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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1610.00243 | Elad Hoffer | Elad Hoffer, Itay Hubara, Nir Ailon | Deep unsupervised learning through spatial contrasting | null | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Convolutional networks have marked their place over the last few years as the
best performing model for various visual tasks. They are, however, most suited
for supervised learning from large amounts of labeled data. Previous attempts
have been made to use unlabeled data to improve model performance by applying
unsupervised techniques. These attempts require different architectures and
training methods. In this work we present a novel approach for unsupervised
training of Convolutional networks that is based on contrasting between spatial
regions within images. This criterion can be employed within conventional
neural networks and trained using standard techniques such as SGD and
back-propagation, thus complementing supervised methods.
| [
{
"created": "Sun, 2 Oct 2016 08:42:59 GMT",
"version": "v1"
},
{
"created": "Tue, 4 Dec 2018 15:38:31 GMT",
"version": "v2"
}
] | 2018-12-05 | [
[
"Hoffer",
"Elad",
""
],
[
"Hubara",
"Itay",
""
],
[
"Ailon",
"Nir",
""
]
] | Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks. They are, however, most suited for supervised learning from large amounts of labeled data. Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques. These attempts require different architectures and training methods. In this work we present a novel approach for unsupervised training of Convolutional networks that is based on contrasting between spatial regions within images. This criterion can be employed within conventional neural networks and trained using standard techniques such as SGD and back-propagation, thus complementing supervised methods. |
1905.01734 | Marcus Scheunemann | Marcus M. Scheunemann and Christoph Salge and Kerstin Dautenhahn | Intrinsically Motivated Autonomy in Human-Robot Interaction: Human
Perception of Predictive Information in Robots | 12 pages, 1 figure, 1 table, Towards Autonomous Robotic Systems
(TAROS), 2019 | null | 10.1007/978-3-030-23807-0_27 | null | cs.HC cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper we present a fully autonomous and intrinsically motivated robot
usable for HRI experiments. We argue that an intrinsically motivated approach
based on the Predictive Information formalism, like the one presented here,
could provide us with a pathway towards autonomous robot behaviour generation,
that is capable of producing behaviour interesting enough for sustaining the
interaction with humans and without the need for a human operator in the loop.
We present a possible reactive baseline behaviour for comparison for future
research. Participants perceive the baseline and the adaptive, intrinsically
motivated behaviour differently. In our exploratory study we see evidence that
participants perceive an intrinsically motivated robot as less intelligent than
the reactive baseline behaviour. We argue that is mostly due to the high
adaptation rate chosen and the design of the environment. However, we also see
that the adaptive robot is perceived as more warm, a factor which carries more
weight in interpersonal interaction than competence.
| [
{
"created": "Sun, 5 May 2019 19:01:24 GMT",
"version": "v1"
}
] | 2019-07-19 | [
[
"Scheunemann",
"Marcus M.",
""
],
[
"Salge",
"Christoph",
""
],
[
"Dautenhahn",
"Kerstin",
""
]
] | In this paper we present a fully autonomous and intrinsically motivated robot usable for HRI experiments. We argue that an intrinsically motivated approach based on the Predictive Information formalism, like the one presented here, could provide us with a pathway towards autonomous robot behaviour generation, that is capable of producing behaviour interesting enough for sustaining the interaction with humans and without the need for a human operator in the loop. We present a possible reactive baseline behaviour for comparison for future research. Participants perceive the baseline and the adaptive, intrinsically motivated behaviour differently. In our exploratory study we see evidence that participants perceive an intrinsically motivated robot as less intelligent than the reactive baseline behaviour. We argue that is mostly due to the high adaptation rate chosen and the design of the environment. However, we also see that the adaptive robot is perceived as more warm, a factor which carries more weight in interpersonal interaction than competence. |
2102.13351 | Micha Sende | Micha Sende, Melanie Schranz, Gianluca Prato, Etienne Brosse, Omar
Morando, Martina Umlauft | Engineering Swarms of Cyber-Physical Systems with the CPSwarm Workbench | null | null | 10.1007/s10846-021-01430-1 | null | cs.MA | http://creativecommons.org/licenses/by-sa/4.0/ | Engineering swarms of cyber-physical systems (CPSs) is a complex process. We
present the CPSwarm workbench that creates an automated design workflow to ease
this process. This formalized workflow guides the user from modeling, to code
generation, to deployment, both in simulation and on CPS hardware platforms.
The workbench combines existing and emerging tools to solve real-world CPS
swarm problems. As a proof-of-concept, we use the workbench to design a swarm
of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for a
search and rescue (SAR) use case. We evaluate the resulting swarm behaviors on
three levels. First, abstract simulations for rapid prototyping. Second,
detailed simulation to test the correctness of the results. Third, deployment
on hardware to demonstrate the applicability. We measure the swarm performance
in terms of area covered and victims rescued. The results show that the
performance of the swarm is proportional to its size. Despite some manual
steps, the proposed workbench shows to be well suited to ease the complicated
task of deploying a swarm of CPSs.
| [
{
"created": "Fri, 26 Feb 2021 08:01:08 GMT",
"version": "v1"
}
] | 2021-09-10 | [
[
"Sende",
"Micha",
""
],
[
"Schranz",
"Melanie",
""
],
[
"Prato",
"Gianluca",
""
],
[
"Brosse",
"Etienne",
""
],
[
"Morando",
"Omar",
""
],
[
"Umlauft",
"Martina",
""
]
] | Engineering swarms of cyber-physical systems (CPSs) is a complex process. We present the CPSwarm workbench that creates an automated design workflow to ease this process. This formalized workflow guides the user from modeling, to code generation, to deployment, both in simulation and on CPS hardware platforms. The workbench combines existing and emerging tools to solve real-world CPS swarm problems. As a proof-of-concept, we use the workbench to design a swarm of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for a search and rescue (SAR) use case. We evaluate the resulting swarm behaviors on three levels. First, abstract simulations for rapid prototyping. Second, detailed simulation to test the correctness of the results. Third, deployment on hardware to demonstrate the applicability. We measure the swarm performance in terms of area covered and victims rescued. The results show that the performance of the swarm is proportional to its size. Despite some manual steps, the proposed workbench shows to be well suited to ease the complicated task of deploying a swarm of CPSs. |
2303.07347 | Dingfeng Shi | Dingfeng Shi, Yujie Zhong, Qiong Cao, Lin Ma, Jia Li, Dacheng Tao | TriDet: Temporal Action Detection with Relative Boundary Modeling | CVPR2023; Temporal Action Detection; Temporal Action Localization | null | null | null | cs.CV cs.AI cs.MM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we present a one-stage framework TriDet for temporal action
detection. Existing methods often suffer from imprecise boundary predictions
due to the ambiguous action boundaries in videos. To alleviate this problem, we
propose a novel Trident-head to model the action boundary via an estimated
relative probability distribution around the boundary. In the feature pyramid
of TriDet, we propose an efficient Scalable-Granularity Perception (SGP) layer
to mitigate the rank loss problem of self-attention that takes place in the
video features and aggregate information across different temporal
granularities. Benefiting from the Trident-head and the SGP-based feature
pyramid, TriDet achieves state-of-the-art performance on three challenging
benchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computational
costs, compared to previous methods. For example, TriDet hits an average mAP of
$69.3\%$ on THUMOS14, outperforming the previous best by $2.5\%$, but with only
$74.6\%$ of its latency. The code is released to
https://github.com/sssste/TriDet.
| [
{
"created": "Mon, 13 Mar 2023 17:59:59 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Mar 2023 11:26:39 GMT",
"version": "v2"
}
] | 2023-03-17 | [
[
"Shi",
"Dingfeng",
""
],
[
"Zhong",
"Yujie",
""
],
[
"Cao",
"Qiong",
""
],
[
"Ma",
"Lin",
""
],
[
"Li",
"Jia",
""
],
[
"Tao",
"Dacheng",
""
]
] | In this paper, we present a one-stage framework TriDet for temporal action detection. Existing methods often suffer from imprecise boundary predictions due to the ambiguous action boundaries in videos. To alleviate this problem, we propose a novel Trident-head to model the action boundary via an estimated relative probability distribution around the boundary. In the feature pyramid of TriDet, we propose an efficient Scalable-Granularity Perception (SGP) layer to mitigate the rank loss problem of self-attention that takes place in the video features and aggregate information across different temporal granularities. Benefiting from the Trident-head and the SGP-based feature pyramid, TriDet achieves state-of-the-art performance on three challenging benchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computational costs, compared to previous methods. For example, TriDet hits an average mAP of $69.3\%$ on THUMOS14, outperforming the previous best by $2.5\%$, but with only $74.6\%$ of its latency. The code is released to https://github.com/sssste/TriDet. |
1512.01568 | Sanjay Sahay | Aruna Govada, Pravin Joshi, Sahil Mittal and Sanjay K Sahay | Hybrid Approach for Inductive Semi Supervised Learning using Label
Propagation and Support Vector Machine | Presented in the 11th International Conference, MLDM, Germany, July
20 - 21, 2015. Springer, Machine Learning and Data Mining in Pattern
Recognition, LNAI Vol. 9166, p. 199-213, 2015 | null | 10.1007/978-3-319-21024-7_14 | null | cs.LG cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Semi supervised learning methods have gained importance in today's world
because of large expenses and time involved in labeling the unlabeled data by
human experts. The proposed hybrid approach uses SVM and Label Propagation to
label the unlabeled data. In the process, at each step SVM is trained to
minimize the error and thus improve the prediction quality. Experiments are
conducted by using SVM and logistic regression(Logreg). Results prove that SVM
performs tremendously better than Logreg. The approach is tested using 12
datasets of different sizes ranging from the order of 1000s to the order of
10000s. Results show that the proposed approach outperforms Label Propagation
by a large margin with F-measure of almost twice on average. The parallel
version of the proposed approach is also designed and implemented, the analysis
shows that the training time decreases significantly when parallel version is
used.
| [
{
"created": "Wed, 2 Dec 2015 12:04:30 GMT",
"version": "v1"
}
] | 2015-12-08 | [
[
"Govada",
"Aruna",
""
],
[
"Joshi",
"Pravin",
""
],
[
"Mittal",
"Sahil",
""
],
[
"Sahay",
"Sanjay K",
""
]
] | Semi supervised learning methods have gained importance in today's world because of large expenses and time involved in labeling the unlabeled data by human experts. The proposed hybrid approach uses SVM and Label Propagation to label the unlabeled data. In the process, at each step SVM is trained to minimize the error and thus improve the prediction quality. Experiments are conducted by using SVM and logistic regression(Logreg). Results prove that SVM performs tremendously better than Logreg. The approach is tested using 12 datasets of different sizes ranging from the order of 1000s to the order of 10000s. Results show that the proposed approach outperforms Label Propagation by a large margin with F-measure of almost twice on average. The parallel version of the proposed approach is also designed and implemented, the analysis shows that the training time decreases significantly when parallel version is used. |
1012.1547 | Martin Hoefer | Martin Hoefer, Michal Penn, Maria Polukarov, Alexander Skopalik,
Berhold V\"ocking | Considerate Equilibrium | 12 pages, 1 figure | null | null | null | cs.GT cs.DS cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the existence and computational complexity of coalitional
stability concepts based on social networks. Our concepts represent a natural
and rich combinatorial generalization of a recent approach termed partition
equilibrium. We assume that players in a strategic game are embedded in a
social network, and there are coordination constraints that restrict the
potential coalitions that can jointly deviate in the game to the set of cliques
in the social network. In addition, players act in a "considerate" fashion to
ignore potentially profitable (group) deviations if the change in their
strategy may cause a decrease of utility to their neighbors.
We study the properties of such considerate equilibria in application to the
class of resource selection games (RSG). Our main result proves existence of a
considerate equilibrium in all symmetric RSG with strictly increasing delays,
for any social network among the players. The existence proof is constructive
and yields an efficient algorithm. In fact, the computed considerate
equilibrium is a Nash equilibrium for the standard RSG showing that there
exists a state that is stable against selfish and considerate behavior
simultaneously. In addition, we show results on convergence of considerate
dynamics.
| [
{
"created": "Tue, 7 Dec 2010 16:44:20 GMT",
"version": "v1"
}
] | 2010-12-08 | [
[
"Hoefer",
"Martin",
""
],
[
"Penn",
"Michal",
""
],
[
"Polukarov",
"Maria",
""
],
[
"Skopalik",
"Alexander",
""
],
[
"Vöcking",
"Berhold",
""
]
] | We consider the existence and computational complexity of coalitional stability concepts based on social networks. Our concepts represent a natural and rich combinatorial generalization of a recent approach termed partition equilibrium. We assume that players in a strategic game are embedded in a social network, and there are coordination constraints that restrict the potential coalitions that can jointly deviate in the game to the set of cliques in the social network. In addition, players act in a "considerate" fashion to ignore potentially profitable (group) deviations if the change in their strategy may cause a decrease of utility to their neighbors. We study the properties of such considerate equilibria in application to the class of resource selection games (RSG). Our main result proves existence of a considerate equilibrium in all symmetric RSG with strictly increasing delays, for any social network among the players. The existence proof is constructive and yields an efficient algorithm. In fact, the computed considerate equilibrium is a Nash equilibrium for the standard RSG showing that there exists a state that is stable against selfish and considerate behavior simultaneously. In addition, we show results on convergence of considerate dynamics. |
1303.2553 | Yi-ying Tseng | Jen-Yeu Chen, Yi-ying Tseng | Distributed Intrusion Detection of Byzantine Attacks in Wireless
Networks with Random Linear Network Coding | null | International Journal of Distributed Sensor Networks, Volume 2012
(2012), Article ID 758340, 10 pages | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Network coding is an elegant technique where, instead of simply relaying the
packets of information they receive, the nodes of a network are allowed to
combine \emph{several} packets together for transmission and this technique can
be used to achieve the maximum possible information flow in a network and save
the needed number of packet transmissions. Moreover, in an energy-constraint
wireless network such as Wireless Sensor Network (a typical type of wireless ad
hoc network), applying network coding to reduce the number of wireless
transmissions can also prolong the life time of sensor nodes. Although applying
network coding in a wireless sensor network is obviously beneficial, due to the
operation that one transmitting information is actually combination of multiple
other information, it is possible that an error propagation may occur in the
network. This special characteristic also exposes network coding system to a
wide range of error attacks, especially Byzantine attacks. When some adversary
nodes generate error data in the network with network coding, those erroneous
information will be mixed at intermeidate nodes and thus corrupt all the
information reaching a destination. Recent research efforts have shown that
network coding can be combined with classical error control codes and
cryptography for secure communication or misbehavior detection. Nevertheless,
when it comes to Byzantine attacks, these results have limited effect. In fact,
unless we find out those adversary nodes and isolate them, network coding may
perform much worse than pure routing in the presence of malicious nodes. In
this paper, a distributed hierarchical algorithm based on random linear network
coding is developed to detect, locate and isolate malicious nodes.
| [
{
"created": "Mon, 11 Mar 2013 15:50:51 GMT",
"version": "v1"
}
] | 2013-03-12 | [
[
"Chen",
"Jen-Yeu",
""
],
[
"Tseng",
"Yi-ying",
""
]
] | Network coding is an elegant technique where, instead of simply relaying the packets of information they receive, the nodes of a network are allowed to combine \emph{several} packets together for transmission and this technique can be used to achieve the maximum possible information flow in a network and save the needed number of packet transmissions. Moreover, in an energy-constraint wireless network such as Wireless Sensor Network (a typical type of wireless ad hoc network), applying network coding to reduce the number of wireless transmissions can also prolong the life time of sensor nodes. Although applying network coding in a wireless sensor network is obviously beneficial, due to the operation that one transmitting information is actually combination of multiple other information, it is possible that an error propagation may occur in the network. This special characteristic also exposes network coding system to a wide range of error attacks, especially Byzantine attacks. When some adversary nodes generate error data in the network with network coding, those erroneous information will be mixed at intermeidate nodes and thus corrupt all the information reaching a destination. Recent research efforts have shown that network coding can be combined with classical error control codes and cryptography for secure communication or misbehavior detection. Nevertheless, when it comes to Byzantine attacks, these results have limited effect. In fact, unless we find out those adversary nodes and isolate them, network coding may perform much worse than pure routing in the presence of malicious nodes. In this paper, a distributed hierarchical algorithm based on random linear network coding is developed to detect, locate and isolate malicious nodes. |
1806.08946 | Ze Wang | Jingyuan Wang, Ze Wang, Jianfeng Li, Junjie Wu | Multilevel Wavelet Decomposition Network for Interpretable Time Series
Analysis | null | null | null | null | cs.LG eess.SP stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent years have witnessed the unprecedented rising of time series from
almost all kindes of academic and industrial fields. Various types of deep
neural network models have been introduced to time series analysis, but the
important frequency information is yet lack of effective modeling. In light of
this, in this paper we propose a wavelet-based neural network structure called
multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware
deep learning models for time series analysis. mWDN preserves the advantage of
multilevel discrete wavelet decomposition in frequency learning while enables
the fine-tuning of all parameters under a deep neural network framework. Based
on mWDN, we further propose two deep learning models called Residual
Classification Flow (RCF) and multi-frequecy Long Short-Term Memory (mLSTM) for
time series classification and forecasting, respectively. The two models take
all or partial mWDN decomposed sub-series in different frequencies as input,
and resort to the back propagation algorithm to learn all the parameters
globally, which enables seamless embedding of wavelet-based frequency analysis
into deep learning frameworks. Extensive experiments on 40 UCR datasets and a
real-world user volume dataset demonstrate the excellent performance of our
time series models based on mWDN. In particular, we propose an importance
analysis method to mWDN based models, which successfully identifies those
time-series elements and mWDN layers that are crucially important to time
series analysis. This indeed indicates the interpretability advantage of mWDN,
and can be viewed as an indepth exploration to interpretable deep learning.
| [
{
"created": "Sat, 23 Jun 2018 11:12:12 GMT",
"version": "v1"
}
] | 2018-06-26 | [
[
"Wang",
"Jingyuan",
""
],
[
"Wang",
"Ze",
""
],
[
"Li",
"Jianfeng",
""
],
[
"Wu",
"Junjie",
""
]
] | Recent years have witnessed the unprecedented rising of time series from almost all kindes of academic and industrial fields. Various types of deep neural network models have been introduced to time series analysis, but the important frequency information is yet lack of effective modeling. In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis. mWDN preserves the advantage of multilevel discrete wavelet decomposition in frequency learning while enables the fine-tuning of all parameters under a deep neural network framework. Based on mWDN, we further propose two deep learning models called Residual Classification Flow (RCF) and multi-frequecy Long Short-Term Memory (mLSTM) for time series classification and forecasting, respectively. The two models take all or partial mWDN decomposed sub-series in different frequencies as input, and resort to the back propagation algorithm to learn all the parameters globally, which enables seamless embedding of wavelet-based frequency analysis into deep learning frameworks. Extensive experiments on 40 UCR datasets and a real-world user volume dataset demonstrate the excellent performance of our time series models based on mWDN. In particular, we propose an importance analysis method to mWDN based models, which successfully identifies those time-series elements and mWDN layers that are crucially important to time series analysis. This indeed indicates the interpretability advantage of mWDN, and can be viewed as an indepth exploration to interpretable deep learning. |
2012.07030 | Pan Cunhua | Kangda Zhi, Cunhua Pan, Hong Ren and Kezhi Wang | Statistical CSI-based Design for Reconfigurable Intelligent
Surface-aided Massive MIMO Systems with Direct Links | Accepted by IEEE Wireless Communications Letters. Keywords:
Intelligent Reflecting Surface (IRS), reconfigurable intelligent surface
(RIS) | null | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/licenses/by/4.0/ | This paper investigates the performance of reconfigurable intelligent surface
(RIS)-aided massive multiple-input multiple-output (MIMO) systems with direct
links, and the phase shifts of the RIS are designed based on the statistical
channel state information (CSI). We first derive the closed-form expression of
the uplink ergodic data rate. Then, based on the derived expression, we use the
genetic algorithm (GA) to solve the sum data rate maximization problem. With
low-complexity maximal-ratio combination (MRC) and low-overhead statistical
CSI-based scheme, we validate that the RIS can still bring significant
performance gains to traditional massive MIMO systems.
| [
{
"created": "Sun, 13 Dec 2020 10:50:06 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Dec 2020 02:13:05 GMT",
"version": "v2"
},
{
"created": "Mon, 15 Feb 2021 12:49:32 GMT",
"version": "v3"
}
] | 2021-02-16 | [
[
"Zhi",
"Kangda",
""
],
[
"Pan",
"Cunhua",
""
],
[
"Ren",
"Hong",
""
],
[
"Wang",
"Kezhi",
""
]
] | This paper investigates the performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with direct links, and the phase shifts of the RIS are designed based on the statistical channel state information (CSI). We first derive the closed-form expression of the uplink ergodic data rate. Then, based on the derived expression, we use the genetic algorithm (GA) to solve the sum data rate maximization problem. With low-complexity maximal-ratio combination (MRC) and low-overhead statistical CSI-based scheme, we validate that the RIS can still bring significant performance gains to traditional massive MIMO systems. |
2208.14637 | Lucas Morillo-Mendez | Tim Schreiter, Lucas Morillo-Mendez, Ravi T. Chadalavada, Andrey
Rudenko, Erik Alexander Billing, and Achim J. Lilienthal | The Effect of Anthropomorphism on Trust in an Industrial Human-Robot
Interaction | in SCRITA Workshop Proceedings (arXiv:2208.11090) held in conjunction
with 31st IEEE International Conference on Robot & Human Interactive
Communication, 29/08 - 02/09 2022, Naples (Italy) | null | null | SCRITA/2022/3783 | cs.RO cs.HC | http://creativecommons.org/licenses/by-sa/4.0/ | Robots are increasingly deployed in spaces shared with humans, including home
settings and industrial environments. In these environments, the interaction
between humans and robots (HRI) is crucial for safety, legibility, and
efficiency. A key factor in HRI is trust, which modulates the acceptance of the
system. Anthropomorphism has been shown to modulate trust development in a
robot, but robots in industrial environments are not usually anthropomorphic.
We designed a simple interaction in an industrial environment in which an
anthropomorphic mock driver (ARMoD) robot simulates to drive an autonomous
guided vehicle (AGV). The task consisted of a human crossing paths with the
AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The
human and the system needed to negotiate trajectories when crossing paths,
meaning that the human had to attend to the trajectory of the robot to avoid a
collision with it. There was a significant increment in the reported trust
scores in the condition where the ARMoD was present, showing that the presence
of an anthropomorphic robot is enough to modulate the trust, even in limited
interactions as the one we present here.
| [
{
"created": "Wed, 31 Aug 2022 05:19:40 GMT",
"version": "v1"
},
{
"created": "Thu, 1 Sep 2022 14:35:07 GMT",
"version": "v2"
}
] | 2022-09-02 | [
[
"Schreiter",
"Tim",
""
],
[
"Morillo-Mendez",
"Lucas",
""
],
[
"Chadalavada",
"Ravi T.",
""
],
[
"Rudenko",
"Andrey",
""
],
[
"Billing",
"Erik Alexander",
""
],
[
"Lilienthal",
"Achim J.",
""
]
] | Robots are increasingly deployed in spaces shared with humans, including home settings and industrial environments. In these environments, the interaction between humans and robots (HRI) is crucial for safety, legibility, and efficiency. A key factor in HRI is trust, which modulates the acceptance of the system. Anthropomorphism has been shown to modulate trust development in a robot, but robots in industrial environments are not usually anthropomorphic. We designed a simple interaction in an industrial environment in which an anthropomorphic mock driver (ARMoD) robot simulates to drive an autonomous guided vehicle (AGV). The task consisted of a human crossing paths with the AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The human and the system needed to negotiate trajectories when crossing paths, meaning that the human had to attend to the trajectory of the robot to avoid a collision with it. There was a significant increment in the reported trust scores in the condition where the ARMoD was present, showing that the presence of an anthropomorphic robot is enough to modulate the trust, even in limited interactions as the one we present here. |
1006.4035 | Uwe Aickelin | Peer-Olaf Siebers, Uwe Aickelin, Helen Celia, Chris Clegg | Towards the Development of a Simulator for Investigating the Impact of
People Management Practices on Retail Performance | 24 pages, 7 figures, 6 tables, Journal of Simulation 2010 | null | null | null | cs.AI cs.CE cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Often models for understanding the impact of management practices on retail
performance are developed under the assumption of stability, equilibrium and
linearity, whereas retail operations are considered in reality to be dynamic,
non-linear and complex. Alternatively, discrete event and agent-based modelling
are approaches that allow the development of simulation models of heterogeneous
non-equilibrium systems for testing out different scenarios. When developing
simulation models one has to abstract and simplify from the real world, which
means that one has to try and capture the 'essence' of the system required for
developing a representation of the mechanisms that drive the progression in the
real system. Simulation models can be developed at different levels of
abstraction. To know the appropriate level of abstraction for a specific
application is often more of an art than a science. We have developed a retail
branch simulation model to investigate which level of model accuracy is
required for such a model to obtain meaningful results for practitioners.
| [
{
"created": "Mon, 21 Jun 2010 11:23:23 GMT",
"version": "v1"
}
] | 2010-07-05 | [
[
"Siebers",
"Peer-Olaf",
""
],
[
"Aickelin",
"Uwe",
""
],
[
"Celia",
"Helen",
""
],
[
"Clegg",
"Chris",
""
]
] | Often models for understanding the impact of management practices on retail performance are developed under the assumption of stability, equilibrium and linearity, whereas retail operations are considered in reality to be dynamic, non-linear and complex. Alternatively, discrete event and agent-based modelling are approaches that allow the development of simulation models of heterogeneous non-equilibrium systems for testing out different scenarios. When developing simulation models one has to abstract and simplify from the real world, which means that one has to try and capture the 'essence' of the system required for developing a representation of the mechanisms that drive the progression in the real system. Simulation models can be developed at different levels of abstraction. To know the appropriate level of abstraction for a specific application is often more of an art than a science. We have developed a retail branch simulation model to investigate which level of model accuracy is required for such a model to obtain meaningful results for practitioners. |
2105.07596 | Zhepei Wang | Zhepei Wang, Jonah Casebeer, Adam Clemmitt, Efthymios Tzinis, Paris
Smaragdis | Sound Event Detection with Adaptive Frequency Selection | Accepted by IEEE Workshop on Applications of Signal Processing to
Audio and Acoustics 2021 | null | null | null | cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we present HIDACT, a novel network architecture for adaptive
computation for efficiently recognizing acoustic events. We evaluate the model
on a sound event detection task where we train it to adaptively process
frequency bands. The model learns to adapt to the input without requesting all
frequency sub-bands provided. It can make confident predictions within fewer
processing steps, hence reducing the amount of computation. Experimental
results show that HIDACT has comparable performance to baseline models with
more parameters and higher computational complexity. Furthermore, the model can
adjust the amount of computation based on the data and computational budget.
| [
{
"created": "Mon, 17 May 2021 03:57:33 GMT",
"version": "v1"
},
{
"created": "Thu, 29 Jul 2021 05:02:59 GMT",
"version": "v2"
}
] | 2021-07-30 | [
[
"Wang",
"Zhepei",
""
],
[
"Casebeer",
"Jonah",
""
],
[
"Clemmitt",
"Adam",
""
],
[
"Tzinis",
"Efthymios",
""
],
[
"Smaragdis",
"Paris",
""
]
] | In this work, we present HIDACT, a novel network architecture for adaptive computation for efficiently recognizing acoustic events. We evaluate the model on a sound event detection task where we train it to adaptively process frequency bands. The model learns to adapt to the input without requesting all frequency sub-bands provided. It can make confident predictions within fewer processing steps, hence reducing the amount of computation. Experimental results show that HIDACT has comparable performance to baseline models with more parameters and higher computational complexity. Furthermore, the model can adjust the amount of computation based on the data and computational budget. |
2010.13631 | Yiwen Liao | Yiwen Liao, Rapha\"el Latty, Bin Yang | Feature Selection Using Batch-Wise Attenuation and Feature Mask
Normalization | accepted by IJCNN2021 | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Feature selection is generally used as one of the most important
preprocessing techniques in machine learning, as it helps to reduce the
dimensionality of data and assists researchers and practitioners in
understanding data. Thereby, by utilizing feature selection, better performance
and reduced computational consumption, memory complexity and even data amount
can be expected. Although there exist approaches leveraging the power of deep
neural networks to carry out feature selection, many of them often suffer from
sensitive hyperparameters. This paper proposes a feature mask module
(FM-module) for feature selection based on a novel batch-wise attenuation and
feature mask normalization. The proposed method is almost free from
hyperparameters and can be easily integrated into common neural networks as an
embedded feature selection method. Experiments on popular image, text and
speech datasets have shown that our approach is easy to use and has superior
performance in comparison with other state-of-the-art deep-learning-based
feature selection methods.
| [
{
"created": "Mon, 26 Oct 2020 14:46:38 GMT",
"version": "v1"
},
{
"created": "Mon, 8 Mar 2021 13:02:04 GMT",
"version": "v2"
},
{
"created": "Fri, 23 Apr 2021 14:28:38 GMT",
"version": "v3"
}
] | 2021-04-26 | [
[
"Liao",
"Yiwen",
""
],
[
"Latty",
"Raphaël",
""
],
[
"Yang",
"Bin",
""
]
] | Feature selection is generally used as one of the most important preprocessing techniques in machine learning, as it helps to reduce the dimensionality of data and assists researchers and practitioners in understanding data. Thereby, by utilizing feature selection, better performance and reduced computational consumption, memory complexity and even data amount can be expected. Although there exist approaches leveraging the power of deep neural networks to carry out feature selection, many of them often suffer from sensitive hyperparameters. This paper proposes a feature mask module (FM-module) for feature selection based on a novel batch-wise attenuation and feature mask normalization. The proposed method is almost free from hyperparameters and can be easily integrated into common neural networks as an embedded feature selection method. Experiments on popular image, text and speech datasets have shown that our approach is easy to use and has superior performance in comparison with other state-of-the-art deep-learning-based feature selection methods. |
2207.09095 | Meng Hua | Meng Hua, Qingqing Wu, Wen Chen, Octavia A. Dobre, A. Lee Swindlehurst | Secure Intelligent Reflecting Surface Aided Integrated Sensing and
Communication | This paper has been submitted to IEEE journal for possible
publication | null | null | null | cs.IT eess.SP math.IT | http://creativecommons.org/licenses/by/4.0/ | In this paper, an intelligent reflecting surface (IRS) is leveraged to
enhance the physical layer security of an integrated sensing and communication
(ISAC) system in which the IRS is deployed to not only assist the downlink
communication for multiple users, but also create a virtual line-of-sight (LoS)
link for target sensing. In particular, we consider a challenging scenario
where the target may be a suspicious eavesdropper that potentially intercepts
the communication-user information transmitted by the base station (BS). We
investigate the joint design of the phase shifts at the IRS and the
communication as well as radar beamformers at the BS to maximize the sensing
beampattern gain towards the target, subject to the maximum information leakage
to the eavesdropping target and the minimum signal-to-interference-plus-noise
ratio (SINR) required by users. Based on the availability of perfect channel
state information (CSI) of all involved user links and the accurate target
location at the BS, two scenarios are considered and two different optimization
algorithms are proposed. For the ideal scenario where the CSI of the user links
and the target location are perfectly known at the BS, a penalty-based
algorithm is proposed to obtain a high-quality solution. In particular, the
beamformers are obtained with a semi-closed-form solution using Lagrange
duality and the IRS phase shifts are solved for in closed form by applying the
majorization-minimization (MM) method. On the other hand, for the more
practical scenario where the CSI is imperfect and the target location is
uncertain, a robust algorithm based on the $\cal S$-procedure and
sign-definiteness approaches is proposed. Simulation results demonstrate the
effectiveness of the proposed scheme in achieving a trade-off between the
communication quality and the sensing quality.
| [
{
"created": "Tue, 19 Jul 2022 06:15:22 GMT",
"version": "v1"
}
] | 2022-07-20 | [
[
"Hua",
"Meng",
""
],
[
"Wu",
"Qingqing",
""
],
[
"Chen",
"Wen",
""
],
[
"Dobre",
"Octavia A.",
""
],
[
"Swindlehurst",
"A. Lee",
""
]
] | In this paper, an intelligent reflecting surface (IRS) is leveraged to enhance the physical layer security of an integrated sensing and communication (ISAC) system in which the IRS is deployed to not only assist the downlink communication for multiple users, but also create a virtual line-of-sight (LoS) link for target sensing. In particular, we consider a challenging scenario where the target may be a suspicious eavesdropper that potentially intercepts the communication-user information transmitted by the base station (BS). We investigate the joint design of the phase shifts at the IRS and the communication as well as radar beamformers at the BS to maximize the sensing beampattern gain towards the target, subject to the maximum information leakage to the eavesdropping target and the minimum signal-to-interference-plus-noise ratio (SINR) required by users. Based on the availability of perfect channel state information (CSI) of all involved user links and the accurate target location at the BS, two scenarios are considered and two different optimization algorithms are proposed. For the ideal scenario where the CSI of the user links and the target location are perfectly known at the BS, a penalty-based algorithm is proposed to obtain a high-quality solution. In particular, the beamformers are obtained with a semi-closed-form solution using Lagrange duality and the IRS phase shifts are solved for in closed form by applying the majorization-minimization (MM) method. On the other hand, for the more practical scenario where the CSI is imperfect and the target location is uncertain, a robust algorithm based on the $\cal S$-procedure and sign-definiteness approaches is proposed. Simulation results demonstrate the effectiveness of the proposed scheme in achieving a trade-off between the communication quality and the sensing quality. |
2203.01680 | Eduardo Esmanhotto | E. Esmanhotto, T. Hirtzlin, N. Castellani, S. Martin, B. Giraud, F.
Andrieu, J.F. Nodin, D. Querlioz, J-M. Portal and E. Vianello | Experimental demonstration of Single-Level and Multi-Level-Cell
RRAM-based In-Memory Computing with up to 16 parallel operations | Preprint for IRPS2022 | null | null | null | cs.ET | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Crossbar arrays of resistive memories (RRAM) hold the promise of enabling
In-Memory Computing (IMC), but essential challenges due to the impact of device
imperfection and device endurance have yet to be overcome. In this work, we
demonstrate experimentally an RRAM-based IMC logic concept with strong
resilience to RRAM variability, even after one million endurance cycles. Our
work relies on a generalization of the concept of in-memory Scouting Logic, and
we demonstrate it experimentally with up to 16 parallel devices (operands), a
new milestone for RRAM in-memory logic. Moreover, we combine IMC with
Multi-Level-Cell programming and demonstrate experimentally, for the first
time, an IMC RRAM-based MLC 2-bit adder.
| [
{
"created": "Thu, 3 Mar 2022 12:38:12 GMT",
"version": "v1"
}
] | 2022-03-04 | [
[
"Esmanhotto",
"E.",
""
],
[
"Hirtzlin",
"T.",
""
],
[
"Castellani",
"N.",
""
],
[
"Martin",
"S.",
""
],
[
"Giraud",
"B.",
""
],
[
"Andrieu",
"F.",
""
],
[
"Nodin",
"J. F.",
""
],
[
"Querlioz",
"D.",
""
],
[
"Portal",
"J-M.",
""
],
[
"Vianello",
"E.",
""
]
] | Crossbar arrays of resistive memories (RRAM) hold the promise of enabling In-Memory Computing (IMC), but essential challenges due to the impact of device imperfection and device endurance have yet to be overcome. In this work, we demonstrate experimentally an RRAM-based IMC logic concept with strong resilience to RRAM variability, even after one million endurance cycles. Our work relies on a generalization of the concept of in-memory Scouting Logic, and we demonstrate it experimentally with up to 16 parallel devices (operands), a new milestone for RRAM in-memory logic. Moreover, we combine IMC with Multi-Level-Cell programming and demonstrate experimentally, for the first time, an IMC RRAM-based MLC 2-bit adder. |
1810.09610 | EPTCS | Eric C.R. Hehner (University of Toronto) | A Theory of Lazy Imperative Timing | In Proceedings Refine 2018, arXiv:1810.08739 | EPTCS 282, 2018, pp. 1-9 | 10.4204/EPTCS.282.1 | null | cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a theory of lazy imperative timing.
| [
{
"created": "Tue, 23 Oct 2018 00:47:36 GMT",
"version": "v1"
}
] | 2018-10-24 | [
[
"Hehner",
"Eric C. R.",
"",
"University of Toronto"
]
] | We present a theory of lazy imperative timing. |
2309.08680 | Joao P. A. Dantas | Joao P. A. Dantas, Diego Geraldo, Andre N. Costa, Marcos R. O. A.
Maximo, Takashi Yoneyama | ASA-SimaaS: Advancing Digital Transformation through Simulation Services
in the Brazilian Air Force | null | null | null | null | cs.CY cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work explores the use of military simulations in predicting and
evaluating the outcomes of potential scenarios. It highlights the evolution of
military simulations and the increased capabilities that have arisen due to the
advancement of artificial intelligence. Also, it discusses the various
applications of military simulations, such as developing tactics and employment
doctrines, training decision-makers, evaluating new acquisitions, and
developing new technologies. The paper then focuses on the Brazilian Air
Force's efforts to create its own simulation tool, the Aerospace Simulation
Environment (Ambiente de Simula\c{c}\~ao Aeroespacial -- ASA in Portuguese),
and how this cloud-based service called ASA Simulation as a Service
(ASA-SimaaS) can provide greater autonomy and economy for the military force.
The main contribution of this work is to present the ASA-SimaaS solution as a
means of empowering digital transformation in defense scenarios, establishing a
partnership network, and improving the military's simulation capabilities and
competitiveness.
| [
{
"created": "Fri, 15 Sep 2023 18:10:13 GMT",
"version": "v1"
}
] | 2023-09-19 | [
[
"Dantas",
"Joao P. A.",
""
],
[
"Geraldo",
"Diego",
""
],
[
"Costa",
"Andre N.",
""
],
[
"Maximo",
"Marcos R. O. A.",
""
],
[
"Yoneyama",
"Takashi",
""
]
] | This work explores the use of military simulations in predicting and evaluating the outcomes of potential scenarios. It highlights the evolution of military simulations and the increased capabilities that have arisen due to the advancement of artificial intelligence. Also, it discusses the various applications of military simulations, such as developing tactics and employment doctrines, training decision-makers, evaluating new acquisitions, and developing new technologies. The paper then focuses on the Brazilian Air Force's efforts to create its own simulation tool, the Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao Aeroespacial -- ASA in Portuguese), and how this cloud-based service called ASA Simulation as a Service (ASA-SimaaS) can provide greater autonomy and economy for the military force. The main contribution of this work is to present the ASA-SimaaS solution as a means of empowering digital transformation in defense scenarios, establishing a partnership network, and improving the military's simulation capabilities and competitiveness. |
2002.07033 | Byungsoo Kim | Youngduck Choi, Youngnam Lee, Junghyun Cho, Jineon Baek, Byungsoo Kim,
Yeongmin Cha, Dongmin Shin, Chan Bae, Jaewe Heo | Towards an Appropriate Query, Key, and Value Computation for Knowledge
Tracing | L@S 2020 | null | 10.1145/3448139.3448188 | null | cs.LG cs.AI cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge tracing, the act of modeling a student's knowledge through learning
activities, is an extensively studied problem in the field of computer-aided
education. Although models with attention mechanism have outperformed
traditional approaches such as Bayesian knowledge tracing and collaborative
filtering, they share two limitations. Firstly, the models rely on shallow
attention layers and fail to capture complex relations among exercises and
responses over time. Secondly, different combinations of queries, keys and
values for the self-attention layer for knowledge tracing were not extensively
explored. Usual practice of using exercises and interactions (exercise-response
pairs) as queries and keys/values respectively lacks empirical support. In this
paper, we propose a novel Transformer based model for knowledge tracing, SAINT:
Separated Self-AttentIve Neural Knowledge Tracing. SAINT has an encoder-decoder
structure where exercise and response embedding sequence separately enter the
encoder and the decoder respectively, which allows to stack attention layers
multiple times. To the best of our knowledge, this is the first work to suggest
an encoder-decoder model for knowledge tracing that applies deep self-attentive
layers to exercises and responses separately. The empirical evaluations on a
large-scale knowledge tracing dataset show that SAINT achieves the
state-of-the-art performance in knowledge tracing with the improvement of AUC
by 1.8% compared to the current state-of-the-art models.
| [
{
"created": "Fri, 14 Feb 2020 09:21:19 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Jun 2020 05:14:09 GMT",
"version": "v2"
},
{
"created": "Wed, 1 Jul 2020 06:57:13 GMT",
"version": "v3"
},
{
"created": "Tue, 25 Aug 2020 01:02:22 GMT",
"version": "v4"
},
{
"created": "Mon, 1 Feb 2021 02:42:50 GMT",
"version": "v5"
}
] | 2021-02-02 | [
[
"Choi",
"Youngduck",
""
],
[
"Lee",
"Youngnam",
""
],
[
"Cho",
"Junghyun",
""
],
[
"Baek",
"Jineon",
""
],
[
"Kim",
"Byungsoo",
""
],
[
"Cha",
"Yeongmin",
""
],
[
"Shin",
"Dongmin",
""
],
[
"Bae",
"Chan",
""
],
[
"Heo",
"Jaewe",
""
]
] | Knowledge tracing, the act of modeling a student's knowledge through learning activities, is an extensively studied problem in the field of computer-aided education. Although models with attention mechanism have outperformed traditional approaches such as Bayesian knowledge tracing and collaborative filtering, they share two limitations. Firstly, the models rely on shallow attention layers and fail to capture complex relations among exercises and responses over time. Secondly, different combinations of queries, keys and values for the self-attention layer for knowledge tracing were not extensively explored. Usual practice of using exercises and interactions (exercise-response pairs) as queries and keys/values respectively lacks empirical support. In this paper, we propose a novel Transformer based model for knowledge tracing, SAINT: Separated Self-AttentIve Neural Knowledge Tracing. SAINT has an encoder-decoder structure where exercise and response embedding sequence separately enter the encoder and the decoder respectively, which allows to stack attention layers multiple times. To the best of our knowledge, this is the first work to suggest an encoder-decoder model for knowledge tracing that applies deep self-attentive layers to exercises and responses separately. The empirical evaluations on a large-scale knowledge tracing dataset show that SAINT achieves the state-of-the-art performance in knowledge tracing with the improvement of AUC by 1.8% compared to the current state-of-the-art models. |
2303.14550 | Yufan Huang | Yufan Huang, C. Seshadhri, David F. Gleich | Theoretical bounds on the network community profile from low-rank
semi-definite programming | null | null | null | null | cs.SI math.OC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study a new connection between a technical measure called
$\mu$-conductance that arises in the study of Markov chains for sampling convex
bodies and the network community profile that characterizes size-resolved
properties of clusters and communities in social and information networks. The
idea of $\mu$-conductance is similar to the traditional graph conductance, but
disregards sets with small volume. We derive a sequence of optimization
problems including a low-rank semi-definite program from which we can derive a
lower bound on the optimal $\mu$-conductance value. These ideas give the first
theoretically sound bound on the behavior of the network community profile for
a wide range of cluster sizes. The algorithm scales up to graphs with hundreds
of thousands of nodes and we demonstrate how our framework validates the
predicted structures of real-world graphs.
| [
{
"created": "Sat, 25 Mar 2023 19:58:18 GMT",
"version": "v1"
}
] | 2023-03-28 | [
[
"Huang",
"Yufan",
""
],
[
"Seshadhri",
"C.",
""
],
[
"Gleich",
"David F.",
""
]
] | We study a new connection between a technical measure called $\mu$-conductance that arises in the study of Markov chains for sampling convex bodies and the network community profile that characterizes size-resolved properties of clusters and communities in social and information networks. The idea of $\mu$-conductance is similar to the traditional graph conductance, but disregards sets with small volume. We derive a sequence of optimization problems including a low-rank semi-definite program from which we can derive a lower bound on the optimal $\mu$-conductance value. These ideas give the first theoretically sound bound on the behavior of the network community profile for a wide range of cluster sizes. The algorithm scales up to graphs with hundreds of thousands of nodes and we demonstrate how our framework validates the predicted structures of real-world graphs. |
2312.13634 | Dale Miller | Matteo Manighetti and Dale Miller | Peano Arithmetic and $\mu$MALL | 21 pages | null | null | null | cs.LO | http://creativecommons.org/licenses/by/4.0/ | Formal theories of arithmetic have traditionally been based on either
classical or intuitionistic logic, leading to the development of Peano and
Heyting arithmetic, respectively. We propose a use $\mu$MALL as a formal theory
of arithmetic based on linear logic. This formal system is presented as a
sequent calculus proof system that extends the standard proof system for
multiplicative-additive linear logic (MALL) with the addition of the logical
connectives universal and existential quantifiers (first-order quantifiers),
term equality and non-equality, and the least and greatest fixed point
operators. We first demonstrate how functions defined using $\mu$MALL
relational specifications can be computed using a simple proof search
algorithm. By incorporating weakening and contraction into $\mu$MALL, we obtain
$\mu$LK+, a natural candidate for a classical sequent calculus for arithmetic.
While important proof theory results are still lacking for $\mu$LK+ (including
cut-elimination and the completeness of focusing), we prove that $\mu$LK+ is
consistent and that it contains Peano arithmetic. We also prove two
conservativity results regarding $\mu$LK+ over $\mu$MALL.
| [
{
"created": "Thu, 21 Dec 2023 07:50:18 GMT",
"version": "v1"
}
] | 2023-12-22 | [
[
"Manighetti",
"Matteo",
""
],
[
"Miller",
"Dale",
""
]
] | Formal theories of arithmetic have traditionally been based on either classical or intuitionistic logic, leading to the development of Peano and Heyting arithmetic, respectively. We propose a use $\mu$MALL as a formal theory of arithmetic based on linear logic. This formal system is presented as a sequent calculus proof system that extends the standard proof system for multiplicative-additive linear logic (MALL) with the addition of the logical connectives universal and existential quantifiers (first-order quantifiers), term equality and non-equality, and the least and greatest fixed point operators. We first demonstrate how functions defined using $\mu$MALL relational specifications can be computed using a simple proof search algorithm. By incorporating weakening and contraction into $\mu$MALL, we obtain $\mu$LK+, a natural candidate for a classical sequent calculus for arithmetic. While important proof theory results are still lacking for $\mu$LK+ (including cut-elimination and the completeness of focusing), we prove that $\mu$LK+ is consistent and that it contains Peano arithmetic. We also prove two conservativity results regarding $\mu$LK+ over $\mu$MALL. |
2406.19317 | Parand A. Alamdari | Parand A. Alamdari, Yanshuai Cao, Kevin H. Wilson | Jump Starting Bandits with LLM-Generated Prior Knowledge | null | null | null | null | cs.LG cs.AI cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | We present substantial evidence demonstrating the benefits of integrating
Large Language Models (LLMs) with a Contextual Multi-Armed Bandit framework.
Contextual bandits have been widely used in recommendation systems to generate
personalized suggestions based on user-specific contexts. We show that LLMs,
pre-trained on extensive corpora rich in human knowledge and preferences, can
simulate human behaviours well enough to jump-start contextual multi-armed
bandits to reduce online learning regret. We propose an initialization
algorithm for contextual bandits by prompting LLMs to produce a pre-training
dataset of approximate human preferences for the bandit. This significantly
reduces online learning regret and data-gathering costs for training such
models. Our approach is validated empirically through two sets of experiments
with different bandit setups: one which utilizes LLMs to serve as an oracle and
a real-world experiment utilizing data from a conjoint survey experiment.
| [
{
"created": "Thu, 27 Jun 2024 16:52:19 GMT",
"version": "v1"
}
] | 2024-06-28 | [
[
"Alamdari",
"Parand A.",
""
],
[
"Cao",
"Yanshuai",
""
],
[
"Wilson",
"Kevin H.",
""
]
] | We present substantial evidence demonstrating the benefits of integrating Large Language Models (LLMs) with a Contextual Multi-Armed Bandit framework. Contextual bandits have been widely used in recommendation systems to generate personalized suggestions based on user-specific contexts. We show that LLMs, pre-trained on extensive corpora rich in human knowledge and preferences, can simulate human behaviours well enough to jump-start contextual multi-armed bandits to reduce online learning regret. We propose an initialization algorithm for contextual bandits by prompting LLMs to produce a pre-training dataset of approximate human preferences for the bandit. This significantly reduces online learning regret and data-gathering costs for training such models. Our approach is validated empirically through two sets of experiments with different bandit setups: one which utilizes LLMs to serve as an oracle and a real-world experiment utilizing data from a conjoint survey experiment. |
2310.12036 | Mohammad Gheshlaghi Azar | Mohammad Gheshlaghi Azar and Mark Rowland and Bilal Piot and Daniel
Guo and Daniele Calandriello and Michal Valko and R\'emi Munos | A General Theoretical Paradigm to Understand Learning from Human
Preferences | null | null | null | null | cs.AI cs.LG stat.ML | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The prevalent deployment of learning from human preferences through
reinforcement learning (RLHF) relies on two important approximations: the first
assumes that pairwise preferences can be substituted with pointwise rewards.
The second assumes that a reward model trained on these pointwise rewards can
generalize from collected data to out-of-distribution data sampled by the
policy. Recently, Direct Preference Optimisation (DPO) has been proposed as an
approach that bypasses the second approximation and learn directly a policy
from collected data without the reward modelling stage. However, this method
still heavily relies on the first approximation.
In this paper we try to gain a deeper theoretical understanding of these
practical algorithms. In particular we derive a new general objective called
$\Psi$PO for learning from human preferences that is expressed in terms of
pairwise preferences and therefore bypasses both approximations. This new
general objective allows us to perform an in-depth analysis of the behavior of
RLHF and DPO (as special cases of $\Psi$PO) and to identify their potential
pitfalls. We then consider another special case for $\Psi$PO by setting $\Psi$
simply to Identity, for which we can derive an efficient optimisation
procedure, prove performance guarantees and demonstrate its empirical
superiority to DPO on some illustrative examples.
| [
{
"created": "Wed, 18 Oct 2023 15:21:28 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Nov 2023 00:02:49 GMT",
"version": "v2"
}
] | 2023-11-23 | [
[
"Azar",
"Mohammad Gheshlaghi",
""
],
[
"Rowland",
"Mark",
""
],
[
"Piot",
"Bilal",
""
],
[
"Guo",
"Daniel",
""
],
[
"Calandriello",
"Daniele",
""
],
[
"Valko",
"Michal",
""
],
[
"Munos",
"Rémi",
""
]
] | The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preferences can be substituted with pointwise rewards. The second assumes that a reward model trained on these pointwise rewards can generalize from collected data to out-of-distribution data sampled by the policy. Recently, Direct Preference Optimisation (DPO) has been proposed as an approach that bypasses the second approximation and learn directly a policy from collected data without the reward modelling stage. However, this method still heavily relies on the first approximation. In this paper we try to gain a deeper theoretical understanding of these practical algorithms. In particular we derive a new general objective called $\Psi$PO for learning from human preferences that is expressed in terms of pairwise preferences and therefore bypasses both approximations. This new general objective allows us to perform an in-depth analysis of the behavior of RLHF and DPO (as special cases of $\Psi$PO) and to identify their potential pitfalls. We then consider another special case for $\Psi$PO by setting $\Psi$ simply to Identity, for which we can derive an efficient optimisation procedure, prove performance guarantees and demonstrate its empirical superiority to DPO on some illustrative examples. |
1701.02149 | Wenpeng Yin | Wenpeng Yin and Hinrich Sch\"utze | Task-Specific Attentive Pooling of Phrase Alignments Contributes to
Sentence Matching | EACL'2017 long paper. arXiv admin note: substantial text overlap with
arXiv:1604.06896 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work studies comparatively two typical sentence matching tasks: textual
entailment (TE) and answer selection (AS), observing that weaker phrase
alignments are more critical in TE, while stronger phrase alignments deserve
more attention in AS. The key to reach this observation lies in phrase
detection, phrase representation, phrase alignment, and more importantly how to
connect those aligned phrases of different matching degrees with the final
classifier. Prior work (i) has limitations in phrase generation and
representation, or (ii) conducts alignment at word and phrase levels by
handcrafted features or (iii) utilizes a single framework of alignment without
considering the characteristics of specific tasks, which limits the framework's
effectiveness across tasks. We propose an architecture based on Gated Recurrent
Unit that supports (i) representation learning of phrases of arbitrary
granularity and (ii) task-specific attentive pooling of phrase alignments
between two sentences. Experimental results on TE and AS match our observation
and show the effectiveness of our approach.
| [
{
"created": "Mon, 9 Jan 2017 12:03:11 GMT",
"version": "v1"
}
] | 2017-01-10 | [
[
"Yin",
"Wenpeng",
""
],
[
"Schütze",
"Hinrich",
""
]
] | This work studies comparatively two typical sentence matching tasks: textual entailment (TE) and answer selection (AS), observing that weaker phrase alignments are more critical in TE, while stronger phrase alignments deserve more attention in AS. The key to reach this observation lies in phrase detection, phrase representation, phrase alignment, and more importantly how to connect those aligned phrases of different matching degrees with the final classifier. Prior work (i) has limitations in phrase generation and representation, or (ii) conducts alignment at word and phrase levels by handcrafted features or (iii) utilizes a single framework of alignment without considering the characteristics of specific tasks, which limits the framework's effectiveness across tasks. We propose an architecture based on Gated Recurrent Unit that supports (i) representation learning of phrases of arbitrary granularity and (ii) task-specific attentive pooling of phrase alignments between two sentences. Experimental results on TE and AS match our observation and show the effectiveness of our approach. |
2301.09515 | Axel Sauer | Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, Timo Aila | StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale
Text-to-Image Synthesis | Project page: https://sites.google.com/view/stylegan-t/ | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Text-to-image synthesis has recently seen significant progress thanks to
large pretrained language models, large-scale training data, and the
introduction of scalable model families such as diffusion and autoregressive
models. However, the best-performing models require iterative evaluation to
generate a single sample. In contrast, generative adversarial networks (GANs)
only need a single forward pass. They are thus much faster, but they currently
remain far behind the state-of-the-art in large-scale text-to-image synthesis.
This paper aims to identify the necessary steps to regain competitiveness. Our
proposed model, StyleGAN-T, addresses the specific requirements of large-scale
text-to-image synthesis, such as large capacity, stable training on diverse
datasets, strong text alignment, and controllable variation vs. text alignment
tradeoff. StyleGAN-T significantly improves over previous GANs and outperforms
distilled diffusion models - the previous state-of-the-art in fast
text-to-image synthesis - in terms of sample quality and speed.
| [
{
"created": "Mon, 23 Jan 2023 16:05:45 GMT",
"version": "v1"
}
] | 2023-01-24 | [
[
"Sauer",
"Axel",
""
],
[
"Karras",
"Tero",
""
],
[
"Laine",
"Samuli",
""
],
[
"Geiger",
"Andreas",
""
],
[
"Aila",
"Timo",
""
]
] | Text-to-image synthesis has recently seen significant progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models. However, the best-performing models require iterative evaluation to generate a single sample. In contrast, generative adversarial networks (GANs) only need a single forward pass. They are thus much faster, but they currently remain far behind the state-of-the-art in large-scale text-to-image synthesis. This paper aims to identify the necessary steps to regain competitiveness. Our proposed model, StyleGAN-T, addresses the specific requirements of large-scale text-to-image synthesis, such as large capacity, stable training on diverse datasets, strong text alignment, and controllable variation vs. text alignment tradeoff. StyleGAN-T significantly improves over previous GANs and outperforms distilled diffusion models - the previous state-of-the-art in fast text-to-image synthesis - in terms of sample quality and speed. |
2305.01962 | Luisa Herrmann | Luisa Herrmann and Vincent Peth and Sebastian Rudolph | Decidable (Ac)counting with Parikh and Muller: Adding Presburger
Arithmetic to Monadic Second-Order Logic over Tree-Interpretable Structures | extended version, accepted at CSL 2024 | null | null | null | cs.LO cs.FL | http://creativecommons.org/licenses/by/4.0/ | We propose $\omega$MSO$\Join$BAPA, an expressive logic for describing
countable structures, which subsumes and transcends both Counting Monadic
Second-Order Logic (CMSO) and Boolean Algebra with Presburger Arithmetic
(BAPA). We show that satisfiability of $\omega$MSO$\Join$BAPA is decidable over
the class of labeled infinite binary trees, whereas it becomes undecidable even
for a rather mild relaxations. The decidability result is established by an
elaborate multi-step transformation into a particular normal form, followed by
the deployment of Parikh-Muller Tree Automata, a novel kind of automaton for
infinite labeled binary trees, integrating and generalizing both Muller and
Parikh automata while still exhibiting a decidable (in fact PSpace-complete)
emptiness problem. By means of MSO-interpretations, we lift the decidability
result to all tree-interpretable classes of structures, including the classes
of finite/countable structures of bounded treewidth/cliquewidth/partitionwidth.
We generalize the result further by showing that decidability is even preserved
when coupling width-restricted $\omega$MSO$\Join$BAPA with width-unrestricted
two-variable logic with advanced counting. A final showcase demonstrates how
our results can be leveraged to harvest decidability results for expressive
$\mu$-calculi extended by global Presburger constraints.
| [
{
"created": "Wed, 3 May 2023 08:23:34 GMT",
"version": "v1"
},
{
"created": "Thu, 23 Nov 2023 13:40:54 GMT",
"version": "v2"
}
] | 2023-11-27 | [
[
"Herrmann",
"Luisa",
""
],
[
"Peth",
"Vincent",
""
],
[
"Rudolph",
"Sebastian",
""
]
] | We propose $\omega$MSO$\Join$BAPA, an expressive logic for describing countable structures, which subsumes and transcends both Counting Monadic Second-Order Logic (CMSO) and Boolean Algebra with Presburger Arithmetic (BAPA). We show that satisfiability of $\omega$MSO$\Join$BAPA is decidable over the class of labeled infinite binary trees, whereas it becomes undecidable even for a rather mild relaxations. The decidability result is established by an elaborate multi-step transformation into a particular normal form, followed by the deployment of Parikh-Muller Tree Automata, a novel kind of automaton for infinite labeled binary trees, integrating and generalizing both Muller and Parikh automata while still exhibiting a decidable (in fact PSpace-complete) emptiness problem. By means of MSO-interpretations, we lift the decidability result to all tree-interpretable classes of structures, including the classes of finite/countable structures of bounded treewidth/cliquewidth/partitionwidth. We generalize the result further by showing that decidability is even preserved when coupling width-restricted $\omega$MSO$\Join$BAPA with width-unrestricted two-variable logic with advanced counting. A final showcase demonstrates how our results can be leveraged to harvest decidability results for expressive $\mu$-calculi extended by global Presburger constraints. |
1608.04236 | Andrew Brock | Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston | Generative and Discriminative Voxel Modeling with Convolutional Neural
Networks | 9 pages, 5 figures, 2 tables | null | null | null | cs.CV cs.HC cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | When working with three-dimensional data, choice of representation is key. We
explore voxel-based models, and present evidence for the viability of
voxellated representations in applications including shape modeling and object
classification. Our key contributions are methods for training voxel-based
variational autoencoders, a user interface for exploring the latent space
learned by the autoencoder, and a deep convolutional neural network
architecture for object classification. We address challenges unique to
voxel-based representations, and empirically evaluate our models on the
ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the
state of the art for object classification.
| [
{
"created": "Mon, 15 Aug 2016 11:14:35 GMT",
"version": "v1"
},
{
"created": "Tue, 16 Aug 2016 08:06:24 GMT",
"version": "v2"
}
] | 2016-08-17 | [
[
"Brock",
"Andrew",
""
],
[
"Lim",
"Theodore",
""
],
[
"Ritchie",
"J. M.",
""
],
[
"Weston",
"Nick",
""
]
] | When working with three-dimensional data, choice of representation is key. We explore voxel-based models, and present evidence for the viability of voxellated representations in applications including shape modeling and object classification. Our key contributions are methods for training voxel-based variational autoencoders, a user interface for exploring the latent space learned by the autoencoder, and a deep convolutional neural network architecture for object classification. We address challenges unique to voxel-based representations, and empirically evaluate our models on the ModelNet benchmark, where we demonstrate a 51.5% relative improvement in the state of the art for object classification. |
2407.13469 | Abderrahmane Issam | Abderrahmane Issam and Yusuf Can Semerci and Jan Scholtes and
Gerasimos Spanakis | Fixed and Adaptive Simultaneous Machine Translation Strategies Using
Adapters | Accepted at IWSLT 2024 | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Simultaneous machine translation aims at solving the task of real-time
translation by starting to translate before consuming the full input, which
poses challenges in terms of balancing quality and latency of the translation.
The wait-$k$ policy offers a solution by starting to translate after consuming
$k$ words, where the choice of the number $k$ directly affects the latency and
quality. In applications where we seek to keep the choice over latency and
quality at inference, the wait-$k$ policy obliges us to train more than one
model. In this paper, we address the challenge of building one model that can
fulfil multiple latency levels and we achieve this by introducing lightweight
adapter modules into the decoder. The adapters are trained to be specialized
for different wait-$k$ values and compared to other techniques they offer more
flexibility to allow for reaping the benefits of parameter sharing and
minimizing interference. Additionally, we show that by combining with an
adaptive strategy, we can further improve the results. Experiments on two
language directions show that our method outperforms or competes with other
strong baselines on most latency values.
| [
{
"created": "Thu, 18 Jul 2024 12:42:45 GMT",
"version": "v1"
}
] | 2024-07-19 | [
[
"Issam",
"Abderrahmane",
""
],
[
"Semerci",
"Yusuf Can",
""
],
[
"Scholtes",
"Jan",
""
],
[
"Spanakis",
"Gerasimos",
""
]
] | Simultaneous machine translation aims at solving the task of real-time translation by starting to translate before consuming the full input, which poses challenges in terms of balancing quality and latency of the translation. The wait-$k$ policy offers a solution by starting to translate after consuming $k$ words, where the choice of the number $k$ directly affects the latency and quality. In applications where we seek to keep the choice over latency and quality at inference, the wait-$k$ policy obliges us to train more than one model. In this paper, we address the challenge of building one model that can fulfil multiple latency levels and we achieve this by introducing lightweight adapter modules into the decoder. The adapters are trained to be specialized for different wait-$k$ values and compared to other techniques they offer more flexibility to allow for reaping the benefits of parameter sharing and minimizing interference. Additionally, we show that by combining with an adaptive strategy, we can further improve the results. Experiments on two language directions show that our method outperforms or competes with other strong baselines on most latency values. |
2008.04582 | Xinzhu Ma | Xinzhu Ma, Shinan Liu, Zhiyi Xia, Hongwen Zhang, Xingyu Zeng and Wanli
Ouyang | Rethinking Pseudo-LiDAR Representation | ECCV2020. Supplemental Material attached | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The recently proposed pseudo-LiDAR based 3D detectors greatly improve the
benchmark of monocular/stereo 3D detection task. However, the underlying
mechanism remains obscure to the research community. In this paper, we perform
an in-depth investigation and observe that the efficacy of pseudo-LiDAR
representation comes from the coordinate transformation, instead of data
representation itself. Based on this observation, we design an image based CNN
detector named Patch-Net, which is more generalized and can be instantiated as
pseudo-LiDAR based 3D detectors. Moreover, the pseudo-LiDAR data in our
PatchNet is organized as the image representation, which means existing 2D CNN
designs can be easily utilized for extracting deep features from input data and
boosting 3D detection performance. We conduct extensive experiments on the
challenging KITTI dataset, where the proposed PatchNet outperforms all existing
pseudo-LiDAR based counterparts. Code has been made available at:
https://github.com/xinzhuma/patchnet.
| [
{
"created": "Tue, 11 Aug 2020 08:44:18 GMT",
"version": "v1"
}
] | 2020-08-12 | [
[
"Ma",
"Xinzhu",
""
],
[
"Liu",
"Shinan",
""
],
[
"Xia",
"Zhiyi",
""
],
[
"Zhang",
"Hongwen",
""
],
[
"Zeng",
"Xingyu",
""
],
[
"Ouyang",
"Wanli",
""
]
] | The recently proposed pseudo-LiDAR based 3D detectors greatly improve the benchmark of monocular/stereo 3D detection task. However, the underlying mechanism remains obscure to the research community. In this paper, we perform an in-depth investigation and observe that the efficacy of pseudo-LiDAR representation comes from the coordinate transformation, instead of data representation itself. Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors. Moreover, the pseudo-LiDAR data in our PatchNet is organized as the image representation, which means existing 2D CNN designs can be easily utilized for extracting deep features from input data and boosting 3D detection performance. We conduct extensive experiments on the challenging KITTI dataset, where the proposed PatchNet outperforms all existing pseudo-LiDAR based counterparts. Code has been made available at: https://github.com/xinzhuma/patchnet. |
2401.14303 | Liliana Cojocaru | Liliana Cojocaru | On Some Complexity Results for Even Linear Languages | 16 pages, no figure. arXiv admin note: substantial text overlap with
arXiv:1512.09207 | null | null | null | cs.FL cs.CC cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We deal with a normal form for context-free grammars, called Dyck normal
form. This normal form is a syntactical restriction of the Chomsky normal form,
in which the two nonterminals occurring on the right-hand side of a rule are
paired nonterminals. This pairwise property, along with several other terminal
rewriting conditions, makes it possible to define a homomorphism from Dyck
words to words generated by a grammar in Dyck normal form. We prove that for
each context-free language L, there exist an integer K and a homomorphism phi
such that L=phi(D'_K), where D'_K is a subset of D_K and D_K is the one-sided
Dyck language over K letters. As an application we give an alternative proof of
the inclusion of the class of even linear languages in AC1.
| [
{
"created": "Thu, 25 Jan 2024 16:50:57 GMT",
"version": "v1"
}
] | 2024-01-26 | [
[
"Cojocaru",
"Liliana",
""
]
] | We deal with a normal form for context-free grammars, called Dyck normal form. This normal form is a syntactical restriction of the Chomsky normal form, in which the two nonterminals occurring on the right-hand side of a rule are paired nonterminals. This pairwise property, along with several other terminal rewriting conditions, makes it possible to define a homomorphism from Dyck words to words generated by a grammar in Dyck normal form. We prove that for each context-free language L, there exist an integer K and a homomorphism phi such that L=phi(D'_K), where D'_K is a subset of D_K and D_K is the one-sided Dyck language over K letters. As an application we give an alternative proof of the inclusion of the class of even linear languages in AC1. |
2005.00196 | EPTCS | Niels Voorneveld | From Equations to Distinctions: Two Interpretations of Effectful
Computations | In Proceedings MSFP 2020, arXiv:2004.14735 | EPTCS 317, 2020, pp. 1-17 | 10.4204/EPTCS.317.1 | null | cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are several ways to define program equivalence for functional programs
with algebraic effects. We consider two complementing ways to specify
behavioural equivalence. One way is to specify a set of axiomatic equations,
and allow proof methods to show that two programs are equivalent. Another way
is to specify an Eilenberg-Moore algebra, which generate tests that could
distinguish programs. These two methods are said to complement each other if
any two programs can be shown to be equivalent if and only if there is no test
to distinguish them.
In this paper, we study a generic method to formulate from a set of axiomatic
equations an Eilenberg-Moore algebra which complements it. We will look at an
additional condition which must be satisfied for this to work. We then apply
this method to a handful of examples of effects, including probability and
global store, and show they coincide with the usual algebras from the
literature. We will moreover study whether or not it is possible to specify a
set of unary Boolean modalities which could function as distinction-tests
complementing the equational theory.
| [
{
"created": "Fri, 1 May 2020 03:41:39 GMT",
"version": "v1"
}
] | 2020-05-04 | [
[
"Voorneveld",
"Niels",
""
]
] | There are several ways to define program equivalence for functional programs with algebraic effects. We consider two complementing ways to specify behavioural equivalence. One way is to specify a set of axiomatic equations, and allow proof methods to show that two programs are equivalent. Another way is to specify an Eilenberg-Moore algebra, which generate tests that could distinguish programs. These two methods are said to complement each other if any two programs can be shown to be equivalent if and only if there is no test to distinguish them. In this paper, we study a generic method to formulate from a set of axiomatic equations an Eilenberg-Moore algebra which complements it. We will look at an additional condition which must be satisfied for this to work. We then apply this method to a handful of examples of effects, including probability and global store, and show they coincide with the usual algebras from the literature. We will moreover study whether or not it is possible to specify a set of unary Boolean modalities which could function as distinction-tests complementing the equational theory. |
2405.15336 | Matthias Hoffmann | Matthias K. Hoffmann, Julian M\"uhlenhoff, Zhaoheng Ding, Thomas
Sattel, Kathrin Fla{\ss}kamp | An iterative closest point algorithm for marker-free 3D shape
registration of continuum robots | 11 pages, 8 figures, 2 algorithms, journal | null | null | null | cs.RO eess.IV | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Continuum robots have emerged as a promising technology in the medical field
due to their potential of accessing deep sited locations of the human body with
low surgical trauma. When deriving physics-based models for these robots,
evaluating the models poses a significant challenge due to the difficulty in
accurately measuring their intricate shapes. In this work, we present an
optimization based 3D shape registration algorithm for estimation of the
backbone shape of slender continuum robots as part of a pho togrammetric
measurement. Our approach to estimating the backbones optimally matches a
parametric three-dimensional curve to images of the robot. Since we incorporate
an iterative closest point algorithm into our method, we do not need prior
knowledge of the robots position within the respective images. In our
experiments with artificial and real images of a concentric tube continuum
robot, we found an average maximum deviation of the reconstruction from
simulation data of 0.665 mm and 0.939 mm from manual measurements. These
results show that our algorithm is well capable of producing high accuracy
positional data from images of continuum robots.
| [
{
"created": "Fri, 24 May 2024 08:17:40 GMT",
"version": "v1"
}
] | 2024-05-27 | [
[
"Hoffmann",
"Matthias K.",
""
],
[
"Mühlenhoff",
"Julian",
""
],
[
"Ding",
"Zhaoheng",
""
],
[
"Sattel",
"Thomas",
""
],
[
"Flaßkamp",
"Kathrin",
""
]
] | Continuum robots have emerged as a promising technology in the medical field due to their potential of accessing deep sited locations of the human body with low surgical trauma. When deriving physics-based models for these robots, evaluating the models poses a significant challenge due to the difficulty in accurately measuring their intricate shapes. In this work, we present an optimization based 3D shape registration algorithm for estimation of the backbone shape of slender continuum robots as part of a pho togrammetric measurement. Our approach to estimating the backbones optimally matches a parametric three-dimensional curve to images of the robot. Since we incorporate an iterative closest point algorithm into our method, we do not need prior knowledge of the robots position within the respective images. In our experiments with artificial and real images of a concentric tube continuum robot, we found an average maximum deviation of the reconstruction from simulation data of 0.665 mm and 0.939 mm from manual measurements. These results show that our algorithm is well capable of producing high accuracy positional data from images of continuum robots. |
1610.08914 | Lucas Dixon | Ellery Wulczyn, Nithum Thain, Lucas Dixon | Ex Machina: Personal Attacks Seen at Scale | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | The damage personal attacks cause to online discourse motivates many
platforms to try to curb the phenomenon. However, understanding the prevalence
and impact of personal attacks in online platforms at scale remains
surprisingly difficult. The contribution of this paper is to develop and
illustrate a method that combines crowdsourcing and machine learning to analyze
personal attacks at scale. We show an evaluation method for a classifier in
terms of the aggregated number of crowd-workers it can approximate. We apply
our methodology to English Wikipedia, generating a corpus of over 100k high
quality human-labeled comments and 63M machine-labeled ones from a classifier
that is as good as the aggregate of 3 crowd-workers, as measured by the area
under the ROC curve and Spearman correlation. Using this corpus of
machine-labeled scores, our methodology allows us to explore some of the open
questions about the nature of online personal attacks. This reveals that the
majority of personal attacks on Wikipedia are not the result of a few malicious
users, nor primarily the consequence of allowing anonymous contributions from
unregistered users.
| [
{
"created": "Thu, 27 Oct 2016 18:18:18 GMT",
"version": "v1"
},
{
"created": "Sat, 25 Feb 2017 18:38:16 GMT",
"version": "v2"
}
] | 2017-02-28 | [
[
"Wulczyn",
"Ellery",
""
],
[
"Thain",
"Nithum",
""
],
[
"Dixon",
"Lucas",
""
]
] | The damage personal attacks cause to online discourse motivates many platforms to try to curb the phenomenon. However, understanding the prevalence and impact of personal attacks in online platforms at scale remains surprisingly difficult. The contribution of this paper is to develop and illustrate a method that combines crowdsourcing and machine learning to analyze personal attacks at scale. We show an evaluation method for a classifier in terms of the aggregated number of crowd-workers it can approximate. We apply our methodology to English Wikipedia, generating a corpus of over 100k high quality human-labeled comments and 63M machine-labeled ones from a classifier that is as good as the aggregate of 3 crowd-workers, as measured by the area under the ROC curve and Spearman correlation. Using this corpus of machine-labeled scores, our methodology allows us to explore some of the open questions about the nature of online personal attacks. This reveals that the majority of personal attacks on Wikipedia are not the result of a few malicious users, nor primarily the consequence of allowing anonymous contributions from unregistered users. |
1104.0199 | Garth Wells | Kristian B. {\O}lgaard and Garth N. Wells | Optimisations for quadrature representations of finite element tensors
through automated code generation | null | ACM Trans. Math. Softw. 37, 1, Article 8 (January 2010), 23 pages | 10.1145/1644001.1644009 | null | cs.MS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine aspects of the computation of finite element matrices and vectors
which are made possible by automated code generation. Given a variational form
in a syntax which resembles standard mathematical notation, the low-level
computer code for building finite element tensors, typically matrices, vectors
and scalars, can be generated automatically via a form compiler. In particular,
the generation of code for computing finite element matrices using a quadrature
approach is addressed. For quadrature representations, a number of optimisation
strategies which are made possible by automated code generation are presented.
The relative performance of two different automatically generated
representations of finite element matrices is examined, with a particular
emphasis on complicated variational forms. It is shown that approaches which
perform best for simple forms are not tractable for more complicated problems
in terms of run time performance, the time required to generate the code or the
size of the generated code. The approach and optimisations elaborated here are
effective for a range of variational forms.
| [
{
"created": "Fri, 1 Apr 2011 15:29:05 GMT",
"version": "v1"
}
] | 2011-04-04 | [
[
"Ølgaard",
"Kristian B.",
""
],
[
"Wells",
"Garth N.",
""
]
] | We examine aspects of the computation of finite element matrices and vectors which are made possible by automated code generation. Given a variational form in a syntax which resembles standard mathematical notation, the low-level computer code for building finite element tensors, typically matrices, vectors and scalars, can be generated automatically via a form compiler. In particular, the generation of code for computing finite element matrices using a quadrature approach is addressed. For quadrature representations, a number of optimisation strategies which are made possible by automated code generation are presented. The relative performance of two different automatically generated representations of finite element matrices is examined, with a particular emphasis on complicated variational forms. It is shown that approaches which perform best for simple forms are not tractable for more complicated problems in terms of run time performance, the time required to generate the code or the size of the generated code. The approach and optimisations elaborated here are effective for a range of variational forms. |
0704.2544 | Vishwambhar Rathi | Vishwambhar Rathi, Ruediger Urbanke | Existence Proofs of Some EXIT Like Functions | To appear in proc. of ISIT 2007 | null | null | null | cs.IT math.IT | null | The Extended BP (EBP) Generalized EXIT (GEXIT) function introduced in
\cite{MMRU05} plays a fundamental role in the asymptotic analysis of sparse
graph codes. For transmission over the binary erasure channel (BEC) the
analytic properties of the EBP GEXIT function are relatively simple and well
understood. The general case is much harder and even the existence of the curve
is not known in general. We introduce some tools from non-linear analysis which
can be useful to prove the existence of EXIT like curves in some cases. The
main tool is the Krasnoselskii-Rabinowitz (KR) bifurcation theorem.
| [
{
"created": "Thu, 19 Apr 2007 14:36:43 GMT",
"version": "v1"
}
] | 2007-07-13 | [
[
"Rathi",
"Vishwambhar",
""
],
[
"Urbanke",
"Ruediger",
""
]
] | The Extended BP (EBP) Generalized EXIT (GEXIT) function introduced in \cite{MMRU05} plays a fundamental role in the asymptotic analysis of sparse graph codes. For transmission over the binary erasure channel (BEC) the analytic properties of the EBP GEXIT function are relatively simple and well understood. The general case is much harder and even the existence of the curve is not known in general. We introduce some tools from non-linear analysis which can be useful to prove the existence of EXIT like curves in some cases. The main tool is the Krasnoselskii-Rabinowitz (KR) bifurcation theorem. |
1506.02876 | Akram Hakiri | Akram Hakiri (LAAS), Pascal Berthou (UPS, LAAS) | Leveraging SDN for The 5G Networks: Trends, Prospects and Challenges | appears in Software Defined Mobile Networks : Beyond LTE Network
Architecture, Wiley Series in Communications Networking \& Distributed
Systems 2015, Mobile \& Wireless Communications, 978-1-118-90028-4 | null | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Today 4G mobile systems are evolving to provide IP connectivity for diverse
applications and services up to 1Gbps. They are designed to optimize the
network performance, improve cost efficiency and facilitate the uptake of mass
market IP-based services. Nevertheless, the growing demand and the diverse
patterns of mobile traffic place an increasing strain on cellular networks. To
cater to the large volumes of traffic delivered by the new services and
applications, the future 5G network will provide the fundamental infrastructure
for billions of new devices with less predictable traffic patterns will join
the network. The 5G technology is presently in its early research stages, so
researches are currently underway exploring different architectural paths to
address their key drivers. SDN techniques have been seen as promising enablers
for this vision of carrier networks, which will likely play a crucial role in
the design of 5G wireless networks. A critical understanding of this emerging
paradigm is necessary to address the multiple challenges of the future
SDN-enabled 5G technology. To address this requirement, a survey the emerging
trends and prospects, followed by in-depth discussion of major challenges in
this area are discussed.
| [
{
"created": "Tue, 9 Jun 2015 12:10:51 GMT",
"version": "v1"
}
] | 2015-06-10 | [
[
"Hakiri",
"Akram",
"",
"LAAS"
],
[
"Berthou",
"Pascal",
"",
"UPS, LAAS"
]
] | Today 4G mobile systems are evolving to provide IP connectivity for diverse applications and services up to 1Gbps. They are designed to optimize the network performance, improve cost efficiency and facilitate the uptake of mass market IP-based services. Nevertheless, the growing demand and the diverse patterns of mobile traffic place an increasing strain on cellular networks. To cater to the large volumes of traffic delivered by the new services and applications, the future 5G network will provide the fundamental infrastructure for billions of new devices with less predictable traffic patterns will join the network. The 5G technology is presently in its early research stages, so researches are currently underway exploring different architectural paths to address their key drivers. SDN techniques have been seen as promising enablers for this vision of carrier networks, which will likely play a crucial role in the design of 5G wireless networks. A critical understanding of this emerging paradigm is necessary to address the multiple challenges of the future SDN-enabled 5G technology. To address this requirement, a survey the emerging trends and prospects, followed by in-depth discussion of major challenges in this area are discussed. |
1712.09775 | Uche Nnolim | Uche A. Nnolim | Sky detection and log illumination refinement for PDE-based hazy image
contrast enhancement | 22 pages, 13 figures, 5 tables | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This report presents the results of a sky detection technique used to improve
the performance of a previously developed partial differential equation
(PDE)-based hazy image enhancement algorithm. Additionally, a proposed
alternative method utilizes a function for log illumination refinement to
improve de-hazing results while avoiding over-enhancement of sky or homogeneous
regions. The algorithms were tested with several benchmark and calibration
images and compared with several standard algorithms from the literature.
Results indicate that the algorithms yield mostly consistent results and
surpasses several of the other algorithms in terms of colour and contrast
enhancement in addition to improved edge visibility.
| [
{
"created": "Thu, 28 Dec 2017 06:30:26 GMT",
"version": "v1"
},
{
"created": "Sat, 10 Mar 2018 08:56:18 GMT",
"version": "v2"
}
] | 2018-03-13 | [
[
"Nnolim",
"Uche A.",
""
]
] | This report presents the results of a sky detection technique used to improve the performance of a previously developed partial differential equation (PDE)-based hazy image enhancement algorithm. Additionally, a proposed alternative method utilizes a function for log illumination refinement to improve de-hazing results while avoiding over-enhancement of sky or homogeneous regions. The algorithms were tested with several benchmark and calibration images and compared with several standard algorithms from the literature. Results indicate that the algorithms yield mostly consistent results and surpasses several of the other algorithms in terms of colour and contrast enhancement in addition to improved edge visibility. |
2103.10847 | Danny Weyns | Danny Weyns, Bradley Schmerl, Masako Kishida, Alberto Leva, Marin
Litoiu, Necmiye Ozay, Colin Paterson, Kenji Tei | Towards Better Adaptive Systems by Combining MAPE, Control Theory, and
Machine Learning | 7 pages | null | null | null | cs.SE cs.LG cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Two established approaches to engineer adaptive systems are
architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing
(MAPE) loop that reasons over architectural models (aka Knowledge) to make
adaptation decisions, and control-based adaptation that relies on principles of
control theory (CT) to realize adaptation. Recently, we also observe a rapidly
growing interest in applying machine learning (ML) to support different
adaptation mechanisms. While MAPE and CT have particular characteristics and
strengths to be applied independently, in this paper, we are concerned with the
question of how these approaches are related with one another and whether
combining them and supporting them with ML can produce better adaptive systems.
We motivate the combined use of different adaptation approaches using a
scenario of a cloud-based enterprise system and illustrate the analysis when
combining the different approaches. To conclude, we offer a set of open
questions for further research in this interesting area.
| [
{
"created": "Fri, 19 Mar 2021 15:00:08 GMT",
"version": "v1"
}
] | 2021-03-22 | [
[
"Weyns",
"Danny",
""
],
[
"Schmerl",
"Bradley",
""
],
[
"Kishida",
"Masako",
""
],
[
"Leva",
"Alberto",
""
],
[
"Litoiu",
"Marin",
""
],
[
"Ozay",
"Necmiye",
""
],
[
"Paterson",
"Colin",
""
],
[
"Tei",
"Kenji",
""
]
] | Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area. |
1905.11519 | Kush Varshney | Kush R. Varshney and Aleksandra Mojsilovic | Open Platforms for Artificial Intelligence for Social Good: Common
Patterns as a Pathway to True Impact | appearing at the 2019 ICML AI for Social Good Workshop | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The AI for social good movement has now reached a state in which a large
number of one-off demonstrations have illustrated that partnerships of AI
practitioners and social change organizations are possible and can address
problems faced in sustainable development. In this paper, we discuss how moving
from demonstrations to true impact on humanity will require a different course
of action, namely open platforms containing foundational AI capabilities to
support common needs of multiple organizations working in similar topical
areas. We lend credence to this proposal by describing three example patterns
of social good problems and their AI-based solutions: natural language
processing for making sense of international development reports, causal
inference for providing guidance to vulnerable individuals, and
discrimination-aware classification for supporting unbiased allocation
decisions. We argue that the development of such platforms will be possible
through convenings of social change organizations, AI companies, and
grantmaking foundations.
| [
{
"created": "Mon, 27 May 2019 21:42:56 GMT",
"version": "v1"
}
] | 2019-05-29 | [
[
"Varshney",
"Kush R.",
""
],
[
"Mojsilovic",
"Aleksandra",
""
]
] | The AI for social good movement has now reached a state in which a large number of one-off demonstrations have illustrated that partnerships of AI practitioners and social change organizations are possible and can address problems faced in sustainable development. In this paper, we discuss how moving from demonstrations to true impact on humanity will require a different course of action, namely open platforms containing foundational AI capabilities to support common needs of multiple organizations working in similar topical areas. We lend credence to this proposal by describing three example patterns of social good problems and their AI-based solutions: natural language processing for making sense of international development reports, causal inference for providing guidance to vulnerable individuals, and discrimination-aware classification for supporting unbiased allocation decisions. We argue that the development of such platforms will be possible through convenings of social change organizations, AI companies, and grantmaking foundations. |
2403.03082 | Haneol Kang | Haneol Kang, Dong-Wan Choi | Recall-Oriented Continual Learning with Generative Adversarial
Meta-Model | Accepted in AAAI-2024 (Oral presentation) | null | null | null | cs.LG cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The stability-plasticity dilemma is a major challenge in continual learning,
as it involves balancing the conflicting objectives of maintaining performance
on previous tasks while learning new tasks. In this paper, we propose the
recall-oriented continual learning framework to address this challenge.
Inspired by the human brain's ability to separate the mechanisms responsible
for stability and plasticity, our framework consists of a two-level
architecture where an inference network effectively acquires new knowledge and
a generative network recalls past knowledge when necessary. In particular, to
maximize the stability of past knowledge, we investigate the complexity of
knowledge depending on different representations, and thereby introducing
generative adversarial meta-model (GAMM) that incrementally learns
task-specific parameters instead of input data samples of the task. Through our
experiments, we show that our framework not only effectively learns new
knowledge without any disruption but also achieves high stability of previous
knowledge in both task-aware and task-agnostic learning scenarios. Our code is
available at: https://github.com/bigdata-inha/recall-oriented-cl-framework.
| [
{
"created": "Tue, 5 Mar 2024 16:08:59 GMT",
"version": "v1"
}
] | 2024-03-06 | [
[
"Kang",
"Haneol",
""
],
[
"Choi",
"Dong-Wan",
""
]
] | The stability-plasticity dilemma is a major challenge in continual learning, as it involves balancing the conflicting objectives of maintaining performance on previous tasks while learning new tasks. In this paper, we propose the recall-oriented continual learning framework to address this challenge. Inspired by the human brain's ability to separate the mechanisms responsible for stability and plasticity, our framework consists of a two-level architecture where an inference network effectively acquires new knowledge and a generative network recalls past knowledge when necessary. In particular, to maximize the stability of past knowledge, we investigate the complexity of knowledge depending on different representations, and thereby introducing generative adversarial meta-model (GAMM) that incrementally learns task-specific parameters instead of input data samples of the task. Through our experiments, we show that our framework not only effectively learns new knowledge without any disruption but also achieves high stability of previous knowledge in both task-aware and task-agnostic learning scenarios. Our code is available at: https://github.com/bigdata-inha/recall-oriented-cl-framework. |
2110.07184 | Eun Sun Lee | Eun Sun Lee, Junho Kim, and Young Min Kim | Self-Supervised Domain Adaptation for Visual Navigation with Global Map
Consistency | Accepted to WACV 2022 | null | null | null | cs.CV cs.RO | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We propose a light-weight, self-supervised adaptation for a visual navigation
agent to generalize to unseen environment. Given an embodied agent trained in a
noiseless environment, our objective is to transfer the agent to a noisy
environment where actuation and odometry sensor noise is present. Our method
encourages the agent to maximize the consistency between the global maps
generated at different time steps in a round-trip trajectory. The proposed task
is completely self-supervised, not requiring any supervision from ground-truth
pose data or explicit noise model. In addition, optimization of the task
objective is extremely light-weight, as training terminates within a few
minutes on a commodity GPU. Our experiments show that the proposed task helps
the agent to successfully transfer to new, noisy environments. The transferred
agent exhibits improved localization and mapping accuracy, further leading to
enhanced performance in downstream visual navigation tasks. Moreover, we
demonstrate test-time adaptation with our self-supervised task to show its
potential applicability in real-world deployment.
| [
{
"created": "Thu, 14 Oct 2021 07:14:36 GMT",
"version": "v1"
}
] | 2021-10-15 | [
[
"Lee",
"Eun Sun",
""
],
[
"Kim",
"Junho",
""
],
[
"Kim",
"Young Min",
""
]
] | We propose a light-weight, self-supervised adaptation for a visual navigation agent to generalize to unseen environment. Given an embodied agent trained in a noiseless environment, our objective is to transfer the agent to a noisy environment where actuation and odometry sensor noise is present. Our method encourages the agent to maximize the consistency between the global maps generated at different time steps in a round-trip trajectory. The proposed task is completely self-supervised, not requiring any supervision from ground-truth pose data or explicit noise model. In addition, optimization of the task objective is extremely light-weight, as training terminates within a few minutes on a commodity GPU. Our experiments show that the proposed task helps the agent to successfully transfer to new, noisy environments. The transferred agent exhibits improved localization and mapping accuracy, further leading to enhanced performance in downstream visual navigation tasks. Moreover, we demonstrate test-time adaptation with our self-supervised task to show its potential applicability in real-world deployment. |
1806.08037 | Qun Liu | Manohar Karki, Qun Liu, Robert DiBiano, Saikat Basu, Supratik
Mukhopadhyay | Pixel-level Reconstruction and Classification for Noisy Handwritten
Bangla Characters | Paper was accepted at the 16th International Conference on Frontiers
in Handwriting Recognition (ICFHR 2018) | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Classification techniques for images of handwritten characters are
susceptible to noise. Quadtrees can be an efficient representation for learning
from sparse features. In this paper, we improve the effectiveness of
probabilistic quadtrees by using a pixel level classifier to extract the
character pixels and remove noise from handwritten character images. The pixel
level denoiser (a deep belief network) uses the map responses obtained from a
pretrained CNN as features for reconstructing the characters eliminating noise.
We experimentally demonstrate the effectiveness of our approach by
reconstructing and classifying a noisy version of handwritten Bangla Numeral
and Basic Character datasets.
| [
{
"created": "Thu, 21 Jun 2018 01:30:30 GMT",
"version": "v1"
}
] | 2018-06-22 | [
[
"Karki",
"Manohar",
""
],
[
"Liu",
"Qun",
""
],
[
"DiBiano",
"Robert",
""
],
[
"Basu",
"Saikat",
""
],
[
"Mukhopadhyay",
"Supratik",
""
]
] | Classification techniques for images of handwritten characters are susceptible to noise. Quadtrees can be an efficient representation for learning from sparse features. In this paper, we improve the effectiveness of probabilistic quadtrees by using a pixel level classifier to extract the character pixels and remove noise from handwritten character images. The pixel level denoiser (a deep belief network) uses the map responses obtained from a pretrained CNN as features for reconstructing the characters eliminating noise. We experimentally demonstrate the effectiveness of our approach by reconstructing and classifying a noisy version of handwritten Bangla Numeral and Basic Character datasets. |
2403.00582 | Nicolas Scharowski | Nicolas Scharowski, Sebastian A. C. Perrig, Lena Fanya Aeschbach, Nick
von Felten, Klaus Opwis, Philipp Wintersberger, and Florian Br\"uhlmann | To Trust or Distrust Trust Measures: Validating Questionnaires for Trust
in AI | null | null | null | null | cs.HC | http://creativecommons.org/licenses/by/4.0/ | Despite the importance of trust in human-AI interactions, researchers must
adopt questionnaires from other disciplines that lack validation in the AI
context. Motivated by the need for reliable and valid measures, we investigated
the psychometric quality of two trust questionnaires, the Trust between People
and Automation scale (TPA) by Jian et al. (2000) and the Trust Scale for the AI
Context (TAI) by Hoffman et al. (2023). In a pre-registered online experiment
(N = 1485), participants observed interactions with trustworthy and
untrustworthy AI (autonomous vehicle and chatbot). Results support the
psychometric quality of the TAI while revealing opportunities to improve the
TPA, which we outline in our recommendations for using the two questionnaires.
Furthermore, our findings provide additional empirical evidence of trust and
distrust as two distinct constructs that may coexist independently. Building on
our findings, we highlight the opportunities and added value of measuring both
trust and distrust in human-AI research and advocate for further work on both
constructs.
| [
{
"created": "Fri, 1 Mar 2024 15:02:36 GMT",
"version": "v1"
}
] | 2024-03-04 | [
[
"Scharowski",
"Nicolas",
""
],
[
"Perrig",
"Sebastian A. C.",
""
],
[
"Aeschbach",
"Lena Fanya",
""
],
[
"von Felten",
"Nick",
""
],
[
"Opwis",
"Klaus",
""
],
[
"Wintersberger",
"Philipp",
""
],
[
"Brühlmann",
"Florian",
""
]
] | Despite the importance of trust in human-AI interactions, researchers must adopt questionnaires from other disciplines that lack validation in the AI context. Motivated by the need for reliable and valid measures, we investigated the psychometric quality of two trust questionnaires, the Trust between People and Automation scale (TPA) by Jian et al. (2000) and the Trust Scale for the AI Context (TAI) by Hoffman et al. (2023). In a pre-registered online experiment (N = 1485), participants observed interactions with trustworthy and untrustworthy AI (autonomous vehicle and chatbot). Results support the psychometric quality of the TAI while revealing opportunities to improve the TPA, which we outline in our recommendations for using the two questionnaires. Furthermore, our findings provide additional empirical evidence of trust and distrust as two distinct constructs that may coexist independently. Building on our findings, we highlight the opportunities and added value of measuring both trust and distrust in human-AI research and advocate for further work on both constructs. |
2305.01877 | Daniel Hader | Daniel Hader and Matthew J. Patitz | The Impacts of Dimensionality, Diffusion, and Directedness on Intrinsic
Cross-Model Simulation in Tile-Based Self-Assembly | To appear in the proceedings of ICALP 2023 | null | null | null | cs.CG cs.ET | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Algorithmic self-assembly occurs when disorganized components autonomously
combine to form structures and, by their design and the dynamics of the system,
are forced to follow the execution of algorithms. Motivated by applications in
DNA-nanotechnology, investigations in algorithmic tile-based self-assembly have
blossomed into a mature theory with research leveraging tools from
computability theory, complexity theory, information theory, and graph theory
to develop a wide range of models and show that many are computationally
universal, while also exposing powers and limitations of each. Beyond
computational universality, the abstract Tile Assembly Model (aTAM) was shown
to be intrinsically universal (IU), a strong notion of completeness where a
single tile set is capable of simulating all systems within the model; however,
this result required non-deterministic tile attachments. This was later
confirmed necessary when it was shown that the class of directed aTAM systems
is not IU. Building on these results to further investigate the impacts of
other dynamics, Hader et al. examined several tile-assembly models which varied
across (1) the numbers of dimensions used, (2) restrictions based on diffusion
of tiles through space, and (3) whether each system is directed, and showed
which models are IU. Such results have shed much light on the roles of various
aspects of the dynamics of tile-assembly and their effects on the intrinsic
universality of each model. Here we provide direct comparisons of the various
models by considering intrinsic simulations between models. We show that in
some cases one model is more powerful than another, and in others, pairs of
models have mutually exclusive capabilities. This comparison helps to expose
the impacts of these three important aspects and further helps define a
hierarchy of tile-assembly models.
| [
{
"created": "Wed, 3 May 2023 03:38:13 GMT",
"version": "v1"
},
{
"created": "Thu, 4 May 2023 13:55:40 GMT",
"version": "v2"
}
] | 2023-05-05 | [
[
"Hader",
"Daniel",
""
],
[
"Patitz",
"Matthew J.",
""
]
] | Algorithmic self-assembly occurs when disorganized components autonomously combine to form structures and, by their design and the dynamics of the system, are forced to follow the execution of algorithms. Motivated by applications in DNA-nanotechnology, investigations in algorithmic tile-based self-assembly have blossomed into a mature theory with research leveraging tools from computability theory, complexity theory, information theory, and graph theory to develop a wide range of models and show that many are computationally universal, while also exposing powers and limitations of each. Beyond computational universality, the abstract Tile Assembly Model (aTAM) was shown to be intrinsically universal (IU), a strong notion of completeness where a single tile set is capable of simulating all systems within the model; however, this result required non-deterministic tile attachments. This was later confirmed necessary when it was shown that the class of directed aTAM systems is not IU. Building on these results to further investigate the impacts of other dynamics, Hader et al. examined several tile-assembly models which varied across (1) the numbers of dimensions used, (2) restrictions based on diffusion of tiles through space, and (3) whether each system is directed, and showed which models are IU. Such results have shed much light on the roles of various aspects of the dynamics of tile-assembly and their effects on the intrinsic universality of each model. Here we provide direct comparisons of the various models by considering intrinsic simulations between models. We show that in some cases one model is more powerful than another, and in others, pairs of models have mutually exclusive capabilities. This comparison helps to expose the impacts of these three important aspects and further helps define a hierarchy of tile-assembly models. |
2310.12684 | Naresh Kshetri | Naresh Kshetri, Vasudha, Denisa Hoxha | knowCC: Knowledge, awareness of computer & cyber ethics between
CS/non-CS university students | 7 pages, 2 figures | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | Technology has advanced dramatically in the previous several years. There are
also cyber assaults. Cyberattacks pose a possible danger to information
security and the general public. Since data practice and internet consumption
rates continue to upswing, cyber awareness has become progressively important.
Furthermore, as businesses pace their digital transformation with mobile
devices, cloud services, communal media, and Internet of Things services,
cybersecurity has appeared as a critical issue in corporate risk management.
This research focuses on the relations between cybersecurity awareness, cyber
knowledge, computer ethics, cyber ethics, and cyber behavior, as well as
protective tools, across university students in general. The findings express
that while internet users are alert of cyber threats, they only take the most
elementary and easy-to-implement precautions. Several knowledge and awareness
have been proposed to knob the issue of cyber security. It also grants the
principles of cybersecurity in terms of its structure, workforces, and evidence
pertaining to the shield of personal information in the cyber world. The first
step is for people to educate themselves about the negative aspects of the
internet and to learn more about cyber threats so that they can notice when an
attack is taking place. To validate the efficiency of the suggested analysis
between CS and non-CS university students, case study along with several
comparisons are provided.
| [
{
"created": "Thu, 19 Oct 2023 12:29:26 GMT",
"version": "v1"
}
] | 2023-10-23 | [
[
"Kshetri",
"Naresh",
""
],
[
"Vasudha",
"",
""
],
[
"Hoxha",
"Denisa",
""
]
] | Technology has advanced dramatically in the previous several years. There are also cyber assaults. Cyberattacks pose a possible danger to information security and the general public. Since data practice and internet consumption rates continue to upswing, cyber awareness has become progressively important. Furthermore, as businesses pace their digital transformation with mobile devices, cloud services, communal media, and Internet of Things services, cybersecurity has appeared as a critical issue in corporate risk management. This research focuses on the relations between cybersecurity awareness, cyber knowledge, computer ethics, cyber ethics, and cyber behavior, as well as protective tools, across university students in general. The findings express that while internet users are alert of cyber threats, they only take the most elementary and easy-to-implement precautions. Several knowledge and awareness have been proposed to knob the issue of cyber security. It also grants the principles of cybersecurity in terms of its structure, workforces, and evidence pertaining to the shield of personal information in the cyber world. The first step is for people to educate themselves about the negative aspects of the internet and to learn more about cyber threats so that they can notice when an attack is taking place. To validate the efficiency of the suggested analysis between CS and non-CS university students, case study along with several comparisons are provided. |
2004.03794 | Kristjan Arumae | Kristjan Arumae and Parminder Bhatia | CALM: Continuous Adaptive Learning for Language Modeling | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training large language representation models has become a standard in the
natural language processing community. This allows for fine tuning on any
number of specific tasks, however, these large high capacity models can
continue to train on domain specific unlabeled data to make initialization even
more robust for supervised tasks. We demonstrate that in practice these
pre-trained models present performance deterioration in the form of
catastrophic forgetting when evaluated on tasks from a general domain such as
GLUE. In this work we propose CALM, Continuous Adaptive Learning for Language
Modeling: techniques to render models which retain knowledge across multiple
domains. With these methods, we are able to reduce the performance gap across
supervised tasks introduced by task specific models which we demonstrate using
a continual learning setting in biomedical and clinical domains.
| [
{
"created": "Wed, 8 Apr 2020 03:51:17 GMT",
"version": "v1"
}
] | 2020-04-09 | [
[
"Arumae",
"Kristjan",
""
],
[
"Bhatia",
"Parminder",
""
]
] | Training large language representation models has become a standard in the natural language processing community. This allows for fine tuning on any number of specific tasks, however, these large high capacity models can continue to train on domain specific unlabeled data to make initialization even more robust for supervised tasks. We demonstrate that in practice these pre-trained models present performance deterioration in the form of catastrophic forgetting when evaluated on tasks from a general domain such as GLUE. In this work we propose CALM, Continuous Adaptive Learning for Language Modeling: techniques to render models which retain knowledge across multiple domains. With these methods, we are able to reduce the performance gap across supervised tasks introduced by task specific models which we demonstrate using a continual learning setting in biomedical and clinical domains. |
2402.16979 | Lucas Monteiro Paes | Juan Felipe Gomez and Caio Vieira Machado and Lucas Monteiro Paes and
Flavio P. Calmon | Algorithmic Arbitrariness in Content Moderation | null | null | null | null | cs.CY cs.LG cs.SI | http://creativecommons.org/licenses/by/4.0/ | Machine learning (ML) is widely used to moderate online content. Despite its
scalability relative to human moderation, the use of ML introduces unique
challenges to content moderation. One such challenge is predictive
multiplicity: multiple competing models for content classification may perform
equally well on average, yet assign conflicting predictions to the same
content. This multiplicity can result from seemingly innocuous choices during
model development, such as random seed selection for parameter initialization.
We experimentally demonstrate how content moderation tools can arbitrarily
classify samples as toxic, leading to arbitrary restrictions on speech. We
discuss these findings in terms of human rights set out by the International
Covenant on Civil and Political Rights (ICCPR), namely freedom of expression,
non-discrimination, and procedural justice. We analyze (i) the extent of
predictive multiplicity among state-of-the-art LLMs used for detecting toxic
content; (ii) the disparate impact of this arbitrariness across social groups;
and (iii) how model multiplicity compares to unambiguous human classifications.
Our findings indicate that the up-scaled algorithmic moderation risks
legitimizing an algorithmic leviathan, where an algorithm disproportionately
manages human rights. To mitigate such risks, our study underscores the need to
identify and increase the transparency of arbitrariness in content moderation
applications. Since algorithmic content moderation is being fueled by pressing
social concerns, such as disinformation and hate speech, our discussion on
harms raises concerns relevant to policy debates. Our findings also contribute
to content moderation and intermediary liability laws being discussed and
passed in many countries, such as the Digital Services Act in the European
Union, the Online Safety Act in the United Kingdom, and the Fake News Bill in
Brazil.
| [
{
"created": "Mon, 26 Feb 2024 19:27:00 GMT",
"version": "v1"
}
] | 2024-02-28 | [
[
"Gomez",
"Juan Felipe",
""
],
[
"Machado",
"Caio Vieira",
""
],
[
"Paes",
"Lucas Monteiro",
""
],
[
"Calmon",
"Flavio P.",
""
]
] | Machine learning (ML) is widely used to moderate online content. Despite its scalability relative to human moderation, the use of ML introduces unique challenges to content moderation. One such challenge is predictive multiplicity: multiple competing models for content classification may perform equally well on average, yet assign conflicting predictions to the same content. This multiplicity can result from seemingly innocuous choices during model development, such as random seed selection for parameter initialization. We experimentally demonstrate how content moderation tools can arbitrarily classify samples as toxic, leading to arbitrary restrictions on speech. We discuss these findings in terms of human rights set out by the International Covenant on Civil and Political Rights (ICCPR), namely freedom of expression, non-discrimination, and procedural justice. We analyze (i) the extent of predictive multiplicity among state-of-the-art LLMs used for detecting toxic content; (ii) the disparate impact of this arbitrariness across social groups; and (iii) how model multiplicity compares to unambiguous human classifications. Our findings indicate that the up-scaled algorithmic moderation risks legitimizing an algorithmic leviathan, where an algorithm disproportionately manages human rights. To mitigate such risks, our study underscores the need to identify and increase the transparency of arbitrariness in content moderation applications. Since algorithmic content moderation is being fueled by pressing social concerns, such as disinformation and hate speech, our discussion on harms raises concerns relevant to policy debates. Our findings also contribute to content moderation and intermediary liability laws being discussed and passed in many countries, such as the Digital Services Act in the European Union, the Online Safety Act in the United Kingdom, and the Fake News Bill in Brazil. |
1302.4258 | Fanny Yang | Fanny Yang, Volker Pohl, Holger Boche | Phase Retrieval via Structured Modulations in Paley-Wiener Spaces | Submitted to SAMPTA 2013 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper considers the recovery of continuous time signals from the
magnitude of its samples. It uses a combination of structured modulation and
oversampling and provides sufficient conditions on the signal and the sampling
system such that signal recovery is possible. In particular, it is shown that
an average sampling rate of four times the Nyquist rate is sufficient to
reconstruct a signal from its magnitude measurements.
| [
{
"created": "Mon, 18 Feb 2013 13:12:54 GMT",
"version": "v1"
}
] | 2013-02-19 | [
[
"Yang",
"Fanny",
""
],
[
"Pohl",
"Volker",
""
],
[
"Boche",
"Holger",
""
]
] | This paper considers the recovery of continuous time signals from the magnitude of its samples. It uses a combination of structured modulation and oversampling and provides sufficient conditions on the signal and the sampling system such that signal recovery is possible. In particular, it is shown that an average sampling rate of four times the Nyquist rate is sufficient to reconstruct a signal from its magnitude measurements. |
1605.07322 | Asahi Takaoka | Asahi Takaoka | Recognizing Simple-Triangle Graphs by Restricted 2-Chain Subgraph Cover | 13 pages, 14 figures, the Author's accepted version of a paper in
WALCOM 2017, Keywords: Chain cover, Graph sandwich problem, PI graphs,
Simple-triangle graphs, Threshold dimension 2 graphs | WALCOM: Algorithms and Computation. Volume 10167 of Lecture Notes
in Computer Science (2017) 177-189 | 10.1007/978-3-319-53925-6_14 | null | cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A simple-triangle graph (also known as a PI graph) is the intersection graph
of a family of triangles defined by a point on a horizontal line and an
interval on another horizontal line. The recognition problem for
simple-triangle graphs was a longstanding open problem, and recently a
polynomial-time algorithm has been given [G. B. Mertzios, The Recognition of
Simple-Triangle Graphs and of Linear-Interval Orders is Polynomial, SIAM J.
Discrete Math., 29(3):1150--1185, 2015]. Along with the approach of this paper,
we show a simpler recognition algorithm for simple-triangle graphs. To do this,
we provide a polynomial-time algorithm to solve the following problem: Given a
bipartite graph $G$ and a set $F$ of edges of $G$, find a 2-chain subgraph
cover of $G$ such that one of two chain subgraphs has no edges in $F$.
| [
{
"created": "Tue, 24 May 2016 07:26:39 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Apr 2017 05:31:52 GMT",
"version": "v2"
}
] | 2017-04-04 | [
[
"Takaoka",
"Asahi",
""
]
] | A simple-triangle graph (also known as a PI graph) is the intersection graph of a family of triangles defined by a point on a horizontal line and an interval on another horizontal line. The recognition problem for simple-triangle graphs was a longstanding open problem, and recently a polynomial-time algorithm has been given [G. B. Mertzios, The Recognition of Simple-Triangle Graphs and of Linear-Interval Orders is Polynomial, SIAM J. Discrete Math., 29(3):1150--1185, 2015]. Along with the approach of this paper, we show a simpler recognition algorithm for simple-triangle graphs. To do this, we provide a polynomial-time algorithm to solve the following problem: Given a bipartite graph $G$ and a set $F$ of edges of $G$, find a 2-chain subgraph cover of $G$ such that one of two chain subgraphs has no edges in $F$. |
2108.00918 | Kai Yue | Kai Yue, Richeng Jin, Chau-Wai Wong, Huaiyu Dai | Communication-Efficient Federated Learning via Predictive Coding | Accepted by JSTSP | null | null | null | cs.DC cs.AI cs.LG eess.SP | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated learning can enable remote workers to collaboratively train a
shared machine learning model while allowing training data to be kept locally.
In the use case of wireless mobile devices, the communication overhead is a
critical bottleneck due to limited power and bandwidth. Prior work has utilized
various data compression tools such as quantization and sparsification to
reduce the overhead. In this paper, we propose a predictive coding based
compression scheme for federated learning. The scheme has shared prediction
functions among all devices and allows each worker to transmit a compressed
residual vector derived from the reference. In each communication round, we
select the predictor and quantizer based on the rate-distortion cost, and
further reduce the redundancy with entropy coding. Extensive simulations reveal
that the communication cost can be reduced up to 99% with even better learning
performance when compared with other baseline methods.
| [
{
"created": "Mon, 2 Aug 2021 14:12:19 GMT",
"version": "v1"
},
{
"created": "Sun, 9 Jan 2022 01:20:06 GMT",
"version": "v2"
}
] | 2022-01-11 | [
[
"Yue",
"Kai",
""
],
[
"Jin",
"Richeng",
""
],
[
"Wong",
"Chau-Wai",
""
],
[
"Dai",
"Huaiyu",
""
]
] | Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhead is a critical bottleneck due to limited power and bandwidth. Prior work has utilized various data compression tools such as quantization and sparsification to reduce the overhead. In this paper, we propose a predictive coding based compression scheme for federated learning. The scheme has shared prediction functions among all devices and allows each worker to transmit a compressed residual vector derived from the reference. In each communication round, we select the predictor and quantizer based on the rate-distortion cost, and further reduce the redundancy with entropy coding. Extensive simulations reveal that the communication cost can be reduced up to 99% with even better learning performance when compared with other baseline methods. |
2305.10349 | Christopher MacLellan | Lane Lawley and Christopher J. MacLellan | Interactive Learning of Hierarchical Tasks from Dialog with GPT | 5 pages, 3 figures | null | null | null | cs.HC cs.AI cs.CL | http://creativecommons.org/licenses/by/4.0/ | We present a system for interpretable, symbolic, interactive task learning
from dialog using a GPT model as a conversational front-end. The learned tasks
are represented as hierarchical decompositions of predicate-argument structures
with scoped variable arguments. By using a GPT model to convert interactive
dialog into a semantic representation, and then recursively asking for
definitions of unknown steps, we show that hierarchical task knowledge can be
acquired and re-used in a natural and unrestrained conversational environment.
We compare our system to a similar architecture using a more conventional
parser and show that our system tolerates a much wider variety of linguistic
variance.
| [
{
"created": "Wed, 17 May 2023 16:32:40 GMT",
"version": "v1"
}
] | 2023-05-18 | [
[
"Lawley",
"Lane",
""
],
[
"MacLellan",
"Christopher J.",
""
]
] | We present a system for interpretable, symbolic, interactive task learning from dialog using a GPT model as a conversational front-end. The learned tasks are represented as hierarchical decompositions of predicate-argument structures with scoped variable arguments. By using a GPT model to convert interactive dialog into a semantic representation, and then recursively asking for definitions of unknown steps, we show that hierarchical task knowledge can be acquired and re-used in a natural and unrestrained conversational environment. We compare our system to a similar architecture using a more conventional parser and show that our system tolerates a much wider variety of linguistic variance. |
2108.03596 | Shuang Li | Qi Wen, Shuang Li, Bingfeng Han, Yi Yuan | ZiGAN: Fine-grained Chinese Calligraphy Font Generation via a Few-shot
Style Transfer Approach | Accepted at ACM MM 2021 | null | 10.1145/3474085.3475225 | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Chinese character style transfer is a very challenging problem because of the
complexity of the glyph shapes or underlying structures and large numbers of
existed characters, when comparing with English letters. Moreover, the
handwriting of calligraphy masters has a more irregular stroke and is difficult
to obtain in real-world scenarios. Recently, several GAN-based methods have
been proposed for font synthesis, but some of them require numerous reference
data and the other part of them have cumbersome preprocessing steps to divide
the character into different parts to be learned and transferred separately. In
this paper, we propose a simple but powerful end-to-end Chinese calligraphy
font generation framework ZiGAN, which does not require any manual operation or
redundant preprocessing to generate fine-grained target-style characters with
few-shot references. To be specific, a few paired samples from different
character styles are leveraged to attain a fine-grained correlation between
structures underlying different glyphs. To capture valuable style knowledge in
target and strengthen the coarse-grained understanding of character content, we
utilize multiple unpaired samples to align the feature distributions belonging
to different character styles. By doing so, only a few target Chinese
calligraphy characters are needed to generated expected style transferred
characters. Experiments demonstrate that our method has a state-of-the-art
generalization ability in few-shot Chinese character style transfer.
| [
{
"created": "Sun, 8 Aug 2021 09:50:20 GMT",
"version": "v1"
}
] | 2021-08-10 | [
[
"Wen",
"Qi",
""
],
[
"Li",
"Shuang",
""
],
[
"Han",
"Bingfeng",
""
],
[
"Yuan",
"Yi",
""
]
] | Chinese character style transfer is a very challenging problem because of the complexity of the glyph shapes or underlying structures and large numbers of existed characters, when comparing with English letters. Moreover, the handwriting of calligraphy masters has a more irregular stroke and is difficult to obtain in real-world scenarios. Recently, several GAN-based methods have been proposed for font synthesis, but some of them require numerous reference data and the other part of them have cumbersome preprocessing steps to divide the character into different parts to be learned and transferred separately. In this paper, we propose a simple but powerful end-to-end Chinese calligraphy font generation framework ZiGAN, which does not require any manual operation or redundant preprocessing to generate fine-grained target-style characters with few-shot references. To be specific, a few paired samples from different character styles are leveraged to attain a fine-grained correlation between structures underlying different glyphs. To capture valuable style knowledge in target and strengthen the coarse-grained understanding of character content, we utilize multiple unpaired samples to align the feature distributions belonging to different character styles. By doing so, only a few target Chinese calligraphy characters are needed to generated expected style transferred characters. Experiments demonstrate that our method has a state-of-the-art generalization ability in few-shot Chinese character style transfer. |
2310.05627 | Shuai Jia | Yujie Ding, Shuai Jia, Tianyi Ma, Bingcheng Mao, Xiuze Zhou, Liuliu Li
and Dongming Han | Integrating Stock Features and Global Information via Large Language
Models for Enhanced Stock Return Prediction | 8 pages, International Joint Conferences on Artificial Intelligence | International Joint Conferences on Artificial Intelligence,2023 | null | null | cs.CL cs.LG q-fin.ST | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The remarkable achievements and rapid advancements of Large Language Models
(LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in
quantitative investment. Traders can effectively leverage these LLMs to analyze
financial news and predict stock returns accurately. However, integrating LLMs
into existing quantitative models presents two primary challenges: the
insufficient utilization of semantic information embedded within LLMs and the
difficulties in aligning the latent information within LLMs with pre-existing
quantitative stock features. We propose a novel framework consisting of two
components to surmount these challenges. The first component, the Local-Global
(LG) model, introduces three distinct strategies for modeling global
information. These approaches are grounded respectively on stock features, the
capabilities of LLMs, and a hybrid method combining the two paradigms. The
second component, Self-Correlated Reinforcement Learning (SCRL), focuses on
aligning the embeddings of financial news generated by LLMs with stock features
within the same semantic space. By implementing our framework, we have
demonstrated superior performance in Rank Information Coefficient and returns,
particularly compared to models relying only on stock features in the China
A-share market.
| [
{
"created": "Mon, 9 Oct 2023 11:34:18 GMT",
"version": "v1"
}
] | 2023-10-11 | [
[
"Ding",
"Yujie",
""
],
[
"Jia",
"Shuai",
""
],
[
"Ma",
"Tianyi",
""
],
[
"Mao",
"Bingcheng",
""
],
[
"Zhou",
"Xiuze",
""
],
[
"Li",
"Liuliu",
""
],
[
"Han",
"Dongming",
""
]
] | The remarkable achievements and rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in quantitative investment. Traders can effectively leverage these LLMs to analyze financial news and predict stock returns accurately. However, integrating LLMs into existing quantitative models presents two primary challenges: the insufficient utilization of semantic information embedded within LLMs and the difficulties in aligning the latent information within LLMs with pre-existing quantitative stock features. We propose a novel framework consisting of two components to surmount these challenges. The first component, the Local-Global (LG) model, introduces three distinct strategies for modeling global information. These approaches are grounded respectively on stock features, the capabilities of LLMs, and a hybrid method combining the two paradigms. The second component, Self-Correlated Reinforcement Learning (SCRL), focuses on aligning the embeddings of financial news generated by LLMs with stock features within the same semantic space. By implementing our framework, we have demonstrated superior performance in Rank Information Coefficient and returns, particularly compared to models relying only on stock features in the China A-share market. |
1612.05568 | Naoise Holohan | Naoise Holohan, Douglas J. Leith, Oliver Mason | Optimal Differentially Private Mechanisms for Randomised Response | null | null | 10.1109/TIFS.2017.2718487 | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine a generalised Randomised Response (RR) technique in the context of
differential privacy and examine the optimality of such mechanisms. Strict and
relaxed differential privacy are considered for binary outputs. By examining
the error of a statistical estimator, we present closed solutions for the
optimal mechanism(s) in both cases. The optimal mechanism is also given for the
specific case of the original RR technique as introduced by Warner in 1965.
| [
{
"created": "Fri, 16 Dec 2016 17:38:49 GMT",
"version": "v1"
}
] | 2017-10-05 | [
[
"Holohan",
"Naoise",
""
],
[
"Leith",
"Douglas J.",
""
],
[
"Mason",
"Oliver",
""
]
] | We examine a generalised Randomised Response (RR) technique in the context of differential privacy and examine the optimality of such mechanisms. Strict and relaxed differential privacy are considered for binary outputs. By examining the error of a statistical estimator, we present closed solutions for the optimal mechanism(s) in both cases. The optimal mechanism is also given for the specific case of the original RR technique as introduced by Warner in 1965. |
2212.09100 | Abdullah Hamdi | Abdullah Hamdi, Bernard Ghanem, Matthias Nie{\ss}ner | SPARF: Large-Scale Learning of 3D Sparse Radiance Fields from Few Input
Images | published at ICCV 2023 workshop proceedings | null | 10.1109/ICCVW60793.2023.00315 | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Recent advances in Neural Radiance Fields (NeRFs) treat the problem of novel
view synthesis as Sparse Radiance Field (SRF) optimization using sparse voxels
for efficient and fast rendering (plenoxels,InstantNGP). In order to leverage
machine learning and adoption of SRFs as a 3D representation, we present SPARF,
a large-scale ShapeNet-based synthetic dataset for novel view synthesis
consisting of $\sim$ 17 million images rendered from nearly 40,000 shapes at
high resolution (400 X 400 pixels). The dataset is orders of magnitude larger
than existing synthetic datasets for novel view synthesis and includes more
than one million 3D-optimized radiance fields with multiple voxel resolutions.
Furthermore, we propose a novel pipeline (SuRFNet) that learns to generate
sparse voxel radiance fields from only few views. This is done by using the
densely collected SPARF dataset and 3D sparse convolutions. SuRFNet employs
partial SRFs from few/one images and a specialized SRF loss to learn to
generate high-quality sparse voxel radiance fields that can be rendered from
novel views. Our approach achieves state-of-the-art results in the task of
unconstrained novel view synthesis based on few views on ShapeNet as compared
to recent baselines. The SPARF dataset is made public with the code and models
on the project website https://abdullahamdi.com/sparf/ .
| [
{
"created": "Sun, 18 Dec 2022 14:56:22 GMT",
"version": "v1"
},
{
"created": "Tue, 14 Mar 2023 12:08:11 GMT",
"version": "v2"
},
{
"created": "Mon, 21 Aug 2023 12:53:09 GMT",
"version": "v3"
}
] | 2024-02-26 | [
[
"Hamdi",
"Abdullah",
""
],
[
"Ghanem",
"Bernard",
""
],
[
"Nießner",
"Matthias",
""
]
] | Recent advances in Neural Radiance Fields (NeRFs) treat the problem of novel view synthesis as Sparse Radiance Field (SRF) optimization using sparse voxels for efficient and fast rendering (plenoxels,InstantNGP). In order to leverage machine learning and adoption of SRFs as a 3D representation, we present SPARF, a large-scale ShapeNet-based synthetic dataset for novel view synthesis consisting of $\sim$ 17 million images rendered from nearly 40,000 shapes at high resolution (400 X 400 pixels). The dataset is orders of magnitude larger than existing synthetic datasets for novel view synthesis and includes more than one million 3D-optimized radiance fields with multiple voxel resolutions. Furthermore, we propose a novel pipeline (SuRFNet) that learns to generate sparse voxel radiance fields from only few views. This is done by using the densely collected SPARF dataset and 3D sparse convolutions. SuRFNet employs partial SRFs from few/one images and a specialized SRF loss to learn to generate high-quality sparse voxel radiance fields that can be rendered from novel views. Our approach achieves state-of-the-art results in the task of unconstrained novel view synthesis based on few views on ShapeNet as compared to recent baselines. The SPARF dataset is made public with the code and models on the project website https://abdullahamdi.com/sparf/ . |
1105.4702 | Joachim Selke | Joachim Selke and Wolf-Tilo Balke | Exploiting Conceptual Knowledge for Querying Information Systems | International Conference on Philosophy's Relevance in Information
Science (PRIS), Paderborn, Germany, 2008 | null | null | null | cs.IR cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Whereas today's information systems are well-equipped for efficient query
handling, their strict mathematical foundations hamper their use for everyday
tasks. In daily life, people expect information to be offered in a personalized
and focused way. But currently, personalization in digital systems still only
takes explicit knowledge into account and does not yet process conceptual
information often naturally implied by users. We discuss how to bridge the gap
between users and today's systems, building on results from cognitive
psychology.
| [
{
"created": "Tue, 24 May 2011 08:01:15 GMT",
"version": "v1"
}
] | 2011-05-25 | [
[
"Selke",
"Joachim",
""
],
[
"Balke",
"Wolf-Tilo",
""
]
] | Whereas today's information systems are well-equipped for efficient query handling, their strict mathematical foundations hamper their use for everyday tasks. In daily life, people expect information to be offered in a personalized and focused way. But currently, personalization in digital systems still only takes explicit knowledge into account and does not yet process conceptual information often naturally implied by users. We discuss how to bridge the gap between users and today's systems, building on results from cognitive psychology. |
2305.09890 | Young-Joo Han | Young-Joo Han and Ha-Jin Yu | SS-BSN: Attentive Blind-Spot Network for Self-Supervised Denoising with
Nonlocal Self-Similarity | Accepted to IJCAI 2023 | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently, numerous studies have been conducted on supervised learning-based
image denoising methods. However, these methods rely on large-scale noisy-clean
image pairs, which are difficult to obtain in practice. Denoising methods with
self-supervised training that can be trained with only noisy images have been
proposed to address the limitation. These methods are based on the
convolutional neural network (CNN) and have shown promising performance.
However, CNN-based methods do not consider using nonlocal self-similarities
essential in the traditional method, which can cause performance limitations.
This paper presents self-similarity attention (SS-Attention), a novel
self-attention module that can capture nonlocal self-similarities to solve the
problem. We focus on designing a lightweight self-attention module in a
pixel-wise manner, which is nearly impossible to implement using the classic
self-attention module due to the quadratically increasing complexity with
spatial resolution. Furthermore, we integrate SS-Attention into the blind-spot
network called self-similarity-based blind-spot network (SS-BSN). We conduct
the experiments on real-world image denoising tasks. The proposed method
quantitatively and qualitatively outperforms state-of-the-art methods in
self-supervised denoising on the Smartphone Image Denoising Dataset (SIDD) and
Darmstadt Noise Dataset (DND) benchmark datasets.
| [
{
"created": "Wed, 17 May 2023 01:55:45 GMT",
"version": "v1"
}
] | 2023-05-18 | [
[
"Han",
"Young-Joo",
""
],
[
"Yu",
"Ha-Jin",
""
]
] | Recently, numerous studies have been conducted on supervised learning-based image denoising methods. However, these methods rely on large-scale noisy-clean image pairs, which are difficult to obtain in practice. Denoising methods with self-supervised training that can be trained with only noisy images have been proposed to address the limitation. These methods are based on the convolutional neural network (CNN) and have shown promising performance. However, CNN-based methods do not consider using nonlocal self-similarities essential in the traditional method, which can cause performance limitations. This paper presents self-similarity attention (SS-Attention), a novel self-attention module that can capture nonlocal self-similarities to solve the problem. We focus on designing a lightweight self-attention module in a pixel-wise manner, which is nearly impossible to implement using the classic self-attention module due to the quadratically increasing complexity with spatial resolution. Furthermore, we integrate SS-Attention into the blind-spot network called self-similarity-based blind-spot network (SS-BSN). We conduct the experiments on real-world image denoising tasks. The proposed method quantitatively and qualitatively outperforms state-of-the-art methods in self-supervised denoising on the Smartphone Image Denoising Dataset (SIDD) and Darmstadt Noise Dataset (DND) benchmark datasets. |
1904.00077 | Dimitar Ho | Dimitar Ho and John C. Doyle | Scalable Robust Adaptive Control from the System Level Perspective | null | null | null | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We will present a new general framework for robust and adaptive control that
allows for distributed and scalable learning and control of large systems of
interconnected linear subsystems. The control method is demonstrated for a
linear time-invariant system with bounded parameter uncertainties, disturbances
and noise. The presented scheme continuously collects measurements to reduce
the uncertainty about the system parameters and adapts dynamic robust
controllers online in a stable and performance-improving way. A key enabler for
our approach is choosing a time-varying dynamic controller implementation,
inspired by recent work on System Level Synthesis. We leverage a new robustness
result for this implementation to propose a general robust adaptive control
algorithm. In particular, the algorithm allows us to impose communication and
delay constraints on the controller implementation and is formulated as a
sequence of robust optimization problems that can be solved in a distributed
manner. The proposed control methodology performs particularly well when the
interconnection between systems is sparse and the dynamics of local regions of
subsystems depend only on a small number of parameters. As we will show on a
five-dimensional exemplary chain-system, the algorithm can utilize system
structure to efficiently learn and control the entire system while respecting
communication and implementation constraints. Moreover, although current
theoretical results require the assumption of small initial uncertainties to
guarantee robustness, we will present simulations that show good closed-loop
performance even in the case of large uncertainties, which suggests that this
assumption is not critical for the presented technique and future work will
focus on providing less conservative guarantees.
| [
{
"created": "Fri, 29 Mar 2019 20:09:39 GMT",
"version": "v1"
}
] | 2019-04-02 | [
[
"Ho",
"Dimitar",
""
],
[
"Doyle",
"John C.",
""
]
] | We will present a new general framework for robust and adaptive control that allows for distributed and scalable learning and control of large systems of interconnected linear subsystems. The control method is demonstrated for a linear time-invariant system with bounded parameter uncertainties, disturbances and noise. The presented scheme continuously collects measurements to reduce the uncertainty about the system parameters and adapts dynamic robust controllers online in a stable and performance-improving way. A key enabler for our approach is choosing a time-varying dynamic controller implementation, inspired by recent work on System Level Synthesis. We leverage a new robustness result for this implementation to propose a general robust adaptive control algorithm. In particular, the algorithm allows us to impose communication and delay constraints on the controller implementation and is formulated as a sequence of robust optimization problems that can be solved in a distributed manner. The proposed control methodology performs particularly well when the interconnection between systems is sparse and the dynamics of local regions of subsystems depend only on a small number of parameters. As we will show on a five-dimensional exemplary chain-system, the algorithm can utilize system structure to efficiently learn and control the entire system while respecting communication and implementation constraints. Moreover, although current theoretical results require the assumption of small initial uncertainties to guarantee robustness, we will present simulations that show good closed-loop performance even in the case of large uncertainties, which suggests that this assumption is not critical for the presented technique and future work will focus on providing less conservative guarantees. |
2202.00379 | Thien-Minh Nguyen | Thien-Minh Nguyen, Shenghai Yuan, Muqing Cao, Yang Lyu, Thien Hoang
Nguyen, Lihua Xie | NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial
Vehicle Viewpoint | IJRR 2021 | null | 10.1177/02783649211052312 | null | cs.RO cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | In recent years, autonomous robots have become ubiquitous in research and
daily life. Among many factors, public datasets play an important role in the
progress of this field, as they waive the tall order of initial investment in
hardware and manpower. However, for research on autonomous aerial systems,
there appears to be a relative lack of public datasets on par with those used
for autonomous driving and ground robots. Thus, to fill in this gap, we conduct
a data collection exercise on an aerial platform equipped with an extensive and
unique set of sensors: two 3D lidars, two hardware-synchronized global-shutter
cameras, multiple Inertial Measurement Units (IMUs), and especially, multiple
Ultra-wideband (UWB) ranging units. The comprehensive sensor suite resembles
that of an autonomous driving car, but features distinct and challenging
characteristics of aerial operations. We record multiple datasets in several
challenging indoor and outdoor conditions. Calibration results and ground truth
from a high-accuracy laser tracker are also included in each package. All
resources can be accessed via our webpage
https://ntu-aris.github.io/ntu_viral_dataset.
| [
{
"created": "Tue, 1 Feb 2022 12:46:52 GMT",
"version": "v1"
}
] | 2022-02-02 | [
[
"Nguyen",
"Thien-Minh",
""
],
[
"Yuan",
"Shenghai",
""
],
[
"Cao",
"Muqing",
""
],
[
"Lyu",
"Yang",
""
],
[
"Nguyen",
"Thien Hoang",
""
],
[
"Xie",
"Lihua",
""
]
] | In recent years, autonomous robots have become ubiquitous in research and daily life. Among many factors, public datasets play an important role in the progress of this field, as they waive the tall order of initial investment in hardware and manpower. However, for research on autonomous aerial systems, there appears to be a relative lack of public datasets on par with those used for autonomous driving and ground robots. Thus, to fill in this gap, we conduct a data collection exercise on an aerial platform equipped with an extensive and unique set of sensors: two 3D lidars, two hardware-synchronized global-shutter cameras, multiple Inertial Measurement Units (IMUs), and especially, multiple Ultra-wideband (UWB) ranging units. The comprehensive sensor suite resembles that of an autonomous driving car, but features distinct and challenging characteristics of aerial operations. We record multiple datasets in several challenging indoor and outdoor conditions. Calibration results and ground truth from a high-accuracy laser tracker are also included in each package. All resources can be accessed via our webpage https://ntu-aris.github.io/ntu_viral_dataset. |
2010.04892 | Jiakun Liu | Jiakun Liu, Xin Xia, David Lo, Haoxiang Zhang, Ying Zou, Ahmed E.
Hassan, and Shanping Li | Broken External Links on Stack Overflow | null | null | 10.1109/TSE.2021.3086494 | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Stack Overflow hosts valuable programming-related knowledge with 11,926,354
links that reference to the third-party websites. The links that reference to
the resources hosted outside the Stack Overflow websites extend the Stack
Overflow knowledge base substantially. However, with the rapid development of
programming-related knowledge, many resources hosted on the Internet are not
available anymore. Based on our analysis of the Stack Overflow data that was
released on Jun. 2, 2019, 14.2% of the links on Stack Overflow are broken
links. The broken links on Stack Overflow can obstruct viewers from obtaining
desired programming-related knowledge, and potentially damage the reputation of
the Stack Overflow as viewers might regard the posts with broken links as
obsolete. In this paper, we characterize the broken links on Stack Overflow.
65% of the broken links in our sampled questions are used to show examples,
e.g., code examples. 70% of the broken links in our sampled answers are used to
provide supporting information, e.g., explaining a certain concept and
describing a step to solve a problem. Only 1.67% of the posts with broken links
are highlighted as such by viewers in the posts' comments. Only 5.8% of the
posts with broken links removed the broken links. Viewers cannot fully rely on
the vote scores to detect broken links, as broken links are common across posts
with different vote scores. The websites that host resources that can be
maintained by their users are referenced by broken links the most on Stack
Overflow -- a prominent example of such websites is GitHub. The posts and
comments related to the web technologies, i.e., JavaScript, HTML, CSS, and
jQuery, are associated with more broken links. Based on our findings, we shed
lights for future directions and provide recommendations for practitioners and
researchers.
| [
{
"created": "Sat, 10 Oct 2020 03:39:29 GMT",
"version": "v1"
}
] | 2022-02-15 | [
[
"Liu",
"Jiakun",
""
],
[
"Xia",
"Xin",
""
],
[
"Lo",
"David",
""
],
[
"Zhang",
"Haoxiang",
""
],
[
"Zou",
"Ying",
""
],
[
"Hassan",
"Ahmed E.",
""
],
[
"Li",
"Shanping",
""
]
] | Stack Overflow hosts valuable programming-related knowledge with 11,926,354 links that reference to the third-party websites. The links that reference to the resources hosted outside the Stack Overflow websites extend the Stack Overflow knowledge base substantially. However, with the rapid development of programming-related knowledge, many resources hosted on the Internet are not available anymore. Based on our analysis of the Stack Overflow data that was released on Jun. 2, 2019, 14.2% of the links on Stack Overflow are broken links. The broken links on Stack Overflow can obstruct viewers from obtaining desired programming-related knowledge, and potentially damage the reputation of the Stack Overflow as viewers might regard the posts with broken links as obsolete. In this paper, we characterize the broken links on Stack Overflow. 65% of the broken links in our sampled questions are used to show examples, e.g., code examples. 70% of the broken links in our sampled answers are used to provide supporting information, e.g., explaining a certain concept and describing a step to solve a problem. Only 1.67% of the posts with broken links are highlighted as such by viewers in the posts' comments. Only 5.8% of the posts with broken links removed the broken links. Viewers cannot fully rely on the vote scores to detect broken links, as broken links are common across posts with different vote scores. The websites that host resources that can be maintained by their users are referenced by broken links the most on Stack Overflow -- a prominent example of such websites is GitHub. The posts and comments related to the web technologies, i.e., JavaScript, HTML, CSS, and jQuery, are associated with more broken links. Based on our findings, we shed lights for future directions and provide recommendations for practitioners and researchers. |
1704.05817 | Wenbin Li | Wenbin Li, Da Chen, Zhihan Lv, Yan Yan, Darren Cosker | Learn to Model Motion from Blurry Footages | Preprint of our paper accepted by Pattern Recognition | null | null | null | cs.CV | http://creativecommons.org/licenses/by-nc-sa/4.0/ | It is difficult to recover the motion field from a real-world footage given a
mixture of camera shake and other photometric effects. In this paper we propose
a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a
traditional optical flow energy. We first conduct a CNN architecture using a
novel learnable directional filtering layer. Such layer encodes the angle and
distance similarity matrix between blur and camera motion, which is able to
enhance the blur features of the camera-shake footages. The proposed CNNs are
then integrated into an iterative optical flow framework, which enable the
capability of modelling and solving both the blind deconvolution and the
optical flow estimation problems simultaneously. Our framework is trained
end-to-end on a synthetic dataset and yields competitive precision and
performance against the state-of-the-art approaches.
| [
{
"created": "Wed, 19 Apr 2017 16:54:54 GMT",
"version": "v1"
}
] | 2017-04-20 | [
[
"Li",
"Wenbin",
""
],
[
"Chen",
"Da",
""
],
[
"Lv",
"Zhihan",
""
],
[
"Yan",
"Yan",
""
],
[
"Cosker",
"Darren",
""
]
] | It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modelling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches. |
2212.03490 | Yue Ma | Yue Ma, Tianyu Yang, Yin Shan, Xiu Li | SimVTP: Simple Video Text Pre-training with Masked Autoencoders | Github: https://github.com/mayuelala/SimVTP | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents SimVTP: a Simple Video-Text Pretraining framework via
masked autoencoders. We randomly mask out the spatial-temporal tubes of input
video and the word tokens of input text and then feed them into a unified
autencoder to reconstruct the missing pixels and words. Our SimVTP has several
properties: 1) Thanks to the unified autoencoder, SimVTP reconstructs the
masked signal of one modality with the help from another modality, which
implicitly learns the cross-modal alignment between video tubes and text
tokens. 2) SimVTP not only benefits from a high video masking ratio (e.g. 90%)
due to the temporal redundancy of video, but also needs a high text masking
ratio (e.g. 75%), which is much higher than BERT (e.g. 15%), to achieve optimal
performance. This is because the aid of video modality makes text
reconstruction less challenging, which thus needs a higher mask ratio to make
the pretext harder for useful feature learning. 3) Equipping SimVTP with
video-text contrastive learning (VTC) and video-text matching (VTM), which are
two commonly used cross-modal training strategies, could further improve the
transferable performance significantly. 4) SimVTP is dataefficent, e.g.,
pre-training only on 10% data of WebVid-2M, SimVTP achieves surprisingly good
results (43.8 R@1) on MSRVTT, which is far above recent state-of-the-art
methods pre-trained on both CC3M and WebVid-2M. We transfer our pre-trained
model to various downstream tasks and achieve superior performance. The codes
and models will be released at https://github.com/mayuelala/SimVTP.
| [
{
"created": "Wed, 7 Dec 2022 07:14:22 GMT",
"version": "v1"
}
] | 2022-12-08 | [
[
"Ma",
"Yue",
""
],
[
"Yang",
"Tianyu",
""
],
[
"Shan",
"Yin",
""
],
[
"Li",
"Xiu",
""
]
] | This paper presents SimVTP: a Simple Video-Text Pretraining framework via masked autoencoders. We randomly mask out the spatial-temporal tubes of input video and the word tokens of input text and then feed them into a unified autencoder to reconstruct the missing pixels and words. Our SimVTP has several properties: 1) Thanks to the unified autoencoder, SimVTP reconstructs the masked signal of one modality with the help from another modality, which implicitly learns the cross-modal alignment between video tubes and text tokens. 2) SimVTP not only benefits from a high video masking ratio (e.g. 90%) due to the temporal redundancy of video, but also needs a high text masking ratio (e.g. 75%), which is much higher than BERT (e.g. 15%), to achieve optimal performance. This is because the aid of video modality makes text reconstruction less challenging, which thus needs a higher mask ratio to make the pretext harder for useful feature learning. 3) Equipping SimVTP with video-text contrastive learning (VTC) and video-text matching (VTM), which are two commonly used cross-modal training strategies, could further improve the transferable performance significantly. 4) SimVTP is dataefficent, e.g., pre-training only on 10% data of WebVid-2M, SimVTP achieves surprisingly good results (43.8 R@1) on MSRVTT, which is far above recent state-of-the-art methods pre-trained on both CC3M and WebVid-2M. We transfer our pre-trained model to various downstream tasks and achieve superior performance. The codes and models will be released at https://github.com/mayuelala/SimVTP. |
1808.02997 | Felipe Campelo | Felipe Campelo and Fernanda Takahashi | Sample size estimation for power and accuracy in the experimental
comparison of algorithms | Main text: 31 pages, 5 figures; Supplemental materials: 20 pages, 3
figures; Submitted to the Journal of Heuristics on October 2017 | null | 10.1007/s10732-018-9396-7 | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Experimental comparisons of performance represent an important aspect of
research on optimization algorithms. In this work we present a methodology for
defining the required sample sizes for designing experiments with desired
statistical properties for the comparison of two methods on a given problem
class. The proposed approach allows the experimenter to define desired levels
of accuracy for estimates of mean performance differences on individual problem
instances, as well as the desired statistical power for comparing mean
performances over a problem class of interest. The method calculates the
required number of problem instances, and runs the algorithms on each test
instance so that the accuracy of the estimated differences in performance is
controlled at the predefined level. Two examples illustrate the application of
the proposed method, and its ability to achieve the desired statistical
properties with a methodologically sound definition of the relevant sample
sizes.
| [
{
"created": "Thu, 9 Aug 2018 02:17:52 GMT",
"version": "v1"
},
{
"created": "Mon, 15 Oct 2018 14:52:32 GMT",
"version": "v2"
}
] | 2018-10-16 | [
[
"Campelo",
"Felipe",
""
],
[
"Takahashi",
"Fernanda",
""
]
] | Experimental comparisons of performance represent an important aspect of research on optimization algorithms. In this work we present a methodology for defining the required sample sizes for designing experiments with desired statistical properties for the comparison of two methods on a given problem class. The proposed approach allows the experimenter to define desired levels of accuracy for estimates of mean performance differences on individual problem instances, as well as the desired statistical power for comparing mean performances over a problem class of interest. The method calculates the required number of problem instances, and runs the algorithms on each test instance so that the accuracy of the estimated differences in performance is controlled at the predefined level. Two examples illustrate the application of the proposed method, and its ability to achieve the desired statistical properties with a methodologically sound definition of the relevant sample sizes. |
1808.08181 | Boxiang Dong | Haipei Sun, Boxiang Dong, Hui (Wendy) Wang, Ting Yu, Zhan Qin | Truth Inference on Sparse Crowdsourcing Data with Local Differential
Privacy | null | null | null | null | cs.CR cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Crowdsourcing has arisen as a new problem-solving paradigm for tasks that are
difficult for computers but easy for humans. However, since the answers
collected from the recruited participants (workers) may contain sensitive
information, crowdsourcing raises serious privacy concerns. In this paper, we
investigate the problem of protecting answer privacy under local differential
privacy (LDP), by which individual workers randomize their answers
independently and send the perturbed answers to the task requester. The utility
goal is to enable to infer the true answer (i.e., truth) from the perturbed
data with high accuracy. One of the challenges of LDP perturbation is the
sparsity of worker answers (i.e., each worker only answers a small number of
tasks). Simple extension of the existing approaches (e.g., Laplace perturbation
and randomized response) may incur large error of truth inference on sparse
data. Thus we design an efficient new matrix factorization (MF) algorithm under
LDP. We prove that our MF algorithm can provide both LDP guarantee and small
error of truth inference, regardless of the sparsity of worker answers. We
perform extensive experiments on real-world and synthetic datasets, and
demonstrate that the MF algorithm performs better than the existing LDP
algorithms on sparse crowdsourcing data.
| [
{
"created": "Fri, 24 Aug 2018 15:48:06 GMT",
"version": "v1"
}
] | 2018-08-27 | [
[
"Sun",
"Haipei",
"",
"Wendy"
],
[
"Dong",
"Boxiang",
"",
"Wendy"
],
[
"Hui",
"",
"",
"Wendy"
],
[
"Wang",
"",
""
],
[
"Yu",
"Ting",
""
],
[
"Qin",
"Zhan",
""
]
] | Crowdsourcing has arisen as a new problem-solving paradigm for tasks that are difficult for computers but easy for humans. However, since the answers collected from the recruited participants (workers) may contain sensitive information, crowdsourcing raises serious privacy concerns. In this paper, we investigate the problem of protecting answer privacy under local differential privacy (LDP), by which individual workers randomize their answers independently and send the perturbed answers to the task requester. The utility goal is to enable to infer the true answer (i.e., truth) from the perturbed data with high accuracy. One of the challenges of LDP perturbation is the sparsity of worker answers (i.e., each worker only answers a small number of tasks). Simple extension of the existing approaches (e.g., Laplace perturbation and randomized response) may incur large error of truth inference on sparse data. Thus we design an efficient new matrix factorization (MF) algorithm under LDP. We prove that our MF algorithm can provide both LDP guarantee and small error of truth inference, regardless of the sparsity of worker answers. We perform extensive experiments on real-world and synthetic datasets, and demonstrate that the MF algorithm performs better than the existing LDP algorithms on sparse crowdsourcing data. |
2011.02749 | Busra Tegin | Busra Tegin, Eduin E. Hernandez, Stefano Rini, Tolga M. Duman | Straggler Mitigation through Unequal Error Protection for Distributed
Matrix Multiplication | 6 pages, 6 figures | null | null | null | cs.IT cs.DC math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large-scale machine learning and data mining methods routinely distribute
computations across multiple agents to parallelize processing. The time
required for computation at the agents is affected by the availability of local
resources giving rise to the "straggler problem" in which the computation
results are held back by unresponsive agents. For this problem, linear coding
of the matrix sub-blocks can be used to introduce resilience toward straggling.
The Parameter Server (PS) utilizes a channel code and distributes the matrices
to the workers for multiplication. It then produces an approximation to the
desired matrix multiplication using the results of the computations received at
a given deadline. In this paper, we propose to employ Unequal Error Protection
(UEP) codes to alleviate the straggler problem. The resiliency level of each
sub-block is chosen according to its norm as blocks with larger norms have
higher effects on the result of the matrix multiplication. We validate the
effectiveness of our scheme both theoretically and through numerical
evaluations. We derive a theoretical characterization of the performance of UEP
using random linear codes, and compare it the case of equal error protection.
We also apply the proposed coding strategy to the computation of the
back-propagation step in the training of a Deep Neural Network (DNN), for which
we investigate the fundamental trade-off between precision and the time
required for the computations.
| [
{
"created": "Thu, 5 Nov 2020 10:43:32 GMT",
"version": "v1"
},
{
"created": "Fri, 19 Mar 2021 08:24:36 GMT",
"version": "v2"
}
] | 2021-03-22 | [
[
"Tegin",
"Busra",
""
],
[
"Hernandez",
"Eduin E.",
""
],
[
"Rini",
"Stefano",
""
],
[
"Duman",
"Tolga M.",
""
]
] | Large-scale machine learning and data mining methods routinely distribute computations across multiple agents to parallelize processing. The time required for computation at the agents is affected by the availability of local resources giving rise to the "straggler problem" in which the computation results are held back by unresponsive agents. For this problem, linear coding of the matrix sub-blocks can be used to introduce resilience toward straggling. The Parameter Server (PS) utilizes a channel code and distributes the matrices to the workers for multiplication. It then produces an approximation to the desired matrix multiplication using the results of the computations received at a given deadline. In this paper, we propose to employ Unequal Error Protection (UEP) codes to alleviate the straggler problem. The resiliency level of each sub-block is chosen according to its norm as blocks with larger norms have higher effects on the result of the matrix multiplication. We validate the effectiveness of our scheme both theoretically and through numerical evaluations. We derive a theoretical characterization of the performance of UEP using random linear codes, and compare it the case of equal error protection. We also apply the proposed coding strategy to the computation of the back-propagation step in the training of a Deep Neural Network (DNN), for which we investigate the fundamental trade-off between precision and the time required for the computations. |
2011.01644 | Juan Quintero | Juan Quintero and Zinaida Benenson | Understanding Usability and User Acceptance of Usage-Based Insurance
from Users' View | null | null | 10.1145/3366750.3366759 | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Intelligent Transportation Systems (ITS) cover a variety of services related
to topics such as traffic control and safe driving, among others. In the
context of car insurance, a recent application for ITS is known as Usage-Based
Insurance (UBI). UBI refers to car insurance policies that enable insurance
companies to collect individual driving data using a telematics device.
Collected data is analysed and used to offer individual discounts based on
driving behaviour and to provide feedback on driving performance. Although
there are plenty of advertising materials about the benefits of UBI, the user
acceptance and the usability of UBI systems have not received research
attention so far. To this end, we conduct two user studies: semi-structured
interviews with UBI users and a qualitative analysis of 186 customer inquiries
from a web forum of a German insurance company. We find that under certain
circumstances, UBI provokes dangerous driving behaviour. These situations could
be mitigated by making UBI transparent and the feedback customisable by
drivers. Moreover, the country driving conditions, the policy conditions, and
the perceived driving style influence UBI acceptance.
| [
{
"created": "Tue, 3 Nov 2020 11:44:27 GMT",
"version": "v1"
}
] | 2020-11-04 | [
[
"Quintero",
"Juan",
""
],
[
"Benenson",
"Zinaida",
""
]
] | Intelligent Transportation Systems (ITS) cover a variety of services related to topics such as traffic control and safe driving, among others. In the context of car insurance, a recent application for ITS is known as Usage-Based Insurance (UBI). UBI refers to car insurance policies that enable insurance companies to collect individual driving data using a telematics device. Collected data is analysed and used to offer individual discounts based on driving behaviour and to provide feedback on driving performance. Although there are plenty of advertising materials about the benefits of UBI, the user acceptance and the usability of UBI systems have not received research attention so far. To this end, we conduct two user studies: semi-structured interviews with UBI users and a qualitative analysis of 186 customer inquiries from a web forum of a German insurance company. We find that under certain circumstances, UBI provokes dangerous driving behaviour. These situations could be mitigated by making UBI transparent and the feedback customisable by drivers. Moreover, the country driving conditions, the policy conditions, and the perceived driving style influence UBI acceptance. |
2312.13490 | Vaggos Chatziafratis | Vaggos Chatziafratis, Piotr Indyk | Dimension-Accuracy Tradeoffs in Contrastive Embeddings for Triplets,
Terminals & Top-k Nearest Neighbors | Abstract shortened for arxiv | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Metric embeddings traditionally study how to map $n$ items to a target metric
space such that distance lengths are not heavily distorted; but what if we only
care to preserve the relative order of the distances (and not their length)? In
this paper, we are motivated by the following basic question: given triplet
comparisons of the form ``item $i$ is closer to item $j$ than to item $k$,''
can we find low-dimensional Euclidean representations for the $n$ items that
respect those distance comparisons? Such order-preserving embeddings naturally
arise in important applications and have been studied since the 1950s, under
the name of ordinal or non-metric embeddings. Our main results are:
1. Nearly-Tight Bounds on Triplet Dimension: We introduce the natural concept
of triplet dimension of a dataset, and surprisingly, we show that in order for
an ordinal embedding to be triplet-preserving, its dimension needs to grow as
$\frac n2$ in the worst case. This is optimal (up to constant) as $n-1$
dimensions always suffice.
2. Tradeoffs for Dimension vs (Ordinal) Relaxation: We then relax the
requirement that every triplet should be exactly preserved and present almost
tight lower bounds for the maximum ratio between distances whose relative order
was inverted by the embedding; this ratio is known as (ordinal) relaxation in
the literature and serves as a counterpart to (metric) distortion.
3. New Bounds on Terminal and Top-$k$-NNs Embeddings: Going beyond triplets,
we then study two well-motivated scenarios where we care about preserving
specific sets of distances (not necessarily triplets). The first scenario is
Terminal Ordinal Embeddings and the second scenario is top-$k$-NNs Ordinal
Embeddings.
To the best of our knowledge, these are some of the first tradeoffs on
triplet-preserving ordinal embeddings and the first study of Terminal and
Top-$k$-NNs Ordinal Embeddings.
| [
{
"created": "Wed, 20 Dec 2023 23:54:18 GMT",
"version": "v1"
},
{
"created": "Fri, 29 Dec 2023 13:47:48 GMT",
"version": "v2"
}
] | 2024-01-01 | [
[
"Chatziafratis",
"Vaggos",
""
],
[
"Indyk",
"Piotr",
""
]
] | Metric embeddings traditionally study how to map $n$ items to a target metric space such that distance lengths are not heavily distorted; but what if we only care to preserve the relative order of the distances (and not their length)? In this paper, we are motivated by the following basic question: given triplet comparisons of the form ``item $i$ is closer to item $j$ than to item $k$,'' can we find low-dimensional Euclidean representations for the $n$ items that respect those distance comparisons? Such order-preserving embeddings naturally arise in important applications and have been studied since the 1950s, under the name of ordinal or non-metric embeddings. Our main results are: 1. Nearly-Tight Bounds on Triplet Dimension: We introduce the natural concept of triplet dimension of a dataset, and surprisingly, we show that in order for an ordinal embedding to be triplet-preserving, its dimension needs to grow as $\frac n2$ in the worst case. This is optimal (up to constant) as $n-1$ dimensions always suffice. 2. Tradeoffs for Dimension vs (Ordinal) Relaxation: We then relax the requirement that every triplet should be exactly preserved and present almost tight lower bounds for the maximum ratio between distances whose relative order was inverted by the embedding; this ratio is known as (ordinal) relaxation in the literature and serves as a counterpart to (metric) distortion. 3. New Bounds on Terminal and Top-$k$-NNs Embeddings: Going beyond triplets, we then study two well-motivated scenarios where we care about preserving specific sets of distances (not necessarily triplets). The first scenario is Terminal Ordinal Embeddings and the second scenario is top-$k$-NNs Ordinal Embeddings. To the best of our knowledge, these are some of the first tradeoffs on triplet-preserving ordinal embeddings and the first study of Terminal and Top-$k$-NNs Ordinal Embeddings. |
cs/0606123 | Atos Alves | Atos Ramos Alves | Use MPLS in Lan's | 9 pages, 0 figures tests in laboratory | null | null | null | cs.NI cs.CR | null | To demonstrate the result of researches in laboratory with the focus in
exhibiting the real impact of the use of the technology MPLS in LAN. Through
these researches we will verify that the investment in this technology is
shown, of the point of view cost/benefit, very interesting, being necessary,
however, the adoption of another measured, in order to settle down a
satisfactory level in the items Quality and safety in the sending of packages
in VPN but assisting to the requirement latency of the net very well being
shown in the tests that it consumes on average one Tuesday leaves of the time
spend for the same function in routing IP.
| [
{
"created": "Thu, 29 Jun 2006 14:44:02 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Alves",
"Atos Ramos",
""
]
] | To demonstrate the result of researches in laboratory with the focus in exhibiting the real impact of the use of the technology MPLS in LAN. Through these researches we will verify that the investment in this technology is shown, of the point of view cost/benefit, very interesting, being necessary, however, the adoption of another measured, in order to settle down a satisfactory level in the items Quality and safety in the sending of packages in VPN but assisting to the requirement latency of the net very well being shown in the tests that it consumes on average one Tuesday leaves of the time spend for the same function in routing IP. |
2003.10870 | Valerio Perrone | Eric Hans Lee, Valerio Perrone, Cedric Archambeau, Matthias Seeger | Cost-aware Bayesian Optimization | null | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Bayesian optimization (BO) is a class of global optimization algorithms,
suitable for minimizing an expensive objective function in as few function
evaluations as possible. While BO budgets are typically given in iterations,
this implicitly measures convergence in terms of iteration count and assumes
each evaluation has identical cost. In practice, evaluation costs may vary in
different regions of the search space. For example, the cost of neural network
training increases quadratically with layer size, which is a typical
hyperparameter. Cost-aware BO measures convergence with alternative cost
metrics such as time, energy, or money, for which vanilla BO methods are
unsuited. We introduce Cost Apportioned BO (CArBO), which attempts to minimize
an objective function in as little cost as possible. CArBO combines a
cost-effective initial design with a cost-cooled optimization phase which
depreciates a learned cost model as iterations proceed. On a set of 20
black-box function optimization problems we show that, given the same cost
budget, CArBO finds significantly better hyperparameter configurations than
competing methods.
| [
{
"created": "Sun, 22 Mar 2020 14:51:04 GMT",
"version": "v1"
}
] | 2020-03-25 | [
[
"Lee",
"Eric Hans",
""
],
[
"Perrone",
"Valerio",
""
],
[
"Archambeau",
"Cedric",
""
],
[
"Seeger",
"Matthias",
""
]
] | Bayesian optimization (BO) is a class of global optimization algorithms, suitable for minimizing an expensive objective function in as few function evaluations as possible. While BO budgets are typically given in iterations, this implicitly measures convergence in terms of iteration count and assumes each evaluation has identical cost. In practice, evaluation costs may vary in different regions of the search space. For example, the cost of neural network training increases quadratically with layer size, which is a typical hyperparameter. Cost-aware BO measures convergence with alternative cost metrics such as time, energy, or money, for which vanilla BO methods are unsuited. We introduce Cost Apportioned BO (CArBO), which attempts to minimize an objective function in as little cost as possible. CArBO combines a cost-effective initial design with a cost-cooled optimization phase which depreciates a learned cost model as iterations proceed. On a set of 20 black-box function optimization problems we show that, given the same cost budget, CArBO finds significantly better hyperparameter configurations than competing methods. |
1412.0781 | Zhizhen Zhao | Zhizhen Zhao, Yoel Shkolnisky, and Amit Singer | Fast Steerable Principal Component Analysis | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cryo-electron microscopy nowadays often requires the analysis of hundreds of
thousands of 2D images as large as a few hundred pixels in each direction. Here
we introduce an algorithm that efficiently and accurately performs principal
component analysis (PCA) for a large set of two-dimensional images, and, for
each image, the set of its uniform rotations in the plane and their
reflections. For a dataset consisting of $n$ images of size $L \times L$
pixels, the computational complexity of our algorithm is $O(nL^3 + L^4)$, while
existing algorithms take $O(nL^4)$. The new algorithm computes the expansion
coefficients of the images in a Fourier-Bessel basis efficiently using the
non-uniform fast Fourier transform. We compare the accuracy and efficiency of
the new algorithm with traditional PCA and existing algorithms for steerable
PCA.
| [
{
"created": "Tue, 2 Dec 2014 04:24:03 GMT",
"version": "v1"
},
{
"created": "Fri, 12 Dec 2014 18:21:40 GMT",
"version": "v2"
},
{
"created": "Sat, 16 May 2015 02:06:04 GMT",
"version": "v3"
},
{
"created": "Fri, 23 Oct 2015 02:14:53 GMT",
"version": "v4"
},
{
"created": "Tue, 15 Dec 2015 19:26:37 GMT",
"version": "v5"
}
] | 2015-12-16 | [
[
"Zhao",
"Zhizhen",
""
],
[
"Shkolnisky",
"Yoel",
""
],
[
"Singer",
"Amit",
""
]
] | Cryo-electron microscopy nowadays often requires the analysis of hundreds of thousands of 2D images as large as a few hundred pixels in each direction. Here we introduce an algorithm that efficiently and accurately performs principal component analysis (PCA) for a large set of two-dimensional images, and, for each image, the set of its uniform rotations in the plane and their reflections. For a dataset consisting of $n$ images of size $L \times L$ pixels, the computational complexity of our algorithm is $O(nL^3 + L^4)$, while existing algorithms take $O(nL^4)$. The new algorithm computes the expansion coefficients of the images in a Fourier-Bessel basis efficiently using the non-uniform fast Fourier transform. We compare the accuracy and efficiency of the new algorithm with traditional PCA and existing algorithms for steerable PCA. |
2007.03659 | Shaun Kane | Shaun Kane, Richard Ladner, and Clayton Lewis | Promoting Strategic Research on Inclusive Access to Rich Online Content
and Services | A Computing Community Consortium (CCC) workshop report, 16 pages | null | null | ccc2014report_5 | cs.CY cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Access to content and services online is increasingly important for everyone,
including people with disabilities. National commitments, including the
Americans with Disabilities Act, and international resolutions, including the
United Nations Declaration of the Rights of Persons with Disabilities, call for
work to ensure that people with disabilities can participate fully in the
online world. Gains in education, employment and health, as well as in civic
engagement, social participation, and personal independence will follow from
enhanced inclusion online. Research in many areas of computer science,
including recognition technology, natural language processing, personalization,
software architecture, and others, is needed to secure these benefits.
Organizing this research calls for partnerships among academic researchers,
federal agencies, and commercial organizations, as well as effective division
of labor and cooperation between computer scientists, behavioral scientists,
advocacy groups, and consumers.
| [
{
"created": "Tue, 7 Jul 2020 17:50:03 GMT",
"version": "v1"
}
] | 2020-07-08 | [
[
"Kane",
"Shaun",
""
],
[
"Ladner",
"Richard",
""
],
[
"Lewis",
"Clayton",
""
]
] | Access to content and services online is increasingly important for everyone, including people with disabilities. National commitments, including the Americans with Disabilities Act, and international resolutions, including the United Nations Declaration of the Rights of Persons with Disabilities, call for work to ensure that people with disabilities can participate fully in the online world. Gains in education, employment and health, as well as in civic engagement, social participation, and personal independence will follow from enhanced inclusion online. Research in many areas of computer science, including recognition technology, natural language processing, personalization, software architecture, and others, is needed to secure these benefits. Organizing this research calls for partnerships among academic researchers, federal agencies, and commercial organizations, as well as effective division of labor and cooperation between computer scientists, behavioral scientists, advocacy groups, and consumers. |
2103.11424 | Mingjie Luo | Mingjie Luo, Siwei Wang, Xinwang Liu, Wenxuan Tu, Yi Zhang, Xifeng
Guo, Sihang Zhou and En Zhu | Deep Distribution-preserving Incomplete Clustering with Optimal
Transport | Data are provided at
https://github.com/wangsiwei2010/Single-view-incomplete-datasets-for-deep-clustering | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Clustering is a fundamental task in the computer vision and machine learning
community. Although various methods have been proposed, the performance of
existing approaches drops dramatically when handling incomplete
high-dimensional data (which is common in real world applications). To solve
the problem, we propose a novel deep incomplete clustering method, named Deep
Distribution-preserving Incomplete Clustering with Optimal Transport (DDIC-OT).
To avoid insufficient sample utilization in existing methods limited by few
fully-observed samples, we propose to measure distribution distance with the
optimal transport for reconstruction evaluation instead of traditional
pixel-wise loss function. Moreover, the clustering loss of the latent feature
is introduced to regularize the embedding with more discrimination capability.
As a consequence, the network becomes more robust against missing features and
the unified framework which combines clustering and sample imputation enables
the two procedures to negotiate to better serve for each other. Extensive
experiments demonstrate that the proposed network achieves superior and stable
clustering performance improvement against existing state-of-the-art incomplete
clustering methods over different missing ratios.
| [
{
"created": "Sun, 21 Mar 2021 15:43:17 GMT",
"version": "v1"
}
] | 2021-03-23 | [
[
"Luo",
"Mingjie",
""
],
[
"Wang",
"Siwei",
""
],
[
"Liu",
"Xinwang",
""
],
[
"Tu",
"Wenxuan",
""
],
[
"Zhang",
"Yi",
""
],
[
"Guo",
"Xifeng",
""
],
[
"Zhou",
"Sihang",
""
],
[
"Zhu",
"En",
""
]
] | Clustering is a fundamental task in the computer vision and machine learning community. Although various methods have been proposed, the performance of existing approaches drops dramatically when handling incomplete high-dimensional data (which is common in real world applications). To solve the problem, we propose a novel deep incomplete clustering method, named Deep Distribution-preserving Incomplete Clustering with Optimal Transport (DDIC-OT). To avoid insufficient sample utilization in existing methods limited by few fully-observed samples, we propose to measure distribution distance with the optimal transport for reconstruction evaluation instead of traditional pixel-wise loss function. Moreover, the clustering loss of the latent feature is introduced to regularize the embedding with more discrimination capability. As a consequence, the network becomes more robust against missing features and the unified framework which combines clustering and sample imputation enables the two procedures to negotiate to better serve for each other. Extensive experiments demonstrate that the proposed network achieves superior and stable clustering performance improvement against existing state-of-the-art incomplete clustering methods over different missing ratios. |
2406.16374 | Dongyang Li | Dongyang Li, Taolin Zhang, Longtao Huang, Chengyu Wang, Xiaofeng He,
Hui Xue | KEHRL: Learning Knowledge-Enhanced Language Representations with
Hierarchical Reinforcement Learning | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation
triples from knowledge graphs (KGs) and integrate these external data sources
into language models via self-supervised learning. Previous works treat
knowledge enhancement as two independent operations, i.e., knowledge injection
and knowledge integration. In this paper, we propose to learn
Knowledge-Enhanced language representations with Hierarchical Reinforcement
Learning (KEHRL), which jointly addresses the problems of detecting positions
for knowledge injection and integrating external knowledge into the model in
order to avoid injecting inaccurate or irrelevant knowledge. Specifically, a
high-level reinforcement learning (RL) agent utilizes both internal and prior
knowledge to iteratively detect essential positions in texts for knowledge
injection, which filters out less meaningful entities to avoid diverting the
knowledge learning direction. Once the entity positions are selected, a
relevant triple filtration module is triggered to perform low-level RL to
dynamically refine the triples associated with polysemic entities through
binary-valued actions. Experiments validate KEHRL's effectiveness in probing
factual knowledge and enhancing the model's performance on various natural
language understanding tasks.
| [
{
"created": "Mon, 24 Jun 2024 07:32:35 GMT",
"version": "v1"
}
] | 2024-06-25 | [
[
"Li",
"Dongyang",
""
],
[
"Zhang",
"Taolin",
""
],
[
"Huang",
"Longtao",
""
],
[
"Wang",
"Chengyu",
""
],
[
"He",
"Xiaofeng",
""
],
[
"Xue",
"Hui",
""
]
] | Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation triples from knowledge graphs (KGs) and integrate these external data sources into language models via self-supervised learning. Previous works treat knowledge enhancement as two independent operations, i.e., knowledge injection and knowledge integration. In this paper, we propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL), which jointly addresses the problems of detecting positions for knowledge injection and integrating external knowledge into the model in order to avoid injecting inaccurate or irrelevant knowledge. Specifically, a high-level reinforcement learning (RL) agent utilizes both internal and prior knowledge to iteratively detect essential positions in texts for knowledge injection, which filters out less meaningful entities to avoid diverting the knowledge learning direction. Once the entity positions are selected, a relevant triple filtration module is triggered to perform low-level RL to dynamically refine the triples associated with polysemic entities through binary-valued actions. Experiments validate KEHRL's effectiveness in probing factual knowledge and enhancing the model's performance on various natural language understanding tasks. |
1811.02007 | Emil Bj\"ornson | Emil Bj\"ornson, Luca Sanguinetti, Jakob Hoydis | Hardware Distortion Correlation Has Negligible Impact on UL Massive MIMO
Spectral Efficiency | Published in IEEE Transactions on Communications, 14 pages, 12
figures. This version corrects a typo in Appendix C. arXiv admin note: text
overlap with arXiv:1805.07958 | null | 10.1109/TCOMM.2018.2877331 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper analyzes how the distortion created by hardware impairments in a
multiple-antenna base station affects the uplink spectral efficiency (SE), with
focus on Massive MIMO. This distortion is correlated across the antennas, but
has been often approximated as uncorrelated to facilitate (tractable) SE
analysis. To determine when this approximation is accurate, basic properties of
distortion correlation are first uncovered. Then, we separately analyze the
distortion correlation caused by third-order non-linearities and by
quantization. Finally, we study the SE numerically and show that the distortion
correlation can be safely neglected in Massive MIMO when there are sufficiently
many users. Under i.i.d. Rayleigh fading and equal signal-to-noise ratios
(SNRs), this occurs for more than five transmitting users. Other channel models
and SNR variations have only minor impact on the accuracy. We also demonstrate
the importance of taking the distortion characteristics into account in the
receive combining.
| [
{
"created": "Mon, 5 Nov 2018 19:52:34 GMT",
"version": "v1"
},
{
"created": "Fri, 24 Dec 2021 07:28:40 GMT",
"version": "v2"
}
] | 2021-12-28 | [
[
"Björnson",
"Emil",
""
],
[
"Sanguinetti",
"Luca",
""
],
[
"Hoydis",
"Jakob",
""
]
] | This paper analyzes how the distortion created by hardware impairments in a multiple-antenna base station affects the uplink spectral efficiency (SE), with focus on Massive MIMO. This distortion is correlated across the antennas, but has been often approximated as uncorrelated to facilitate (tractable) SE analysis. To determine when this approximation is accurate, basic properties of distortion correlation are first uncovered. Then, we separately analyze the distortion correlation caused by third-order non-linearities and by quantization. Finally, we study the SE numerically and show that the distortion correlation can be safely neglected in Massive MIMO when there are sufficiently many users. Under i.i.d. Rayleigh fading and equal signal-to-noise ratios (SNRs), this occurs for more than five transmitting users. Other channel models and SNR variations have only minor impact on the accuracy. We also demonstrate the importance of taking the distortion characteristics into account in the receive combining. |
2206.01002 | Ali Karimi | Ali Karimi and Zahra Mousavi Kouzehkanan and Reshad Hosseini and Hadi
Asheri | Introducing One Sided Margin Loss for Solving Classification Problems in
Deep Networks | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces a new loss function, OSM (One-Sided Margin), to solve
maximum-margin classification problems effectively. Unlike the hinge loss, in
OSM the margin is explicitly determined with corresponding hyperparameters and
then the classification problem is solved. In experiments, we observe that
using OSM loss leads to faster training speeds and better accuracies than
binary and categorical cross-entropy in several commonly used deep models for
classification and optical character recognition problems.
OSM has consistently shown better classification accuracies over
cross-entropy and hinge losses for small to large neural networks. it has also
led to a more efficient training procedure. We achieved state-of-the-art
accuracies for small networks on several benchmark datasets of
CIFAR10(98.82\%), CIFAR100(91.56\%), Flowers(98.04\%), Stanford Cars(93.91\%)
with considerable improvements over other loss functions. Moreover, the
accuracies are rather better than cross-entropy and hinge loss for large
networks. Therefore, we strongly believe that OSM is a powerful alternative to
hinge and cross-entropy losses to train deep neural networks on classification
tasks.
| [
{
"created": "Thu, 2 Jun 2022 12:03:39 GMT",
"version": "v1"
}
] | 2022-06-03 | [
[
"Karimi",
"Ali",
""
],
[
"Kouzehkanan",
"Zahra Mousavi",
""
],
[
"Hosseini",
"Reshad",
""
],
[
"Asheri",
"Hadi",
""
]
] | This paper introduces a new loss function, OSM (One-Sided Margin), to solve maximum-margin classification problems effectively. Unlike the hinge loss, in OSM the margin is explicitly determined with corresponding hyperparameters and then the classification problem is solved. In experiments, we observe that using OSM loss leads to faster training speeds and better accuracies than binary and categorical cross-entropy in several commonly used deep models for classification and optical character recognition problems. OSM has consistently shown better classification accuracies over cross-entropy and hinge losses for small to large neural networks. it has also led to a more efficient training procedure. We achieved state-of-the-art accuracies for small networks on several benchmark datasets of CIFAR10(98.82\%), CIFAR100(91.56\%), Flowers(98.04\%), Stanford Cars(93.91\%) with considerable improvements over other loss functions. Moreover, the accuracies are rather better than cross-entropy and hinge loss for large networks. Therefore, we strongly believe that OSM is a powerful alternative to hinge and cross-entropy losses to train deep neural networks on classification tasks. |
1710.09718 | Yuhang Song | Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman | Learning Approximate Stochastic Transition Models | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine the problem of learning mappings from state to state, suitable for
use in a model-based reinforcement-learning setting, that simultaneously
generalize to novel states and can capture stochastic transitions. We show that
currently popular generative adversarial networks struggle to learn these
stochastic transition models but a modification to their loss functions results
in a powerful learning algorithm for this class of problems.
| [
{
"created": "Thu, 26 Oct 2017 14:06:52 GMT",
"version": "v1"
}
] | 2017-10-27 | [
[
"Song",
"Yuhang",
""
],
[
"Grimm",
"Christopher",
""
],
[
"Wang",
"Xianming",
""
],
[
"Littman",
"Michael L.",
""
]
] | We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions. We show that currently popular generative adversarial networks struggle to learn these stochastic transition models but a modification to their loss functions results in a powerful learning algorithm for this class of problems. |
1906.09996 | Unai Lopez-Novoa | Unai Lopez-Novoa, Cyril Charron, John Evans, Leandro Beltrachini | The BIDS Toolbox: A web service to manage brain imaging datasets | Paper for the Workshop on Data Preprocessing for Big Biomedical Data
2019, held in conjunction with the IEEE Smart World Congress 2019, Leicester,
UK | null | null | null | cs.DL q-bio.NC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data sharing is a key factor for ensuring reproducibility and transparency of
scientific experiments, and neuroimaging is no exception. The vast
heterogeneity of data formats and imaging modalities utilised in the field
makes it a very challenging problem. In this context, the Brain Imaging Data
Structure (BIDS) appears as a solution for organising and describing
neuroimaging datasets. Since its publication in 2015, BIDS has gained
widespread attention in the field, as it provides a common way to arrange and
share multimodal brain images. Although the evident benefits it presents, BIDS
has not been widely adopted in the field of MRI yet and we believe that this is
due to the lack of a go-to tool to create and managed BIDS datasets. Motivated
by this, we present the BIDS Toolbox, a web service to manage brain imaging
datasets in BIDS format. Different from other tools, the BIDS Toolbox allows
the creation and modification of BIDS-compliant datasets based on MRI data. It
provides both a web interface and REST endpoints for its use. In this paper we
describe its design and early prototype, and provide a link to the public
source code repository.
| [
{
"created": "Mon, 24 Jun 2019 14:34:38 GMT",
"version": "v1"
}
] | 2019-06-25 | [
[
"Lopez-Novoa",
"Unai",
""
],
[
"Charron",
"Cyril",
""
],
[
"Evans",
"John",
""
],
[
"Beltrachini",
"Leandro",
""
]
] | Data sharing is a key factor for ensuring reproducibility and transparency of scientific experiments, and neuroimaging is no exception. The vast heterogeneity of data formats and imaging modalities utilised in the field makes it a very challenging problem. In this context, the Brain Imaging Data Structure (BIDS) appears as a solution for organising and describing neuroimaging datasets. Since its publication in 2015, BIDS has gained widespread attention in the field, as it provides a common way to arrange and share multimodal brain images. Although the evident benefits it presents, BIDS has not been widely adopted in the field of MRI yet and we believe that this is due to the lack of a go-to tool to create and managed BIDS datasets. Motivated by this, we present the BIDS Toolbox, a web service to manage brain imaging datasets in BIDS format. Different from other tools, the BIDS Toolbox allows the creation and modification of BIDS-compliant datasets based on MRI data. It provides both a web interface and REST endpoints for its use. In this paper we describe its design and early prototype, and provide a link to the public source code repository. |
1003.4074 | William Jackson | Shivaji P. Mirashe, N. V. Kalyankar | Cloud Computing | null | Journal of Computing, Volume 2, Issue 3, March 2010 | null | null | cs.DC cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computing as you know it is about to change, your applications and documents
are going to move from the desktop into the cloud. I'm talking about cloud
computing, where applications and files are hosted on a "cloud" consisting of
thousands of computers and servers, all linked together and accessible via the
Internet. With cloud computing, everything you do is now web based instead of
being desktop based. You can access all your programs and documents from any
computer that's connected to the Internet. How will cloud computing change the
way you work? For one thing, you're no longer tied to a single computer. You
can take your work anywhere because it's always accessible via the web. In
addition, cloud computing facilitates group collaboration, as all group members
can access the same programs and documents from wherever they happen to be
located. Cloud computing might sound far-fetched, but chances are you're
already using some cloud applications. If you're using a web-based email
program, such as Gmail or Hotmail, you're computing in the cloud. If you're
using a web-based application such as Google Calendar or Apple Mobile Me,
you're computing in the cloud. If you're using a file- or photo-sharing site,
such as Flickr or Picasa Web Albums, you're computing in the cloud. It's the
technology of the future, available to use today.
| [
{
"created": "Mon, 22 Mar 2010 06:16:48 GMT",
"version": "v1"
}
] | 2010-03-23 | [
[
"Mirashe",
"Shivaji P.",
""
],
[
"Kalyankar",
"N. V.",
""
]
] | Computing as you know it is about to change, your applications and documents are going to move from the desktop into the cloud. I'm talking about cloud computing, where applications and files are hosted on a "cloud" consisting of thousands of computers and servers, all linked together and accessible via the Internet. With cloud computing, everything you do is now web based instead of being desktop based. You can access all your programs and documents from any computer that's connected to the Internet. How will cloud computing change the way you work? For one thing, you're no longer tied to a single computer. You can take your work anywhere because it's always accessible via the web. In addition, cloud computing facilitates group collaboration, as all group members can access the same programs and documents from wherever they happen to be located. Cloud computing might sound far-fetched, but chances are you're already using some cloud applications. If you're using a web-based email program, such as Gmail or Hotmail, you're computing in the cloud. If you're using a web-based application such as Google Calendar or Apple Mobile Me, you're computing in the cloud. If you're using a file- or photo-sharing site, such as Flickr or Picasa Web Albums, you're computing in the cloud. It's the technology of the future, available to use today. |
2204.06046 | Bryce Ferguson | Bryce L. Ferguson, Philip N. Brown, Jason R. Marden | Avoiding Unintended Consequences: How Incentives Aid Information
Provisioning in Bayesian Congestion Games | null | null | 10.1109/CDC51059.2022.9992777 | null | cs.GT cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | When users lack specific knowledge of various system parameters, their
uncertainty may lead them to make undesirable deviations in their decision
making. To alleviate this, an informed system operator may elect to signal
information to uninformed users with the hope of persuading them to take more
preferable actions. In this work, we study public and truthful signalling
mechanisms in the context of Bayesian congestion games on parallel networks. We
provide bounds on the possible benefit a signalling policy can provide with and
without the concurrent use of monetary incentives. We find that though
revealing information can reduce system cost in some settings, it can also be
detrimental and cause worse performance than not signalling at all. However, by
utilizing both signalling and incentive mechanisms, the system operator can
guarantee that revealing information does not worsen performance while offering
similar opportunities for improvement. These findings emerge from the closed
form bounds we derive on the benefit a signalling policy can provide. We
provide a numerical example which illustrates the phenomenon that revealing
more information can degrade performance when incentives are not used and
improves performance when incentives are used.
| [
{
"created": "Tue, 12 Apr 2022 19:06:24 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Mar 2023 04:02:08 GMT",
"version": "v2"
}
] | 2023-03-31 | [
[
"Ferguson",
"Bryce L.",
""
],
[
"Brown",
"Philip N.",
""
],
[
"Marden",
"Jason R.",
""
]
] | When users lack specific knowledge of various system parameters, their uncertainty may lead them to make undesirable deviations in their decision making. To alleviate this, an informed system operator may elect to signal information to uninformed users with the hope of persuading them to take more preferable actions. In this work, we study public and truthful signalling mechanisms in the context of Bayesian congestion games on parallel networks. We provide bounds on the possible benefit a signalling policy can provide with and without the concurrent use of monetary incentives. We find that though revealing information can reduce system cost in some settings, it can also be detrimental and cause worse performance than not signalling at all. However, by utilizing both signalling and incentive mechanisms, the system operator can guarantee that revealing information does not worsen performance while offering similar opportunities for improvement. These findings emerge from the closed form bounds we derive on the benefit a signalling policy can provide. We provide a numerical example which illustrates the phenomenon that revealing more information can degrade performance when incentives are not used and improves performance when incentives are used. |
1807.04561 | Fabio Patrizi | Giuseppe De Giacomo, Brian Logan, Paolo Felli, Fabio Patrizi,
Sebastian Sardina | Situation Calculus for Synthesis of Manufacturing Controllers | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Manufacturing is transitioning from a mass production model to a
manufacturing as a service model in which manufacturing facilities 'bid' to
produce products. To decide whether to bid for a complex, previously unseen
product, a manufacturing facility must be able to synthesize, 'on the fly', a
process plan controller that delegates abstract manufacturing tasks in the
supplied process recipe to the appropriate manufacturing resources, e.g., CNC
machines, robots etc. Previous work in applying AI behaviour composition to
synthesize process plan controllers has considered only finite state ad-hoc
representations. Here, we study the problem in the relational setting of the
Situation Calculus. By taking advantage of recent work on abstraction in the
Situation Calculus, process recipes and available resources are represented by
ConGolog programs over, respectively, an abstract and a concrete action theory.
This allows us to capture the problem in a formal, general framework, and show
decidability for the case of bounded action theories. We also provide
techniques for actually synthesizing the controller.
| [
{
"created": "Thu, 12 Jul 2018 12:05:41 GMT",
"version": "v1"
}
] | 2018-07-13 | [
[
"De Giacomo",
"Giuseppe",
""
],
[
"Logan",
"Brian",
""
],
[
"Felli",
"Paolo",
""
],
[
"Patrizi",
"Fabio",
""
],
[
"Sardina",
"Sebastian",
""
]
] | Manufacturing is transitioning from a mass production model to a manufacturing as a service model in which manufacturing facilities 'bid' to produce products. To decide whether to bid for a complex, previously unseen product, a manufacturing facility must be able to synthesize, 'on the fly', a process plan controller that delegates abstract manufacturing tasks in the supplied process recipe to the appropriate manufacturing resources, e.g., CNC machines, robots etc. Previous work in applying AI behaviour composition to synthesize process plan controllers has considered only finite state ad-hoc representations. Here, we study the problem in the relational setting of the Situation Calculus. By taking advantage of recent work on abstraction in the Situation Calculus, process recipes and available resources are represented by ConGolog programs over, respectively, an abstract and a concrete action theory. This allows us to capture the problem in a formal, general framework, and show decidability for the case of bounded action theories. We also provide techniques for actually synthesizing the controller. |
2105.11836 | Cyrus Vahidi | Cyrus Vahidi, Charalampos Saitis, Gy\"orgy Fazekas | A Modulation Front-End for Music Audio Tagging | null | null | null | null | cs.SD cs.LG eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Convolutional Neural Networks have been extensively explored in the task of
automatic music tagging. The problem can be approached by using either
engineered time-frequency features or raw audio as input. Modulation filter
bank representations that have been actively researched as a basis for timbre
perception have the potential to facilitate the extraction of perceptually
salient features. We explore end-to-end learned front-ends for audio
representation learning, ModNet and SincModNet, that incorporate a temporal
modulation processing block. The structure is effectively analogous to a
modulation filter bank, where the FIR filter center frequencies are learned in
a data-driven manner. The expectation is that a perceptually motivated filter
bank can provide a useful representation for identifying music features. Our
experimental results provide a fully visualisable and interpretable front-end
temporal modulation decomposition of raw audio. We evaluate the performance of
our model against the state-of-the-art of music tagging on the MagnaTagATune
dataset. We analyse the impact on performance for particular tags when
time-frequency bands are subsampled by the modulation filters at a
progressively reduced rate. We demonstrate that modulation filtering provides
promising results for music tagging and feature representation, without using
extensive musical domain knowledge in the design of this front-end.
| [
{
"created": "Tue, 25 May 2021 11:05:24 GMT",
"version": "v1"
}
] | 2021-05-26 | [
[
"Vahidi",
"Cyrus",
""
],
[
"Saitis",
"Charalampos",
""
],
[
"Fazekas",
"György",
""
]
] | Convolutional Neural Networks have been extensively explored in the task of automatic music tagging. The problem can be approached by using either engineered time-frequency features or raw audio as input. Modulation filter bank representations that have been actively researched as a basis for timbre perception have the potential to facilitate the extraction of perceptually salient features. We explore end-to-end learned front-ends for audio representation learning, ModNet and SincModNet, that incorporate a temporal modulation processing block. The structure is effectively analogous to a modulation filter bank, where the FIR filter center frequencies are learned in a data-driven manner. The expectation is that a perceptually motivated filter bank can provide a useful representation for identifying music features. Our experimental results provide a fully visualisable and interpretable front-end temporal modulation decomposition of raw audio. We evaluate the performance of our model against the state-of-the-art of music tagging on the MagnaTagATune dataset. We analyse the impact on performance for particular tags when time-frequency bands are subsampled by the modulation filters at a progressively reduced rate. We demonstrate that modulation filtering provides promising results for music tagging and feature representation, without using extensive musical domain knowledge in the design of this front-end. |
1012.0027 | Fen Zhou | Fen Zhou (IRISA), Miklos Molnar (IRISA), Bernard Cousin (IRISA) | Avoidance of multicast incapable branching nodes for multicast routing
in WDM networks | null | Photonic Network Communication 18, 3 (2009) 378-392 | 10.1007/s11107-009-0200-3 | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this articlewestudy themulticast routing problem in all-opticalWDMnetworks
under the spare light splitting constraint. To implement a multicast session,
several light-trees may have to be used due to the limited fanouts of network
nodes. Although many multicast routing algorithms have been proposed in order
to reduce the total number of wavelength channels used (total cost) for a
multicast session, the maximum number of wavelengths required in one fiber link
(link stress) and the end-to-end delay are two parameters which are not always
taken into consideration. It is known that the shortest path tree (SPT) results
in the optimal end-to-end delay, but it can not be employed directly for
multicast routing in sparse light splitting WDM networks. Hence, we propose a
novel wavelength routing algorithm which tries to avoid the multicast incapable
branching nodes (MIBs, branching nodes without splitting capability) in the
shortest-path-based multicast tree to diminish the link stress. Good parts of
the shortest-path-tree are retained by the algorithm to reduce the end-to-end
delay. The algorithm consists of tree steps: (1) aDijkstraPro algorithmwith
priority assignment and node adoption is introduced to produce a SPT with up to
38% fewer MIB nodes in the NSF topology and 46% fewerMIB nodes in the USA
Longhaul topology, (2) critical articulation and deepest branch heuristics are
used to process the MIB nodes, (3) a distance-based light-tree reconnection
algorithm is proposed to create the multicast light-trees. Extensive
simulations demonstrate the algorithm's efficiency in terms of link stress and
end-to-end delay.
| [
{
"created": "Tue, 30 Nov 2010 21:34:53 GMT",
"version": "v1"
}
] | 2010-12-02 | [
[
"Zhou",
"Fen",
"",
"IRISA"
],
[
"Molnar",
"Miklos",
"",
"IRISA"
],
[
"Cousin",
"Bernard",
"",
"IRISA"
]
] | In this articlewestudy themulticast routing problem in all-opticalWDMnetworks under the spare light splitting constraint. To implement a multicast session, several light-trees may have to be used due to the limited fanouts of network nodes. Although many multicast routing algorithms have been proposed in order to reduce the total number of wavelength channels used (total cost) for a multicast session, the maximum number of wavelengths required in one fiber link (link stress) and the end-to-end delay are two parameters which are not always taken into consideration. It is known that the shortest path tree (SPT) results in the optimal end-to-end delay, but it can not be employed directly for multicast routing in sparse light splitting WDM networks. Hence, we propose a novel wavelength routing algorithm which tries to avoid the multicast incapable branching nodes (MIBs, branching nodes without splitting capability) in the shortest-path-based multicast tree to diminish the link stress. Good parts of the shortest-path-tree are retained by the algorithm to reduce the end-to-end delay. The algorithm consists of tree steps: (1) aDijkstraPro algorithmwith priority assignment and node adoption is introduced to produce a SPT with up to 38% fewer MIB nodes in the NSF topology and 46% fewerMIB nodes in the USA Longhaul topology, (2) critical articulation and deepest branch heuristics are used to process the MIB nodes, (3) a distance-based light-tree reconnection algorithm is proposed to create the multicast light-trees. Extensive simulations demonstrate the algorithm's efficiency in terms of link stress and end-to-end delay. |
1110.1360 | Aravindan Vijayaraghavan | Aditya Bhaskara, Moses Charikar, Venkatesan Guruswami, Aravindan
Vijayaraghavan, Yuan Zhou | Polynomial integrality gaps for strong SDP relaxations of Densest
k-subgraph | 26 ages, 1 figure. To appear in Symposium on Discrete Algorithms
(SODA) 2012 | null | null | null | cs.DS cs.CC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The densest k-subgraph (DkS) problem (i.e. find a size k subgraph with
maximum number of edges), is one of the notorious problems in approximation
algorithms. There is a significant gap between known upper and lower bounds for
DkS: the current best algorithm gives an ~ O(n^{1/4}) approximation, while even
showing a small constant factor hardness requires significantly stronger
assumptions than P != NP. In addition to interest in designing better
algorithms, a number of recent results have exploited the conjectured hardness
of densest k-subgraph and its variants. Thus, understanding the approximability
of DkS is an important challenge.
In this work, we give evidence for the hardness of approximating DkS within
polynomial factors. Specifically, we expose the limitations of strong
semidefinite programs from SDP hierarchies in solving densest k-subgraph. Our
results include:
* A lower bound of Omega(n^{1/4}/log^3 n) on the integrality gap for
Omega(log n/log log n) rounds of the Sherali-Adams relaxation for DkS. This
also holds for the relaxation obtained from Sherali-Adams with an added SDP
constraint. Our gap instances are in fact Erdos-Renyi random graphs.
* For every epsilon > 0, a lower bound of n^{2/53-eps} on the integrality gap
of n^{Omega(eps)} rounds of the Lasserre SDP relaxation for DkS, and an
n^{Omega_eps(1)} gap for n^{1-eps} rounds. Our construction proceeds via a
reduction from random instances of a certain Max-CSP over large domains.
In the absence of inapproximability results for DkS, our results show that
even the most powerful SDPs are unable to beat a factor of n^{Omega(1)}, and in
fact even improving the best known n^{1/4} factor is a barrier for current
techniques.
| [
{
"created": "Thu, 6 Oct 2011 19:29:01 GMT",
"version": "v1"
}
] | 2011-10-07 | [
[
"Bhaskara",
"Aditya",
""
],
[
"Charikar",
"Moses",
""
],
[
"Guruswami",
"Venkatesan",
""
],
[
"Vijayaraghavan",
"Aravindan",
""
],
[
"Zhou",
"Yuan",
""
]
] | The densest k-subgraph (DkS) problem (i.e. find a size k subgraph with maximum number of edges), is one of the notorious problems in approximation algorithms. There is a significant gap between known upper and lower bounds for DkS: the current best algorithm gives an ~ O(n^{1/4}) approximation, while even showing a small constant factor hardness requires significantly stronger assumptions than P != NP. In addition to interest in designing better algorithms, a number of recent results have exploited the conjectured hardness of densest k-subgraph and its variants. Thus, understanding the approximability of DkS is an important challenge. In this work, we give evidence for the hardness of approximating DkS within polynomial factors. Specifically, we expose the limitations of strong semidefinite programs from SDP hierarchies in solving densest k-subgraph. Our results include: * A lower bound of Omega(n^{1/4}/log^3 n) on the integrality gap for Omega(log n/log log n) rounds of the Sherali-Adams relaxation for DkS. This also holds for the relaxation obtained from Sherali-Adams with an added SDP constraint. Our gap instances are in fact Erdos-Renyi random graphs. * For every epsilon > 0, a lower bound of n^{2/53-eps} on the integrality gap of n^{Omega(eps)} rounds of the Lasserre SDP relaxation for DkS, and an n^{Omega_eps(1)} gap for n^{1-eps} rounds. Our construction proceeds via a reduction from random instances of a certain Max-CSP over large domains. In the absence of inapproximability results for DkS, our results show that even the most powerful SDPs are unable to beat a factor of n^{Omega(1)}, and in fact even improving the best known n^{1/4} factor is a barrier for current techniques. |
2108.09746 | Godfred Amankwaa | Godfred Amankwaa, Richard Heeks and Alison L. Browne | Digitalising the Water Sector: Implications for Water Service Management
and Governance | In proceedings of the 1st Virtual Conference on Implications of
Information and Digital Technologies for Development, 2021 | null | null | null | cs.CY | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Digital technologies are becoming central to water governance and management,
yet their impact and developmental implications are under-researched,
particularly in the global South. This paper addresses this knowledge gap by
examining the process of water service digitalisation and the resulting effects
on service providers. Drawing on qualitative methods, we apply ideas on
digitalisation, value, and power to investigate the implementation and impact
of digital technologies in Ghana's state water utility company. We find digital
water innovations to be recent, and delivering relatively limited impacts as
yet, with value mainly accruing at the utility's operational rather than
strategic level. The digital technologies present avenues for power shifts and
struggles internally and externally as well as some changes in water management
structures and responsibilities. We end with a brief discussion on the
implications for water service governance and research.
| [
{
"created": "Sun, 22 Aug 2021 15:05:11 GMT",
"version": "v1"
}
] | 2021-08-24 | [
[
"Amankwaa",
"Godfred",
""
],
[
"Heeks",
"Richard",
""
],
[
"Browne",
"Alison L.",
""
]
] | Digital technologies are becoming central to water governance and management, yet their impact and developmental implications are under-researched, particularly in the global South. This paper addresses this knowledge gap by examining the process of water service digitalisation and the resulting effects on service providers. Drawing on qualitative methods, we apply ideas on digitalisation, value, and power to investigate the implementation and impact of digital technologies in Ghana's state water utility company. We find digital water innovations to be recent, and delivering relatively limited impacts as yet, with value mainly accruing at the utility's operational rather than strategic level. The digital technologies present avenues for power shifts and struggles internally and externally as well as some changes in water management structures and responsibilities. We end with a brief discussion on the implications for water service governance and research. |
1812.11284 | Zhongang Cai | Zhongang Cai, Cunjun Yu, Quang-Cuong Pham | 3D Convolution on RGB-D Point Clouds for Accurate Model-free Object Pose
Estimation | null | null | null | null | cs.RO cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The conventional pose estimation of a 3D object usually requires the
knowledge of the 3D model of the object. Even with the recent development in
convolutional neural networks (CNNs), a 3D model is often necessary in the
final estimation. In this paper, we propose a two-stage pipeline that takes in
raw colored point cloud data and estimates an object's translation and rotation
by running 3D convolutions on voxels. The pipeline is simple yet highly
accurate: translation error is reduced to the voxel resolution (around 1 cm)
and rotation error is around 5 degrees. The pipeline is also put to actual
robotic grasping tests where it achieves above 90% success rate for test
objects. Another innovation is that a motion capture system is used to
automatically label the point cloud samples which makes it possible to rapidly
collect a large amount of highly accurate real data for training the neural
networks.
| [
{
"created": "Sat, 29 Dec 2018 04:46:51 GMT",
"version": "v1"
}
] | 2019-01-01 | [
[
"Cai",
"Zhongang",
""
],
[
"Yu",
"Cunjun",
""
],
[
"Pham",
"Quang-Cuong",
""
]
] | The conventional pose estimation of a 3D object usually requires the knowledge of the 3D model of the object. Even with the recent development in convolutional neural networks (CNNs), a 3D model is often necessary in the final estimation. In this paper, we propose a two-stage pipeline that takes in raw colored point cloud data and estimates an object's translation and rotation by running 3D convolutions on voxels. The pipeline is simple yet highly accurate: translation error is reduced to the voxel resolution (around 1 cm) and rotation error is around 5 degrees. The pipeline is also put to actual robotic grasping tests where it achieves above 90% success rate for test objects. Another innovation is that a motion capture system is used to automatically label the point cloud samples which makes it possible to rapidly collect a large amount of highly accurate real data for training the neural networks. |
2102.12519 | Diego S. D'antonio | Diego S. D'antonio, Gustavo A. Cardona, and David Salda\~na | The Catenary Robot: Design and Control of a Cable Propelled by Two
Quadrotors | Supplementary video: https://youtu.be/RjKkjZuCDV4 | null | null | null | cs.RO | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Transporting objects using aerial robots has been widely studied in the
literature. Still, those approaches always assume that the connection between
the quadrotor and the load is made in a previous stage. However, that previous
stage usually requires human intervention, and autonomous procedures to locate
and attach the object are not considered. Additionally, most of the approaches
assume cables as rigid links, but manipulating cables requires considering the
state when the cables are hanging. In this work, we design and control a
catenary robot. Our robot is able to transport hook-shaped objects in the
environment. The robotic system is composed of two quadrotors attached to the
two ends of a cable. By defining the catenary curve with five degrees of
freedom, position in 3-D, orientation in the z-axis, and span, we can drive the
two quadrotors to track a given trajectory. We validate our approach with
simulations and real robots. We present four different scenarios of
experiments. Our numerical solution is computationally fast and can be executed
in real-time.
| [
{
"created": "Wed, 24 Feb 2021 19:25:37 GMT",
"version": "v1"
},
{
"created": "Tue, 2 Mar 2021 16:56:30 GMT",
"version": "v2"
}
] | 2021-03-03 | [
[
"D'antonio",
"Diego S.",
""
],
[
"Cardona",
"Gustavo A.",
""
],
[
"Saldaña",
"David",
""
]
] | Transporting objects using aerial robots has been widely studied in the literature. Still, those approaches always assume that the connection between the quadrotor and the load is made in a previous stage. However, that previous stage usually requires human intervention, and autonomous procedures to locate and attach the object are not considered. Additionally, most of the approaches assume cables as rigid links, but manipulating cables requires considering the state when the cables are hanging. In this work, we design and control a catenary robot. Our robot is able to transport hook-shaped objects in the environment. The robotic system is composed of two quadrotors attached to the two ends of a cable. By defining the catenary curve with five degrees of freedom, position in 3-D, orientation in the z-axis, and span, we can drive the two quadrotors to track a given trajectory. We validate our approach with simulations and real robots. We present four different scenarios of experiments. Our numerical solution is computationally fast and can be executed in real-time. |
2003.02973 | Kazuhiro Seki | Kazuhiro Seki and Yusuke Ikuta | S-APIR: News-based Business Sentiment Index | null | null | null | null | cs.CL cs.CY cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes our work on developing a new business sentiment index
using daily newspaper articles. We adopt a recurrent neural network (RNN) with
Gated Recurrent Units to predict the business sentiment of a given text. An RNN
is initially trained on Economy Watchers Survey and then fine-tuned on news
texts for domain adaptation. Also, a one-class support vector machine is
applied to filter out texts deemed irrelevant to business sentiment. Moreover,
we propose a simple approach to temporally analyzing how much and when any
given factor influences the predicted business sentiment. The validity and
utility of the proposed approaches are empirically demonstrated through a
series of experiments on Nikkei Newspaper articles published from 2013 to 2018.
| [
{
"created": "Fri, 6 Mar 2020 00:18:50 GMT",
"version": "v1"
}
] | 2020-03-09 | [
[
"Seki",
"Kazuhiro",
""
],
[
"Ikuta",
"Yusuke",
""
]
] | This paper describes our work on developing a new business sentiment index using daily newspaper articles. We adopt a recurrent neural network (RNN) with Gated Recurrent Units to predict the business sentiment of a given text. An RNN is initially trained on Economy Watchers Survey and then fine-tuned on news texts for domain adaptation. Also, a one-class support vector machine is applied to filter out texts deemed irrelevant to business sentiment. Moreover, we propose a simple approach to temporally analyzing how much and when any given factor influences the predicted business sentiment. The validity and utility of the proposed approaches are empirically demonstrated through a series of experiments on Nikkei Newspaper articles published from 2013 to 2018. |
2106.11703 | Raussen Martin | Martin Raussen | Connectivity of spaces of directed paths in geometric models for
concurrent computation | null | Computational Geometry: Theory and Applications 109 (2023) 101942 | 10.1016/j.comgeo.2022.101942 | null | cs.FL math.AT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Higher Dimensional Automata (HDA) are higher dimensional relatives to
transition systems in concurrency theory taking into account to which degree
various actions commute. Mathematically, they take the form of labelled cubical
complexes. It is important to know, and challenging from a
geometric/topological perspective, whether the space of directed paths
(executions in the model) between two vertices (states) is connected; more
generally, to estimate higher connectedness of these path spaces.
This paper presents an approach for such an estimation for particularly
simple HDA modelling the access of a number of processors to a number of
resources with given limited capacity each. It defines a spare capacity for a
concurrent program with prescribed periods of access of the processors to the
resources. It shows that the connectedness of spaces of directed paths can be
estimated (from above) by spare capacities. Moreover, spare capacities can also
be used to detect deadlocks and critical states in such a HDA.
The key theoretical ingredient is a transition from the calculation of local
connectedness bounds (of the upper links of vertices of an HDA) to global ones
by applying a version of the nerve lemma due to Anders Bj\"orner.
| [
{
"created": "Tue, 22 Jun 2021 12:18:49 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Apr 2022 09:09:24 GMT",
"version": "v2"
},
{
"created": "Tue, 6 Sep 2022 13:30:27 GMT",
"version": "v3"
}
] | 2022-09-07 | [
[
"Raussen",
"Martin",
""
]
] | Higher Dimensional Automata (HDA) are higher dimensional relatives to transition systems in concurrency theory taking into account to which degree various actions commute. Mathematically, they take the form of labelled cubical complexes. It is important to know, and challenging from a geometric/topological perspective, whether the space of directed paths (executions in the model) between two vertices (states) is connected; more generally, to estimate higher connectedness of these path spaces. This paper presents an approach for such an estimation for particularly simple HDA modelling the access of a number of processors to a number of resources with given limited capacity each. It defines a spare capacity for a concurrent program with prescribed periods of access of the processors to the resources. It shows that the connectedness of spaces of directed paths can be estimated (from above) by spare capacities. Moreover, spare capacities can also be used to detect deadlocks and critical states in such a HDA. The key theoretical ingredient is a transition from the calculation of local connectedness bounds (of the upper links of vertices of an HDA) to global ones by applying a version of the nerve lemma due to Anders Bj\"orner. |
2310.14230 | Haoran Wang | Haoran Wang, Qiuye Jin, Shiman Li, Siyu Liu, Manning Wang, Zhijian
Song | A comprehensive survey on deep active learning in medical image analysis | More papers and contents of medical image analysis & performance
analysis on medical imaging datasets with experiments | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Deep learning has achieved widespread success in medical image analysis,
leading to an increasing demand for large-scale expert-annotated medical image
datasets. Yet, the high cost of annotating medical images severely hampers the
development of deep learning in this field. To reduce annotation costs, active
learning aims to select the most informative samples for annotation and train
high-performance models with as few labeled samples as possible. In this
survey, we review the core methods of active learning, including the evaluation
of informativeness and sampling strategy. For the first time, we provide a
detailed summary of the integration of active learning with other
label-efficient techniques, such as semi-supervised, self-supervised learning,
and so on. We also summarize active learning works that are specifically
tailored to medical image analysis. Additionally, we conduct a thorough
comparative analysis of the performance of different AL methods in medical
image analysis with experiments. In the end, we offer our perspectives on the
future trends and challenges of active learning and its applications in medical
image analysis.
| [
{
"created": "Sun, 22 Oct 2023 08:46:40 GMT",
"version": "v1"
},
{
"created": "Tue, 24 Oct 2023 01:36:19 GMT",
"version": "v2"
},
{
"created": "Wed, 13 Mar 2024 09:23:10 GMT",
"version": "v3"
}
] | 2024-03-14 | [
[
"Wang",
"Haoran",
""
],
[
"Jin",
"Qiuye",
""
],
[
"Li",
"Shiman",
""
],
[
"Liu",
"Siyu",
""
],
[
"Wang",
"Manning",
""
],
[
"Song",
"Zhijian",
""
]
] | Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative samples for annotation and train high-performance models with as few labeled samples as possible. In this survey, we review the core methods of active learning, including the evaluation of informativeness and sampling strategy. For the first time, we provide a detailed summary of the integration of active learning with other label-efficient techniques, such as semi-supervised, self-supervised learning, and so on. We also summarize active learning works that are specifically tailored to medical image analysis. Additionally, we conduct a thorough comparative analysis of the performance of different AL methods in medical image analysis with experiments. In the end, we offer our perspectives on the future trends and challenges of active learning and its applications in medical image analysis. |
1004.4614 | Ashley Smith | Vitthal J. Gond and Aditya Goel | Performance Evaluation of Wavelength Routed Optical Network with
Wavelength Conversion | Vitthal J. Gond and Aditya Goel, "Performance Evaluation of
Wavelength Routed Optical Network with Wavelength Conversion", Journal of
Telecommunications, Volume 2, Issue 1, p110-114, April 2010 | Journal of Telecommunications, Volume 2, Issue 1, p110-114, April
2010 | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The rapid development of telecommunication networks is driven by user demands
for new applications and advances in technologies. The explosive growth of the
internet traffic is due to its use for collecting the information,
communication, multimedia application, entertainment, etc. These applications
are imposing a tremendous demand for bandwidth capacity on telecommunication
network. The introduction of fiber optics had proved to meet the huge demand of
bandwidth. These requirement can be meet by all optical network which is
capable of transmitting enormous data at very high speed, around 50 Tera bits
per seconds (Tbps) A wavelength conversion technique is addressed in this paper
to reduced the blocking probability in wavelength routed networks. It is seen
that the blocking probability of traffic requests decreases as the wavelength
conversion factor increases. We explode the possibility for network with
different size with variation in wavelength per link. In this work the
evaluation of wavelength routed optical network with varying number of
wavelength converters, different traffic types are carried out and results are
shown that the blocking probability is minimum with 50% to 60% wavelength
convertible nodes. Wavelength convertible nodes more than 60% are not showing
much effect on reduction in blocking probability rather it results in increase
in overall cost of network.
| [
{
"created": "Mon, 26 Apr 2010 19:29:59 GMT",
"version": "v1"
}
] | 2010-04-27 | [
[
"Gond",
"Vitthal J.",
""
],
[
"Goel",
"Aditya",
""
]
] | The rapid development of telecommunication networks is driven by user demands for new applications and advances in technologies. The explosive growth of the internet traffic is due to its use for collecting the information, communication, multimedia application, entertainment, etc. These applications are imposing a tremendous demand for bandwidth capacity on telecommunication network. The introduction of fiber optics had proved to meet the huge demand of bandwidth. These requirement can be meet by all optical network which is capable of transmitting enormous data at very high speed, around 50 Tera bits per seconds (Tbps) A wavelength conversion technique is addressed in this paper to reduced the blocking probability in wavelength routed networks. It is seen that the blocking probability of traffic requests decreases as the wavelength conversion factor increases. We explode the possibility for network with different size with variation in wavelength per link. In this work the evaluation of wavelength routed optical network with varying number of wavelength converters, different traffic types are carried out and results are shown that the blocking probability is minimum with 50% to 60% wavelength convertible nodes. Wavelength convertible nodes more than 60% are not showing much effect on reduction in blocking probability rather it results in increase in overall cost of network. |
1906.05986 | Omar Alonso | Omar Alonso, Vasileios Kandylas, Serge-Eric Tremblay | Scalable Knowledge Graph Construction from Twitter | null | null | null | null | cs.IR cs.SI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a knowledge graph derived from Twitter data with the goal of
discovering relationships between people, links, and topics. The goal is to
filter out noise from Twitter and surface an inside-out view that relies on
high quality content. The generated graph contains many relationships where the
user can query and traverse the structure from different angles allowing the
development of new applications.
| [
{
"created": "Fri, 14 Jun 2019 02:28:55 GMT",
"version": "v1"
}
] | 2019-06-17 | [
[
"Alonso",
"Omar",
""
],
[
"Kandylas",
"Vasileios",
""
],
[
"Tremblay",
"Serge-Eric",
""
]
] | We describe a knowledge graph derived from Twitter data with the goal of discovering relationships between people, links, and topics. The goal is to filter out noise from Twitter and surface an inside-out view that relies on high quality content. The generated graph contains many relationships where the user can query and traverse the structure from different angles allowing the development of new applications. |
1507.07348 | Andreas Schwarz | Sam Nees, Andreas Schwarz, Walter Kellermann | A model for the temporal evolution of the spatial coherence in decaying
reverberant sound fields | Accepted for JASA Express Letters | null | 10.1121/1.4929733 | null | cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reverberant sound fields are often modeled as isotropic. However, it has been
observed that spatial properties change during the decay of the sound field
energy, due to non-isotropic attenuation in non-ideal rooms. In this letter, a
model for the spatial coherence between two sensors in a decaying reverberant
sound field is developed for rectangular rooms. The modeled coherence function
depends on room dimensions, surface reflectivity and orientation of the sensor
pair, but is independent of the position of source and sensors in the room. The
model includes the spherically isotropic (diffuse) and cylindrically isotropic
sound field models as special cases.
| [
{
"created": "Mon, 27 Jul 2015 10:08:24 GMT",
"version": "v1"
}
] | 2015-08-26 | [
[
"Nees",
"Sam",
""
],
[
"Schwarz",
"Andreas",
""
],
[
"Kellermann",
"Walter",
""
]
] | Reverberant sound fields are often modeled as isotropic. However, it has been observed that spatial properties change during the decay of the sound field energy, due to non-isotropic attenuation in non-ideal rooms. In this letter, a model for the spatial coherence between two sensors in a decaying reverberant sound field is developed for rectangular rooms. The modeled coherence function depends on room dimensions, surface reflectivity and orientation of the sensor pair, but is independent of the position of source and sensors in the room. The model includes the spherically isotropic (diffuse) and cylindrically isotropic sound field models as special cases. |
2106.04565 | Juri Opitz | Sarah Uhrig, Yoalli Rezepka Garcia, Juri Opitz, Anette Frank | Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing | IWPT 2021 | null | null | null | cs.CL cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In cross-lingual Abstract Meaning Representation (AMR) parsing, researchers
develop models that project sentences from various languages onto their AMRs to
capture their essential semantic structures: given a sentence in any language,
we aim to capture its core semantic content through concepts connected by
manifold types of semantic relations. Methods typically leverage large silver
training data to learn a single model that is able to project non-English
sentences to AMRs. However, we find that a simple baseline tends to be
over-looked: translating the sentences to English and projecting their AMR with
a monolingual AMR parser (translate+parse,T+P). In this paper, we revisit this
simple two-step base-line, and enhance it with a strong NMT system and a strong
AMR parser. Our experiments show that T+P outperforms a recent state-of-the-art
system across all tested languages: German, Italian, Spanish and Mandarin with
+14.6, +12.6, +14.3 and +16.0 Smatch points.
| [
{
"created": "Tue, 8 Jun 2021 17:52:48 GMT",
"version": "v1"
}
] | 2021-06-09 | [
[
"Uhrig",
"Sarah",
""
],
[
"Garcia",
"Yoalli Rezepka",
""
],
[
"Opitz",
"Juri",
""
],
[
"Frank",
"Anette",
""
]
] | In cross-lingual Abstract Meaning Representation (AMR) parsing, researchers develop models that project sentences from various languages onto their AMRs to capture their essential semantic structures: given a sentence in any language, we aim to capture its core semantic content through concepts connected by manifold types of semantic relations. Methods typically leverage large silver training data to learn a single model that is able to project non-English sentences to AMRs. However, we find that a simple baseline tends to be over-looked: translating the sentences to English and projecting their AMR with a monolingual AMR parser (translate+parse,T+P). In this paper, we revisit this simple two-step base-line, and enhance it with a strong NMT system and a strong AMR parser. Our experiments show that T+P outperforms a recent state-of-the-art system across all tested languages: German, Italian, Spanish and Mandarin with +14.6, +12.6, +14.3 and +16.0 Smatch points. |
2211.14823 | Bowen Cai | Bowen Cai, Yujie Li, Yuqin Liang, Rongfei Jia, Binqiang Zhao, Mingming
Gong, and Huan Fu | 3D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer
Avenue | Accepted by IEEE Transactions on Pattern Analysis and Machine
Intelligence (T-PAMI), project page:
http://3d-front-future.github.io/LighTNet | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies how to flexibly integrate reconstructed 3D models into
practical 3D modeling pipelines such as 3D scene creation and rendering. Due to
the technical difficulty, one can only obtain rough 3D models (R3DMs) for most
real objects using existing 3D reconstruction techniques. As a result,
physically-based rendering (PBR) would render low-quality images or videos for
scenes that are constructed by R3DMs. One promising solution would be
representing real-world objects as Neural Fields such as NeRFs, which are able
to generate photo-realistic renderings of an object under desired viewpoints.
However, a drawback is that the synthesized views through Neural Fields
Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR
pipelines, especially when object interactions in the 3D scene creation cause
local shadows. To solve this dilemma, we propose a lighting transfer network
(LighTNet) to bridge NFR and PBR, such that they can benefit from each other.
LighTNet reasons about a simplified image composition model, remedies the
uneven surface issue caused by R3DMs, and is empowered by several
perceptual-motivated constraints and a new Lab angle loss which enhances the
contrast between lighting strength and colors. Comparisons demonstrate that
LighTNet is superior in synthesizing impressive lighting, and is promising in
pushing NFR further in practical 3D modeling workflows.
| [
{
"created": "Sun, 27 Nov 2022 13:31:00 GMT",
"version": "v1"
},
{
"created": "Sun, 4 Dec 2022 07:13:01 GMT",
"version": "v2"
},
{
"created": "Tue, 19 Mar 2024 15:02:04 GMT",
"version": "v3"
}
] | 2024-03-20 | [
[
"Cai",
"Bowen",
""
],
[
"Li",
"Yujie",
""
],
[
"Liang",
"Yuqin",
""
],
[
"Jia",
"Rongfei",
""
],
[
"Zhao",
"Binqiang",
""
],
[
"Gong",
"Mingming",
""
],
[
"Fu",
"Huan",
""
]
] | This paper studies how to flexibly integrate reconstructed 3D models into practical 3D modeling pipelines such as 3D scene creation and rendering. Due to the technical difficulty, one can only obtain rough 3D models (R3DMs) for most real objects using existing 3D reconstruction techniques. As a result, physically-based rendering (PBR) would render low-quality images or videos for scenes that are constructed by R3DMs. One promising solution would be representing real-world objects as Neural Fields such as NeRFs, which are able to generate photo-realistic renderings of an object under desired viewpoints. However, a drawback is that the synthesized views through Neural Fields Rendering (NFR) cannot reflect the simulated lighting details on R3DMs in PBR pipelines, especially when object interactions in the 3D scene creation cause local shadows. To solve this dilemma, we propose a lighting transfer network (LighTNet) to bridge NFR and PBR, such that they can benefit from each other. LighTNet reasons about a simplified image composition model, remedies the uneven surface issue caused by R3DMs, and is empowered by several perceptual-motivated constraints and a new Lab angle loss which enhances the contrast between lighting strength and colors. Comparisons demonstrate that LighTNet is superior in synthesizing impressive lighting, and is promising in pushing NFR further in practical 3D modeling workflows. |
1105.4301 | Neil J. Gunther | Neil J. Gunther, Shanti Subramanyam, Stefan Parvu | A Methodology for Optimizing Multithreaded System Scalability on
Multi-cores | 21 pages, 11 figures. To appear in "Programming Multi-core and
Many-core Computing Systems," eds. S. Pllana and F. Xhafa, Wiley Series on
Parallel and Distributed Computing | null | null | null | cs.DC cs.PF | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show how to quantify scalability with the Universal Scalability Law (USL)
by applying it to performance measurements of memcached, J2EE, and Weblogic on
multi-core platforms. Since commercial multicores are essentially black-boxes,
the accessible performance gains are primarily available at the application
level. We also demonstrate how our methodology can identify the most
significant performance tuning opportunities to optimize application
scalability, as well as providing an easy means for exploring other aspects of
the multi-core system design space.
| [
{
"created": "Sun, 22 May 2011 01:12:11 GMT",
"version": "v1"
}
] | 2011-05-24 | [
[
"Gunther",
"Neil J.",
""
],
[
"Subramanyam",
"Shanti",
""
],
[
"Parvu",
"Stefan",
""
]
] | We show how to quantify scalability with the Universal Scalability Law (USL) by applying it to performance measurements of memcached, J2EE, and Weblogic on multi-core platforms. Since commercial multicores are essentially black-boxes, the accessible performance gains are primarily available at the application level. We also demonstrate how our methodology can identify the most significant performance tuning opportunities to optimize application scalability, as well as providing an easy means for exploring other aspects of the multi-core system design space. |
1705.00571 | Andrew Moore | Andrew Moore, Paul Rayson | Lancaster A at SemEval-2017 Task 5: Evaluation metrics matter:
predicting sentiment from financial news headlines | 5 pages, to Appear in the Proceedings of the 11th International
Workshop on Semantic Evaluation (SemEval 2017), August 2017, Vancouver, BC | null | 10.18653/v1/S17-2095 | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | This paper describes our participation in Task 5 track 2 of SemEval 2017 to
predict the sentiment of financial news headlines for a specific company on a
continuous scale between -1 and 1. We tackled the problem using a number of
approaches, utilising a Support Vector Regression (SVR) and a Bidirectional
Long Short-Term Memory (BLSTM). We found an improvement of 4-6% using the LSTM
model over the SVR and came fourth in the track. We report a number of
different evaluations using a finance specific word embedding model and reflect
on the effects of using different evaluation metrics.
| [
{
"created": "Mon, 1 May 2017 15:57:41 GMT",
"version": "v1"
}
] | 2018-06-15 | [
[
"Moore",
"Andrew",
""
],
[
"Rayson",
"Paul",
""
]
] | This paper describes our participation in Task 5 track 2 of SemEval 2017 to predict the sentiment of financial news headlines for a specific company on a continuous scale between -1 and 1. We tackled the problem using a number of approaches, utilising a Support Vector Regression (SVR) and a Bidirectional Long Short-Term Memory (BLSTM). We found an improvement of 4-6% using the LSTM model over the SVR and came fourth in the track. We report a number of different evaluations using a finance specific word embedding model and reflect on the effects of using different evaluation metrics. |
1209.1198 | Chong-Dao Lee | Yaotsu Chang, Chong-Dao Lee, Keqin Feng | Multivariate Interpolation Formula over Finite Fields and Its
Applications in Coding Theory | 11 pages. This work is supported by the grant of the NSFC no.10990011
and the Tsinghua National Lab. of Information Science and Technology | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A multivariate interpolation formula (MVIF) over finite fields is presented
by using the proposed Kronecker delta function. The MVIF can be applied to
yield polynomial relations over the base field among homogeneous symmetric
rational functions. Besides the property that all the coefficients are coming
from the base field, there is also a significant one on the degrees of the
obtained polynomial; namely, the degree of each term satisfies certain
condition. Next, for any cyclic codes the unknown syndrome representation can
also be provided by the proposed MVIF and also has the same properties. By
applying the unknown syndrome representation and the Berlekamp-Massey
algorithm, one-step decoding algorithms can be developed to determine the error
locator polynomials for arbitrary cyclic codes.
| [
{
"created": "Thu, 6 Sep 2012 07:08:08 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Dec 2012 15:05:55 GMT",
"version": "v2"
}
] | 2012-12-21 | [
[
"Chang",
"Yaotsu",
""
],
[
"Lee",
"Chong-Dao",
""
],
[
"Feng",
"Keqin",
""
]
] | A multivariate interpolation formula (MVIF) over finite fields is presented by using the proposed Kronecker delta function. The MVIF can be applied to yield polynomial relations over the base field among homogeneous symmetric rational functions. Besides the property that all the coefficients are coming from the base field, there is also a significant one on the degrees of the obtained polynomial; namely, the degree of each term satisfies certain condition. Next, for any cyclic codes the unknown syndrome representation can also be provided by the proposed MVIF and also has the same properties. By applying the unknown syndrome representation and the Berlekamp-Massey algorithm, one-step decoding algorithms can be developed to determine the error locator polynomials for arbitrary cyclic codes. |
2407.10332 | Ryan Hare | Ryan Hare and Ying Tang | Ontology-driven Reinforcement Learning for Personalized Student Support | 6 pages, 3 figures, in press for IEEE Systems, Man, and Cybernetics
2024 Conference | null | null | null | cs.CY cs.LG cs.MA | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the search for more effective education, there is a widespread effort to
develop better approaches to personalize student education. Unassisted,
educators often do not have time or resources to personally support every
student in a given classroom. Motivated by this issue, and by recent
advancements in artificial intelligence, this paper presents a general-purpose
framework for personalized student support, applicable to any virtual
educational system such as a serious game or an intelligent tutoring system. To
fit any educational situation, we apply ontologies for their semantic
organization, combining them with data collection considerations and
multi-agent reinforcement learning. The result is a modular system that can be
adapted to any virtual educational software to provide useful personalized
assistance to students.
| [
{
"created": "Sun, 14 Jul 2024 21:11:44 GMT",
"version": "v1"
}
] | 2024-07-16 | [
[
"Hare",
"Ryan",
""
],
[
"Tang",
"Ying",
""
]
] | In the search for more effective education, there is a widespread effort to develop better approaches to personalize student education. Unassisted, educators often do not have time or resources to personally support every student in a given classroom. Motivated by this issue, and by recent advancements in artificial intelligence, this paper presents a general-purpose framework for personalized student support, applicable to any virtual educational system such as a serious game or an intelligent tutoring system. To fit any educational situation, we apply ontologies for their semantic organization, combining them with data collection considerations and multi-agent reinforcement learning. The result is a modular system that can be adapted to any virtual educational software to provide useful personalized assistance to students. |
2107.02777 | W. Damayantha Kularatne | W. D. Kularatne, Lasanthika H. Dissawa, T.M.S.S.K. Ekanayake, Janaka
B. Ekanayake | Developing and delivering a remote experiment based on the experiential
learning framework during COVID-19 pandemic | 15 pages | null | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The students following Engineering disciplines should not only acquire the
conceptual understanding of the concepts but also the processors and attitudes.
There are two recognizable learning environments for students, namely,
classroom environment and laboratory environment. With the COVID-19 pandemic,
both environments merged to online environments, impacting students'
development of processes and characteristic attitudes. This paper introduces a
theoretical framework based on experiential learning to plan and deliver
processes through an online environment. A case study based on the power factor
correction experiment was presented. The traditional experiment that runs for 3
hours was broken into smaller tasks such as a pre-lab activity, a simulation
exercise, a PowerPoint presentation, a remote laboratory activity, and a final
report based on the experiential learning approach. A questionnaire that
carries close and open-ended questions were administered to obtain students'
reflections about developing the processes through an online-friendly
experiential learning approach. The majority of the students like the approach
followed and praise for providing them with an opportunity to perform the
experiment in a novel way during the COVID-19 situation.
| [
{
"created": "Tue, 6 Jul 2021 17:39:48 GMT",
"version": "v1"
}
] | 2021-07-07 | [
[
"Kularatne",
"W. D.",
""
],
[
"Dissawa",
"Lasanthika H.",
""
],
[
"Ekanayake",
"T. M. S. S. K.",
""
],
[
"Ekanayake",
"Janaka B.",
""
]
] | The students following Engineering disciplines should not only acquire the conceptual understanding of the concepts but also the processors and attitudes. There are two recognizable learning environments for students, namely, classroom environment and laboratory environment. With the COVID-19 pandemic, both environments merged to online environments, impacting students' development of processes and characteristic attitudes. This paper introduces a theoretical framework based on experiential learning to plan and deliver processes through an online environment. A case study based on the power factor correction experiment was presented. The traditional experiment that runs for 3 hours was broken into smaller tasks such as a pre-lab activity, a simulation exercise, a PowerPoint presentation, a remote laboratory activity, and a final report based on the experiential learning approach. A questionnaire that carries close and open-ended questions were administered to obtain students' reflections about developing the processes through an online-friendly experiential learning approach. The majority of the students like the approach followed and praise for providing them with an opportunity to perform the experiment in a novel way during the COVID-19 situation. |
2110.02861 | Tim Dettmers | Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer | 8-bit Optimizers via Block-wise Quantization | ICLR2022 spotlight version | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Stateful optimizers maintain gradient statistics over time, e.g., the
exponentially smoothed sum (SGD with momentum) or squared sum (Adam) of past
gradient values. This state can be used to accelerate optimization compared to
plain stochastic gradient descent but uses memory that might otherwise be
allocated to model parameters, thereby limiting the maximum size of models
trained in practice. In this paper, we develop the first optimizers that use
8-bit statistics while maintaining the performance levels of using 32-bit
optimizer states. To overcome the resulting computational, quantization, and
stability challenges, we develop block-wise dynamic quantization. Block-wise
quantization divides input tensors into smaller blocks that are independently
quantized. Each block is processed in parallel across cores, yielding faster
optimization and high precision quantization. To maintain stability and
performance, we combine block-wise quantization with two additional changes:
(1) dynamic quantization, a form of non-linear optimization that is precise for
both large and small magnitude values, and (2) a stable embedding layer to
reduce gradient variance that comes from the highly non-uniform distribution of
input tokens in language models. As a result, our 8-bit optimizers maintain
32-bit performance with a small fraction of the memory footprint on a range of
tasks, including 1.5B parameter language modeling, GLUE finetuning, ImageNet
classification, WMT'14 machine translation, MoCo v2 contrastive ImageNet
pretraining+finetuning, and RoBERTa pretraining, without changes to the
original optimizer hyperparameters. We open-source our 8-bit optimizers as a
drop-in replacement that only requires a two-line code change.
| [
{
"created": "Wed, 6 Oct 2021 15:43:20 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Jun 2022 16:05:15 GMT",
"version": "v2"
}
] | 2022-06-22 | [
[
"Dettmers",
"Tim",
""
],
[
"Lewis",
"Mike",
""
],
[
"Shleifer",
"Sam",
""
],
[
"Zettlemoyer",
"Luke",
""
]
] | Stateful optimizers maintain gradient statistics over time, e.g., the exponentially smoothed sum (SGD with momentum) or squared sum (Adam) of past gradient values. This state can be used to accelerate optimization compared to plain stochastic gradient descent but uses memory that might otherwise be allocated to model parameters, thereby limiting the maximum size of models trained in practice. In this paper, we develop the first optimizers that use 8-bit statistics while maintaining the performance levels of using 32-bit optimizer states. To overcome the resulting computational, quantization, and stability challenges, we develop block-wise dynamic quantization. Block-wise quantization divides input tensors into smaller blocks that are independently quantized. Each block is processed in parallel across cores, yielding faster optimization and high precision quantization. To maintain stability and performance, we combine block-wise quantization with two additional changes: (1) dynamic quantization, a form of non-linear optimization that is precise for both large and small magnitude values, and (2) a stable embedding layer to reduce gradient variance that comes from the highly non-uniform distribution of input tokens in language models. As a result, our 8-bit optimizers maintain 32-bit performance with a small fraction of the memory footprint on a range of tasks, including 1.5B parameter language modeling, GLUE finetuning, ImageNet classification, WMT'14 machine translation, MoCo v2 contrastive ImageNet pretraining+finetuning, and RoBERTa pretraining, without changes to the original optimizer hyperparameters. We open-source our 8-bit optimizers as a drop-in replacement that only requires a two-line code change. |
1909.08048 | Rasheed Hussain | Fatima Hussain and Rasheed Hussain and Brett Noye and Salah Sharieh | Enterprise API Security and GDPR Compliance: Design and Implementation
Perspective | 7 pages | null | null | null | cs.CR cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the advancements in the enterprise-level business development, the
demand for new applications and services is overwhelming. For the development
and delivery of such applications and services, enterprise businesses rely on
Application Programming Interfaces (APIs). In essence, API is a double-edged
sword. On one hand, API provides ease of expanding the business through sharing
value and utility, but on another hand it raises security and privacy issues.
Since the applications usually use APIs to retrieve important data, therefore
it is extremely important to make sure that an effective access control and
security mechanism are in place , and the data does not fall into wrong hands.
In this article, we discuss the current state of the enterprise API security
and the role of Machine Learning (ML) in API security. We also discuss the
General Data Protection Regulation (GDPR) compliance and its effect on the API
security.
| [
{
"created": "Tue, 17 Sep 2019 19:36:12 GMT",
"version": "v1"
}
] | 2019-09-19 | [
[
"Hussain",
"Fatima",
""
],
[
"Hussain",
"Rasheed",
""
],
[
"Noye",
"Brett",
""
],
[
"Sharieh",
"Salah",
""
]
] | With the advancements in the enterprise-level business development, the demand for new applications and services is overwhelming. For the development and delivery of such applications and services, enterprise businesses rely on Application Programming Interfaces (APIs). In essence, API is a double-edged sword. On one hand, API provides ease of expanding the business through sharing value and utility, but on another hand it raises security and privacy issues. Since the applications usually use APIs to retrieve important data, therefore it is extremely important to make sure that an effective access control and security mechanism are in place , and the data does not fall into wrong hands. In this article, we discuss the current state of the enterprise API security and the role of Machine Learning (ML) in API security. We also discuss the General Data Protection Regulation (GDPR) compliance and its effect on the API security. |
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