112.07130 | Jean Belo Klamti | Jean Belo Klamti and M. Anwar Hasan | A code-based hybrid signcryption scheme | We made some improvment in the paper | null | null | null | cs.CR cs.IT math.IT math.NT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A key encapsulation mechanism (KEM) that takes as input an arbitrary string,
i.e., a tag, is known as tag-KEM, while a scheme that combines signature and
encryption is called signcryption. In this paper, we present a code-based
signcryption tag-KEM scheme. We utilize a code-based signature and an IND-CCA2
(adaptive chosen ciphertext attack) secure version of McEliece's encryption
scheme. The proposed scheme uses an equivalent subcode as a public code for the
receiver, making the NPcompleteness of the subcode equivalence problem to be
one of our main security assumptions. We then base the signcryption tag-KEM to
design a code-based hybrid signcryption scheme. A hybrid scheme deploys
asymmetric- as well as symmetric-key encryption. We give security analyses of
both our schemes in the standard model and prove that they are secure against
IND-CCA2 (indistinguishability under adaptive chosen ciphertext attack) and
SUF-CMA (strong existential unforgeability under chosen message attack).
| [
{
"created": "Tue, 14 Dec 2021 03:02:24 GMT",
"version": "v1"
},
{
"created": "Tue, 21 Mar 2023 14:05:21 GMT",
"version": "v2"
}
] | 2023-03-22 | [
[
"Klamti",
"Jean Belo",
""
],
[
"Hasan",
"M. Anwar",
""
]
] | A key encapsulation mechanism (KEM) that takes as input an arbitrary string, i.e., a tag, is known as tag-KEM, while a scheme that combines signature and encryption is called signcryption. In this paper, we present a code-based signcryption tag-KEM scheme. We utilize a code-based signature and an IND-CCA2 (adaptive chosen ciphertext attack) secure version of McEliece's encryption scheme. The proposed scheme uses an equivalent subcode as a public code for the receiver, making the NPcompleteness of the subcode equivalence problem to be one of our main security assumptions. We then base the signcryption tag-KEM to design a code-based hybrid signcryption scheme. A hybrid scheme deploys asymmetric- as well as symmetric-key encryption. We give security analyses of both our schemes in the standard model and prove that they are secure against IND-CCA2 (indistinguishability under adaptive chosen ciphertext attack) and SUF-CMA (strong existential unforgeability under chosen message attack). |
1601.05748 | Wentao Wu | Wentao Wu, Jeffrey F. Naughton, Harneet Singh | Sampling-Based Query Re-Optimization | This is the extended version of a paper with the same title and
authors that appears in the Proceedings of the ACM SIGMOD International
Conference on Management of Data (SIGMOD 2016) | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite of decades of work, query optimizers still make mistakes on
"difficult" queries because of bad cardinality estimates, often due to the
interaction of multiple predicates and correlations in the data. In this paper,
we propose a low-cost post-processing step that can take a plan produced by the
optimizer, detect when it is likely to have made such a mistake, and take steps
to fix it. Specifically, our solution is a sampling-based iterative procedure
that requires almost no changes to the original query optimizer or query
evaluation mechanism of the system. We show that this indeed imposes low
overhead and catches cases where three widely used optimizers (PostgreSQL and
two commercial systems) make large errors.
| [
{
"created": "Thu, 21 Jan 2016 18:46:18 GMT",
"version": "v1"
}
] | 2016-01-22 | [
[
"Wu",
"Wentao",
""
],
[
"Naughton",
"Jeffrey F.",
""
],
[
"Singh",
"Harneet",
""
]
] | Despite of decades of work, query optimizers still make mistakes on "difficult" queries because of bad cardinality estimates, often due to the interaction of multiple predicates and correlations in the data. In this paper, we propose a low-cost post-processing step that can take a plan produced by the optimizer, detect when it is likely to have made such a mistake, and take steps to fix it. Specifically, our solution is a sampling-based iterative procedure that requires almost no changes to the original query optimizer or query evaluation mechanism of the system. We show that this indeed imposes low overhead and catches cases where three widely used optimizers (PostgreSQL and two commercial systems) make large errors. |
2403.01738 | Qihe Huang | Zhengyang Zhou, Qihe Huang, Binwu Wang, Jianpeng Hou, Kuo Yang, Yuxuan
Liang, Yang Wang | ComS2T: A complementary spatiotemporal learning system for data-adaptive
model evolution | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatiotemporal (ST) learning has become a crucial technique to enable smart
cities and sustainable urban development. Current ST learning models capture
the heterogeneity via various spatial convolution and temporal evolution
blocks. However, rapid urbanization leads to fluctuating distributions in urban
data and city structures over short periods, resulting in existing methods
suffering generalization and data adaptation issues. Despite efforts, existing
methods fail to deal with newly arrived observations and those methods with
generalization capacity are limited in repeated training. Motivated by
complementary learning in neuroscience, we introduce a prompt-based
complementary spatiotemporal learning termed ComS2T, to empower the evolution
of models for data adaptation. ComS2T partitions the neural architecture into a
stable neocortex for consolidating historical memory and a dynamic hippocampus
for new knowledge update. We first disentangle two disjoint structures into
stable and dynamic weights, and then train spatial and temporal prompts by
characterizing distribution of main observations to enable prompts adaptive to
new data. This data-adaptive prompt mechanism, combined with a two-stage
training process, facilitates fine-tuning of the neural architecture
conditioned on prompts, thereby enabling efficient adaptation during testing.
Extensive experiments validate the efficacy of ComS2T in adapting to various
spatiotemporal out-of-distribution scenarios while maintaining efficient
inference capabilities.
| [
{
"created": "Mon, 4 Mar 2024 05:31:29 GMT",
"version": "v1"
}
] | 2024-03-05 | [
[
"Zhou",
"Zhengyang",
""
],
[
"Huang",
"Qihe",
""
],
[
"Wang",
"Binwu",
""
],
[
"Hou",
"Jianpeng",
""
],
[
"Yang",
"Kuo",
""
],
[
"Liang",
"Yuxuan",
""
],
[
"Wang",
"Yang",
""
]
] | Spatiotemporal (ST) learning has become a crucial technique to enable smart cities and sustainable urban development. Current ST learning models capture the heterogeneity via various spatial convolution and temporal evolution blocks. However, rapid urbanization leads to fluctuating distributions in urban data and city structures over short periods, resulting in existing methods suffering generalization and data adaptation issues. Despite efforts, existing methods fail to deal with newly arrived observations and those methods with generalization capacity are limited in repeated training. Motivated by complementary learning in neuroscience, we introduce a prompt-based complementary spatiotemporal learning termed ComS2T, to empower the evolution of models for data adaptation. ComS2T partitions the neural architecture into a stable neocortex for consolidating historical memory and a dynamic hippocampus for new knowledge update. We first disentangle two disjoint structures into stable and dynamic weights, and then train spatial and temporal prompts by characterizing distribution of main observations to enable prompts adaptive to new data. This data-adaptive prompt mechanism, combined with a two-stage training process, facilitates fine-tuning of the neural architecture conditioned on prompts, thereby enabling efficient adaptation during testing. Extensive experiments validate the efficacy of ComS2T in adapting to various spatiotemporal out-of-distribution scenarios while maintaining efficient inference capabilities. |
1801.03003 | Lise Verlaet | Lise Verlaet (LERASS), Sidonie Gallot (LERASS) | Between collective intelligence and semantic web : hypermediating sites.
Contribution to technologies of intelligence | null | EJDE - Electronic Journal of Digital Enterprise (ISSN: 1776-2960),
Academic e-Journal eJ.D.E. (www.scientifics.fr/ejde), 2013,
http://www.scientifics.fr/ejde/html/1776-2960%20R374.htm | null | null | cs.AI cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a new form of access to knowledge through what we
call "hypermediator websites". These hypermediator sites are intermediate
between information devices that just scan the book culture and a "real"
hypertext writing format.
| [
{
"created": "Mon, 8 Jan 2018 13:53:41 GMT",
"version": "v1"
}
] | 2018-01-10 | [
[
"Verlaet",
"Lise",
"",
"LERASS"
],
[
"Gallot",
"Sidonie",
"",
"LERASS"
]
] | In this paper we present a new form of access to knowledge through what we call "hypermediator websites". These hypermediator sites are intermediate between information devices that just scan the book culture and a "real" hypertext writing format. |
1403.1896 | Tao Qin Dr. | Weidong Ma, Bo Zheng, Tao Qin, Pingzhong Tang, Tie-Yan Liu | Online Mechanism Design for Cloud Computing | null | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we study the problem of online mechanism design for resources
allocation and pricing in cloud computing (RAPCC). We show that in general the
allocation problems in RAPCC are NP-hard, and therefore we focus on designing
dominant-strategy incentive compatible (DSIC) mechanisms with good competitive
ratios compared to the offline optimal allocation (with the prior knowledge
about the future jobs). We propose two kinds of DSIC online mechanisms. The
first mechanism, which is based on a greedy allocation rule and leverages a
priority function for allocation, is very fast and has a tight competitive
bound. We discuss several priority functions including exponential and linear
priority functions, and show that the former one has a better competitive
ratio. The second mechanism, which is based on a dynamic program for
allocation, also has a tight competitive ratio and performs better than the
first one when the maximum demand of cloud customers is close to the capacity
of the cloud provider.
| [
{
"created": "Fri, 7 Mar 2014 23:17:15 GMT",
"version": "v1"
}
] | 2014-03-11 | [
[
"Ma",
"Weidong",
""
],
[
"Zheng",
"Bo",
""
],
[
"Qin",
"Tao",
""
],
[
"Tang",
"Pingzhong",
""
],
[
"Liu",
"Tie-Yan",
""
]
] | In this work, we study the problem of online mechanism design for resources allocation and pricing in cloud computing (RAPCC). We show that in general the allocation problems in RAPCC are NP-hard, and therefore we focus on designing dominant-strategy incentive compatible (DSIC) mechanisms with good competitive ratios compared to the offline optimal allocation (with the prior knowledge about the future jobs). We propose two kinds of DSIC online mechanisms. The first mechanism, which is based on a greedy allocation rule and leverages a priority function for allocation, is very fast and has a tight competitive bound. We discuss several priority functions including exponential and linear priority functions, and show that the former one has a better competitive ratio. The second mechanism, which is based on a dynamic program for allocation, also has a tight competitive ratio and performs better than the first one when the maximum demand of cloud customers is close to the capacity of the cloud provider. |
2408.00673 | Shailendra Bhandari | Shailendra Bhandari, Pedro Lincastre and Pedro Lind | Modeling stochastic eye tracking data: A comparison of quantum
generative adversarial networks and Markov models | 8 pages | null | 10.1145/3638530.3664134 | null | cs.NE quant-ph | http://creativecommons.org/licenses/by/4.0/ | We explore the use of quantum generative adversarial networks QGANs for
modeling eye movement velocity data. We assess whether the advanced
computational capabilities of QGANs can enhance the modeling of complex
stochastic distribution beyond the traditional mathematical models,
particularly the Markov model. The findings indicate that while QGANs
demonstrate potential in approximating complex distributions, the Markov model
consistently outperforms in accurately replicating the real data distribution.
This comparison underlines the challenges and avenues for refinement in time
series data generation using quantum computing techniques. It emphasizes the
need for further optimization of quantum models to better align with real-world
data characteristics.
| [
{
"created": "Thu, 1 Aug 2024 16:15:07 GMT",
"version": "v1"
}
] | 2024-08-02 | [
[
"Bhandari",
"Shailendra",
""
],
[
"Lincastre",
"Pedro",
""
],
[
"Lind",
"Pedro",
""
]
] | We explore the use of quantum generative adversarial networks QGANs for modeling eye movement velocity data. We assess whether the advanced computational capabilities of QGANs can enhance the modeling of complex stochastic distribution beyond the traditional mathematical models, particularly the Markov model. The findings indicate that while QGANs demonstrate potential in approximating complex distributions, the Markov model consistently outperforms in accurately replicating the real data distribution. This comparison underlines the challenges and avenues for refinement in time series data generation using quantum computing techniques. It emphasizes the need for further optimization of quantum models to better align with real-world data characteristics. |
2210.17161 | Thokozile Manaka Ms | Thokozile Manaka, Terence van Zyl, Deepak Kar | Improving Cause-of-Death Classification from Verbal Autopsy Reports | Southern African Conference for Artificial Intelligence Research | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | In many lower-and-middle income countries including South Africa, data access
in health facilities is restricted due to patient privacy and confidentiality
policies. Further, since clinical data is unique to individual institutions and
laboratories, there are insufficient data annotation standards and conventions.
As a result of the scarcity of textual data, natural language processing (NLP)
techniques have fared poorly in the health sector. A cause of death (COD) is
often determined by a verbal autopsy (VA) report in places without reliable
death registration systems. A non-clinician field worker does a VA report using
a set of standardized questions as a guide to uncover symptoms of a COD. This
analysis focuses on the textual part of the VA report as a case study to
address the challenge of adapting NLP techniques in the health domain. We
present a system that relies on two transfer learning paradigms of monolingual
learning and multi-source domain adaptation to improve VA narratives for the
target task of the COD classification. We use the Bidirectional Encoder
Representations from Transformers (BERT) and Embeddings from Language Models
(ELMo) models pre-trained on the general English and health domains to extract
features from the VA narratives. Our findings suggest that this transfer
learning system improves the COD classification tasks and that the narrative
text contains valuable information for figuring out a COD. Our results further
show that combining binary VA features and narrative text features learned via
this framework boosts the classification task of COD.
| [
{
"created": "Mon, 31 Oct 2022 09:14:08 GMT",
"version": "v1"
}
] | 2022-11-01 | [
[
"Manaka",
"Thokozile",
""
],
[
"van Zyl",
"Terence",
""
],
[
"Kar",
"Deepak",
""
]
] | In many lower-and-middle income countries including South Africa, data access in health facilities is restricted due to patient privacy and confidentiality policies. Further, since clinical data is unique to individual institutions and laboratories, there are insufficient data annotation standards and conventions. As a result of the scarcity of textual data, natural language processing (NLP) techniques have fared poorly in the health sector. A cause of death (COD) is often determined by a verbal autopsy (VA) report in places without reliable death registration systems. A non-clinician field worker does a VA report using a set of standardized questions as a guide to uncover symptoms of a COD. This analysis focuses on the textual part of the VA report as a case study to address the challenge of adapting NLP techniques in the health domain. We present a system that relies on two transfer learning paradigms of monolingual learning and multi-source domain adaptation to improve VA narratives for the target task of the COD classification. We use the Bidirectional Encoder Representations from Transformers (BERT) and Embeddings from Language Models (ELMo) models pre-trained on the general English and health domains to extract features from the VA narratives. Our findings suggest that this transfer learning system improves the COD classification tasks and that the narrative text contains valuable information for figuring out a COD. Our results further show that combining binary VA features and narrative text features learned via this framework boosts the classification task of COD. |
2008.09020 | Md. Khaledur Rahman | Md. Khaledur Rahman | Training Sensitivity in Graph Isomorphism Network | Accepted for publication in CIKM 2020 | CIKM 2020 | null | null | cs.LG cs.SI stat.ML | http://creativecommons.org/licenses/by-sa/4.0/ | Graph neural network (GNN) is a popular tool to learn the lower-dimensional
representation of a graph. It facilitates the applicability of machine learning
tasks on graphs by incorporating domain-specific features. There are various
options for underlying procedures (such as optimization functions, activation
functions, etc.) that can be considered in the implementation of GNN. However,
most of the existing tools are confined to one approach without any analysis.
Thus, this emerging field lacks a robust implementation ignoring the highly
irregular structure of the real-world graphs. In this paper, we attempt to fill
this gap by studying various alternative functions for a respective module
using a diverse set of benchmark datasets. Our empirical results suggest that
the generally used underlying techniques do not always perform well to capture
the overall structure from a set of graphs.
| [
{
"created": "Wed, 19 Aug 2020 03:50:28 GMT",
"version": "v1"
}
] | 2020-08-21 | [
[
"Rahman",
"Md. Khaledur",
""
]
] | Graph neural network (GNN) is a popular tool to learn the lower-dimensional representation of a graph. It facilitates the applicability of machine learning tasks on graphs by incorporating domain-specific features. There are various options for underlying procedures (such as optimization functions, activation functions, etc.) that can be considered in the implementation of GNN. However, most of the existing tools are confined to one approach without any analysis. Thus, this emerging field lacks a robust implementation ignoring the highly irregular structure of the real-world graphs. In this paper, we attempt to fill this gap by studying various alternative functions for a respective module using a diverse set of benchmark datasets. Our empirical results suggest that the generally used underlying techniques do not always perform well to capture the overall structure from a set of graphs. |
1809.10841 | Jun Zhao Dr | Jun Zhao and Ulrik Lyngs and Nigel Shadbolt | What privacy concerns do parents have about children's mobile apps, and
how can they stay SHARP? | 13 pages, 12 figures, report | null | null | null | cs.CY | http://creativecommons.org/licenses/by-sa/4.0/ | Tablet computers are widely used by young children. A report in 2016 shows
that children aged 5 to 15 years are spending more time online than watching
TV. A 2017 update of the same report shows that parents are becoming more
concerned about their children's online risks compared to the previous year.
Parents are working hard to protect their children's online safety. An
increasing number of parents are setting up content filtering at home or having
regular discussions with their children regarding online risks. However,
although risks related to Social Media platforms or social video sharing sites
(like YouTube) are widely known, risks posed by mobile applications or games
(i.e. `apps') are less known. Behind the cute characters, apps used by children
can not only have the possibility of exposing them to age-inappropriate content
or excessive in-app promotions, but may also make a large amount of their
personal information accessible to third-party online marketing and advertising
industry. Such practices are not unique to children's apps, but young children
are probably less capable of resisting the resulting personalised
advertisements and game promotions. In this report, we present findings from
our online survey of 220 parents with children aged 6-10, mainly from the U.K.
and other western countries, regarding their privacy concerns and expectations
of their children's use of mobile apps. Parents play a key role in children's
use of digital technology, especially for children under 10 years old. Recent
reports have highlighted parents' lack of sufficient support for choosing
appropriate digital content for their children. Our report sheds some initial
light on parents' key struggles and points to immediate steps and possible
areas of future development.
| [
{
"created": "Fri, 28 Sep 2018 03:30:22 GMT",
"version": "v1"
}
] | 2018-10-01 | [
[
"Zhao",
"Jun",
""
],
[
"Lyngs",
"Ulrik",
""
],
[
"Shadbolt",
"Nigel",
""
]
] | Tablet computers are widely used by young children. A report in 2016 shows that children aged 5 to 15 years are spending more time online than watching TV. A 2017 update of the same report shows that parents are becoming more concerned about their children's online risks compared to the previous year. Parents are working hard to protect their children's online safety. An increasing number of parents are setting up content filtering at home or having regular discussions with their children regarding online risks. However, although risks related to Social Media platforms or social video sharing sites (like YouTube) are widely known, risks posed by mobile applications or games (i.e. `apps') are less known. Behind the cute characters, apps used by children can not only have the possibility of exposing them to age-inappropriate content or excessive in-app promotions, but may also make a large amount of their personal information accessible to third-party online marketing and advertising industry. Such practices are not unique to children's apps, but young children are probably less capable of resisting the resulting personalised advertisements and game promotions. In this report, we present findings from our online survey of 220 parents with children aged 6-10, mainly from the U.K. and other western countries, regarding their privacy concerns and expectations of their children's use of mobile apps. Parents play a key role in children's use of digital technology, especially for children under 10 years old. Recent reports have highlighted parents' lack of sufficient support for choosing appropriate digital content for their children. Our report sheds some initial light on parents' key struggles and points to immediate steps and possible areas of future development. |
2104.14928 | Joris Gu\'erin | Joris Guerin, Kevin Delmas and J\'er\'emie Guiochet | Certifying Emergency Landing for Safe Urban UAV | 8 pages, 4 figure, 4 tables To appear in the proceedings of the 7th
international workshop on Safety and Security of Intelligent Vehicles (SSIV
2021) at DSN 2021 | null | null | null | cs.RO cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Unmanned Aerial Vehicles (UAVs) have the potential to be used for many
applications in urban environments. However, allowing UAVs to fly above densely
populated areas raises concerns regarding safety. One of the main safety issues
is the possibility for a failure to cause the loss of navigation capabilities,
which can result in the UAV falling/landing in hazardous areas such as busy
roads, where it can cause fatal accidents. Current standards, such as the SORA
published in 2019, do not consider applicable mitigation techniques to handle
this kind of hazardous situations. Consequently, certifying UAV urban
operations implies to demonstrate very high levels of integrity, which results
in prohibitive development costs. To address this issue, this paper explores
the concept of Emergency Landing (EL). A safety analysis is conducted on an
urban UAV case study, and requirements are proposed to enable the integration
of EL as an acceptable mitigation mean in the SORA. Based on these
requirements, an EL implementation was developed, together with a runtime
monitoring architecture to enhance confidence in the system. Preliminary
qualitative results are presented and the monitor seem to be able to detect
errors of the EL system effectively.
| [
{
"created": "Fri, 30 Apr 2021 11:47:46 GMT",
"version": "v1"
}
] | 2021-05-03 | [
[
"Guerin",
"Joris",
""
],
[
"Delmas",
"Kevin",
""
],
[
"Guiochet",
"Jérémie",
""
]
] | Unmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments. However, allowing UAVs to fly above densely populated areas raises concerns regarding safety. One of the main safety issues is the possibility for a failure to cause the loss of navigation capabilities, which can result in the UAV falling/landing in hazardous areas such as busy roads, where it can cause fatal accidents. Current standards, such as the SORA published in 2019, do not consider applicable mitigation techniques to handle this kind of hazardous situations. Consequently, certifying UAV urban operations implies to demonstrate very high levels of integrity, which results in prohibitive development costs. To address this issue, this paper explores the concept of Emergency Landing (EL). A safety analysis is conducted on an urban UAV case study, and requirements are proposed to enable the integration of EL as an acceptable mitigation mean in the SORA. Based on these requirements, an EL implementation was developed, together with a runtime monitoring architecture to enhance confidence in the system. Preliminary qualitative results are presented and the monitor seem to be able to detect errors of the EL system effectively. |
2110.09470 | Meera Hahn | Meera Hahn, Devendra Chaplot, Shubham Tulsiani, Mustafa Mukadam, James
M. Rehg, Abhinav Gupta | No RL, No Simulation: Learning to Navigate without Navigating | null | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Most prior methods for learning navigation policies require access to
simulation environments, as they need online policy interaction and rely on
ground-truth maps for rewards. However, building simulators is expensive
(requires manual effort for each and every scene) and creates challenges in
transferring learned policies to robotic platforms in the real-world, due to
the sim-to-real domain gap. In this paper, we pose a simple question: Do we
really need active interaction, ground-truth maps or even
reinforcement-learning (RL) in order to solve the image-goal navigation task?
We propose a self-supervised approach to learn to navigate from only passive
videos of roaming. Our approach, No RL, No Simulator (NRNS), is simple and
scalable, yet highly effective. NRNS outperforms RL-based formulations by a
significant margin. We present NRNS as a strong baseline for any future
image-based navigation tasks that use RL or Simulation.
| [
{
"created": "Mon, 18 Oct 2021 17:04:06 GMT",
"version": "v1"
},
{
"created": "Fri, 22 Oct 2021 15:35:03 GMT",
"version": "v2"
}
] | 2021-10-25 | [
[
"Hahn",
"Meera",
""
],
[
"Chaplot",
"Devendra",
""
],
[
"Tulsiani",
"Shubham",
""
],
[
"Mukadam",
"Mustafa",
""
],
[
"Rehg",
"James M.",
""
],
[
"Gupta",
"Abhinav",
""
]
] | Most prior methods for learning navigation policies require access to simulation environments, as they need online policy interaction and rely on ground-truth maps for rewards. However, building simulators is expensive (requires manual effort for each and every scene) and creates challenges in transferring learned policies to robotic platforms in the real-world, due to the sim-to-real domain gap. In this paper, we pose a simple question: Do we really need active interaction, ground-truth maps or even reinforcement-learning (RL) in order to solve the image-goal navigation task? We propose a self-supervised approach to learn to navigate from only passive videos of roaming. Our approach, No RL, No Simulator (NRNS), is simple and scalable, yet highly effective. NRNS outperforms RL-based formulations by a significant margin. We present NRNS as a strong baseline for any future image-based navigation tasks that use RL or Simulation. |
2305.11667 | Jochen Hoenicke | Elisabeth Henkel, Jochen Hoenicke, Tanja Schindler | Choose your Colour: Tree Interpolation for Quantified Formulas in SMT | This is the preprint for the submission published in CADE-29 and also
includes the proofs in the appendix. It has not undergone peer review or any
post-submission improvements or corrections. The Version of Record of this
contribution will be published in CADE-29 | null | null | null | cs.LO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We present a generic tree-interpolation algorithm in the SMT context with
quantifiers. The algorithm takes a proof of unsatisfiability using resolution
and quantifier instantiation and computes interpolants (which may contain
quantifiers). Arbitrary SMT theories are supported, as long as each theory
itself supports tree interpolation for its lemmas. In particular, we show this
for the theory combination of equality with uninterpreted functions and linear
arithmetic. The interpolants can be tweaked by virtually assigning each literal
in the proof to interpolation partitions (colouring the literals) in arbitrary
ways. The algorithm is implemented in SMTInterpol.
| [
{
"created": "Fri, 19 May 2023 13:32:13 GMT",
"version": "v1"
}
] | 2023-05-22 | [
[
"Henkel",
"Elisabeth",
""
],
[
"Hoenicke",
"Jochen",
""
],
[
"Schindler",
"Tanja",
""
]
] | We present a generic tree-interpolation algorithm in the SMT context with quantifiers. The algorithm takes a proof of unsatisfiability using resolution and quantifier instantiation and computes interpolants (which may contain quantifiers). Arbitrary SMT theories are supported, as long as each theory itself supports tree interpolation for its lemmas. In particular, we show this for the theory combination of equality with uninterpreted functions and linear arithmetic. The interpolants can be tweaked by virtually assigning each literal in the proof to interpolation partitions (colouring the literals) in arbitrary ways. The algorithm is implemented in SMTInterpol. |
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