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1805.01965 | Zhongxing Yu | Zhongxing Yu, Chenggang Bai, Lionel Seinturier, Martin Monperrus | Characterizing the Usage, Evolution and Impact of Java Annotations in
Practice | TO APPEAR IN IEEE TRANSACTIONS ON SOFTWARE ENGINEERING | IEEE Transactions on Software Engineering, 2019 | 10.1109/TSE.2019.2910516 | null | cs.SE cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Annotations have been formally introduced into Java since Java 5. Since then,
annotations have been widely used by the Java community for different purposes,
such as compiler guidance and runtime processing. Despite the ever-growing use,
there is still limited empirical knowledge about the actual usage of
annotations in practice, the changes made to annotations during software
evolution, and the potential impact of annotations on code quality. To fill
this gap, we perform the first large-scale empirical study about Java
annotations on 1,094 notable open-source projects hosted on GitHub. Our study
systematically investigates annotation usage, annotation evolution, and
annotation impact, and generates 10 novel and important findings. We also
present the implications of our findings, which shed light for developers,
researchers, tool builders, and language or library designers in order to
improve all facets of Java annotation engineering.
| [
{
"created": "Fri, 4 May 2018 23:29:19 GMT",
"version": "v1"
},
{
"created": "Fri, 5 Apr 2019 15:02:59 GMT",
"version": "v2"
}
] | 2019-04-16 | [
[
"Yu",
"Zhongxing",
""
],
[
"Bai",
"Chenggang",
""
],
[
"Seinturier",
"Lionel",
""
],
[
"Monperrus",
"Martin",
""
]
] | Annotations have been formally introduced into Java since Java 5. Since then, annotations have been widely used by the Java community for different purposes, such as compiler guidance and runtime processing. Despite the ever-growing use, there is still limited empirical knowledge about the actual usage of annotations in practice, the changes made to annotations during software evolution, and the potential impact of annotations on code quality. To fill this gap, we perform the first large-scale empirical study about Java annotations on 1,094 notable open-source projects hosted on GitHub. Our study systematically investigates annotation usage, annotation evolution, and annotation impact, and generates 10 novel and important findings. We also present the implications of our findings, which shed light for developers, researchers, tool builders, and language or library designers in order to improve all facets of Java annotation engineering. |
cs/0601135 | Robert Brijder | Robert Brijder, Hendrik Jan Hoogeboom, Michael Muskulus | Strategies of Loop Recombination in Ciliates | 22 pages, 14 figures | Discrete Applied Mathematics, v. 156, 1736-1753, 2008 | 10.1016/j.dam.2007.08.032 | LIACS Technical Report 2006-01 | cs.LO q-bio.GN | null | Gene assembly in ciliates is an extremely involved DNA transformation
process, which transforms a nucleus, the micronucleus, to another functionally
different nucleus, the macronucleus. In this paper we characterize which loop
recombination operations (one of the three types of molecular operations that
accomplish gene assembly) can possibly be applied in the transformation of a
given gene from its micronuclear form to its macronuclear form. We also
characterize in which order these loop recombination operations are applicable.
This is done in the abstract and more general setting of so-called legal
strings.
| [
{
"created": "Tue, 31 Jan 2006 17:43:36 GMT",
"version": "v1"
}
] | 2014-03-26 | [
[
"Brijder",
"Robert",
""
],
[
"Hoogeboom",
"Hendrik Jan",
""
],
[
"Muskulus",
"Michael",
""
]
] | Gene assembly in ciliates is an extremely involved DNA transformation process, which transforms a nucleus, the micronucleus, to another functionally different nucleus, the macronucleus. In this paper we characterize which loop recombination operations (one of the three types of molecular operations that accomplish gene assembly) can possibly be applied in the transformation of a given gene from its micronuclear form to its macronuclear form. We also characterize in which order these loop recombination operations are applicable. This is done in the abstract and more general setting of so-called legal strings. |
1912.09539 | Hamidreza Kasaei | S. Hamidreza Kasaei | Interactive Open-Ended Learning for 3D Object Recognition | PhD thesis | null | null | null | cs.RO cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The thesis contributes in several important ways to the research area of 3D
object category learning and recognition. To cope with the mentioned
limitations, we look at human cognition, in particular at the fact that human
beings learn to recognize object categories ceaselessly over time. This ability
to refine knowledge from the set of accumulated experiences facilitates the
adaptation to new environments. Inspired by this capability, we seek to create
a cognitive object perception and perceptual learning architecture that can
learn 3D object categories in an open-ended fashion. In this context,
``open-ended'' implies that the set of categories to be learned is not known in
advance, and the training instances are extracted from actual experiences of a
robot, and thus become gradually available, rather than being available since
the beginning of the learning process. In particular, this architecture
provides perception capabilities that will allow robots to incrementally learn
object categories from the set of accumulated experiences and reason about how
to perform complex tasks. This framework integrates detection, tracking,
teaching, learning, and recognition of objects. An extensive set of systematic
experiments, in multiple experimental settings, was carried out to thoroughly
evaluate the described learning approaches. Experimental results show that the
proposed system is able to interact with human users, learn new object
categories over time, as well as perform complex tasks. The contributions
presented in this thesis have been fully implemented and evaluated on different
standard object and scene datasets and empirically evaluated on different
robotic platforms.
| [
{
"created": "Thu, 19 Dec 2019 20:46:51 GMT",
"version": "v1"
}
] | 2019-12-23 | [
[
"Kasaei",
"S. Hamidreza",
""
]
] | The thesis contributes in several important ways to the research area of 3D object category learning and recognition. To cope with the mentioned limitations, we look at human cognition, in particular at the fact that human beings learn to recognize object categories ceaselessly over time. This ability to refine knowledge from the set of accumulated experiences facilitates the adaptation to new environments. Inspired by this capability, we seek to create a cognitive object perception and perceptual learning architecture that can learn 3D object categories in an open-ended fashion. In this context, ``open-ended'' implies that the set of categories to be learned is not known in advance, and the training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available since the beginning of the learning process. In particular, this architecture provides perception capabilities that will allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform complex tasks. This framework integrates detection, tracking, teaching, learning, and recognition of objects. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks. The contributions presented in this thesis have been fully implemented and evaluated on different standard object and scene datasets and empirically evaluated on different robotic platforms. |
2312.15242 | Rashik Shrestha | Rashik Shrestha, Bishad Koju, Abhigyan Bhusal, Danda Pani Paudel,
Fran\c{c}ois Rameau | CaLDiff: Camera Localization in NeRF via Pose Diffusion | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the widespread use of NeRF-based implicit 3D representation, the need
for camera localization in the same representation becomes manifestly apparent.
Doing so not only simplifies the localization process -- by avoiding an
outside-the-NeRF-based localization -- but also has the potential to offer the
benefit of enhanced localization. This paper studies the problem of localizing
cameras in NeRF using a diffusion model for camera pose adjustment. More
specifically, given a pre-trained NeRF model, we train a diffusion model that
iteratively updates randomly initialized camera poses, conditioned upon the
image to be localized. At test time, a new camera is localized in two steps:
first, coarse localization using the proposed pose diffusion process, followed
by local refinement steps of a pose inversion process in NeRF. In fact, the
proposed camera localization by pose diffusion (CaLDiff) method also integrates
the pose inversion steps within the diffusion process. Such integration offers
significantly better localization, thanks to our downstream refinement-aware
diffusion process. Our exhaustive experiments on challenging real-world data
validate our method by providing significantly better results than the compared
methods and the established baselines. Our source code will be made publicly
available.
| [
{
"created": "Sat, 23 Dec 2023 12:36:01 GMT",
"version": "v1"
}
] | 2023-12-27 | [
[
"Shrestha",
"Rashik",
""
],
[
"Koju",
"Bishad",
""
],
[
"Bhusal",
"Abhigyan",
""
],
[
"Paudel",
"Danda Pani",
""
],
[
"Rameau",
"François",
""
]
] | With the widespread use of NeRF-based implicit 3D representation, the need for camera localization in the same representation becomes manifestly apparent. Doing so not only simplifies the localization process -- by avoiding an outside-the-NeRF-based localization -- but also has the potential to offer the benefit of enhanced localization. This paper studies the problem of localizing cameras in NeRF using a diffusion model for camera pose adjustment. More specifically, given a pre-trained NeRF model, we train a diffusion model that iteratively updates randomly initialized camera poses, conditioned upon the image to be localized. At test time, a new camera is localized in two steps: first, coarse localization using the proposed pose diffusion process, followed by local refinement steps of a pose inversion process in NeRF. In fact, the proposed camera localization by pose diffusion (CaLDiff) method also integrates the pose inversion steps within the diffusion process. Such integration offers significantly better localization, thanks to our downstream refinement-aware diffusion process. Our exhaustive experiments on challenging real-world data validate our method by providing significantly better results than the compared methods and the established baselines. Our source code will be made publicly available. |
2010.05119 | Ad\'in Ram\'irez Rivera | Ad\'in Ram\'irez Rivera, Adil Khan, Imad E. I. Bekkouch, Taimoor S.
Sheikh | Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical
Feature Distillation | To appear in IEEE Trans. on Neural Networks and Learning Systems | null | 10.1109/TNNLS.2020.3027667 | null | cs.CV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Anomaly detection suffers from unbalanced data since anomalies are quite
rare. Synthetically generated anomalies are a solution to such ill or not fully
defined data. However, synthesis requires an expressive representation to
guarantee the quality of the generated data. In this paper, we propose a
two-level hierarchical latent space representation that distills inliers'
feature-descriptors (through autoencoders) into more robust representations
based on a variational family of distributions (through a variational
autoencoder) for zero-shot anomaly generation. From the learned latent
distributions, we select those that lie on the outskirts of the training data
as synthetic-outlier generators. And, we synthesize from them, i.e., generate
negative samples without seen them before, to train binary classifiers. We
found that the use of the proposed hierarchical structure for feature
distillation and fusion creates robust and general representations that allow
us to synthesize pseudo outlier samples. And in turn, train robust binary
classifiers for true outlier detection (without the need for actual outliers
during training). We demonstrate the performance of our proposal on several
benchmarks for anomaly detection.
| [
{
"created": "Sat, 10 Oct 2020 23:34:02 GMT",
"version": "v1"
}
] | 2020-10-13 | [
[
"Rivera",
"Adín Ramírez",
""
],
[
"Khan",
"Adil",
""
],
[
"Bekkouch",
"Imad E. I.",
""
],
[
"Sheikh",
"Taimoor S.",
""
]
] | Anomaly detection suffers from unbalanced data since anomalies are quite rare. Synthetically generated anomalies are a solution to such ill or not fully defined data. However, synthesis requires an expressive representation to guarantee the quality of the generated data. In this paper, we propose a two-level hierarchical latent space representation that distills inliers' feature-descriptors (through autoencoders) into more robust representations based on a variational family of distributions (through a variational autoencoder) for zero-shot anomaly generation. From the learned latent distributions, we select those that lie on the outskirts of the training data as synthetic-outlier generators. And, we synthesize from them, i.e., generate negative samples without seen them before, to train binary classifiers. We found that the use of the proposed hierarchical structure for feature distillation and fusion creates robust and general representations that allow us to synthesize pseudo outlier samples. And in turn, train robust binary classifiers for true outlier detection (without the need for actual outliers during training). We demonstrate the performance of our proposal on several benchmarks for anomaly detection. |
1809.05762 | John Kingston | John KC Kingston | Using Artificial Intelligence to Support Compliance with the General
Data Protection Regulation | null | Artificial Intelligence and Law (2017) 25, 429 - 443 | 10.1007/s10506-017-9206-9 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The General Data Protection Regulation (GDPR) is a European Union regulation
that will replace the existing Data Protection Directive on 25 May 2018. The
most significant change is a huge increase in the maximum fine that can be
levied for breaches of the regulation. Yet fewer than half of UK companies are
fully aware of GDPR - and a number of those who were preparing for it stopped
doing so when the Brexit vote was announced. A last-minute rush to become
compliant is therefore expected, and numerous companies are starting to offer
advice, checklists and consultancy on how to comply with GDPR. In such an
environment, artificial intelligence technologies ought to be able to assist by
providing best advice; asking all and only the relevant questions; monitoring
activities; and carrying out assessments. The paper considers four areas of
GDPR compliance where rule based technologies and/or machine learning
techniques may be relevant: * Following compliance checklists and codes of
conduct; * Supporting risk assessments; * Complying with the new regulations
regarding technologies that perform automatic profiling; * Complying with the
new regulations concerning recognising and reporting breaches of security. It
concludes that AI technology can support each of these four areas. The
requirements that GDPR (or organisations that need to comply with GDPR) state
for explanation and justification of reasoning imply that rule-based approaches
are likely to be more helpful than machine learning approaches. However, there
may be good business reasons to take a different approach in some
circumstances.
| [
{
"created": "Sat, 15 Sep 2018 19:57:02 GMT",
"version": "v1"
}
] | 2018-09-18 | [
[
"Kingston",
"John KC",
""
]
] | The General Data Protection Regulation (GDPR) is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR - and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is therefore expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: * Following compliance checklists and codes of conduct; * Supporting risk assessments; * Complying with the new regulations regarding technologies that perform automatic profiling; * Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR (or organisations that need to comply with GDPR) state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances. |
2406.18060 | Yifan Yang | Yifan Yang, Kai Zhen, Ershad Banijamal, Athanasios Mouchtaris, Zheng
Zhang | AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for
Memory-Efficient Large Language Models Fine-Tuning | null | null | null | null | cs.CL cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | Fine-tuning large language models (LLMs) has achieved remarkable performance
across various natural language processing tasks, yet it demands more and more
memory as model sizes keep growing. To address this issue, the recently
proposed Memory-efficient Zeroth-order (MeZO) methods attempt to fine-tune LLMs
using only forward passes, thereby avoiding the need for a backpropagation
graph. However, significant performance drops and a high risk of divergence
have limited their widespread adoption. In this paper, we propose the Adaptive
Zeroth-order Tensor-Train Adaption (AdaZeta) framework, specifically designed
to improve the performance and convergence of the ZO methods. To enhance
dimension-dependent ZO estimation accuracy, we introduce a fast-forward,
low-parameter tensorized adapter. To tackle the frequently observed divergence
issue in large-scale ZO fine-tuning tasks, we propose an adaptive query number
schedule that guarantees convergence. Detailed theoretical analysis and
extensive experimental results on Roberta-Large and Llama-2-7B models
substantiate the efficacy of our AdaZeta framework in terms of accuracy, memory
efficiency, and convergence speed.
| [
{
"created": "Wed, 26 Jun 2024 04:33:13 GMT",
"version": "v1"
}
] | 2024-06-27 | [
[
"Yang",
"Yifan",
""
],
[
"Zhen",
"Kai",
""
],
[
"Banijamal",
"Ershad",
""
],
[
"Mouchtaris",
"Athanasios",
""
],
[
"Zhang",
"Zheng",
""
]
] | Fine-tuning large language models (LLMs) has achieved remarkable performance across various natural language processing tasks, yet it demands more and more memory as model sizes keep growing. To address this issue, the recently proposed Memory-efficient Zeroth-order (MeZO) methods attempt to fine-tune LLMs using only forward passes, thereby avoiding the need for a backpropagation graph. However, significant performance drops and a high risk of divergence have limited their widespread adoption. In this paper, we propose the Adaptive Zeroth-order Tensor-Train Adaption (AdaZeta) framework, specifically designed to improve the performance and convergence of the ZO methods. To enhance dimension-dependent ZO estimation accuracy, we introduce a fast-forward, low-parameter tensorized adapter. To tackle the frequently observed divergence issue in large-scale ZO fine-tuning tasks, we propose an adaptive query number schedule that guarantees convergence. Detailed theoretical analysis and extensive experimental results on Roberta-Large and Llama-2-7B models substantiate the efficacy of our AdaZeta framework in terms of accuracy, memory efficiency, and convergence speed. |
2109.08295 | EPTCS | Tobias Grubenmann (SDA Research Group, Department of Computer Science,
University of Bonn, Germany), Jens Lehmann (SDA Research Group, Department of
Computer Science, University of Bonn, Germany) | Geolog: Scalable Logic Programming on Spatial Data | In Proceedings ICLP 2021, arXiv:2109.07914 | EPTCS 345, 2021, pp. 191-204 | 10.4204/EPTCS.345.34 | null | cs.LO | http://creativecommons.org/licenses/by/4.0/ | Spatial data is ubiquitous in our data-driven society. The Logic Programming
community has been investigating the use of spatial data in different settings.
Despite the success of this research, the Geographic Information System (GIS)
community has rarely made use of these new approaches. This has mainly two
reasons. First, there is a lack of tools that tightly integrate logical
reasoning into state-of-the-art GIS software. Second, the scalability of
solutions has often not been tested and hence, some solutions might work on toy
examples but do not scale well to real-world settings. The two main
contributions of this paper are (1) the Relation Based Programming paradigm,
expressing rules on relations instead of individual entities, and (2) Geolog, a
tool for spatio-logical reasoning that can be installed on top of ArcMap, which
is an industry standard GIS. We evaluate our new Relation Based Programming
paradigm in four real-world scenarios and show that up to two orders of
magnitude in performance gain can be achieved compared to the prevalent Entity
Based Programming paradigm.
| [
{
"created": "Fri, 17 Sep 2021 01:49:06 GMT",
"version": "v1"
}
] | 2021-09-20 | [
[
"Grubenmann",
"Tobias",
"",
"SDA Research Group, Department of Computer Science,\n University of Bonn, Germany"
],
[
"Lehmann",
"Jens",
"",
"SDA Research Group, Department of\n Computer Science, University of Bonn, Germany"
]
] | Spatial data is ubiquitous in our data-driven society. The Logic Programming community has been investigating the use of spatial data in different settings. Despite the success of this research, the Geographic Information System (GIS) community has rarely made use of these new approaches. This has mainly two reasons. First, there is a lack of tools that tightly integrate logical reasoning into state-of-the-art GIS software. Second, the scalability of solutions has often not been tested and hence, some solutions might work on toy examples but do not scale well to real-world settings. The two main contributions of this paper are (1) the Relation Based Programming paradigm, expressing rules on relations instead of individual entities, and (2) Geolog, a tool for spatio-logical reasoning that can be installed on top of ArcMap, which is an industry standard GIS. We evaluate our new Relation Based Programming paradigm in four real-world scenarios and show that up to two orders of magnitude in performance gain can be achieved compared to the prevalent Entity Based Programming paradigm. |
2104.01482 | Edgar A. Bernal | Edgar A. Bernal | Training Deep Normalizing Flow Models in Highly Incomplete Data
Scenarios with Prior Regularization | null | null | null | null | cs.LG cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep generative frameworks including GANs and normalizing flow models have
proven successful at filling in missing values in partially observed data
samples by effectively learning -- either explicitly or implicitly -- complex,
high-dimensional statistical distributions. In tasks where the data available
for learning is only partially observed, however, their performance decays
monotonically as a function of the data missingness rate. In high missing data
rate regimes (e.g., 60% and above), it has been observed that state-of-the-art
models tend to break down and produce unrealistic and/or semantically
inaccurate data. We propose a novel framework to facilitate the learning of
data distributions in high paucity scenarios that is inspired by traditional
formulations of solutions to ill-posed problems. The proposed framework
naturally stems from posing the process of learning from incomplete data as a
joint optimization task of the parameters of the model being learned and the
missing data values. The method involves enforcing a prior regularization term
that seamlessly integrates with objectives used to train explicit and tractable
deep generative frameworks such as deep normalizing flow models. We demonstrate
via extensive experimental validation that the proposed framework outperforms
competing techniques, particularly as the rate of data paucity approaches
unity.
| [
{
"created": "Sat, 3 Apr 2021 20:57:57 GMT",
"version": "v1"
}
] | 2021-04-06 | [
[
"Bernal",
"Edgar A.",
""
]
] | Deep generative frameworks including GANs and normalizing flow models have proven successful at filling in missing values in partially observed data samples by effectively learning -- either explicitly or implicitly -- complex, high-dimensional statistical distributions. In tasks where the data available for learning is only partially observed, however, their performance decays monotonically as a function of the data missingness rate. In high missing data rate regimes (e.g., 60% and above), it has been observed that state-of-the-art models tend to break down and produce unrealistic and/or semantically inaccurate data. We propose a novel framework to facilitate the learning of data distributions in high paucity scenarios that is inspired by traditional formulations of solutions to ill-posed problems. The proposed framework naturally stems from posing the process of learning from incomplete data as a joint optimization task of the parameters of the model being learned and the missing data values. The method involves enforcing a prior regularization term that seamlessly integrates with objectives used to train explicit and tractable deep generative frameworks such as deep normalizing flow models. We demonstrate via extensive experimental validation that the proposed framework outperforms competing techniques, particularly as the rate of data paucity approaches unity. |
2402.07108 | Wenzhi Gao | Wenzhi Gao, Chunlin Sun, Chenyu Xue, Dongdong Ge, Yinyu Ye | Decoupling Learning and Decision-Making: Breaking the
$\mathcal{O}(\sqrt{T})$ Barrier in Online Resource Allocation with
First-Order Methods | null | null | null | null | cs.LG math.OC | http://creativecommons.org/licenses/by/4.0/ | Online linear programming plays an important role in both revenue management
and resource allocation, and recent research has focused on developing
efficient first-order online learning algorithms. Despite the empirical success
of first-order methods, they typically achieve a regret no better than
$\mathcal{O}(\sqrt{T})$, which is suboptimal compared to the $\mathcal{O}(\log
T)$ bound guaranteed by the state-of-the-art linear programming (LP)-based
online algorithms. This paper establishes several important facts about online
linear programming, which unveils the challenge for first-order-method-based
online algorithms to achieve beyond $\mathcal{O}(\sqrt{T})$ regret. To address
the challenge, we introduce a new algorithmic framework that decouples learning
from decision-making. For the first time, we show that first-order methods can
attain regret $\mathcal{O}(T^{1/3})$ with this new framework.
| [
{
"created": "Sun, 11 Feb 2024 05:35:50 GMT",
"version": "v1"
},
{
"created": "Tue, 28 May 2024 20:43:21 GMT",
"version": "v2"
}
] | 2024-05-30 | [
[
"Gao",
"Wenzhi",
""
],
[
"Sun",
"Chunlin",
""
],
[
"Xue",
"Chenyu",
""
],
[
"Ge",
"Dongdong",
""
],
[
"Ye",
"Yinyu",
""
]
] | Online linear programming plays an important role in both revenue management and resource allocation, and recent research has focused on developing efficient first-order online learning algorithms. Despite the empirical success of first-order methods, they typically achieve a regret no better than $\mathcal{O}(\sqrt{T})$, which is suboptimal compared to the $\mathcal{O}(\log T)$ bound guaranteed by the state-of-the-art linear programming (LP)-based online algorithms. This paper establishes several important facts about online linear programming, which unveils the challenge for first-order-method-based online algorithms to achieve beyond $\mathcal{O}(\sqrt{T})$ regret. To address the challenge, we introduce a new algorithmic framework that decouples learning from decision-making. For the first time, we show that first-order methods can attain regret $\mathcal{O}(T^{1/3})$ with this new framework. |
2201.11674 | Xin Du | Xin Du, Benedicte Legastelois, Bhargavi Ganesh, Ajitha Rajan, Hana
Chockler, Vaishak Belle, Stuart Anderson, Subramanian Ramamoorthy | Vision Checklist: Towards Testable Error Analysis of Image Models to
Help System Designers Interrogate Model Capabilities | 17 pages, 18 figures | null | null | null | cs.CV cs.LG | http://creativecommons.org/licenses/by/4.0/ | Using large pre-trained models for image recognition tasks is becoming
increasingly common owing to the well acknowledged success of recent models
like vision transformers and other CNN-based models like VGG and Resnet. The
high accuracy of these models on benchmark tasks has translated into their
practical use across many domains including safety-critical applications like
autonomous driving and medical diagnostics. Despite their widespread use, image
models have been shown to be fragile to changes in the operating environment,
bringing their robustness into question. There is an urgent need for methods
that systematically characterise and quantify the capabilities of these models
to help designers understand and provide guarantees about their safety and
robustness. In this paper, we propose Vision Checklist, a framework aimed at
interrogating the capabilities of a model in order to produce a report that can
be used by a system designer for robustness evaluations. This framework
proposes a set of perturbation operations that can be applied on the underlying
data to generate test samples of different types. The perturbations reflect
potential changes in operating environments, and interrogate various properties
ranging from the strictly quantitative to more qualitative. Our framework is
evaluated on multiple datasets like Tinyimagenet, CIFAR10, CIFAR100 and
Camelyon17 and for models like ViT and Resnet. Our Vision Checklist proposes a
specific set of evaluations that can be integrated into the previously proposed
concept of a model card. Robustness evaluations like our checklist will be
crucial in future safety evaluations of visual perception modules, and be
useful for a wide range of stakeholders including designers, deployers, and
regulators involved in the certification of these systems. Source code of
Vision Checklist would be open for public use.
| [
{
"created": "Thu, 27 Jan 2022 17:20:16 GMT",
"version": "v1"
},
{
"created": "Fri, 28 Jan 2022 13:48:59 GMT",
"version": "v2"
},
{
"created": "Mon, 31 Jan 2022 11:09:19 GMT",
"version": "v3"
}
] | 2022-02-01 | [
[
"Du",
"Xin",
""
],
[
"Legastelois",
"Benedicte",
""
],
[
"Ganesh",
"Bhargavi",
""
],
[
"Rajan",
"Ajitha",
""
],
[
"Chockler",
"Hana",
""
],
[
"Belle",
"Vaishak",
""
],
[
"Anderson",
"Stuart",
""
],
[
"Ramamoorthy",
"Subramanian",
""
]
] | Using large pre-trained models for image recognition tasks is becoming increasingly common owing to the well acknowledged success of recent models like vision transformers and other CNN-based models like VGG and Resnet. The high accuracy of these models on benchmark tasks has translated into their practical use across many domains including safety-critical applications like autonomous driving and medical diagnostics. Despite their widespread use, image models have been shown to be fragile to changes in the operating environment, bringing their robustness into question. There is an urgent need for methods that systematically characterise and quantify the capabilities of these models to help designers understand and provide guarantees about their safety and robustness. In this paper, we propose Vision Checklist, a framework aimed at interrogating the capabilities of a model in order to produce a report that can be used by a system designer for robustness evaluations. This framework proposes a set of perturbation operations that can be applied on the underlying data to generate test samples of different types. The perturbations reflect potential changes in operating environments, and interrogate various properties ranging from the strictly quantitative to more qualitative. Our framework is evaluated on multiple datasets like Tinyimagenet, CIFAR10, CIFAR100 and Camelyon17 and for models like ViT and Resnet. Our Vision Checklist proposes a specific set of evaluations that can be integrated into the previously proposed concept of a model card. Robustness evaluations like our checklist will be crucial in future safety evaluations of visual perception modules, and be useful for a wide range of stakeholders including designers, deployers, and regulators involved in the certification of these systems. Source code of Vision Checklist would be open for public use. |
2112.08458 | Onofrio Semeraro | Alessandro Bucci, Onofrio Semeraro, Alexandre Allauzen, Sergio
Chibbaro and Lionel Mathelin | Curriculum learning for data-driven modeling of dynamical systems | null | Eur. Phys. J. E 46, 12 (2023) | 10.1140/epje/s10189-023-00269-8 | null | cs.LG nlin.CD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The reliable prediction of the temporal behavior of complex systems is key in
numerous scientific fields. This strong interest is however hindered by
modeling issues: often, the governing equations describing the physics of the
system under consideration are not accessible or, if known, their solution
might require a computational time incompatible with the prediction time
constraints. Not surprisingly, approximating complex systems in a generic
functional format and informing it ex-nihilo from available observations has
become common practice in the age of machine learning, as illustrated by the
numerous successful examples based on deep neural networks. However,
generalizability of the models, margins of guarantee and the impact of data are
often overlooked or examined mainly by relying on prior knowledge of the
physics. We tackle these issues from a different viewpoint, by adopting a
curriculum learning strategy. In curriculum learning, the dataset is structured
such that the training process starts from simple samples towards more complex
ones in order to favor convergence and generalization. The concept has been
developed and successfully applied in robotics and control of systems. Here, we
apply this concept for the learning of complex dynamical systems in a
systematic way. First, leveraging insights from the ergodic theory, we assess
the amount of data sufficient for a-priori guaranteeing a faithful model of the
physical system and thoroughly investigate the impact of the training set and
its structure on the quality of long-term predictions. Based on that, we
consider entropy as a metric of complexity of the dataset; we show how an
informed design of the training set based on the analysis of the entropy
significantly improves the resulting models in terms of generalizability, and
provide insights on the amount and the choice of data required for an effective
data-driven modeling.
| [
{
"created": "Wed, 15 Dec 2021 20:09:20 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Apr 2022 09:56:54 GMT",
"version": "v2"
},
{
"created": "Tue, 22 Nov 2022 17:02:30 GMT",
"version": "v3"
},
{
"created": "Tue, 14 Feb 2023 21:06:53 GMT",
"version": "v4"
}
] | 2023-05-29 | [
[
"Bucci",
"Alessandro",
""
],
[
"Semeraro",
"Onofrio",
""
],
[
"Allauzen",
"Alexandre",
""
],
[
"Chibbaro",
"Sergio",
""
],
[
"Mathelin",
"Lionel",
""
]
] | The reliable prediction of the temporal behavior of complex systems is key in numerous scientific fields. This strong interest is however hindered by modeling issues: often, the governing equations describing the physics of the system under consideration are not accessible or, if known, their solution might require a computational time incompatible with the prediction time constraints. Not surprisingly, approximating complex systems in a generic functional format and informing it ex-nihilo from available observations has become common practice in the age of machine learning, as illustrated by the numerous successful examples based on deep neural networks. However, generalizability of the models, margins of guarantee and the impact of data are often overlooked or examined mainly by relying on prior knowledge of the physics. We tackle these issues from a different viewpoint, by adopting a curriculum learning strategy. In curriculum learning, the dataset is structured such that the training process starts from simple samples towards more complex ones in order to favor convergence and generalization. The concept has been developed and successfully applied in robotics and control of systems. Here, we apply this concept for the learning of complex dynamical systems in a systematic way. First, leveraging insights from the ergodic theory, we assess the amount of data sufficient for a-priori guaranteeing a faithful model of the physical system and thoroughly investigate the impact of the training set and its structure on the quality of long-term predictions. Based on that, we consider entropy as a metric of complexity of the dataset; we show how an informed design of the training set based on the analysis of the entropy significantly improves the resulting models in terms of generalizability, and provide insights on the amount and the choice of data required for an effective data-driven modeling. |
2203.08512 | Wenpeng Yin | Wenpeng Yin, Jia Li, Caiming Xiong | ConTinTin: Continual Learning from Task Instructions | ACL'2022 camera-ready | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | The mainstream machine learning paradigms for NLP often work with two
underlying presumptions. First, the target task is predefined and static; a
system merely needs to learn to solve it exclusively. Second, the supervision
of a task mainly comes from a set of labeled examples. A question arises: how
to build a system that can keep learning new tasks from their instructions?
This work defines a new learning paradigm ConTinTin (Continual Learning from
Task Instructions), in which a system should learn a sequence of new tasks one
by one, each task is explained by a piece of textual instruction. The system is
required to (i) generate the expected outputs of a new task by learning from
its instruction, (ii) transfer the knowledge acquired from upstream tasks to
help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even
improve the performance on earlier tasks after learning new tasks (i.e.,
backward-transfer). This new problem is studied on a stream of more than 60
tasks, each equipped with an instruction. Technically, our method
InstructionSpeak contains two strategies that make full use of task
instructions to improve forward-transfer and backward-transfer: one is to learn
from negative outputs, the other is to re-visit instructions of previous tasks.
To our knowledge, this is the first time to study ConTinTin in NLP. In addition
to the problem formulation and our promising approach, this work also
contributes to providing rich analyses for the community to better understand
this novel learning problem.
| [
{
"created": "Wed, 16 Mar 2022 10:27:18 GMT",
"version": "v1"
},
{
"created": "Fri, 18 Mar 2022 19:15:47 GMT",
"version": "v2"
}
] | 2022-03-22 | [
[
"Yin",
"Wenpeng",
""
],
[
"Li",
"Jia",
""
],
[
"Xiong",
"Caiming",
""
]
] | The mainstream machine learning paradigms for NLP often work with two underlying presumptions. First, the target task is predefined and static; a system merely needs to learn to solve it exclusively. Second, the supervision of a task mainly comes from a set of labeled examples. A question arises: how to build a system that can keep learning new tasks from their instructions? This work defines a new learning paradigm ConTinTin (Continual Learning from Task Instructions), in which a system should learn a sequence of new tasks one by one, each task is explained by a piece of textual instruction. The system is required to (i) generate the expected outputs of a new task by learning from its instruction, (ii) transfer the knowledge acquired from upstream tasks to help solve downstream tasks (i.e., forward-transfer), and (iii) retain or even improve the performance on earlier tasks after learning new tasks (i.e., backward-transfer). This new problem is studied on a stream of more than 60 tasks, each equipped with an instruction. Technically, our method InstructionSpeak contains two strategies that make full use of task instructions to improve forward-transfer and backward-transfer: one is to learn from negative outputs, the other is to re-visit instructions of previous tasks. To our knowledge, this is the first time to study ConTinTin in NLP. In addition to the problem formulation and our promising approach, this work also contributes to providing rich analyses for the community to better understand this novel learning problem. |
0901.3990 | Bernard Jacquemin | Bernard Jacquemin (LIMSI), Sabine Ploux (L2C2) | Du corpus au dictionnaire | null | Cahiers de Linguistique. Revue de sociolinguistique et de
sociologie de la langue fran\c{c}aise 33, 1 (2008) 63-84 | null | null | cs.CL cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, we propose an automatic process to build multi-lingual
lexico-semantic resources. The goal of these resources is to browse
semantically textual information contained in texts of different languages.
This method uses a mathematical model called Atlas s\'emantiques in order to
represent the different senses of each word. It uses the linguistic relations
between words to create graphs that are projected into a semantic space. These
projections constitute semantic maps that denote the sense trends of each given
word. This model is fed with syntactic relations between words extracted from a
corpus. Therefore, the lexico-semantic resource produced describes all the
words and all their meanings observed in the corpus. The sense trends are
expressed by syntactic contexts, typical for a given meaning. The link between
each sense trend and the utterances used to build the sense trend are also
stored in an index. Thus all the instances of a word in a particular sense are
linked and can be browsed easily. And by using several corpora of different
languages, several resources are built that correspond with each other through
languages. It makes it possible to browse information through languages thanks
to syntactic contexts translations (even if some of them are partial).
| [
{
"created": "Mon, 26 Jan 2009 15:52:21 GMT",
"version": "v1"
}
] | 2009-01-27 | [
[
"Jacquemin",
"Bernard",
"",
"LIMSI"
],
[
"Ploux",
"Sabine",
"",
"L2C2"
]
] | In this article, we propose an automatic process to build multi-lingual lexico-semantic resources. The goal of these resources is to browse semantically textual information contained in texts of different languages. This method uses a mathematical model called Atlas s\'emantiques in order to represent the different senses of each word. It uses the linguistic relations between words to create graphs that are projected into a semantic space. These projections constitute semantic maps that denote the sense trends of each given word. This model is fed with syntactic relations between words extracted from a corpus. Therefore, the lexico-semantic resource produced describes all the words and all their meanings observed in the corpus. The sense trends are expressed by syntactic contexts, typical for a given meaning. The link between each sense trend and the utterances used to build the sense trend are also stored in an index. Thus all the instances of a word in a particular sense are linked and can be browsed easily. And by using several corpora of different languages, several resources are built that correspond with each other through languages. It makes it possible to browse information through languages thanks to syntactic contexts translations (even if some of them are partial). |
2304.06167 | Ravi Sahita | Ravi Sahita, Atish Patra, Vedvyas Shanbhogue, Samuel Ortiz, Andrew
Bresticker, Dylan Reid, Atul Khare, Rajnesh Kanwal | CoVE: Towards Confidential Computing on RISC-V Platforms | null | null | null | null | cs.CR cs.AR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-tenant computing platforms are typically comprised of several software
and hardware components including platform firmware, host operating system
kernel, virtualization monitor, and the actual tenant payloads that run on them
(typically in a virtual machine, container, or application). This model is well
established in large scale commercial deployment, but the downside is that all
platform components and operators are in the Trusted Computing Base (TCB) of
the tenant. This aspect is ill-suited for privacy-oriented workloads that aim
to minimize the TCB footprint. Confidential computing presents a good
stepping-stone towards providing a quantifiable TCB for computing. Confidential
computing [1] requires the use of a HW-attested Trusted Execution Environments
for data-in-use protection. The RISC-V architecture presents a strong
foundation for meeting the requirements for Confidential Computing and other
security paradigms in a clean slate manner. This paper describes a reference
architecture and discusses ISA, non-ISA and system-on-chip (SoC) requirements
for confidential computing on RISC-V Platforms. It discusses proposed ISA and
non-ISA Extension for Confidential Virtual Machine for RISC-V platforms,
referred to as CoVE.
| [
{
"created": "Wed, 12 Apr 2023 21:35:44 GMT",
"version": "v1"
}
] | 2023-04-14 | [
[
"Sahita",
"Ravi",
""
],
[
"Patra",
"Atish",
""
],
[
"Shanbhogue",
"Vedvyas",
""
],
[
"Ortiz",
"Samuel",
""
],
[
"Bresticker",
"Andrew",
""
],
[
"Reid",
"Dylan",
""
],
[
"Khare",
"Atul",
""
],
[
"Kanwal",
"Rajnesh",
""
]
] | Multi-tenant computing platforms are typically comprised of several software and hardware components including platform firmware, host operating system kernel, virtualization monitor, and the actual tenant payloads that run on them (typically in a virtual machine, container, or application). This model is well established in large scale commercial deployment, but the downside is that all platform components and operators are in the Trusted Computing Base (TCB) of the tenant. This aspect is ill-suited for privacy-oriented workloads that aim to minimize the TCB footprint. Confidential computing presents a good stepping-stone towards providing a quantifiable TCB for computing. Confidential computing [1] requires the use of a HW-attested Trusted Execution Environments for data-in-use protection. The RISC-V architecture presents a strong foundation for meeting the requirements for Confidential Computing and other security paradigms in a clean slate manner. This paper describes a reference architecture and discusses ISA, non-ISA and system-on-chip (SoC) requirements for confidential computing on RISC-V Platforms. It discusses proposed ISA and non-ISA Extension for Confidential Virtual Machine for RISC-V platforms, referred to as CoVE. |
1608.02784 | Nikos Papasarantopoulos | Nikos Papasarantopoulos, Helen Jiang, Shay B. Cohen | Canonical Correlation Inference for Mapping Abstract Scenes to Text | 10 pages, accepted to AAAI 2018 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We describe a technique for structured prediction, based on canonical
correlation analysis. Our learning algorithm finds two projections for the
input and the output spaces that aim at projecting a given input and its
correct output into points close to each other. We demonstrate our technique on
a language-vision problem, namely the problem of giving a textual description
to an "abstract scene".
| [
{
"created": "Tue, 9 Aug 2016 12:26:19 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Nov 2017 19:53:13 GMT",
"version": "v2"
}
] | 2017-11-21 | [
[
"Papasarantopoulos",
"Nikos",
""
],
[
"Jiang",
"Helen",
""
],
[
"Cohen",
"Shay B.",
""
]
] | We describe a technique for structured prediction, based on canonical correlation analysis. Our learning algorithm finds two projections for the input and the output spaces that aim at projecting a given input and its correct output into points close to each other. We demonstrate our technique on a language-vision problem, namely the problem of giving a textual description to an "abstract scene". |
2210.03246 | Joseph Gatto | Joseph Gatto, Parker Seegmiller, Garrett Johnston, Sarah M. Preum | HealthE: Classifying Entities in Online Textual Health Advice | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | The processing of entities in natural language is essential to many medical
NLP systems. Unfortunately, existing datasets vastly under-represent the
entities required to model public health relevant texts such as health advice
often found on sites like WebMD. People rely on such information for personal
health management and clinically relevant decision making. In this work, we
release a new annotated dataset, HealthE, consisting of 6,756 health advice.
HealthE has a more granular label space compared to existing medical NER
corpora and contains annotation for diverse health phrases. Additionally, we
introduce a new health entity classification model, EP S-BERT, which leverages
textual context patterns in the classification of entity classes. EP S-BERT
provides a 4-point increase in F1 score over the nearest baseline and a
34-point increase in F1 when compared to off-the-shelf medical NER tools
trained to extract disease and medication mentions from clinical texts. All
code and data are publicly available on Github.
| [
{
"created": "Thu, 6 Oct 2022 23:18:24 GMT",
"version": "v1"
}
] | 2022-10-10 | [
[
"Gatto",
"Joseph",
""
],
[
"Seegmiller",
"Parker",
""
],
[
"Johnston",
"Garrett",
""
],
[
"Preum",
"Sarah M.",
""
]
] | The processing of entities in natural language is essential to many medical NLP systems. Unfortunately, existing datasets vastly under-represent the entities required to model public health relevant texts such as health advice often found on sites like WebMD. People rely on such information for personal health management and clinically relevant decision making. In this work, we release a new annotated dataset, HealthE, consisting of 6,756 health advice. HealthE has a more granular label space compared to existing medical NER corpora and contains annotation for diverse health phrases. Additionally, we introduce a new health entity classification model, EP S-BERT, which leverages textual context patterns in the classification of entity classes. EP S-BERT provides a 4-point increase in F1 score over the nearest baseline and a 34-point increase in F1 when compared to off-the-shelf medical NER tools trained to extract disease and medication mentions from clinical texts. All code and data are publicly available on Github. |
1609.05135 | Mark Vousden | Mark Vousden, Marc-Antonio Bisotti, Maximilian Albert, Hans Fangohr | Virtual Micromagnetics: A Framework for Accessible and Reproducible
Micromagnetic Simulation | 12 pages, 1 figure | Journal of Open Research Software, 4(1), p.e41 (2016) | 10.5334/jors.141 | null | cs.OH | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computational micromagnetics requires numerical solution of partial
differential equations to resolve complex interactions in magnetic
nanomaterials. The Virtual Micromagnetics project described here provides
virtual machine simulation environments to run open-source micromagnetic
simulation packages. These environments allow easy access to simulation
packages that are often difficult to compile and install, and enable
simulations and their data to be shared and stored in a single virtual hard
disk file, which encourages reproducible research. Virtual Micromagnetics can
be extended to automate the installation of micromagnetic simulation packages
on non-virtual machines, and to support closed-source and new open-source
simulation packages, including packages from disciplines other than
micromagnetics, encouraging reuse. Virtual Micromagnetics is stored in a public
GitHub repository under a three-clause Berkeley Software Distribution (BSD)
license.
| [
{
"created": "Thu, 11 Aug 2016 10:59:40 GMT",
"version": "v1"
},
{
"created": "Fri, 25 Nov 2016 10:15:16 GMT",
"version": "v2"
}
] | 2016-11-28 | [
[
"Vousden",
"Mark",
""
],
[
"Bisotti",
"Marc-Antonio",
""
],
[
"Albert",
"Maximilian",
""
],
[
"Fangohr",
"Hans",
""
]
] | Computational micromagnetics requires numerical solution of partial differential equations to resolve complex interactions in magnetic nanomaterials. The Virtual Micromagnetics project described here provides virtual machine simulation environments to run open-source micromagnetic simulation packages. These environments allow easy access to simulation packages that are often difficult to compile and install, and enable simulations and their data to be shared and stored in a single virtual hard disk file, which encourages reproducible research. Virtual Micromagnetics can be extended to automate the installation of micromagnetic simulation packages on non-virtual machines, and to support closed-source and new open-source simulation packages, including packages from disciplines other than micromagnetics, encouraging reuse. Virtual Micromagnetics is stored in a public GitHub repository under a three-clause Berkeley Software Distribution (BSD) license. |
2212.04316 | Martino Sorbaro | Francesco L\"assig, Pau Vilimelis Aceituno, Martino Sorbaro, Benjamin
F. Grewe | Bio-Inspired, Task-Free Continual Learning through Activity
Regularization | null | null | null | null | cs.NE cs.CV q-bio.NC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The ability to sequentially learn multiple tasks without forgetting is a key
skill of biological brains, whereas it represents a major challenge to the
field of deep learning. To avoid catastrophic forgetting, various continual
learning (CL) approaches have been devised. However, these usually require
discrete task boundaries. This requirement seems biologically implausible and
often limits the application of CL methods in the real world where tasks are
not always well defined. Here, we take inspiration from neuroscience, where
sparse, non-overlapping neuronal representations have been suggested to prevent
catastrophic forgetting. As in the brain, we argue that these sparse
representations should be chosen on the basis of feed forward
(stimulus-specific) as well as top-down (context-specific) information. To
implement such selective sparsity, we use a bio-plausible form of hierarchical
credit assignment known as Deep Feedback Control (DFC) and combine it with a
winner-take-all sparsity mechanism. In addition to sparsity, we introduce
lateral recurrent connections within each layer to further protect previously
learned representations. We evaluate the new sparse-recurrent version of DFC on
the split-MNIST computer vision benchmark and show that only the combination of
sparsity and intra-layer recurrent connections improves CL performance with
respect to standard backpropagation. Our method achieves similar performance to
well-known CL methods, such as Elastic Weight Consolidation and Synaptic
Intelligence, without requiring information about task boundaries. Overall, we
showcase the idea of adopting computational principles from the brain to derive
new, task-free learning algorithms for CL.
| [
{
"created": "Thu, 8 Dec 2022 15:14:20 GMT",
"version": "v1"
}
] | 2022-12-09 | [
[
"Lässig",
"Francesco",
""
],
[
"Aceituno",
"Pau Vilimelis",
""
],
[
"Sorbaro",
"Martino",
""
],
[
"Grewe",
"Benjamin F.",
""
]
] | The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approaches have been devised. However, these usually require discrete task boundaries. This requirement seems biologically implausible and often limits the application of CL methods in the real world where tasks are not always well defined. Here, we take inspiration from neuroscience, where sparse, non-overlapping neuronal representations have been suggested to prevent catastrophic forgetting. As in the brain, we argue that these sparse representations should be chosen on the basis of feed forward (stimulus-specific) as well as top-down (context-specific) information. To implement such selective sparsity, we use a bio-plausible form of hierarchical credit assignment known as Deep Feedback Control (DFC) and combine it with a winner-take-all sparsity mechanism. In addition to sparsity, we introduce lateral recurrent connections within each layer to further protect previously learned representations. We evaluate the new sparse-recurrent version of DFC on the split-MNIST computer vision benchmark and show that only the combination of sparsity and intra-layer recurrent connections improves CL performance with respect to standard backpropagation. Our method achieves similar performance to well-known CL methods, such as Elastic Weight Consolidation and Synaptic Intelligence, without requiring information about task boundaries. Overall, we showcase the idea of adopting computational principles from the brain to derive new, task-free learning algorithms for CL. |
2405.01324 | Philipp Meyer | Philipp Meyer, Timo H\"ackel, Teresa L\"ubeck, Franz Korf, Thomas C.
Schmidt | A Framework for the Systematic Assessment of Anomaly Detectors in
Time-Sensitive Automotive Networks | null | null | null | null | cs.NI cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Connected cars are susceptible to cyberattacks. Security and safety of future
vehicles highly depend on a holistic protection of automotive components, of
which the time-sensitive backbone network takes a significant role. These
onboard Time-Sensitive Networks (TSNs) require monitoring for safety and -- as
versatile platforms to host Network Anomaly Detection Systems (NADSs) -- for
security. Still a thorough evaluation of anomaly detection methods in the
context of hard real-time operations, automotive protocol stacks, and domain
specific attack vectors is missing along with appropriate input datasets. In
this paper, we present an assessment framework that allows for reproducible,
comparable, and rapid evaluation of detection algorithms. It is based on a
simulation toolchain, which contributes configurable topologies, traffic
streams, anomalies, attacks, and detectors. We demonstrate the assessment of
NADSs in a comprehensive in-vehicular network with its communication flows, on
which we model traffic anomalies. We evaluate exemplary detection mechanisms
and reveal how the detection performance is influenced by different
combinations of TSN traffic flows and anomaly types. Our approach translates to
other real-time Ethernet domains, such as industrial facilities, airplanes, and
UAVs.
| [
{
"created": "Thu, 2 May 2024 14:29:42 GMT",
"version": "v1"
}
] | 2024-05-03 | [
[
"Meyer",
"Philipp",
""
],
[
"Häckel",
"Timo",
""
],
[
"Lübeck",
"Teresa",
""
],
[
"Korf",
"Franz",
""
],
[
"Schmidt",
"Thomas C.",
""
]
] | Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and -- as versatile platforms to host Network Anomaly Detection Systems (NADSs) -- for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs. |
1403.0783 | Antoine Amarilli | Antoine Amarilli, Yael Amsterdamer, Tova Milo | Uncertainty in Crowd Data Sourcing under Structural Constraints | 8 pages, vision paper. To appear at UnCrowd 2014 | null | 10.1007/978-3-662-43984-5_27 | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Applications extracting data from crowdsourcing platforms must deal with the
uncertainty of crowd answers in two different ways: first, by deriving
estimates of the correct value from the answers; second, by choosing crowd
questions whose answers are expected to minimize this uncertainty relative to
the overall data collection goal. Such problems are already challenging when we
assume that questions are unrelated and answers are independent, but they are
even more complicated when we assume that the unknown values follow hard
structural constraints (such as monotonicity).
In this vision paper, we examine how to formally address this issue with an
approach inspired by [Amsterdamer et al., 2013]. We describe a generalized
setting where we model constraints as linear inequalities, and use them to
guide the choice of crowd questions and the processing of answers. We present
the main challenges arising in this setting, and propose directions to solve
them.
| [
{
"created": "Tue, 4 Mar 2014 13:21:39 GMT",
"version": "v1"
}
] | 2016-07-19 | [
[
"Amarilli",
"Antoine",
""
],
[
"Amsterdamer",
"Yael",
""
],
[
"Milo",
"Tova",
""
]
] | Applications extracting data from crowdsourcing platforms must deal with the uncertainty of crowd answers in two different ways: first, by deriving estimates of the correct value from the answers; second, by choosing crowd questions whose answers are expected to minimize this uncertainty relative to the overall data collection goal. Such problems are already challenging when we assume that questions are unrelated and answers are independent, but they are even more complicated when we assume that the unknown values follow hard structural constraints (such as monotonicity). In this vision paper, we examine how to formally address this issue with an approach inspired by [Amsterdamer et al., 2013]. We describe a generalized setting where we model constraints as linear inequalities, and use them to guide the choice of crowd questions and the processing of answers. We present the main challenges arising in this setting, and propose directions to solve them. |
2003.06705 | Djordje Batic | Djordje Batic, Dubravko Culibrk | Identifying Individual Dogs in Social Media Images | Presented at BMVC 2019: Workshop on Visual AI and Entrepreneurship,
Cardiff, UK | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present the results of an initial study focused on developing a visual AI
solution able to recognize individual dogs in unconstrained (wild) images
occurring on social media.
The work described here is part of joint project done with Pet2Net, a social
network focused on pets and their owners. In order to detect and recognize
individual dogs we combine transfer learning and object detection approaches on
Inception v3 and SSD Inception v2 architectures respectively and evaluate the
proposed pipeline using a new data set containing real data that the users
uploaded to Pet2Net platform. We show that it can achieve 94.59% accuracy in
identifying individual dogs. Our approach has been designed with simplicity in
mind and the goal of easy deployment on all the images uploaded to Pet2Net
platform.
A purely visual approach to identifying dogs in images, will enhance Pet2Net
features aimed at finding lost dogs, as well as form the basis of future work
focused on identifying social relationships between dogs, which cannot be
inferred from other data collected by the platform.
| [
{
"created": "Sat, 14 Mar 2020 21:11:02 GMT",
"version": "v1"
}
] | 2020-03-17 | [
[
"Batic",
"Djordje",
""
],
[
"Culibrk",
"Dubravko",
""
]
] | We present the results of an initial study focused on developing a visual AI solution able to recognize individual dogs in unconstrained (wild) images occurring on social media. The work described here is part of joint project done with Pet2Net, a social network focused on pets and their owners. In order to detect and recognize individual dogs we combine transfer learning and object detection approaches on Inception v3 and SSD Inception v2 architectures respectively and evaluate the proposed pipeline using a new data set containing real data that the users uploaded to Pet2Net platform. We show that it can achieve 94.59% accuracy in identifying individual dogs. Our approach has been designed with simplicity in mind and the goal of easy deployment on all the images uploaded to Pet2Net platform. A purely visual approach to identifying dogs in images, will enhance Pet2Net features aimed at finding lost dogs, as well as form the basis of future work focused on identifying social relationships between dogs, which cannot be inferred from other data collected by the platform. |
2006.08456 | Dimitrios Michael Manias | Dimitrios Michael Manias, Hassan Hawilo, Abdallah Shami | A Machine Learning-Based Migration Strategy for Virtual Network Function
Instances | Accepted - Future Technologies Conference 2020 | null | null | null | cs.NI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the growing demand for data connectivity, network service providers are
faced with the task of reducing their capital and operational expenses while
simultaneously improving network performance and addressing the increased
demand. Although Network Function Virtualization (NFV) has been identified as a
promising solution, several challenges must be addressed to ensure its
feasibility. In this paper, we address the Virtual Network Function (VNF)
migration problem by developing the VNF Neural Network for Instance Migration
(VNNIM), a migration strategy for VNF instances. The performance of VNNIM is
further improved through the optimization of the learning rate hyperparameter
through particle swarm optimization. Results show that the VNNIM is very
effective in predicting the post-migration server exhibiting a binary accuracy
of 99.07% and a delay difference distribution that is centered around a mean of
zero when compared to the optimization model. The greatest advantage of VNNIM,
however, is its run-time efficiency highlighted through a run-time analysis.
| [
{
"created": "Mon, 15 Jun 2020 15:03:27 GMT",
"version": "v1"
}
] | 2020-06-16 | [
[
"Manias",
"Dimitrios Michael",
""
],
[
"Hawilo",
"Hassan",
""
],
[
"Shami",
"Abdallah",
""
]
] | With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased demand. Although Network Function Virtualization (NFV) has been identified as a promising solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) migration problem by developing the VNF Neural Network for Instance Migration (VNNIM), a migration strategy for VNF instances. The performance of VNNIM is further improved through the optimization of the learning rate hyperparameter through particle swarm optimization. Results show that the VNNIM is very effective in predicting the post-migration server exhibiting a binary accuracy of 99.07% and a delay difference distribution that is centered around a mean of zero when compared to the optimization model. The greatest advantage of VNNIM, however, is its run-time efficiency highlighted through a run-time analysis. |
2312.11500 | Mathew Walter | Mathew J. Walter, Aaron Barrett and Kimberly Tam | A Red Teaming Framework for Securing AI in Maritime Autonomous Systems | null | null | null | null | cs.CR cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Artificial intelligence (AI) is being ubiquitously adopted to automate
processes in science and industry. However, due to its often intricate and
opaque nature, AI has been shown to possess inherent vulnerabilities which can
be maliciously exploited with adversarial AI, potentially putting AI users and
developers at both cyber and physical risk. In addition, there is insufficient
comprehension of the real-world effects of adversarial AI and an inadequacy of
AI security examinations; therefore, the growing threat landscape is unknown
for many AI solutions. To mitigate this issue, we propose one of the first red
team frameworks for evaluating the AI security of maritime autonomous systems.
The framework provides operators with a proactive (secure by design) and
reactive (post-deployment evaluation) response to securing AI technology today
and in the future. This framework is a multi-part checklist, which can be
tailored to different systems and requirements. We demonstrate this framework
to be highly effective for a red team to use to uncover numerous
vulnerabilities within a real-world maritime autonomous systems AI, ranging
from poisoning to adversarial patch attacks. The lessons learned from
systematic AI red teaming can help prevent MAS-related catastrophic events in a
world with increasing uptake and reliance on mission-critical AI.
| [
{
"created": "Fri, 8 Dec 2023 14:59:07 GMT",
"version": "v1"
}
] | 2023-12-20 | [
[
"Walter",
"Mathew J.",
""
],
[
"Barrett",
"Aaron",
""
],
[
"Tam",
"Kimberly",
""
]
] | Artificial intelligence (AI) is being ubiquitously adopted to automate processes in science and industry. However, due to its often intricate and opaque nature, AI has been shown to possess inherent vulnerabilities which can be maliciously exploited with adversarial AI, potentially putting AI users and developers at both cyber and physical risk. In addition, there is insufficient comprehension of the real-world effects of adversarial AI and an inadequacy of AI security examinations; therefore, the growing threat landscape is unknown for many AI solutions. To mitigate this issue, we propose one of the first red team frameworks for evaluating the AI security of maritime autonomous systems. The framework provides operators with a proactive (secure by design) and reactive (post-deployment evaluation) response to securing AI technology today and in the future. This framework is a multi-part checklist, which can be tailored to different systems and requirements. We demonstrate this framework to be highly effective for a red team to use to uncover numerous vulnerabilities within a real-world maritime autonomous systems AI, ranging from poisoning to adversarial patch attacks. The lessons learned from systematic AI red teaming can help prevent MAS-related catastrophic events in a world with increasing uptake and reliance on mission-critical AI. |
2009.06855 | Madison Elliott | Madison Elliott, Christine Nothelfer, Cindy Xiong and Danielle Szafir | A Design Space of Vision Science Methods for Visualization Research | 11 pages, 6 figures | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A growing number of efforts aim to understand what people see when using a
visualization. These efforts provide scientific grounding to complement design
intuitions, leading to more effective visualization practice. However,
published visualization research currently reflects a limited set of available
methods for understanding how people process visualized data. Alternative
methods from vision science offer a rich suite of tools for understanding
visualizations, but no curated collection of these methods exists in either
perception or visualization research. We introduce a design space of
experimental methods for empirically investigating the perceptual processes
involved with viewing data visualizations to ultimately inform visualization
design guidelines. This paper provides a shared lexicon for facilitating
experimental visualization research. We discuss popular experimental paradigms,
adjustment types, response types, and dependent measures used in vision science
research, rooting each in visualization examples. We then discuss the
advantages and limitations of each technique. Researchers can use this design
space to create innovative studies and progress scientific understanding of
design choices and evaluations in visualization. We highlight a history of
collaborative success between visualization and vision science research and
advocate for a deeper relationship between the two fields that can elaborate on
and extend the methodological design space for understanding visualization and
vision.
| [
{
"created": "Tue, 15 Sep 2020 03:51:15 GMT",
"version": "v1"
}
] | 2020-09-16 | [
[
"Elliott",
"Madison",
""
],
[
"Nothelfer",
"Christine",
""
],
[
"Xiong",
"Cindy",
""
],
[
"Szafir",
"Danielle",
""
]
] | A growing number of efforts aim to understand what people see when using a visualization. These efforts provide scientific grounding to complement design intuitions, leading to more effective visualization practice. However, published visualization research currently reflects a limited set of available methods for understanding how people process visualized data. Alternative methods from vision science offer a rich suite of tools for understanding visualizations, but no curated collection of these methods exists in either perception or visualization research. We introduce a design space of experimental methods for empirically investigating the perceptual processes involved with viewing data visualizations to ultimately inform visualization design guidelines. This paper provides a shared lexicon for facilitating experimental visualization research. We discuss popular experimental paradigms, adjustment types, response types, and dependent measures used in vision science research, rooting each in visualization examples. We then discuss the advantages and limitations of each technique. Researchers can use this design space to create innovative studies and progress scientific understanding of design choices and evaluations in visualization. We highlight a history of collaborative success between visualization and vision science research and advocate for a deeper relationship between the two fields that can elaborate on and extend the methodological design space for understanding visualization and vision. |
1906.02040 | Yifan Hu | Yifan Hu and Yefeng Zheng | A GLCM Embedded CNN Strategy for Computer-aided Diagnosis in
Intracerebral Hemorrhage | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Computer-aided diagnosis (CADx) systems have been shown to assist
radiologists by providing classifications of all kinds of medical images like
Computed tomography (CT) and Magnetic resonance (MR). Currently, convolutional
neural networks play an important role in CADx. However, since CNN model should
have a square-like input, it is usually difficult to directly apply the CNN
algorithms on the irregular segmentation region of interests (ROIs) where the
radiologists are interested in. In this paper, we propose a new approach to
construct the model by extracting and converting the information of the
irregular region into a fixed-size Gray-Level Co-Occurrence Matrix (GLCM) and
then utilize the GLCM as one input of our CNN model. In this way, as an useful
implementary to the original CNN, a couple of GLCM-based features are also
extracted by CNN. Meanwhile, the network will pay more attention to the
important lesion area and achieve a higher accuracy in classification.
Experiments are performed on three classification databases: Hemorrhage,
BraTS18 and Cervix to validate the universality of our innovative model. In
conclusion, the proposed framework outperforms the corresponding state-of-art
algorithms on each database with both test losses and classification accuracy
as the evaluation criteria.
| [
{
"created": "Wed, 5 Jun 2019 14:12:21 GMT",
"version": "v1"
}
] | 2019-06-06 | [
[
"Hu",
"Yifan",
""
],
[
"Zheng",
"Yefeng",
""
]
] | Computer-aided diagnosis (CADx) systems have been shown to assist radiologists by providing classifications of all kinds of medical images like Computed tomography (CT) and Magnetic resonance (MR). Currently, convolutional neural networks play an important role in CADx. However, since CNN model should have a square-like input, it is usually difficult to directly apply the CNN algorithms on the irregular segmentation region of interests (ROIs) where the radiologists are interested in. In this paper, we propose a new approach to construct the model by extracting and converting the information of the irregular region into a fixed-size Gray-Level Co-Occurrence Matrix (GLCM) and then utilize the GLCM as one input of our CNN model. In this way, as an useful implementary to the original CNN, a couple of GLCM-based features are also extracted by CNN. Meanwhile, the network will pay more attention to the important lesion area and achieve a higher accuracy in classification. Experiments are performed on three classification databases: Hemorrhage, BraTS18 and Cervix to validate the universality of our innovative model. In conclusion, the proposed framework outperforms the corresponding state-of-art algorithms on each database with both test losses and classification accuracy as the evaluation criteria. |
2402.03283 | Rohit Verma | Rohit Verma, Arun Raghunath | Towards a Flexible Scale-out Framework for Efficient Visual Data Query
Processing | null | null | null | null | cs.DB cs.CV | http://creativecommons.org/licenses/by-sa/4.0/ | There is growing interest in visual data management systems that support
queries with specialized operations ranging from resizing an image to running
complex machine learning models. With a plethora of such operations, the basic
need to receive query responses in minimal time takes a hit, especially when
the client desires to run multiple such operations in a single query. Existing
systems provide an ad-hoc approach where different solutions are clubbed
together to provide an end-to-end visual data management system. Unlike such
solutions, the Visual Data Management System (VDMS) natively executes queries
with multiple operations, thus providing an end-to-end solution. However, a
fixed subset of native operations and a synchronous threading architecture
limit its generality and scalability.
In this paper, we develop VDMS-Async that adds the capability to run
user-defined operations with VDMS and execute operations within a query on a
remote server. VDMS-Async utilizes an event-driven architecture to create an
efficient pipeline for executing operations within a query. Our experiments
have shown that VDMS-Async reduces the query execution time by 2-3X compared to
existing state-of-the-art systems. Further, remote operations coupled with an
event-driven architecture enables VDMS-Async to scale query execution time
linearly with the addition of every new remote server. We demonstrate a 64X
reduction in query execution time when adding 64 remote servers.
| [
{
"created": "Mon, 5 Feb 2024 18:39:04 GMT",
"version": "v1"
}
] | 2024-02-06 | [
[
"Verma",
"Rohit",
""
],
[
"Raghunath",
"Arun",
""
]
] | There is growing interest in visual data management systems that support queries with specialized operations ranging from resizing an image to running complex machine learning models. With a plethora of such operations, the basic need to receive query responses in minimal time takes a hit, especially when the client desires to run multiple such operations in a single query. Existing systems provide an ad-hoc approach where different solutions are clubbed together to provide an end-to-end visual data management system. Unlike such solutions, the Visual Data Management System (VDMS) natively executes queries with multiple operations, thus providing an end-to-end solution. However, a fixed subset of native operations and a synchronous threading architecture limit its generality and scalability. In this paper, we develop VDMS-Async that adds the capability to run user-defined operations with VDMS and execute operations within a query on a remote server. VDMS-Async utilizes an event-driven architecture to create an efficient pipeline for executing operations within a query. Our experiments have shown that VDMS-Async reduces the query execution time by 2-3X compared to existing state-of-the-art systems. Further, remote operations coupled with an event-driven architecture enables VDMS-Async to scale query execution time linearly with the addition of every new remote server. We demonstrate a 64X reduction in query execution time when adding 64 remote servers. |
2111.10882 | Rishabh Garg | Rishabh Garg, Ruohan Gao, Kristen Grauman | Geometry-Aware Multi-Task Learning for Binaural Audio Generation from
Video | Published in BMVC 2021, project page:
http://vision.cs.utexas.edu/projects/geometry-aware-binaural/ | null | null | null | cs.CV cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Binaural audio provides human listeners with an immersive spatial sound
experience, but most existing videos lack binaural audio recordings. We propose
an audio spatialization method that draws on visual information in videos to
convert their monaural (single-channel) audio to binaural audio. Whereas
existing approaches leverage visual features extracted directly from video
frames, our approach explicitly disentangles the geometric cues present in the
visual stream to guide the learning process. In particular, we develop a
multi-task framework that learns geometry-aware features for binaural audio
generation by accounting for the underlying room impulse response, the visual
stream's coherence with the sound source(s) positions, and the consistency in
geometry of the sounding objects over time. Furthermore, we introduce a new
large video dataset with realistic binaural audio simulated for real-world
scanned environments. On two datasets, we demonstrate the efficacy of our
method, which achieves state-of-the-art results.
| [
{
"created": "Sun, 21 Nov 2021 19:26:45 GMT",
"version": "v1"
}
] | 2021-11-23 | [
[
"Garg",
"Rishabh",
""
],
[
"Gao",
"Ruohan",
""
],
[
"Grauman",
"Kristen",
""
]
] | Binaural audio provides human listeners with an immersive spatial sound experience, but most existing videos lack binaural audio recordings. We propose an audio spatialization method that draws on visual information in videos to convert their monaural (single-channel) audio to binaural audio. Whereas existing approaches leverage visual features extracted directly from video frames, our approach explicitly disentangles the geometric cues present in the visual stream to guide the learning process. In particular, we develop a multi-task framework that learns geometry-aware features for binaural audio generation by accounting for the underlying room impulse response, the visual stream's coherence with the sound source(s) positions, and the consistency in geometry of the sounding objects over time. Furthermore, we introduce a new large video dataset with realistic binaural audio simulated for real-world scanned environments. On two datasets, we demonstrate the efficacy of our method, which achieves state-of-the-art results. |
1410.5387 | M\'aria Svore\v{n}ov\'a | Maria Svorenova, Jan Kretinsky, Martin Chmelik, Krishnendu Chatterjee,
Ivana Cerna, Calin Belta | Temporal Logic Control for Stochastic Linear Systems using Abstraction
Refinement of Probabilistic Games | Technical report accompanying HSCC'15 paper | null | null | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider the problem of computing the set of initial states of a dynamical
system such that there exists a control strategy to ensure that the
trajectories satisfy a temporal logic specification with probability 1
(almost-surely). We focus on discrete-time, stochastic linear dynamics and
specifications given as formulas of the Generalized Reactivity(1) fragment of
Linear Temporal Logic over linear predicates in the states of the system. We
propose a solution based on iterative abstraction-refinement, and turn-based
2-player probabilistic games. While the theoretical guarantee of our algorithm
after any finite number of iterations is only a partial solution, we show that
if our algorithm terminates, then the result is the set of satisfying initial
states. Moreover, for any (partial) solution our algorithm synthesizes witness
control strategies to ensure almost-sure satisfaction of the temporal logic
specification. We demonstrate our approach on an illustrative case study.
| [
{
"created": "Mon, 20 Oct 2014 18:45:55 GMT",
"version": "v1"
},
{
"created": "Wed, 22 Oct 2014 12:01:39 GMT",
"version": "v2"
},
{
"created": "Mon, 23 Feb 2015 11:54:41 GMT",
"version": "v3"
}
] | 2015-02-24 | [
[
"Svorenova",
"Maria",
""
],
[
"Kretinsky",
"Jan",
""
],
[
"Chmelik",
"Martin",
""
],
[
"Chatterjee",
"Krishnendu",
""
],
[
"Cerna",
"Ivana",
""
],
[
"Belta",
"Calin",
""
]
] | We consider the problem of computing the set of initial states of a dynamical system such that there exists a control strategy to ensure that the trajectories satisfy a temporal logic specification with probability 1 (almost-surely). We focus on discrete-time, stochastic linear dynamics and specifications given as formulas of the Generalized Reactivity(1) fragment of Linear Temporal Logic over linear predicates in the states of the system. We propose a solution based on iterative abstraction-refinement, and turn-based 2-player probabilistic games. While the theoretical guarantee of our algorithm after any finite number of iterations is only a partial solution, we show that if our algorithm terminates, then the result is the set of satisfying initial states. Moreover, for any (partial) solution our algorithm synthesizes witness control strategies to ensure almost-sure satisfaction of the temporal logic specification. We demonstrate our approach on an illustrative case study. |
1610.01175 | Guangwu Xu | Guangwu Xu, Bao Li | On the Algorithmic Significance and Analysis of the Method of DaYan
Deriving One | 9 Pages, in Chinese | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Modulo inverse is an important arithmetic operation. Many famous algorithms
in public key cryptography require to compute modulo inverse. It is argued that
the method of DaYan deriving one of Jiushao Qin provides the most concise and
transparent way of computing modulo inverse. Based on the rule of taking the
least positive remainder in division, this paper presents a more precise
algorithmic description of the method of DaYan deriving one to reflect Qin's
original idea. Our form of the algorithm is straightforward and different from
the ones in the literature. Some additional information can be revealed easily
from the process of DaYan deriving one, e.g., the invariance property of the
permanent of the state, natural connection to continued fractions. Comparison
of Qin'a algorithm and the modern form of the Extended Euclidean algorithm is
also given. Since DaYan deriving one is the key technical ingredient of Jiushao
Qin's DaYan aggregation method (aka the Chinese Remainder Theorem), we include
some explanation to the latter as well.
| [
{
"created": "Tue, 4 Oct 2016 20:04:12 GMT",
"version": "v1"
},
{
"created": "Thu, 31 Aug 2017 19:50:59 GMT",
"version": "v2"
}
] | 2017-09-04 | [
[
"Xu",
"Guangwu",
""
],
[
"Li",
"Bao",
""
]
] | Modulo inverse is an important arithmetic operation. Many famous algorithms in public key cryptography require to compute modulo inverse. It is argued that the method of DaYan deriving one of Jiushao Qin provides the most concise and transparent way of computing modulo inverse. Based on the rule of taking the least positive remainder in division, this paper presents a more precise algorithmic description of the method of DaYan deriving one to reflect Qin's original idea. Our form of the algorithm is straightforward and different from the ones in the literature. Some additional information can be revealed easily from the process of DaYan deriving one, e.g., the invariance property of the permanent of the state, natural connection to continued fractions. Comparison of Qin'a algorithm and the modern form of the Extended Euclidean algorithm is also given. Since DaYan deriving one is the key technical ingredient of Jiushao Qin's DaYan aggregation method (aka the Chinese Remainder Theorem), we include some explanation to the latter as well. |
2211.09334 | Koki Kizawa | Koki Kizawa, Ryoichi Shinkuma, Gabriele Trovato | Estimation of physical activities of people in offices from time-series
point-cloud data | null | null | null | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper proposes an edge computing system that enables estimating physical
activities of people in offices from time-series point-cloud data, obtained by
using a light-detection-and-ranging (LIDAR) sensor network. The paper presents
that the proposed system successfully constructs the model for estimating the
number of typed characters from time-series point-cloud data, through an
experiment using real LIDAR sensors.
| [
{
"created": "Thu, 17 Nov 2022 04:49:51 GMT",
"version": "v1"
}
] | 2022-11-18 | [
[
"Kizawa",
"Koki",
""
],
[
"Shinkuma",
"Ryoichi",
""
],
[
"Trovato",
"Gabriele",
""
]
] | This paper proposes an edge computing system that enables estimating physical activities of people in offices from time-series point-cloud data, obtained by using a light-detection-and-ranging (LIDAR) sensor network. The paper presents that the proposed system successfully constructs the model for estimating the number of typed characters from time-series point-cloud data, through an experiment using real LIDAR sensors. |
2304.14791 | Naif Mehanna | Naif Mehanna (CRIStAL, CNRS, SPIRALS), Walter Rudametkin (UR, IUF,
CNRS, IRISA, DiverSe) | Caught in the Game: On the History and Evolution of Web Browser Gaming | null | TheWebConference 2023, Apr 2023, Austin (TX), United States | null | null | cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Web browsers have come a long way since their inception, evolving from a
simple means of displaying text documents over the network to complex software
stacks with advanced graphics and network capabilities. As personal computers
grew in popularity, developers jumped at the opportunity to deploy
cross-platform games with centralized management and a low barrier to entry.
Simply going to the right address is now enough to start a game. From
text-based to GPU-powered 3D games, browser gaming has evolved to become a
strong alternative to traditional console and mobile-based gaming, targeting
both casual and advanced gamers. Browser technology has also evolved to
accommodate more demanding applications, sometimes even supplanting functions
typically left to the operating system. Today, websites display rich,
computationally intensive, hardware-accelerated graphics, allowing developers
to build ever-more impressive applications and games.In this paper, we present
the evolution of browser gaming and the technologies that enabled it, from the
release of the first text-based games in the early 1990s to current open-world
and game-engine-powered browser games. We discuss the societal impact of
browser gaming and how it has allowed a new target audience to accessdigital
gaming. Finally, we review the potential future evolution ofthe browser gaming
industry.
| [
{
"created": "Fri, 28 Apr 2023 12:02:16 GMT",
"version": "v1"
}
] | 2023-05-01 | [
[
"Mehanna",
"Naif",
"",
"CRIStAL, CNRS, SPIRALS"
],
[
"Rudametkin",
"Walter",
"",
"UR, IUF,\n CNRS, IRISA, DiverSe"
]
] | Web browsers have come a long way since their inception, evolving from a simple means of displaying text documents over the network to complex software stacks with advanced graphics and network capabilities. As personal computers grew in popularity, developers jumped at the opportunity to deploy cross-platform games with centralized management and a low barrier to entry. Simply going to the right address is now enough to start a game. From text-based to GPU-powered 3D games, browser gaming has evolved to become a strong alternative to traditional console and mobile-based gaming, targeting both casual and advanced gamers. Browser technology has also evolved to accommodate more demanding applications, sometimes even supplanting functions typically left to the operating system. Today, websites display rich, computationally intensive, hardware-accelerated graphics, allowing developers to build ever-more impressive applications and games.In this paper, we present the evolution of browser gaming and the technologies that enabled it, from the release of the first text-based games in the early 1990s to current open-world and game-engine-powered browser games. We discuss the societal impact of browser gaming and how it has allowed a new target audience to accessdigital gaming. Finally, we review the potential future evolution ofthe browser gaming industry. |
2102.02502 | Sebastian Bullinger | Sebastian Bullinger, Christoph Bodensteiner, Michael Arens | 3D Surface Reconstruction From Multi-Date Satellite Images | Accepted at ISPRS Congress 2021 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The reconstruction of accurate three-dimensional environment models is one of
the most fundamental goals in the field of photogrammetry. Since satellite
images provide suitable properties for obtaining large-scale environment
reconstructions, there exist a variety of Stereo Matching based methods to
reconstruct point clouds for satellite image pairs. Recently, the first
Structure from Motion (SfM) based approach has been proposed, which allows to
reconstruct point clouds from multiple satellite images. In this work, we
propose an extension of this SfM based pipeline that allows us to reconstruct
not only point clouds but watertight meshes including texture information. We
provide a detailed description of several steps that are mandatory to exploit
state-of-the-art mesh reconstruction algorithms in the context of satellite
imagery. This includes a decomposition of finite projective camera calibration
matrices, a skew correction of corresponding depth maps and input images as
well as the recovery of real-world depth maps from reparameterized depth
values. The paper presents an extensive quantitative evaluation on multi-date
satellite images demonstrating that the proposed pipeline combined with current
meshing algorithms outperforms state-of-the-art point cloud reconstruction
algorithms in terms of completeness and median error. We make the source code
of our pipeline publicly available.
| [
{
"created": "Thu, 4 Feb 2021 09:23:21 GMT",
"version": "v1"
},
{
"created": "Sat, 3 Apr 2021 12:50:05 GMT",
"version": "v2"
}
] | 2021-04-06 | [
[
"Bullinger",
"Sebastian",
""
],
[
"Bodensteiner",
"Christoph",
""
],
[
"Arens",
"Michael",
""
]
] | The reconstruction of accurate three-dimensional environment models is one of the most fundamental goals in the field of photogrammetry. Since satellite images provide suitable properties for obtaining large-scale environment reconstructions, there exist a variety of Stereo Matching based methods to reconstruct point clouds for satellite image pairs. Recently, the first Structure from Motion (SfM) based approach has been proposed, which allows to reconstruct point clouds from multiple satellite images. In this work, we propose an extension of this SfM based pipeline that allows us to reconstruct not only point clouds but watertight meshes including texture information. We provide a detailed description of several steps that are mandatory to exploit state-of-the-art mesh reconstruction algorithms in the context of satellite imagery. This includes a decomposition of finite projective camera calibration matrices, a skew correction of corresponding depth maps and input images as well as the recovery of real-world depth maps from reparameterized depth values. The paper presents an extensive quantitative evaluation on multi-date satellite images demonstrating that the proposed pipeline combined with current meshing algorithms outperforms state-of-the-art point cloud reconstruction algorithms in terms of completeness and median error. We make the source code of our pipeline publicly available. |
1906.01602 | Sarabjot Singh | Sarabjot Singh | On Provisioning Cellular Networks for Distributed Inference | null | null | null | null | cs.NI cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Wireless traffic attributable to machine learning (ML) inference workloads is
increasing with the proliferation of applications and smart wireless devices
leveraging ML inference. Owing to limited compute capabilities at these "edge"
devices, achieving high inference accuracy often requires coordination with a
remote compute node or "cloud" over the wireless cellular network. The accuracy
of this distributed inference is, thus, impacted by the communication rate and
reliability offered by the cellular network. In this paper, an analytical
framework is proposed to characterize inference accuracy as a function of
cellular network design. Using the developed framework, it is shown that
cellular network should be provisioned with a minimum density of access points
(APs) to guarantee a target inference accuracy, and the inference accuracy
achievable at asymptotically high AP density is limited by the air-interface
bandwidth. Furthermore, the minimum accuracy required of edge inference to
deliver a target inference accuracy is shown to be inversely proportional to
the density of APs and the bandwidth.
| [
{
"created": "Tue, 4 Jun 2019 17:31:13 GMT",
"version": "v1"
}
] | 2019-06-05 | [
[
"Singh",
"Sarabjot",
""
]
] | Wireless traffic attributable to machine learning (ML) inference workloads is increasing with the proliferation of applications and smart wireless devices leveraging ML inference. Owing to limited compute capabilities at these "edge" devices, achieving high inference accuracy often requires coordination with a remote compute node or "cloud" over the wireless cellular network. The accuracy of this distributed inference is, thus, impacted by the communication rate and reliability offered by the cellular network. In this paper, an analytical framework is proposed to characterize inference accuracy as a function of cellular network design. Using the developed framework, it is shown that cellular network should be provisioned with a minimum density of access points (APs) to guarantee a target inference accuracy, and the inference accuracy achievable at asymptotically high AP density is limited by the air-interface bandwidth. Furthermore, the minimum accuracy required of edge inference to deliver a target inference accuracy is shown to be inversely proportional to the density of APs and the bandwidth. |
1909.00333 | Hangfeng He | Hangfeng He, Qiang Ning, Dan Roth | QuASE: Question-Answer Driven Sentence Encoding | null | null | null | null | cs.CL cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Question-answering (QA) data often encodes essential information in many
facets. This paper studies a natural question: Can we get supervision from QA
data for other tasks (typically, non-QA ones)? For example, {\em can we use
QAMR (Michael et al., 2017) to improve named entity recognition?} We suggest
that simply further pre-training BERT is often not the best option, and propose
the {\em question-answer driven sentence encoding (QuASE)} framework. QuASE
learns representations from QA data, using BERT or other state-of-the-art
contextual language models. In particular, we observe the need to distinguish
between two types of sentence encodings, depending on whether the target task
is a single- or multi-sentence input; in both cases, the resulting encoding is
shown to be an easy-to-use plugin for many downstream tasks. This work may
point out an alternative way to supervise NLP tasks.
| [
{
"created": "Sun, 1 Sep 2019 06:30:57 GMT",
"version": "v1"
},
{
"created": "Mon, 4 May 2020 15:40:12 GMT",
"version": "v2"
},
{
"created": "Thu, 3 Dec 2020 21:12:24 GMT",
"version": "v3"
}
] | 2020-12-07 | [
[
"He",
"Hangfeng",
""
],
[
"Ning",
"Qiang",
""
],
[
"Roth",
"Dan",
""
]
] | Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)? For example, {\em can we use QAMR (Michael et al., 2017) to improve named entity recognition?} We suggest that simply further pre-training BERT is often not the best option, and propose the {\em question-answer driven sentence encoding (QuASE)} framework. QuASE learns representations from QA data, using BERT or other state-of-the-art contextual language models. In particular, we observe the need to distinguish between two types of sentence encodings, depending on whether the target task is a single- or multi-sentence input; in both cases, the resulting encoding is shown to be an easy-to-use plugin for many downstream tasks. This work may point out an alternative way to supervise NLP tasks. |
1711.05073 | Wei He | Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan
Liu, Yizhong Wang, Hua Wu, Qiaoqiao She, Xuan Liu, Tian Wu, Haifeng Wang | DuReader: a Chinese Machine Reading Comprehension Dataset from
Real-world Applications | 10 pages, ACL 2018 MRQA Workshop camera-ready version | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper introduces DuReader, a new large-scale, open-domain Chinese ma-
chine reading comprehension (MRC) dataset, designed to address real-world MRC.
DuReader has three advantages over previous MRC datasets: (1) data sources:
questions and documents are based on Baidu Search and Baidu Zhidao; answers are
manually generated. (2) question types: it provides rich annotations for more
question types, especially yes-no and opinion questions, that leaves more
opportunity for the research community. (3) scale: it contains 200K questions,
420K answers and 1M documents; it is the largest Chinese MRC dataset so far.
Experiments show that human performance is well above current state-of-the-art
baseline systems, leaving plenty of room for the community to make
improvements. To help the community make these improvements, both DuReader and
baseline systems have been posted online. We also organize a shared competition
to encourage the exploration of more models. Since the release of the task,
there are significant improvements over the baselines.
| [
{
"created": "Tue, 14 Nov 2017 12:13:44 GMT",
"version": "v1"
},
{
"created": "Wed, 15 Nov 2017 11:45:41 GMT",
"version": "v2"
},
{
"created": "Wed, 23 May 2018 12:07:19 GMT",
"version": "v3"
},
{
"created": "Mon, 11 Jun 2018 03:26:30 GMT",
"version": "v4"
}
] | 2018-06-12 | [
[
"He",
"Wei",
""
],
[
"Liu",
"Kai",
""
],
[
"Liu",
"Jing",
""
],
[
"Lyu",
"Yajuan",
""
],
[
"Zhao",
"Shiqi",
""
],
[
"Xiao",
"Xinyan",
""
],
[
"Liu",
"Yuan",
""
],
[
"Wang",
"Yizhong",
""
],
[
"Wu",
"Hua",
""
],
[
"She",
"Qiaoqiao",
""
],
[
"Liu",
"Xuan",
""
],
[
"Wu",
"Tian",
""
],
[
"Wang",
"Haifeng",
""
]
] | This paper introduces DuReader, a new large-scale, open-domain Chinese ma- chine reading comprehension (MRC) dataset, designed to address real-world MRC. DuReader has three advantages over previous MRC datasets: (1) data sources: questions and documents are based on Baidu Search and Baidu Zhidao; answers are manually generated. (2) question types: it provides rich annotations for more question types, especially yes-no and opinion questions, that leaves more opportunity for the research community. (3) scale: it contains 200K questions, 420K answers and 1M documents; it is the largest Chinese MRC dataset so far. Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements. To help the community make these improvements, both DuReader and baseline systems have been posted online. We also organize a shared competition to encourage the exploration of more models. Since the release of the task, there are significant improvements over the baselines. |
2203.16796 | Nikhil Tripathi | Nikhil Tripathi | Delays have Dangerous Ends: Slow HTTP/2 DoS attacks into the Wild and
their Real-Time Detection using Event Sequence Analysis | 11 pages, 8 figures | null | null | null | cs.CR | http://creativecommons.org/licenses/by/4.0/ | The robustness principle, written by Jon Postel in an early version of TCP
implementation, states that the communicating entities should be liberal while
accepting the data. Several entities on the Internet do follow this principle.
For instance, in this work, we show that many popular web servers on the
Internet are generous as they wait for a substantial time period to receive the
remaining portion of an incomplete web request. Unfortunately, this behavior
also makes them vulnerable to a class of cyber attacks, commonly known as Slow
Rate DoS attacks. HTTP/2, the recent version of HTTP, is recently found
vulnerable to these attacks. However, the impact of Slow HTTP/2 DoS attacks on
real web servers on the Internet has not been studied yet. Also, to the best of
our knowledge, there is no defense scheme known to detect Slow Rate DoS attacks
against HTTP/2 in real-time. To bridge these gaps, we first test the behavior
of HTTP/2 supporting web servers on the Internet against Slow HTTP/2 DoS
attacks. Subsequently, we propose a scheme to detect these attacks in
real-time. We show that the proposed detection scheme can detect attacks in
real-time with high accuracy and marginal computational overhead.
| [
{
"created": "Thu, 31 Mar 2022 04:53:35 GMT",
"version": "v1"
}
] | 2022-04-01 | [
[
"Tripathi",
"Nikhil",
""
]
] | The robustness principle, written by Jon Postel in an early version of TCP implementation, states that the communicating entities should be liberal while accepting the data. Several entities on the Internet do follow this principle. For instance, in this work, we show that many popular web servers on the Internet are generous as they wait for a substantial time period to receive the remaining portion of an incomplete web request. Unfortunately, this behavior also makes them vulnerable to a class of cyber attacks, commonly known as Slow Rate DoS attacks. HTTP/2, the recent version of HTTP, is recently found vulnerable to these attacks. However, the impact of Slow HTTP/2 DoS attacks on real web servers on the Internet has not been studied yet. Also, to the best of our knowledge, there is no defense scheme known to detect Slow Rate DoS attacks against HTTP/2 in real-time. To bridge these gaps, we first test the behavior of HTTP/2 supporting web servers on the Internet against Slow HTTP/2 DoS attacks. Subsequently, we propose a scheme to detect these attacks in real-time. We show that the proposed detection scheme can detect attacks in real-time with high accuracy and marginal computational overhead. |
2203.04305 | Jiang Lianlian | Lianlian Jiang, Yuexuan Wang, Wenyi Zheng, Chao Jin, Zengxiang Li, Sin
G. Teo | LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series
Data | null | null | null | null | cs.LG cs.AI cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Federated learning (FL) and split learning (SL) are the two popular
distributed machine learning (ML) approaches that provide some data privacy
protection mechanisms. In the time-series classification problem, many
researchers typically use 1D convolutional neural networks (1DCNNs) based on
the SL approach with a single client to reduce the computational overhead at
the client-side while still preserving data privacy. Another method, recurrent
neural network (RNN), is utilized on sequentially partitioned data where
segments of multiple-segment sequential data are distributed across various
clients. However, to the best of our knowledge, it is still not much work done
in SL with long short-term memory (LSTM) network, even the LSTM network is
practically effective in processing time-series data. In this work, we propose
a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to
classify time-series data with multiple clients. The differential privacy (DP)
is applied to solve the data privacy leakage. The proposed method, LSTMSPLIT,
has achieved better or reasonable accuracy compared to the Split-1DCNN method
using the electrocardiogram dataset and the human activity recognition dataset.
Furthermore, the proposed method, LSTMSPLIT, can also achieve good accuracy
after applying differential privacy to preserve the user privacy of the cut
layer of the LSTMSPLIT.
| [
{
"created": "Tue, 8 Mar 2022 11:44:12 GMT",
"version": "v1"
}
] | 2022-03-10 | [
[
"Jiang",
"Lianlian",
""
],
[
"Wang",
"Yuexuan",
""
],
[
"Zheng",
"Wenyi",
""
],
[
"Jin",
"Chao",
""
],
[
"Li",
"Zengxiang",
""
],
[
"Teo",
"Sin G.",
""
]
] | Federated learning (FL) and split learning (SL) are the two popular distributed machine learning (ML) approaches that provide some data privacy protection mechanisms. In the time-series classification problem, many researchers typically use 1D convolutional neural networks (1DCNNs) based on the SL approach with a single client to reduce the computational overhead at the client-side while still preserving data privacy. Another method, recurrent neural network (RNN), is utilized on sequentially partitioned data where segments of multiple-segment sequential data are distributed across various clients. However, to the best of our knowledge, it is still not much work done in SL with long short-term memory (LSTM) network, even the LSTM network is practically effective in processing time-series data. In this work, we propose a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to classify time-series data with multiple clients. The differential privacy (DP) is applied to solve the data privacy leakage. The proposed method, LSTMSPLIT, has achieved better or reasonable accuracy compared to the Split-1DCNN method using the electrocardiogram dataset and the human activity recognition dataset. Furthermore, the proposed method, LSTMSPLIT, can also achieve good accuracy after applying differential privacy to preserve the user privacy of the cut layer of the LSTMSPLIT. |
1308.1779 | Christoph Lange | Marco B. Caminati, Manfred Kerber, Christoph Lange, Colin Rowat | Proving soundness of combinatorial Vickrey auctions and generating
verified executable code | null | null | null | null | cs.GT cs.CE cs.LO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Using mechanised reasoning we prove that combinatorial Vickrey auctions are
soundly specified in that they associate a unique outcome (allocation and
transfers) to any valid input (bids). Having done so, we auto-generate verified
executable code from the formally defined auction. This removes a source of
error in implementing the auction design. We intend to use formal methods to
verify new auction designs. Here, our contribution is to introduce and
demonstrate the use of formal methods for auction verification in the familiar
setting of a well-known auction.
| [
{
"created": "Thu, 8 Aug 2013 08:00:55 GMT",
"version": "v1"
},
{
"created": "Mon, 2 Sep 2013 10:47:25 GMT",
"version": "v2"
}
] | 2013-09-03 | [
[
"Caminati",
"Marco B.",
""
],
[
"Kerber",
"Manfred",
""
],
[
"Lange",
"Christoph",
""
],
[
"Rowat",
"Colin",
""
]
] | Using mechanised reasoning we prove that combinatorial Vickrey auctions are soundly specified in that they associate a unique outcome (allocation and transfers) to any valid input (bids). Having done so, we auto-generate verified executable code from the formally defined auction. This removes a source of error in implementing the auction design. We intend to use formal methods to verify new auction designs. Here, our contribution is to introduce and demonstrate the use of formal methods for auction verification in the familiar setting of a well-known auction. |
1806.10756 | Bin Li | Chaoqiong Fan, Bin Li, Jia Hou, Yi Wu, Weisi Guo, Chenglin Zhao | Robust Fuzzy-Learning For Partially Overlapping Channels Allocation In
UAV Communication Networks | null | null | null | null | cs.NI cs.GT cs.LG | http://creativecommons.org/licenses/by/4.0/ | In this paper, we consider a mesh-structured unmanned aerial vehicle (UAV)
networks exploiting partially overlapping channels (POCs). For general
data-collection tasks in UAV networks, we aim to optimize the network
throughput with constraints on transmission power and quality of service (QoS).
As far as the highly mobile and constantly changing UAV networks are concerned,
unfortunately, most existing methods rely on definite information which is
vulnerable to the dynamic environment, rendering system performance to be less
effective. In order to combat dynamic topology and varying interference of UAV
networks, a robust and distributed learning scheme is proposed. Rather than the
perfect channel state information (CSI), we introduce uncertainties to
characterize the dynamic channel gains among UAV nodes, which are then
interpreted with fuzzy numbers. Instead of the traditional observation space
where the channel capacity is a crisp reward, we implement the learning and
decision process in a mapped fuzzy space. This allows the system to achieve a
smoother and more robust performance by optimizing in an alternate space. To
this end, we design a fuzzy payoffs function (FPF) to describe the fluctuated
utility, and the problem of POCs assignment is formulated as a fuzzy payoffs
game (FPG). Assisted by an attractive property of fuzzy bi-matrix games, the
existence of fuzzy Nash equilibrium (FNE) for our formulated FPG is proved. Our
robust fuzzy-learning algorithm could reach the equilibrium solution via a
least-deviation method. Finally, numerical simulations are provided to
demonstrate the advantages of our new scheme over the existing scheme.
| [
{
"created": "Thu, 28 Jun 2018 03:35:09 GMT",
"version": "v1"
}
] | 2018-06-29 | [
[
"Fan",
"Chaoqiong",
""
],
[
"Li",
"Bin",
""
],
[
"Hou",
"Jia",
""
],
[
"Wu",
"Yi",
""
],
[
"Guo",
"Weisi",
""
],
[
"Zhao",
"Chenglin",
""
]
] | In this paper, we consider a mesh-structured unmanned aerial vehicle (UAV) networks exploiting partially overlapping channels (POCs). For general data-collection tasks in UAV networks, we aim to optimize the network throughput with constraints on transmission power and quality of service (QoS). As far as the highly mobile and constantly changing UAV networks are concerned, unfortunately, most existing methods rely on definite information which is vulnerable to the dynamic environment, rendering system performance to be less effective. In order to combat dynamic topology and varying interference of UAV networks, a robust and distributed learning scheme is proposed. Rather than the perfect channel state information (CSI), we introduce uncertainties to characterize the dynamic channel gains among UAV nodes, which are then interpreted with fuzzy numbers. Instead of the traditional observation space where the channel capacity is a crisp reward, we implement the learning and decision process in a mapped fuzzy space. This allows the system to achieve a smoother and more robust performance by optimizing in an alternate space. To this end, we design a fuzzy payoffs function (FPF) to describe the fluctuated utility, and the problem of POCs assignment is formulated as a fuzzy payoffs game (FPG). Assisted by an attractive property of fuzzy bi-matrix games, the existence of fuzzy Nash equilibrium (FNE) for our formulated FPG is proved. Our robust fuzzy-learning algorithm could reach the equilibrium solution via a least-deviation method. Finally, numerical simulations are provided to demonstrate the advantages of our new scheme over the existing scheme. |
2311.16473 | Zhihao Liang | Zhihao Liang, Qi Zhang, Ying Feng, Ying Shan, Kui Jia | GS-IR: 3D Gaussian Splatting for Inverse Rendering | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian
Splatting (GS) that leverages forward mapping volume rendering to achieve
photorealistic novel view synthesis and relighting results. Unlike previous
works that use implicit neural representations and volume rendering (e.g.
NeRF), which suffer from low expressive power and high computational
complexity, we extend GS, a top-performance representation for novel view
synthesis, to estimate scene geometry, surface material, and environment
illumination from multi-view images captured under unknown lighting conditions.
There are two main problems when introducing GS to inverse rendering: 1) GS
does not support producing plausible normal natively; 2) forward mapping (e.g.
rasterization and splatting) cannot trace the occlusion like backward mapping
(e.g. ray tracing). To address these challenges, our GS-IR proposes an
efficient optimization scheme that incorporates a depth-derivation-based
regularization for normal estimation and a baking-based occlusion to model
indirect lighting. The flexible and expressive GS representation allows us to
achieve fast and compact geometry reconstruction, photorealistic novel view
synthesis, and effective physically-based rendering. We demonstrate the
superiority of our method over baseline methods through qualitative and
quantitative evaluations on various challenging scenes.
| [
{
"created": "Sun, 26 Nov 2023 02:35:09 GMT",
"version": "v1"
},
{
"created": "Mon, 4 Dec 2023 10:35:53 GMT",
"version": "v2"
},
{
"created": "Thu, 28 Mar 2024 05:47:24 GMT",
"version": "v3"
}
] | 2024-03-29 | [
[
"Liang",
"Zhihao",
""
],
[
"Zhang",
"Qi",
""
],
[
"Feng",
"Ying",
""
],
[
"Shan",
"Ying",
""
],
[
"Jia",
"Kui",
""
]
] | We propose GS-IR, a novel inverse rendering approach based on 3D Gaussian Splatting (GS) that leverages forward mapping volume rendering to achieve photorealistic novel view synthesis and relighting results. Unlike previous works that use implicit neural representations and volume rendering (e.g. NeRF), which suffer from low expressive power and high computational complexity, we extend GS, a top-performance representation for novel view synthesis, to estimate scene geometry, surface material, and environment illumination from multi-view images captured under unknown lighting conditions. There are two main problems when introducing GS to inverse rendering: 1) GS does not support producing plausible normal natively; 2) forward mapping (e.g. rasterization and splatting) cannot trace the occlusion like backward mapping (e.g. ray tracing). To address these challenges, our GS-IR proposes an efficient optimization scheme that incorporates a depth-derivation-based regularization for normal estimation and a baking-based occlusion to model indirect lighting. The flexible and expressive GS representation allows us to achieve fast and compact geometry reconstruction, photorealistic novel view synthesis, and effective physically-based rendering. We demonstrate the superiority of our method over baseline methods through qualitative and quantitative evaluations on various challenging scenes. |
1907.12850 | Jongho Im | Jongho Im, Taikgun Song, Youngsu Lee, Jewoo Kim | Confirmatory Aspect-based Opinion Mining Processes | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by-nc-sa/4.0/ | A new opinion extraction method is proposed to summarize unstructured,
user-generated content (i.e., online customer reviews) in the fixed topic
domains. To differentiate the current approach from other opinion extraction
approaches, which are often exposed to a sparsity problem and lack of sentiment
scores, a confirmatory aspect-based opinion mining framework is introduced
along with its practical algorithm called DiSSBUS. In this procedure, 1) each
customer review is disintegrated into a set of clauses; 2) each clause is
summarized to bi-terms-a topic word and an evaluation word-using a
part-of-speech (POS) tagger; and 3) each bi-term is matched to a pre-specified
topic relevant to a specific domain. The proposed processes have two primary
advantages over existing methods: 1) they can decompose a single review into a
set of bi-terms related to pre-specified topics in the domain of interest and,
therefore, 2) allow identification of the reviewer's opinions on the topics via
evaluation words within the set of bi-terms. The proposed aspect-based opinion
mining is applied to customer reviews of restaurants in Hawaii obtained from
TripAdvisor, and the empirical findings validate the effectiveness of the
method.
Keywords: Clause-based sentiment analysis, Customer review, Opinion mining,
Topic modeling, User-generate-contents.
| [
{
"created": "Tue, 30 Jul 2019 12:00:03 GMT",
"version": "v1"
}
] | 2019-07-31 | [
[
"Im",
"Jongho",
""
],
[
"Song",
"Taikgun",
""
],
[
"Lee",
"Youngsu",
""
],
[
"Kim",
"Jewoo",
""
]
] | A new opinion extraction method is proposed to summarize unstructured, user-generated content (i.e., online customer reviews) in the fixed topic domains. To differentiate the current approach from other opinion extraction approaches, which are often exposed to a sparsity problem and lack of sentiment scores, a confirmatory aspect-based opinion mining framework is introduced along with its practical algorithm called DiSSBUS. In this procedure, 1) each customer review is disintegrated into a set of clauses; 2) each clause is summarized to bi-terms-a topic word and an evaluation word-using a part-of-speech (POS) tagger; and 3) each bi-term is matched to a pre-specified topic relevant to a specific domain. The proposed processes have two primary advantages over existing methods: 1) they can decompose a single review into a set of bi-terms related to pre-specified topics in the domain of interest and, therefore, 2) allow identification of the reviewer's opinions on the topics via evaluation words within the set of bi-terms. The proposed aspect-based opinion mining is applied to customer reviews of restaurants in Hawaii obtained from TripAdvisor, and the empirical findings validate the effectiveness of the method. Keywords: Clause-based sentiment analysis, Customer review, Opinion mining, Topic modeling, User-generate-contents. |
1105.0826 | Radu Arsinte | Radu Arsinte, Eugen Lupu | Streaming Multimedia Information Using the Features of the DVB-S Card | 4 pages, 5 figures | Scientific Bulletin of the "Politehnica" University Timi\c{s}oara,
Transaction on Electronics and Telecomunications, Tom 51(65), Fascicola 1-2,
pag. 181-184, 2006 | null | null | cs.MM cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a study of audio-video streaming using the additional
possibilities of a DVB-S card. The board used for experiments (Technisat
SkyStar 2) is one of the most frequently used cards for this purpose. Using the
main blocks of the board's software support it is possible the implement a
really useful and full functional system for audio-video streaming. The
streaming is possible to be implemented either for decoded MPEG stream or for
transport stream. In this last case it is possible to view not only a program,
but any program from the same multiplex. This allows us to implement
| [
{
"created": "Wed, 4 May 2011 13:46:31 GMT",
"version": "v1"
}
] | 2011-05-05 | [
[
"Arsinte",
"Radu",
""
],
[
"Lupu",
"Eugen",
""
]
] | This paper presents a study of audio-video streaming using the additional possibilities of a DVB-S card. The board used for experiments (Technisat SkyStar 2) is one of the most frequently used cards for this purpose. Using the main blocks of the board's software support it is possible the implement a really useful and full functional system for audio-video streaming. The streaming is possible to be implemented either for decoded MPEG stream or for transport stream. In this last case it is possible to view not only a program, but any program from the same multiplex. This allows us to implement |
1312.5946 | Kathrin Bujna | Johannes Bl\"omer and Kathrin Bujna | Adaptive Seeding for Gaussian Mixture Models | This is a preprint of a paper that has been accepted for publication
in the Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery
and Data Mining (PAKDD) 2016. The final publication is available at
link.springer.com (http://link.springer.com/chapter/10.1007/978-3-319-31750-2
24) | null | 10.1007/978-3-319-31750-2_24 | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present new initialization methods for the expectation-maximization
algorithm for multivariate Gaussian mixture models. Our methods are adaptions
of the well-known $K$-means++ initialization and the Gonzalez algorithm.
Thereby we aim to close the gap between simple random, e.g. uniform, and
complex methods, that crucially depend on the right choice of hyperparameters.
Our extensive experiments indicate the usefulness of our methods compared to
common techniques and methods, which e.g. apply the original $K$-means++ and
Gonzalez directly, with respect to artificial as well as real-world data sets.
| [
{
"created": "Fri, 20 Dec 2013 14:08:48 GMT",
"version": "v1"
},
{
"created": "Mon, 1 Aug 2016 08:33:13 GMT",
"version": "v2"
},
{
"created": "Tue, 30 May 2017 07:44:37 GMT",
"version": "v3"
}
] | 2017-05-31 | [
[
"Blömer",
"Johannes",
""
],
[
"Bujna",
"Kathrin",
""
]
] | We present new initialization methods for the expectation-maximization algorithm for multivariate Gaussian mixture models. Our methods are adaptions of the well-known $K$-means++ initialization and the Gonzalez algorithm. Thereby we aim to close the gap between simple random, e.g. uniform, and complex methods, that crucially depend on the right choice of hyperparameters. Our extensive experiments indicate the usefulness of our methods compared to common techniques and methods, which e.g. apply the original $K$-means++ and Gonzalez directly, with respect to artificial as well as real-world data sets. |
1008.5073 | Everardo Barcenas | Everardo Barcenas (INRIA Rh\^one-Alpes / LIG Laboratoire
d'Informatique de Grenoble), Pierre Geneves (INRIA Rh\^one-Alpes / LIG
Laboratoire d'Informatique de Grenoble), Nabil Layaida (INRIA Rh\^one-Alpes /
LIG Laboratoire d'Informatique de Grenoble), Alan Schmitt (INRIA
Rh\^one-Alpes / LIG Laboratoire d'Informatique de Grenoble) | On the Count of Trees | null | null | null | RR-7251 | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Regular tree grammars and regular path expressions constitute core constructs
widely used in programming languages and type systems. Nevertheless, there has
been little research so far on frameworks for reasoning about path expressions
where node cardinality constraints occur along a path in a tree. We present a
logic capable of expressing deep counting along paths which may include
arbitrary recursive forward and backward navigation. The counting extensions
can be seen as a generalization of graded modalities that count immediate
successor nodes. While the combination of graded modalities, nominals, and
inverse modalities yields undecidable logics over graphs, we show that these
features can be combined in a decidable tree logic whose main features can be
decided in exponential time. Our logic being closed under negation, it may be
used to decide typical problems on XPath queries such as satisfiability, type
checking with relation to regular types, containment, or equivalence.
| [
{
"created": "Mon, 30 Aug 2010 12:58:17 GMT",
"version": "v1"
}
] | 2010-08-31 | [
[
"Barcenas",
"Everardo",
"",
"INRIA Rhône-Alpes / LIG Laboratoire\n d'Informatique de Grenoble"
],
[
"Geneves",
"Pierre",
"",
"INRIA Rhône-Alpes / LIG\n Laboratoire d'Informatique de Grenoble"
],
[
"Layaida",
"Nabil",
"",
"INRIA Rhône-Alpes /\n LIG Laboratoire d'Informatique de Grenoble"
],
[
"Schmitt",
"Alan",
"",
"INRIA\n Rhône-Alpes / LIG Laboratoire d'Informatique de Grenoble"
]
] | Regular tree grammars and regular path expressions constitute core constructs widely used in programming languages and type systems. Nevertheless, there has been little research so far on frameworks for reasoning about path expressions where node cardinality constraints occur along a path in a tree. We present a logic capable of expressing deep counting along paths which may include arbitrary recursive forward and backward navigation. The counting extensions can be seen as a generalization of graded modalities that count immediate successor nodes. While the combination of graded modalities, nominals, and inverse modalities yields undecidable logics over graphs, we show that these features can be combined in a decidable tree logic whose main features can be decided in exponential time. Our logic being closed under negation, it may be used to decide typical problems on XPath queries such as satisfiability, type checking with relation to regular types, containment, or equivalence. |
2305.11347 | Elise Bishoff | Elise Bishoff, Charles Godfrey, Myles McKay, Eleanor Byler | Quantifying the robustness of deep multispectral segmentation models
against natural perturbations and data poisoning | null | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by/4.0/ | In overhead image segmentation tasks, including additional spectral bands
beyond the traditional RGB channels can improve model performance. However, it
is still unclear how incorporating this additional data impacts model
robustness to adversarial attacks and natural perturbations. For adversarial
robustness, the additional information could improve the model's ability to
distinguish malicious inputs, or simply provide new attack avenues and
vulnerabilities. For natural perturbations, the additional information could
better inform model decisions and weaken perturbation effects or have no
significant influence at all. In this work, we seek to characterize the
performance and robustness of a multispectral (RGB and near infrared) image
segmentation model subjected to adversarial attacks and natural perturbations.
While existing adversarial and natural robustness research has focused
primarily on digital perturbations, we prioritize on creating realistic
perturbations designed with physical world conditions in mind. For adversarial
robustness, we focus on data poisoning attacks whereas for natural robustness,
we focus on extending ImageNet-C common corruptions for fog and snow that
coherently and self-consistently perturbs the input data. Overall, we find both
RGB and multispectral models are vulnerable to data poisoning attacks
regardless of input or fusion architectures and that while physically
realizable natural perturbations still degrade model performance, the impact
differs based on fusion architecture and input data.
| [
{
"created": "Thu, 18 May 2023 23:43:33 GMT",
"version": "v1"
}
] | 2023-05-22 | [
[
"Bishoff",
"Elise",
""
],
[
"Godfrey",
"Charles",
""
],
[
"McKay",
"Myles",
""
],
[
"Byler",
"Eleanor",
""
]
] | In overhead image segmentation tasks, including additional spectral bands beyond the traditional RGB channels can improve model performance. However, it is still unclear how incorporating this additional data impacts model robustness to adversarial attacks and natural perturbations. For adversarial robustness, the additional information could improve the model's ability to distinguish malicious inputs, or simply provide new attack avenues and vulnerabilities. For natural perturbations, the additional information could better inform model decisions and weaken perturbation effects or have no significant influence at all. In this work, we seek to characterize the performance and robustness of a multispectral (RGB and near infrared) image segmentation model subjected to adversarial attacks and natural perturbations. While existing adversarial and natural robustness research has focused primarily on digital perturbations, we prioritize on creating realistic perturbations designed with physical world conditions in mind. For adversarial robustness, we focus on data poisoning attacks whereas for natural robustness, we focus on extending ImageNet-C common corruptions for fog and snow that coherently and self-consistently perturbs the input data. Overall, we find both RGB and multispectral models are vulnerable to data poisoning attacks regardless of input or fusion architectures and that while physically realizable natural perturbations still degrade model performance, the impact differs based on fusion architecture and input data. |
2311.17200 | Steve Huntsman | Steve Huntsman | Greybox fuzzing time-intensive programs | null | null | null | null | cs.SE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We examine (directed) greybox fuzzing from a geometrical perspective, viewing
dissimilarities on inputs and on control flow graphs (with dynamical
statistics) as primitive objects of interest. We prototype and evaluate
GoExploreFuzz, a greybox fuzzer for time-intensive programs that incorporates
this perspective. The results indicate useful capabilities for greybox fuzzing
that have hitherto been underutilized, notably quantifying the diversity of
paths and autonomously tuning the "bandwidth" of mutations.
| [
{
"created": "Tue, 28 Nov 2023 20:10:38 GMT",
"version": "v1"
}
] | 2023-11-30 | [
[
"Huntsman",
"Steve",
""
]
] | We examine (directed) greybox fuzzing from a geometrical perspective, viewing dissimilarities on inputs and on control flow graphs (with dynamical statistics) as primitive objects of interest. We prototype and evaluate GoExploreFuzz, a greybox fuzzer for time-intensive programs that incorporates this perspective. The results indicate useful capabilities for greybox fuzzing that have hitherto been underutilized, notably quantifying the diversity of paths and autonomously tuning the "bandwidth" of mutations. |
1906.01926 | Yoshinari Fujinuma | Yoshinari Fujinuma, Jordan Boyd-Graber, Michael J. Paul | A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings
Based on Graph Modularity | Accepted to ACL 2019, camera-ready | null | 10.18653/v1/P19-1489 | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cross-lingual word embeddings encode the meaning of words from different
languages into a shared low-dimensional space. An important requirement for
many downstream tasks is that word similarity should be independent of language
- i.e., word vectors within one language should not be more similar to each
other than to words in another language. We measure this characteristic using
modularity, a network measurement that measures the strength of clusters in a
graph. Modularity has a moderate to strong correlation with three downstream
tasks, even though modularity is based only on the structure of embeddings and
does not require any external resources. We show through experiments that
modularity can serve as an intrinsic validation metric to improve unsupervised
cross-lingual word embeddings, particularly on distant language pairs in
low-resource settings.
| [
{
"created": "Wed, 5 Jun 2019 10:34:56 GMT",
"version": "v1"
}
] | 2022-03-24 | [
[
"Fujinuma",
"Yoshinari",
""
],
[
"Boyd-Graber",
"Jordan",
""
],
[
"Paul",
"Michael J.",
""
]
] | Cross-lingual word embeddings encode the meaning of words from different languages into a shared low-dimensional space. An important requirement for many downstream tasks is that word similarity should be independent of language - i.e., word vectors within one language should not be more similar to each other than to words in another language. We measure this characteristic using modularity, a network measurement that measures the strength of clusters in a graph. Modularity has a moderate to strong correlation with three downstream tasks, even though modularity is based only on the structure of embeddings and does not require any external resources. We show through experiments that modularity can serve as an intrinsic validation metric to improve unsupervised cross-lingual word embeddings, particularly on distant language pairs in low-resource settings. |
2011.06235 | Weiming Zhi | Weiming Zhi, Tin Lai, Lionel Ott, Fabio Ramos | Anticipatory Navigation in Crowds by Probabilistic Prediction of
Pedestrian Future Movements | null | null | null | null | cs.RO cs.LG | http://creativecommons.org/licenses/by/4.0/ | Critical for the coexistence of humans and robots in dynamic environments is
the capability for agents to understand each other's actions, and anticipate
their movements. This paper presents Stochastic Process Anticipatory Navigation
(SPAN), a framework that enables nonholonomic robots to navigate in
environments with crowds, while anticipating and accounting for the motion
patterns of pedestrians. To this end, we learn a predictive model to predict
continuous-time stochastic processes to model future movement of pedestrians.
Anticipated pedestrian positions are used to conduct chance constrained
collision-checking, and are incorporated into a time-to-collision control
problem. An occupancy map is also integrated to allow for probabilistic
collision-checking with static obstacles. We demonstrate the capability of SPAN
in crowded simulation environments, as well as with a real-world pedestrian
dataset.
| [
{
"created": "Thu, 12 Nov 2020 07:18:20 GMT",
"version": "v1"
}
] | 2020-11-13 | [
[
"Zhi",
"Weiming",
""
],
[
"Lai",
"Tin",
""
],
[
"Ott",
"Lionel",
""
],
[
"Ramos",
"Fabio",
""
]
] | Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This paper presents Stochastic Process Anticipatory Navigation (SPAN), a framework that enables nonholonomic robots to navigate in environments with crowds, while anticipating and accounting for the motion patterns of pedestrians. To this end, we learn a predictive model to predict continuous-time stochastic processes to model future movement of pedestrians. Anticipated pedestrian positions are used to conduct chance constrained collision-checking, and are incorporated into a time-to-collision control problem. An occupancy map is also integrated to allow for probabilistic collision-checking with static obstacles. We demonstrate the capability of SPAN in crowded simulation environments, as well as with a real-world pedestrian dataset. |
2401.14520 | Cori Faklaris | Cori Faklaris | Mitigating Smishing: Challenges and Future Work | 5 pages. In submission to ConPro: 8th Workshop on Technology and
Consumer Protection, co-located with the 45th IEEE Symposium on Security and
Privacy, San Francisco, CA USA | null | null | null | cs.CR cs.CY cs.HC | http://creativecommons.org/licenses/by-sa/4.0/ | This paper describes three principal challenges in smishing mitigation -
limitations of device affordances, complexity of infrastructure, and cognitive
and contextual factors of mobile device use. We give a high-level overview of
ideas that can mitigate smishing and work around these challenges.
| [
{
"created": "Thu, 25 Jan 2024 21:26:36 GMT",
"version": "v1"
}
] | 2024-01-29 | [
[
"Faklaris",
"Cori",
""
]
] | This paper describes three principal challenges in smishing mitigation - limitations of device affordances, complexity of infrastructure, and cognitive and contextual factors of mobile device use. We give a high-level overview of ideas that can mitigate smishing and work around these challenges. |
1306.5702 | Vijay Manikandan Janakiraman | Vijay Manikandan Janakiraman, XuanLong Nguyen, Jeff Sterniak, and
Dennis Assanis | Modeling The Stable Operating Envelope For Partially Stable Combustion
Engines Using Class Imbalance Learning | In a Journal review | null | null | null | cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Advanced combustion technologies such as homogeneous charge compression
ignition (HCCI) engines have a narrow stable operating region defined by
complex control strategies such as exhaust gas recirculation (EGR) and variable
valve timing among others. For such systems, it is important to identify the
operating envelope or the boundary of stable operation for diagnostics and
control purposes. Obtaining a good model of the operating envelope using
physics becomes intractable owing to engine transient effects. In this paper, a
machine learning based approach is employed to identify the stable operating
boundary of HCCI combustion directly from experimental data. Owing to imbalance
in class proportions in the data, two approaches are considered. A re-sampling
(under-sampling, over-sampling) based approach is used to develop models using
existing algorithms while a cost-sensitive approach is used to modify the
learning algorithm without modifying the data set. Support vector machines and
recently developed extreme learning machines are used for model development and
results compared against linear classification methods show that cost-sensitive
versions of ELM and SVM algorithms are well suited to model the HCCI operating
envelope. The prediction results indicate that the models have the potential to
be used for predicting HCCI instability based on sensor measurement history.
| [
{
"created": "Mon, 24 Jun 2013 18:34:28 GMT",
"version": "v1"
}
] | 2013-06-25 | [
[
"Janakiraman",
"Vijay Manikandan",
""
],
[
"Nguyen",
"XuanLong",
""
],
[
"Sterniak",
"Jeff",
""
],
[
"Assanis",
"Dennis",
""
]
] | Advanced combustion technologies such as homogeneous charge compression ignition (HCCI) engines have a narrow stable operating region defined by complex control strategies such as exhaust gas recirculation (EGR) and variable valve timing among others. For such systems, it is important to identify the operating envelope or the boundary of stable operation for diagnostics and control purposes. Obtaining a good model of the operating envelope using physics becomes intractable owing to engine transient effects. In this paper, a machine learning based approach is employed to identify the stable operating boundary of HCCI combustion directly from experimental data. Owing to imbalance in class proportions in the data, two approaches are considered. A re-sampling (under-sampling, over-sampling) based approach is used to develop models using existing algorithms while a cost-sensitive approach is used to modify the learning algorithm without modifying the data set. Support vector machines and recently developed extreme learning machines are used for model development and results compared against linear classification methods show that cost-sensitive versions of ELM and SVM algorithms are well suited to model the HCCI operating envelope. The prediction results indicate that the models have the potential to be used for predicting HCCI instability based on sensor measurement history. |
2008.07958 | Fran Casino | Lamprini Zarpala and Fran Casino | A blockchain-based Forensic Model for Financial Crime Investigation: The
Embezzlement Scenario | Digit Finance (2021) | null | 10.1007/s42521-021-00035-5 | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The financial crime landscape is evolving along with the digitization in
financial services. In this context, laws and regulations cannot efficiently
cope with a fast-moving industry such as finance, which translates in late
adoption of measures and legal voids, providing a fruitful landscape for
malicious actors. In parallel, blockchain technology and its promising features
such as immutability, verifiability, and authentication, enhance the
opportunities of financial forensics. In this paper, we focus on an
embezzlement scheme and we provide a forensic-by-design methodology for its
investigation. In addition, the feasibility and adaptability of our approach
can be extended and embrace digital investigations on other types of schemes.
We provide a functional implementation based on smart contracts and we
integrate standardised forensic flows and chain of custody preservation
mechanisms. Finally, we discuss the benefits and challenges of the symbiotic
relationship between blockchain and financial investigations, along with future
research directions.
| [
{
"created": "Tue, 18 Aug 2020 14:38:01 GMT",
"version": "v1"
},
{
"created": "Sun, 23 Aug 2020 15:27:59 GMT",
"version": "v2"
},
{
"created": "Mon, 19 Jul 2021 11:42:41 GMT",
"version": "v3"
}
] | 2021-07-20 | [
[
"Zarpala",
"Lamprini",
""
],
[
"Casino",
"Fran",
""
]
] | The financial crime landscape is evolving along with the digitization in financial services. In this context, laws and regulations cannot efficiently cope with a fast-moving industry such as finance, which translates in late adoption of measures and legal voids, providing a fruitful landscape for malicious actors. In parallel, blockchain technology and its promising features such as immutability, verifiability, and authentication, enhance the opportunities of financial forensics. In this paper, we focus on an embezzlement scheme and we provide a forensic-by-design methodology for its investigation. In addition, the feasibility and adaptability of our approach can be extended and embrace digital investigations on other types of schemes. We provide a functional implementation based on smart contracts and we integrate standardised forensic flows and chain of custody preservation mechanisms. Finally, we discuss the benefits and challenges of the symbiotic relationship between blockchain and financial investigations, along with future research directions. |
1401.3448 | Robert Mateescu | Robert Mateescu, Rina Dechter, Radu Marinescu | AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models | null | Journal Of Artificial Intelligence Research, Volume 33, pages
465-519, 2008 | 10.1613/jair.2605 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inspired by the recently introduced framework of AND/OR search spaces for
graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD)
with AND nodes, in order to capture function decomposition structure and to
extend these compiled data structures to general weighted graphical models
(e.g., probabilistic models). We present the AND/OR Multi-Valued Decision
Diagram (AOMDD) which compiles a graphical model into a canonical form that
supports polynomial (e.g., solution counting, belief updating) or constant time
(e.g. equivalence of graphical models) queries. We provide two algorithms for
compiling the AOMDD of a graphical model. The first is search-based, and works
by applying reduction rules to the trace of the memory intensive AND/OR search
algorithm. The second is inference-based and uses a Bucket Elimination schedule
to combine the AOMDDs of the input functions via the the APPLY operator. For
both algorithms, the compilation time and the size of the AOMDD are, in the
worst case, exponential in the treewidth of the graphical model, rather than
pathwidth as is known for ordered binary decision diagrams (OBDDs). We
introduce the concept of semantic treewidth, which helps explain why the size
of a decision diagram is often much smaller than the worst case bound. We
provide an experimental evaluation that demonstrates the potential of AOMDDs.
| [
{
"created": "Wed, 15 Jan 2014 05:09:35 GMT",
"version": "v1"
}
] | 2014-01-16 | [
[
"Mateescu",
"Robert",
""
],
[
"Dechter",
"Rina",
""
],
[
"Marinescu",
"Radu",
""
]
] | Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR Multi-Valued Decision Diagram (AOMDD) which compiles a graphical model into a canonical form that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. equivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphical model. The first is search-based, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. The second is inference-based and uses a Bucket Elimination schedule to combine the AOMDDs of the input functions via the the APPLY operator. For both algorithms, the compilation time and the size of the AOMDD are, in the worst case, exponential in the treewidth of the graphical model, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs). We introduce the concept of semantic treewidth, which helps explain why the size of a decision diagram is often much smaller than the worst case bound. We provide an experimental evaluation that demonstrates the potential of AOMDDs. |
2111.00160 | Xuxi Chen | Xuxi Chen, Tianlong Chen, Weizhu Chen, Ahmed Hassan Awadallah,
Zhangyang Wang, Yu Cheng | DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language
Models | Accepted by ACL 2023 | null | null | null | cs.LG cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Gigantic pre-trained models have become central to natural language
processing (NLP), serving as the starting point for fine-tuning towards a range
of downstream tasks. However, two pain points persist for this paradigm: (a) as
the pre-trained models grow bigger (e.g., 175B parameters for GPT-3), even the
fine-tuning process can be time-consuming and computationally expensive; (b)
the fine-tuned model has the same size as its starting point by default, which
is neither sensible due to its more specialized functionality, nor practical
since many fine-tuned models will be deployed in resource-constrained
environments. To address these pain points, we propose a framework for
resource- and parameter-efficient fine-tuning by leveraging the sparsity prior
in both weight updates and the final model weights. Our proposed framework,
dubbed Dually Sparsity-Embedded Efficient Tuning (DSEE), aims to achieve two
key objectives: (i) parameter efficient fine-tuning - by enforcing
sparsity-aware low-rank updates on top of the pre-trained weights; and (ii)
resource-efficient inference - by encouraging a sparse weight structure towards
the final fine-tuned model. We leverage sparsity in these two directions by
exploiting both unstructured and structured sparse patterns in pre-trained
language models via a unified approach. Extensive experiments and in-depth
investigations, with diverse network backbones (i.e., BERT, RoBERTa, and GPT-2)
on dozens of datasets, consistently demonstrate impressive
parameter-/inference-efficiency, while maintaining competitive downstream
performance. For instance, DSEE saves about 25% inference FLOPs while achieving
comparable performance, with 0.5% trainable parameters on BERT. Codes are
available in https://github.com/VITA-Group/DSEE.
| [
{
"created": "Sat, 30 Oct 2021 03:29:47 GMT",
"version": "v1"
},
{
"created": "Sun, 31 Jul 2022 16:30:56 GMT",
"version": "v2"
},
{
"created": "Wed, 24 May 2023 02:29:37 GMT",
"version": "v3"
}
] | 2023-05-25 | [
[
"Chen",
"Xuxi",
""
],
[
"Chen",
"Tianlong",
""
],
[
"Chen",
"Weizhu",
""
],
[
"Awadallah",
"Ahmed Hassan",
""
],
[
"Wang",
"Zhangyang",
""
],
[
"Cheng",
"Yu",
""
]
] | Gigantic pre-trained models have become central to natural language processing (NLP), serving as the starting point for fine-tuning towards a range of downstream tasks. However, two pain points persist for this paradigm: (a) as the pre-trained models grow bigger (e.g., 175B parameters for GPT-3), even the fine-tuning process can be time-consuming and computationally expensive; (b) the fine-tuned model has the same size as its starting point by default, which is neither sensible due to its more specialized functionality, nor practical since many fine-tuned models will be deployed in resource-constrained environments. To address these pain points, we propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights. Our proposed framework, dubbed Dually Sparsity-Embedded Efficient Tuning (DSEE), aims to achieve two key objectives: (i) parameter efficient fine-tuning - by enforcing sparsity-aware low-rank updates on top of the pre-trained weights; and (ii) resource-efficient inference - by encouraging a sparse weight structure towards the final fine-tuned model. We leverage sparsity in these two directions by exploiting both unstructured and structured sparse patterns in pre-trained language models via a unified approach. Extensive experiments and in-depth investigations, with diverse network backbones (i.e., BERT, RoBERTa, and GPT-2) on dozens of datasets, consistently demonstrate impressive parameter-/inference-efficiency, while maintaining competitive downstream performance. For instance, DSEE saves about 25% inference FLOPs while achieving comparable performance, with 0.5% trainable parameters on BERT. Codes are available in https://github.com/VITA-Group/DSEE. |
1812.05901 | Antoine Deleforge | Romain Lebarbenchon, Ewen Camberlein, Diego di Carlo, Cl\'ement
Gaultier, Antoine Deleforge, Nancy Bertin | Evaluation of an open-source implementation of the SRP-PHAT algorithm
within the 2018 LOCATA challenge | In Proceedings of the LOCATA Challenge Workshop - a satellite event
of IWAENC 2018 (arXiv:1811.08482 ) | null | null | LOCATAchallenge/2018/01 | cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This short paper presents an efficient, flexible implementation of the
SRP-PHAT multichannel sound source localization method. The method is evaluated
on the single-source tasks of the LOCATA 2018 development dataset, and an
associated Matlab toolbox is made available online.
| [
{
"created": "Fri, 14 Dec 2018 13:15:45 GMT",
"version": "v1"
}
] | 2018-12-17 | [
[
"Lebarbenchon",
"Romain",
""
],
[
"Camberlein",
"Ewen",
""
],
[
"di Carlo",
"Diego",
""
],
[
"Gaultier",
"Clément",
""
],
[
"Deleforge",
"Antoine",
""
],
[
"Bertin",
"Nancy",
""
]
] | This short paper presents an efficient, flexible implementation of the SRP-PHAT multichannel sound source localization method. The method is evaluated on the single-source tasks of the LOCATA 2018 development dataset, and an associated Matlab toolbox is made available online. |
2306.06811 | Samuel Reinders | Samuel Reinders, Swamy Ananthanarayan, Matthew Butler, Kim Marriott | Designing Conversational Multimodal 3D Printed Models with People who
are Blind | To appear in ACM Designing Interactive Systems Conference (DIS '23),
July 10-14, 2023, Pittsburgh, PA, USA | null | 10.1145/3563657.3595989 | null | cs.HC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 3D printed models have been used to improve access to graphical information
by people who are blind, offering benefits over conventional accessible
graphics. Here we investigate an interactive 3D printed model (I3M) that
combines a conversational interface with haptic vibration and touch to provide
more natural and accessible experiences. Specifically, we co-designed a
multimodal model of the Solar System with nine blind people and evaluated the
prototype with another seven blind participants. We discuss our journey from a
design perspective, focusing on touch, conversational and multimodal
interactions. Based on our experience, we suggest design recommendations that
consider blind users' desire for independence and control, customisation,
comfort and use of prior experience
| [
{
"created": "Mon, 12 Jun 2023 00:44:57 GMT",
"version": "v1"
}
] | 2023-06-13 | [
[
"Reinders",
"Samuel",
""
],
[
"Ananthanarayan",
"Swamy",
""
],
[
"Butler",
"Matthew",
""
],
[
"Marriott",
"Kim",
""
]
] | 3D printed models have been used to improve access to graphical information by people who are blind, offering benefits over conventional accessible graphics. Here we investigate an interactive 3D printed model (I3M) that combines a conversational interface with haptic vibration and touch to provide more natural and accessible experiences. Specifically, we co-designed a multimodal model of the Solar System with nine blind people and evaluated the prototype with another seven blind participants. We discuss our journey from a design perspective, focusing on touch, conversational and multimodal interactions. Based on our experience, we suggest design recommendations that consider blind users' desire for independence and control, customisation, comfort and use of prior experience |
1807.03546 | Oskar Schirmer | Oskar Schirmer | Parallel Architecture Hardware and General Purpose Operating System
Co-design | 66 pages, 30 figures and tables | null | null | null | cs.DC cs.OS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Because most optimisations to achieve higher computational performance
eventually are limited, parallelism that scales is required. Parallelised
hardware alone is not sufficient, but software that matches the architecture is
required to gain best performance. For decades now, hardware design has been
guided by the basic design of existing software, to avoid the higher cost to
redesign the latter. In doing so, however, quite a variety of superior concepts
is excluded a priori. Consequently, co-design of both hardware and software is
crucial where highest performance is the goal. For special purpose application,
this co-design is common practice. For general purpose application, however, a
precondition for usability of a computer system is an operating system which is
both comprehensive and dynamic. As no such operating system has ever been
designed, a sketch for a comprehensive dynamic operating system is presented,
based on a straightforward hardware architecture to demonstrate how design
decisions regarding software and hardware do coexist and harmonise.
| [
{
"created": "Tue, 10 Jul 2018 09:24:33 GMT",
"version": "v1"
}
] | 2018-07-11 | [
[
"Schirmer",
"Oskar",
""
]
] | Because most optimisations to achieve higher computational performance eventually are limited, parallelism that scales is required. Parallelised hardware alone is not sufficient, but software that matches the architecture is required to gain best performance. For decades now, hardware design has been guided by the basic design of existing software, to avoid the higher cost to redesign the latter. In doing so, however, quite a variety of superior concepts is excluded a priori. Consequently, co-design of both hardware and software is crucial where highest performance is the goal. For special purpose application, this co-design is common practice. For general purpose application, however, a precondition for usability of a computer system is an operating system which is both comprehensive and dynamic. As no such operating system has ever been designed, a sketch for a comprehensive dynamic operating system is presented, based on a straightforward hardware architecture to demonstrate how design decisions regarding software and hardware do coexist and harmonise. |
2405.00172 | David Liu | David Liu, Arjun Seshadri, Tina Eliassi-Rad, Johan Ugander | Re-visiting Skip-Gram Negative Sampling: Dimension Regularization for
More Efficient Dissimilarity Preservation in Graph Embeddings | null | null | null | null | cs.LG cs.SI stat.ML | http://creativecommons.org/licenses/by/4.0/ | A wide range of graph embedding objectives decompose into two components: one
that attracts the embeddings of nodes that are perceived as similar, and
another that repels embeddings of nodes that are perceived as dissimilar.
Because real-world graphs are sparse and the number of dissimilar pairs grows
quadratically with the number of nodes, Skip-Gram Negative Sampling (SGNS) has
emerged as a popular and efficient repulsion approach. SGNS repels each node
from a sample of dissimilar nodes, as opposed to all dissimilar nodes. In this
work, we show that node-wise repulsion is, in aggregate, an approximate
re-centering of the node embedding dimensions. Such dimension operations are
much more scalable than node operations. The dimension approach, in addition to
being more efficient, yields a simpler geometric interpretation of the
repulsion. Our result extends findings from the self-supervised learning
literature to the skip-gram model, establishing a connection between skip-gram
node contrast and dimension regularization. We show that in the limit of large
graphs, under mild regularity conditions, the original node repulsion objective
converges to optimization with dimension regularization. We use this
observation to propose an algorithm augmentation framework that speeds up any
existing algorithm, supervised or unsupervised, using SGNS. The framework
prioritizes node attraction and replaces SGNS with dimension regularization. We
instantiate this generic framework for LINE and node2vec and show that the
augmented algorithms preserve downstream performance while dramatically
increasing efficiency.
| [
{
"created": "Tue, 30 Apr 2024 19:43:01 GMT",
"version": "v1"
}
] | 2024-05-02 | [
[
"Liu",
"David",
""
],
[
"Seshadri",
"Arjun",
""
],
[
"Eliassi-Rad",
"Tina",
""
],
[
"Ugander",
"Johan",
""
]
] | A wide range of graph embedding objectives decompose into two components: one that attracts the embeddings of nodes that are perceived as similar, and another that repels embeddings of nodes that are perceived as dissimilar. Because real-world graphs are sparse and the number of dissimilar pairs grows quadratically with the number of nodes, Skip-Gram Negative Sampling (SGNS) has emerged as a popular and efficient repulsion approach. SGNS repels each node from a sample of dissimilar nodes, as opposed to all dissimilar nodes. In this work, we show that node-wise repulsion is, in aggregate, an approximate re-centering of the node embedding dimensions. Such dimension operations are much more scalable than node operations. The dimension approach, in addition to being more efficient, yields a simpler geometric interpretation of the repulsion. Our result extends findings from the self-supervised learning literature to the skip-gram model, establishing a connection between skip-gram node contrast and dimension regularization. We show that in the limit of large graphs, under mild regularity conditions, the original node repulsion objective converges to optimization with dimension regularization. We use this observation to propose an algorithm augmentation framework that speeds up any existing algorithm, supervised or unsupervised, using SGNS. The framework prioritizes node attraction and replaces SGNS with dimension regularization. We instantiate this generic framework for LINE and node2vec and show that the augmented algorithms preserve downstream performance while dramatically increasing efficiency. |
2403.06734 | Keshara Weerasinghe | Keshara Weerasinghe, Saahith Janapati, Xueren Ge, Sion Kim, Sneha
Iyer, John A. Stankovic, Homa Alemzadeh | Real-Time Multimodal Cognitive Assistant for Emergency Medical Services | This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible | null | null | null | cs.AI cs.CL cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Emergency Medical Services (EMS) responders often operate under
time-sensitive conditions, facing cognitive overload and inherent risks,
requiring essential skills in critical thinking and rapid decision-making. This
paper presents CognitiveEMS, an end-to-end wearable cognitive assistant system
that can act as a collaborative virtual partner engaging in the real-time
acquisition and analysis of multimodal data from an emergency scene and
interacting with EMS responders through Augmented Reality (AR) smart glasses.
CognitiveEMS processes the continuous streams of data in real-time and
leverages edge computing to provide assistance in EMS protocol selection and
intervention recognition. We address key technical challenges in real-time
cognitive assistance by introducing three novel components: (i) a Speech
Recognition model that is fine-tuned for real-world medical emergency
conversations using simulated EMS audio recordings, augmented with synthetic
data generated by large language models (LLMs); (ii) an EMS Protocol Prediction
model that combines state-of-the-art (SOTA) tiny language models with EMS
domain knowledge using graph-based attention mechanisms; (iii) an EMS Action
Recognition module which leverages multimodal audio and video data and protocol
predictions to infer the intervention/treatment actions taken by the responders
at the incident scene. Our results show that for speech recognition we achieve
superior performance compared to SOTA (WER of 0.290 vs. 0.618) on
conversational data. Our protocol prediction component also significantly
outperforms SOTA (top-3 accuracy of 0.800 vs. 0.200) and the action recognition
achieves an accuracy of 0.727, while maintaining an end-to-end latency of 3.78s
for protocol prediction on the edge and 0.31s on the server.
| [
{
"created": "Mon, 11 Mar 2024 13:56:57 GMT",
"version": "v1"
}
] | 2024-03-12 | [
[
"Weerasinghe",
"Keshara",
""
],
[
"Janapati",
"Saahith",
""
],
[
"Ge",
"Xueren",
""
],
[
"Kim",
"Sion",
""
],
[
"Iyer",
"Sneha",
""
],
[
"Stankovic",
"John A.",
""
],
[
"Alemzadeh",
"Homa",
""
]
] | Emergency Medical Services (EMS) responders often operate under time-sensitive conditions, facing cognitive overload and inherent risks, requiring essential skills in critical thinking and rapid decision-making. This paper presents CognitiveEMS, an end-to-end wearable cognitive assistant system that can act as a collaborative virtual partner engaging in the real-time acquisition and analysis of multimodal data from an emergency scene and interacting with EMS responders through Augmented Reality (AR) smart glasses. CognitiveEMS processes the continuous streams of data in real-time and leverages edge computing to provide assistance in EMS protocol selection and intervention recognition. We address key technical challenges in real-time cognitive assistance by introducing three novel components: (i) a Speech Recognition model that is fine-tuned for real-world medical emergency conversations using simulated EMS audio recordings, augmented with synthetic data generated by large language models (LLMs); (ii) an EMS Protocol Prediction model that combines state-of-the-art (SOTA) tiny language models with EMS domain knowledge using graph-based attention mechanisms; (iii) an EMS Action Recognition module which leverages multimodal audio and video data and protocol predictions to infer the intervention/treatment actions taken by the responders at the incident scene. Our results show that for speech recognition we achieve superior performance compared to SOTA (WER of 0.290 vs. 0.618) on conversational data. Our protocol prediction component also significantly outperforms SOTA (top-3 accuracy of 0.800 vs. 0.200) and the action recognition achieves an accuracy of 0.727, while maintaining an end-to-end latency of 3.78s for protocol prediction on the edge and 0.31s on the server. |
2001.05119 | Zan Gojcic | Zan Gojcic, Caifa Zhou, Jan D. Wegner, Leonidas J. Guibas, Tolga
Birdal | Learning multiview 3D point cloud registration | CVPR2020 - Camera Ready | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a novel, end-to-end learnable, multiview 3D point cloud
registration algorithm. Registration of multiple scans typically follows a
two-stage pipeline: the initial pairwise alignment and the globally consistent
refinement. The former is often ambiguous due to the low overlap of neighboring
point clouds, symmetries and repetitive scene parts. Therefore, the latter
global refinement aims at establishing the cyclic consistency across multiple
scans and helps in resolving the ambiguous cases. In this paper we propose, to
the best of our knowledge, the first end-to-end algorithm for joint learning of
both parts of this two-stage problem. Experimental evaluation on well accepted
benchmark datasets shows that our approach outperforms the state-of-the-art by
a significant margin, while being end-to-end trainable and computationally less
costly. Moreover, we present detailed analysis and an ablation study that
validate the novel components of our approach. The source code and pretrained
models are publicly available under
https://github.com/zgojcic/3D_multiview_reg.
| [
{
"created": "Wed, 15 Jan 2020 03:42:14 GMT",
"version": "v1"
},
{
"created": "Tue, 31 Mar 2020 07:53:36 GMT",
"version": "v2"
}
] | 2020-04-01 | [
[
"Gojcic",
"Zan",
""
],
[
"Zhou",
"Caifa",
""
],
[
"Wegner",
"Jan D.",
""
],
[
"Guibas",
"Leonidas J.",
""
],
[
"Birdal",
"Tolga",
""
]
] | We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm. Registration of multiple scans typically follows a two-stage pipeline: the initial pairwise alignment and the globally consistent refinement. The former is often ambiguous due to the low overlap of neighboring point clouds, symmetries and repetitive scene parts. Therefore, the latter global refinement aims at establishing the cyclic consistency across multiple scans and helps in resolving the ambiguous cases. In this paper we propose, to the best of our knowledge, the first end-to-end algorithm for joint learning of both parts of this two-stage problem. Experimental evaluation on well accepted benchmark datasets shows that our approach outperforms the state-of-the-art by a significant margin, while being end-to-end trainable and computationally less costly. Moreover, we present detailed analysis and an ablation study that validate the novel components of our approach. The source code and pretrained models are publicly available under https://github.com/zgojcic/3D_multiview_reg. |
1802.07384 | Xin Zhang | Xin Zhang, Armando Solar-Lezama, and Rishabh Singh | Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic
Corrections | 24 pages | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new algorithm to generate minimal, stable, and symbolic
corrections to an input that will cause a neural network with ReLU activations
to change its output. We argue that such a correction is a useful way to
provide feedback to a user when the network's output is different from a
desired output. Our algorithm generates such a correction by solving a series
of linear constraint satisfaction problems. The technique is evaluated on three
neural network models: one predicting whether an applicant will pay a mortgage,
one predicting whether a first-order theorem can be proved efficiently by a
solver using certain heuristics, and the final one judging whether a drawing is
an accurate rendition of a canonical drawing of a cat.
| [
{
"created": "Wed, 21 Feb 2018 00:47:32 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Aug 2018 21:33:26 GMT",
"version": "v2"
}
] | 2018-09-03 | [
[
"Zhang",
"Xin",
""
],
[
"Solar-Lezama",
"Armando",
""
],
[
"Singh",
"Rishabh",
""
]
] | We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output. We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output. Our algorithm generates such a correction by solving a series of linear constraint satisfaction problems. The technique is evaluated on three neural network models: one predicting whether an applicant will pay a mortgage, one predicting whether a first-order theorem can be proved efficiently by a solver using certain heuristics, and the final one judging whether a drawing is an accurate rendition of a canonical drawing of a cat. |
1909.03877 | Ting Yao | Fuchen Long and Ting Yao and Zhaofan Qiu and Xinmei Tian and Jiebo Luo
and Tao Mei | Gaussian Temporal Awareness Networks for Action Localization | CVPR 2019 Oral | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporally localizing actions in a video is a fundamental challenge in video
understanding. Most existing approaches have often drawn inspiration from image
object detection and extended the advances, e.g., SSD and Faster R-CNN, to
produce temporal locations of an action in a 1D sequence. Nevertheless, the
results can suffer from robustness problem due to the design of predetermined
temporal scales, which overlooks the temporal structure of an action and limits
the utility on detecting actions with complex variations. In this paper, we
propose to address the problem by introducing Gaussian kernels to dynamically
optimize temporal scale of each action proposal. Specifically, we present
Gaussian Temporal Awareness Networks (GTAN) --- a new architecture that novelly
integrates the exploitation of temporal structure into an one-stage action
localization framework. Technically, GTAN models the temporal structure through
learning a set of Gaussian kernels, each for a cell in the feature maps. Each
Gaussian kernel corresponds to a particular interval of an action proposal and
a mixture of Gaussian kernels could further characterize action proposals with
various length. Moreover, the values in each Gaussian curve reflect the
contextual contributions to the localization of an action proposal. Extensive
experiments are conducted on both THUMOS14 and ActivityNet v1.3 datasets, and
superior results are reported when comparing to state-of-the-art approaches.
More remarkably, GTAN achieves 1.9% and 1.1% improvements in mAP on testing set
of the two datasets.
| [
{
"created": "Mon, 9 Sep 2019 14:13:48 GMT",
"version": "v1"
}
] | 2019-09-10 | [
[
"Long",
"Fuchen",
""
],
[
"Yao",
"Ting",
""
],
[
"Qiu",
"Zhaofan",
""
],
[
"Tian",
"Xinmei",
""
],
[
"Luo",
"Jiebo",
""
],
[
"Mei",
"Tao",
""
]
] | Temporally localizing actions in a video is a fundamental challenge in video understanding. Most existing approaches have often drawn inspiration from image object detection and extended the advances, e.g., SSD and Faster R-CNN, to produce temporal locations of an action in a 1D sequence. Nevertheless, the results can suffer from robustness problem due to the design of predetermined temporal scales, which overlooks the temporal structure of an action and limits the utility on detecting actions with complex variations. In this paper, we propose to address the problem by introducing Gaussian kernels to dynamically optimize temporal scale of each action proposal. Specifically, we present Gaussian Temporal Awareness Networks (GTAN) --- a new architecture that novelly integrates the exploitation of temporal structure into an one-stage action localization framework. Technically, GTAN models the temporal structure through learning a set of Gaussian kernels, each for a cell in the feature maps. Each Gaussian kernel corresponds to a particular interval of an action proposal and a mixture of Gaussian kernels could further characterize action proposals with various length. Moreover, the values in each Gaussian curve reflect the contextual contributions to the localization of an action proposal. Extensive experiments are conducted on both THUMOS14 and ActivityNet v1.3 datasets, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, GTAN achieves 1.9% and 1.1% improvements in mAP on testing set of the two datasets. |
2011.04349 | Hieu Phung | Hieu Trong Phung (1 and 2), Anh Tuan Vu (1), Tung Dinh Nguyen (1), Lam
Thanh Do (1 and 2), Giang Nam Ngo (1), Trung Thanh Tran (1) and Ngoc C. L\^e
(1 and 2) ((1) PIXTA Vietnam, Hanoi, Vietnam. (2) Hanoi University of Science
and Technology, Ha Noi, Viet Nam.) | MAGNeto: An Efficient Deep Learning Method for the Extractive Tags
Summarization Problem | null | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we study a new image annotation task named Extractive Tags
Summarization (ETS). The goal is to extract important tags from the context
lying in an image and its corresponding tags. We adjust some state-of-the-art
deep learning models to utilize both visual and textual information. Our
proposed solution consists of different widely used blocks like convolutional
and self-attention layers, together with a novel idea of combining auxiliary
loss functions and the gating mechanism to glue and elevate these fundamental
components and form a unified architecture. Besides, we introduce a loss
function that aims to reduce the imbalance of the training data and a simple
but effective data augmentation technique dedicated to alleviates the effect of
outliers on the final results. Last but not least, we explore an unsupervised
pre-training strategy to further boost the performance of the model by making
use of the abundant amount of available unlabeled data. Our model shows the
good results as 90% $F_\text{1}$ score on the public NUS-WIDE benchmark, and
50% $F_\text{1}$ score on a noisy large-scale real-world private dataset.
Source code for reproducing the experiments is publicly available at:
https://github.com/pixta-dev/labteam
| [
{
"created": "Mon, 9 Nov 2020 11:34:21 GMT",
"version": "v1"
}
] | 2020-11-10 | [
[
"Phung",
"Hieu Trong",
"",
"1 and 2"
],
[
"Vu",
"Anh Tuan",
"",
"1 and 2"
],
[
"Nguyen",
"Tung Dinh",
"",
"1 and 2"
],
[
"Do",
"Lam Thanh",
"",
"1 and 2"
],
[
"Ngo",
"Giang Nam",
"",
"1 and 2"
],
[
"Tran",
"Trung Thanh",
"",
"1 and 2"
],
[
"Lê",
"Ngoc C.",
"",
"1 and 2"
]
] | In this work, we study a new image annotation task named Extractive Tags Summarization (ETS). The goal is to extract important tags from the context lying in an image and its corresponding tags. We adjust some state-of-the-art deep learning models to utilize both visual and textual information. Our proposed solution consists of different widely used blocks like convolutional and self-attention layers, together with a novel idea of combining auxiliary loss functions and the gating mechanism to glue and elevate these fundamental components and form a unified architecture. Besides, we introduce a loss function that aims to reduce the imbalance of the training data and a simple but effective data augmentation technique dedicated to alleviates the effect of outliers on the final results. Last but not least, we explore an unsupervised pre-training strategy to further boost the performance of the model by making use of the abundant amount of available unlabeled data. Our model shows the good results as 90% $F_\text{1}$ score on the public NUS-WIDE benchmark, and 50% $F_\text{1}$ score on a noisy large-scale real-world private dataset. Source code for reproducing the experiments is publicly available at: https://github.com/pixta-dev/labteam |
1905.04284 | Mohsen Joneidi | Mohsen Joneidi, Nazanin Rahnavard | Primary User Localization and Online Radio Cartography via Structured
Tensor Decomposition | Submitted to the 2019 IEEE Global Communications Conference
(GLOBECOM) | null | null | null | cs.IT eess.SP math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Source localization and radio cartography using multi-way representation of
spectrum is the subject of study in this paper. A joint matrix factorization
and tensor decomposition problem is proposed and solved using an iterative
algorithm. The multi-way measured spectrum is organized in a tensor and it is
modeled by multiplication of a propagation tensor and a channel gain matrix.
The tensor indicates the propagating power from each location and each
frequency over time and the channel matrix links the propagating tensor to the
sensed spectrum. We utilize sparsity and other intrinsic characteristics of
spectrum to identify the solution of the proposed problem. Moreover, The online
implementation of the proposed framework results in online radio cartography
which is a powerful tool for efficient spectrum awareness and utilization. The
simulation results show that our algorithm is a promising technique for dynamic
primary user localization and online radio cartography.
| [
{
"created": "Fri, 10 May 2019 17:51:39 GMT",
"version": "v1"
}
] | 2019-05-13 | [
[
"Joneidi",
"Mohsen",
""
],
[
"Rahnavard",
"Nazanin",
""
]
] | Source localization and radio cartography using multi-way representation of spectrum is the subject of study in this paper. A joint matrix factorization and tensor decomposition problem is proposed and solved using an iterative algorithm. The multi-way measured spectrum is organized in a tensor and it is modeled by multiplication of a propagation tensor and a channel gain matrix. The tensor indicates the propagating power from each location and each frequency over time and the channel matrix links the propagating tensor to the sensed spectrum. We utilize sparsity and other intrinsic characteristics of spectrum to identify the solution of the proposed problem. Moreover, The online implementation of the proposed framework results in online radio cartography which is a powerful tool for efficient spectrum awareness and utilization. The simulation results show that our algorithm is a promising technique for dynamic primary user localization and online radio cartography. |
1605.00802 | Aseem Sharma | Aseem Sharma, Krishna Jagannathan, Lav R. Varshney | Queuing Approaches to Principal-Agent Communication under Information
Overload | 33 pages excluding the main page, 5 figures | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the information overload regime, human communication tasks such as
responding to email are well-modeled as priority queues, where priority is
determined by a mix of intrinsic motivation and extrinsic motivation
corresponding to the task's importance to the sender. We view priority queuing
from a principal-agent perspective, and characterize the effect of
priority-misalignment and information asymmetry between task senders and task
receivers in both single-agent and multi-agent settings. In the single-agent
setting, we find that discipline can override misalignment. Although variation
in human interests leads to performance loss in the single-agent setting, the
same variability is useful to the principal with optimal routing of tasks, if
the principal has suitable information about agents' priorities. Our approach
starts to quantitatively address the effect of human dynamics in routine
communication tasks.
| [
{
"created": "Tue, 3 May 2016 09:23:01 GMT",
"version": "v1"
}
] | 2016-05-04 | [
[
"Sharma",
"Aseem",
""
],
[
"Jagannathan",
"Krishna",
""
],
[
"Varshney",
"Lav R.",
""
]
] | In the information overload regime, human communication tasks such as responding to email are well-modeled as priority queues, where priority is determined by a mix of intrinsic motivation and extrinsic motivation corresponding to the task's importance to the sender. We view priority queuing from a principal-agent perspective, and characterize the effect of priority-misalignment and information asymmetry between task senders and task receivers in both single-agent and multi-agent settings. In the single-agent setting, we find that discipline can override misalignment. Although variation in human interests leads to performance loss in the single-agent setting, the same variability is useful to the principal with optimal routing of tasks, if the principal has suitable information about agents' priorities. Our approach starts to quantitatively address the effect of human dynamics in routine communication tasks. |
2001.01211 | Lizhao Gao | Lizhao Gao, Haihua Xu, Chong Sun, Junling Liu, Yu-Wing Tai | Spatial-Scale Aligned Network for Fine-Grained Recognition | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existing approaches for fine-grained visual recognition focus on learning
marginal region-based representations while neglecting the spatial and scale
misalignments, leading to inferior performance. In this paper, we propose the
spatial-scale aligned network (SSANET) and implicitly address misalignments
during the recognition process. Especially, SSANET consists of 1) a
self-supervised proposal mining formula with Morphological Alignment
Constraints; 2) a discriminative scale mining (DSM) module, which exploits the
feature pyramid via a circulant matrix, and provides the Fourier solver for
fast scale alignments; 3) an oriented pooling (OP) module, that performs the
pooling operation in several pre-defined orientations. Each orientation defines
one kind of spatial alignment, and the network automatically determines which
is the optimal alignments through learning. With the proposed two modules, our
algorithm can automatically determine the accurate local proposal regions and
generate more robust target representations being invariant to various
appearance variances. Extensive experiments verify that SSANET is competent at
learning better spatial-scale invariant target representations, yielding
superior performance on the fine-grained recognition task on several
benchmarks.
| [
{
"created": "Sun, 5 Jan 2020 11:12:08 GMT",
"version": "v1"
}
] | 2020-01-07 | [
[
"Gao",
"Lizhao",
""
],
[
"Xu",
"Haihua",
""
],
[
"Sun",
"Chong",
""
],
[
"Liu",
"Junling",
""
],
[
"Tai",
"Yu-Wing",
""
]
] | Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance. In this paper, we propose the spatial-scale aligned network (SSANET) and implicitly address misalignments during the recognition process. Especially, SSANET consists of 1) a self-supervised proposal mining formula with Morphological Alignment Constraints; 2) a discriminative scale mining (DSM) module, which exploits the feature pyramid via a circulant matrix, and provides the Fourier solver for fast scale alignments; 3) an oriented pooling (OP) module, that performs the pooling operation in several pre-defined orientations. Each orientation defines one kind of spatial alignment, and the network automatically determines which is the optimal alignments through learning. With the proposed two modules, our algorithm can automatically determine the accurate local proposal regions and generate more robust target representations being invariant to various appearance variances. Extensive experiments verify that SSANET is competent at learning better spatial-scale invariant target representations, yielding superior performance on the fine-grained recognition task on several benchmarks. |
1809.03470 | Marek Wydmuch | Marek Wydmuch, Micha{\l} Kempka, Wojciech Ja\'skowski | ViZDoom Competitions: Playing Doom from Pixels | null | null | 10.1109/TG.2018.2877047 | null | cs.AI cs.CV cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the first two editions of Visual Doom AI Competition,
held in 2016 and 2017. The challenge was to create bots that compete in a
multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots
had to make their decisions based solely on visual information, i.e., a raw
screen buffer. To play well, the bots needed to understand their surroundings,
navigate, explore, and handle the opponents at the same time. These aspects,
together with the competitive multi-agent aspect of the game, make the
competition a unique platform for evaluating the state of the art reinforcement
learning algorithms. The paper discusses the rules, solutions, results, and
statistics that give insight into the agents' behaviors. Best-performing agents
are described in more detail. The results of the competition lead to the
conclusion that, although reinforcement learning can produce capable Doom bots,
they still are not yet able to successfully compete against humans in this
game. The paper also revisits the ViZDoom environment, which is a flexible,
easy to use, and efficient 3D platform for research for vision-based
reinforcement learning, based on a well-recognized first-person perspective
game Doom.
| [
{
"created": "Mon, 10 Sep 2018 17:41:39 GMT",
"version": "v1"
}
] | 2022-07-28 | [
[
"Wydmuch",
"Marek",
""
],
[
"Kempka",
"Michał",
""
],
[
"Jaśkowski",
"Wojciech",
""
]
] | This paper presents the first two editions of Visual Doom AI Competition, held in 2016 and 2017. The challenge was to create bots that compete in a multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots had to make their decisions based solely on visual information, i.e., a raw screen buffer. To play well, the bots needed to understand their surroundings, navigate, explore, and handle the opponents at the same time. These aspects, together with the competitive multi-agent aspect of the game, make the competition a unique platform for evaluating the state of the art reinforcement learning algorithms. The paper discusses the rules, solutions, results, and statistics that give insight into the agents' behaviors. Best-performing agents are described in more detail. The results of the competition lead to the conclusion that, although reinforcement learning can produce capable Doom bots, they still are not yet able to successfully compete against humans in this game. The paper also revisits the ViZDoom environment, which is a flexible, easy to use, and efficient 3D platform for research for vision-based reinforcement learning, based on a well-recognized first-person perspective game Doom. |
2310.16252 | Arnab Maiti | Arnab Maiti, Ross Boczar, Kevin Jamieson, Lillian J. Ratliff | Near-Optimal Pure Exploration in Matrix Games: A Generalization of
Stochastic Bandits & Dueling Bandits | 22 pages, 5 figures | null | null | null | cs.LG cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We study the sample complexity of identifying the pure strategy Nash
equilibrium (PSNE) in a two-player zero-sum matrix game with noise. Formally,
we are given a stochastic model where any learner can sample an entry $(i,j)$
of the input matrix $A\in[-1,1]^{n\times m}$ and observe $A_{i,j}+\eta$ where
$\eta$ is a zero-mean 1-sub-Gaussian noise. The aim of the learner is to
identify the PSNE of $A$, whenever it exists, with high probability while
taking as few samples as possible. Zhou et al. (2017) presents an
instance-dependent sample complexity lower bound that depends only on the
entries in the row and column in which the PSNE lies. We design a near-optimal
algorithm whose sample complexity matches the lower bound, up to log factors.
The problem of identifying the PSNE also generalizes the problem of pure
exploration in stochastic multi-armed bandits and dueling bandits, and our
result matches the optimal bounds, up to log factors, in both the settings.
| [
{
"created": "Wed, 25 Oct 2023 00:05:37 GMT",
"version": "v1"
},
{
"created": "Mon, 27 Nov 2023 21:33:05 GMT",
"version": "v2"
}
] | 2023-11-29 | [
[
"Maiti",
"Arnab",
""
],
[
"Boczar",
"Ross",
""
],
[
"Jamieson",
"Kevin",
""
],
[
"Ratliff",
"Lillian J.",
""
]
] | We study the sample complexity of identifying the pure strategy Nash equilibrium (PSNE) in a two-player zero-sum matrix game with noise. Formally, we are given a stochastic model where any learner can sample an entry $(i,j)$ of the input matrix $A\in[-1,1]^{n\times m}$ and observe $A_{i,j}+\eta$ where $\eta$ is a zero-mean 1-sub-Gaussian noise. The aim of the learner is to identify the PSNE of $A$, whenever it exists, with high probability while taking as few samples as possible. Zhou et al. (2017) presents an instance-dependent sample complexity lower bound that depends only on the entries in the row and column in which the PSNE lies. We design a near-optimal algorithm whose sample complexity matches the lower bound, up to log factors. The problem of identifying the PSNE also generalizes the problem of pure exploration in stochastic multi-armed bandits and dueling bandits, and our result matches the optimal bounds, up to log factors, in both the settings. |
2403.17249 | Theodoros Stouraitis | Lei Yan, Theodoros Stouraitis, Jo\~ao Moura, Wenfu Xu, Michael
Gienger, and Sethu Vijayakumar | Impact-Aware Bimanual Catching of Large-Momentum Objects | null | null | null | null | cs.RO cs.SY eess.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper investigates one of the most challenging tasks in dynamic
manipulation -- catching large-momentum moving objects. Beyond the realm of
quasi-static manipulation, dealing with highly dynamic objects can
significantly improve the robot's capability of interacting with its
surrounding environment. Yet, the inevitable motion mismatch between the fast
moving object and the approaching robot will result in large impulsive forces,
which lead to the unstable contacts and irreversible damage to both the object
and the robot. To address the above problems, we propose an online optimization
framework to: 1) estimate and predict the linear and angular motion of the
object; 2) search and select the optimal contact locations across every surface
of the object to mitigate impact through sequential quadratic programming
(SQP); 3) simultaneously optimize the end-effector motion, stiffness, and
contact force for both robots using multi-mode trajectory optimization (MMTO);
and 4) realise the impact-aware catching motion on the compliant robotic system
based on indirect force controller. We validate the impulse distribution,
contact selection, and impact-aware MMTO algorithms in simulation and
demonstrate the benefits of the proposed framework in real-world experiments
including catching large-momentum moving objects with well-defined motion,
constrained motion and free-flying motion.
| [
{
"created": "Mon, 25 Mar 2024 22:51:27 GMT",
"version": "v1"
}
] | 2024-03-27 | [
[
"Yan",
"Lei",
""
],
[
"Stouraitis",
"Theodoros",
""
],
[
"Moura",
"João",
""
],
[
"Xu",
"Wenfu",
""
],
[
"Gienger",
"Michael",
""
],
[
"Vijayakumar",
"Sethu",
""
]
] | This paper investigates one of the most challenging tasks in dynamic manipulation -- catching large-momentum moving objects. Beyond the realm of quasi-static manipulation, dealing with highly dynamic objects can significantly improve the robot's capability of interacting with its surrounding environment. Yet, the inevitable motion mismatch between the fast moving object and the approaching robot will result in large impulsive forces, which lead to the unstable contacts and irreversible damage to both the object and the robot. To address the above problems, we propose an online optimization framework to: 1) estimate and predict the linear and angular motion of the object; 2) search and select the optimal contact locations across every surface of the object to mitigate impact through sequential quadratic programming (SQP); 3) simultaneously optimize the end-effector motion, stiffness, and contact force for both robots using multi-mode trajectory optimization (MMTO); and 4) realise the impact-aware catching motion on the compliant robotic system based on indirect force controller. We validate the impulse distribution, contact selection, and impact-aware MMTO algorithms in simulation and demonstrate the benefits of the proposed framework in real-world experiments including catching large-momentum moving objects with well-defined motion, constrained motion and free-flying motion. |
2006.05679 | Bertrand Jouve | Djellabi Mehdi, Jouve Bertrand, Amblard Fr\'ed\'eric | Dense and sparse vertex connectivity in networks | null | null | null | null | cs.SI cs.DM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The different approaches developed to analyze the structure of complex
networks have generated a large number of studies. In the field of social
networks at least, studies mainly address the detection and analysis of
communities. In this paper, we challenge these approaches and focus on nodes
that have meaningful local interactions able to identify the internal
organization of communities or the way communities are assembled. We propose an
algorithm, ItRich, to identify this type of nodes, based on the decomposition
of a graph into successive, less and less dense, layers. Our method is tested
on synthetic and real data sets and meshes well with other methods such as
community detection or k-core decomposition.
| [
{
"created": "Wed, 10 Jun 2020 06:27:07 GMT",
"version": "v1"
}
] | 2020-06-11 | [
[
"Mehdi",
"Djellabi",
""
],
[
"Bertrand",
"Jouve",
""
],
[
"Frédéric",
"Amblard",
""
]
] | The different approaches developed to analyze the structure of complex networks have generated a large number of studies. In the field of social networks at least, studies mainly address the detection and analysis of communities. In this paper, we challenge these approaches and focus on nodes that have meaningful local interactions able to identify the internal organization of communities or the way communities are assembled. We propose an algorithm, ItRich, to identify this type of nodes, based on the decomposition of a graph into successive, less and less dense, layers. Our method is tested on synthetic and real data sets and meshes well with other methods such as community detection or k-core decomposition. |
1001.2625 | Arnab Bhattacharya | Arnab Bhattacharya, Abhishek Bhowmick, Ambuj K. Singh | Finding top-k similar pairs of objects annotated with terms from an
ontology | 17 pages, 13 figures | null | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the growing focus on semantic searches and interpretations, an
increasing number of standardized vocabularies and ontologies are being
designed and used to describe data. We investigate the querying of objects
described by a tree-structured ontology. Specifically, we consider the case of
finding the top-k best pairs of objects that have been annotated with terms
from such an ontology when the object descriptions are available only at
runtime. We consider three distance measures. The first one defines the object
distance as the minimum pairwise distance between the sets of terms describing
them, and the second one defines the distance as the average pairwise term
distance. The third and most useful distance measure, earth mover's distance,
finds the best way of matching the terms and computes the distance
corresponding to this best matching. We develop lower bounds that can be
aggregated progressively and utilize them to speed up the search for top-k
object pairs when the earth mover's distance is used. For the minimum pairwise
distance, we devise an algorithm that runs in O(D + Tk log k) time, where D is
the total information size and T is the total number of terms in the ontology.
We also develop a novel best-first search strategy for the average pairwise
distance that utilizes lower bounds generated in an ordered manner. Experiments
on real and synthetic datasets demonstrate the practicality and scalability of
our algorithms.
| [
{
"created": "Fri, 15 Jan 2010 07:01:37 GMT",
"version": "v1"
},
{
"created": "Sat, 6 Mar 2010 11:23:28 GMT",
"version": "v2"
}
] | 2010-03-09 | [
[
"Bhattacharya",
"Arnab",
""
],
[
"Bhowmick",
"Abhishek",
""
],
[
"Singh",
"Ambuj K.",
""
]
] | With the growing focus on semantic searches and interpretations, an increasing number of standardized vocabularies and ontologies are being designed and used to describe data. We investigate the querying of objects described by a tree-structured ontology. Specifically, we consider the case of finding the top-k best pairs of objects that have been annotated with terms from such an ontology when the object descriptions are available only at runtime. We consider three distance measures. The first one defines the object distance as the minimum pairwise distance between the sets of terms describing them, and the second one defines the distance as the average pairwise term distance. The third and most useful distance measure, earth mover's distance, finds the best way of matching the terms and computes the distance corresponding to this best matching. We develop lower bounds that can be aggregated progressively and utilize them to speed up the search for top-k object pairs when the earth mover's distance is used. For the minimum pairwise distance, we devise an algorithm that runs in O(D + Tk log k) time, where D is the total information size and T is the total number of terms in the ontology. We also develop a novel best-first search strategy for the average pairwise distance that utilizes lower bounds generated in an ordered manner. Experiments on real and synthetic datasets demonstrate the practicality and scalability of our algorithms. |
2009.08371 | Nils Eckstein | Nils Eckstein and Julia Buhmann and Matthew Cook and Jan Funke | Microtubule Tracking in Electron Microscopy Volumes | Accepted at MICCAI 2020 | null | null | null | cs.CV cs.LG eess.IV q-bio.QM | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a method for microtubule tracking in electron microscopy volumes.
Our method first identifies a sparse set of voxels that likely belong to
microtubules. Similar to prior work, we then enumerate potential edges between
these voxels, which we represent in a candidate graph. Tracks of microtubules
are found by selecting nodes and edges in the candidate graph by solving a
constrained optimization problem incorporating biological priors on microtubule
structure. For this, we present a novel integer linear programming formulation,
which results in speed-ups of three orders of magnitude and an increase of 53%
in accuracy compared to prior art (evaluated on three 1.2 x 4 x 4$\mu$m volumes
of Drosophila neural tissue). We also propose a scheme to solve the
optimization problem in a block-wise fashion, which allows distributed tracking
and is necessary to process very large electron microscopy volumes. Finally, we
release a benchmark dataset for microtubule tracking, here used for training,
testing and validation, consisting of eight 30 x 1000 x 1000 voxel blocks (1.2
x 4 x 4$\mu$m) of densely annotated microtubules in the CREMI data set
(https://github.com/nilsec/micron).
| [
{
"created": "Thu, 17 Sep 2020 15:37:30 GMT",
"version": "v1"
}
] | 2020-09-18 | [
[
"Eckstein",
"Nils",
""
],
[
"Buhmann",
"Julia",
""
],
[
"Cook",
"Matthew",
""
],
[
"Funke",
"Jan",
""
]
] | We present a method for microtubule tracking in electron microscopy volumes. Our method first identifies a sparse set of voxels that likely belong to microtubules. Similar to prior work, we then enumerate potential edges between these voxels, which we represent in a candidate graph. Tracks of microtubules are found by selecting nodes and edges in the candidate graph by solving a constrained optimization problem incorporating biological priors on microtubule structure. For this, we present a novel integer linear programming formulation, which results in speed-ups of three orders of magnitude and an increase of 53% in accuracy compared to prior art (evaluated on three 1.2 x 4 x 4$\mu$m volumes of Drosophila neural tissue). We also propose a scheme to solve the optimization problem in a block-wise fashion, which allows distributed tracking and is necessary to process very large electron microscopy volumes. Finally, we release a benchmark dataset for microtubule tracking, here used for training, testing and validation, consisting of eight 30 x 1000 x 1000 voxel blocks (1.2 x 4 x 4$\mu$m) of densely annotated microtubules in the CREMI data set (https://github.com/nilsec/micron). |
2406.17548 | Lachlan Gunn | Vasisht Duddu, Oskari J\"arvinen, Lachlan J Gunn, N Asokan | Laminator: Verifiable ML Property Cards using Hardware-assisted
Attestations | null | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Regulations increasingly call for various assurances from machine learning
(ML) model providers about their training data, training process, and the
behavior of resulting models during inference. For better transparency,
companies (e.g., Huggingface and Google) have adopted model cards and
datasheets which describe different properties of the training datasets and
models. In the same vein, we introduce the notion of an inference card to
describe the properties of a given inference (e.g., binding output to the model
and its corresponding input). We collectively refer to these as ML property
cards.
A malicious model provider can include false information in ML property
cards, raising a need for verifiable ML property cards. We show how to realized
them using property attestation, technical mechanisms by which a prover (e.g.,
a model provider) can attest different ML properties during training and
inference to a verifier (e.g., an auditor). However, prior attestation
mechanisms based purely on cryptography are often narrowly focused (lacking
versatility) and inefficient. There is a need to efficiently attest different
types properties across the ML model training and inference pipeline.
Recent developments make it possible to run and even train models inside
hardware-assisted trusted execution environments (TEEs), which can provide
highly efficient attestation. We propose Laminator, the first framework for
verifiable ML property cards using hardware-assisted ML property attestations
to efficiently furnish attestations for various ML properties for training and
inference. It scales to multiple verifiers, and is independent of the model
configuration.
| [
{
"created": "Tue, 25 Jun 2024 13:36:53 GMT",
"version": "v1"
}
] | 2024-06-26 | [
[
"Duddu",
"Vasisht",
""
],
[
"Järvinen",
"Oskari",
""
],
[
"Gunn",
"Lachlan J",
""
],
[
"Asokan",
"N",
""
]
] | Regulations increasingly call for various assurances from machine learning (ML) model providers about their training data, training process, and the behavior of resulting models during inference. For better transparency, companies (e.g., Huggingface and Google) have adopted model cards and datasheets which describe different properties of the training datasets and models. In the same vein, we introduce the notion of an inference card to describe the properties of a given inference (e.g., binding output to the model and its corresponding input). We collectively refer to these as ML property cards. A malicious model provider can include false information in ML property cards, raising a need for verifiable ML property cards. We show how to realized them using property attestation, technical mechanisms by which a prover (e.g., a model provider) can attest different ML properties during training and inference to a verifier (e.g., an auditor). However, prior attestation mechanisms based purely on cryptography are often narrowly focused (lacking versatility) and inefficient. There is a need to efficiently attest different types properties across the ML model training and inference pipeline. Recent developments make it possible to run and even train models inside hardware-assisted trusted execution environments (TEEs), which can provide highly efficient attestation. We propose Laminator, the first framework for verifiable ML property cards using hardware-assisted ML property attestations to efficiently furnish attestations for various ML properties for training and inference. It scales to multiple verifiers, and is independent of the model configuration. |
2009.07769 | Sarah Alnegheimish | Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo
Cuesta-Infante, Kalyan Veeramachaneni | TadGAN: Time Series Anomaly Detection Using Generative Adversarial
Networks | Alexander Geiger and Dongyu Liu contributed equally. To appear in the
proceedings of IEEE International Conference on Big Data | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Time series anomalies can offer information relevant to critical situations
facing various fields, from finance and aerospace to the IT, security, and
medical domains. However, detecting anomalies in time series data is
particularly challenging due to the vague definition of anomalies and said
data's frequent lack of labels and highly complex temporal correlations.
Current state-of-the-art unsupervised machine learning methods for anomaly
detection suffer from scalability and portability issues, and may have high
false positive rates. In this paper, we propose TadGAN, an unsupervised anomaly
detection approach built on Generative Adversarial Networks (GANs). To capture
the temporal correlations of time series distributions, we use LSTM Recurrent
Neural Networks as base models for Generators and Critics. TadGAN is trained
with cycle consistency loss to allow for effective time-series data
reconstruction. We further propose several novel methods to compute
reconstruction errors, as well as different approaches to combine
reconstruction errors and Critic outputs to compute anomaly scores. To
demonstrate the performance and generalizability of our approach, we test
several anomaly scoring techniques and report the best-suited one. We compare
our approach to 8 baseline anomaly detection methods on 11 datasets from
multiple reputable sources such as NASA, Yahoo, Numenta, Amazon, and Twitter.
The results show that our approach can effectively detect anomalies and
outperform baseline methods in most cases (6 out of 11). Notably, our method
has the highest averaged F1 score across all the datasets. Our code is open
source and is available as a benchmarking tool.
| [
{
"created": "Wed, 16 Sep 2020 15:52:04 GMT",
"version": "v1"
},
{
"created": "Sat, 19 Sep 2020 23:25:06 GMT",
"version": "v2"
},
{
"created": "Sat, 14 Nov 2020 23:05:29 GMT",
"version": "v3"
}
] | 2020-11-17 | [
[
"Geiger",
"Alexander",
""
],
[
"Liu",
"Dongyu",
""
],
[
"Alnegheimish",
"Sarah",
""
],
[
"Cuesta-Infante",
"Alfredo",
""
],
[
"Veeramachaneni",
"Kalyan",
""
]
] | Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations. Current state-of-the-art unsupervised machine learning methods for anomaly detection suffer from scalability and portability issues, and may have high false positive rates. In this paper, we propose TadGAN, an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs). To capture the temporal correlations of time series distributions, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. TadGAN is trained with cycle consistency loss to allow for effective time-series data reconstruction. We further propose several novel methods to compute reconstruction errors, as well as different approaches to combine reconstruction errors and Critic outputs to compute anomaly scores. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one. We compare our approach to 8 baseline anomaly detection methods on 11 datasets from multiple reputable sources such as NASA, Yahoo, Numenta, Amazon, and Twitter. The results show that our approach can effectively detect anomalies and outperform baseline methods in most cases (6 out of 11). Notably, our method has the highest averaged F1 score across all the datasets. Our code is open source and is available as a benchmarking tool. |
1912.05759 | Hanchi Liu | Bin Liu, Yuxiao Ren, Hanchi Liu, Hui Xu, Zhengfang Wang, Anthony G.
Cohn, and Peng Jiang | GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion
for Tunnel Lining | null | IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no.
10, pp. 8305-8325, Oct. 2021 | 10.1109/TGRS.2020.3046454 | null | cs.CV cs.LG eess.IV physics.geo-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A DNN architecture referred to as GPRInvNet was proposed to tackle the
challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex
permittivity maps of subsurface structures. The GPRInvNet consisted of a
trace-to-trace encoder and a decoder. It was specially designed to take into
account the characteristics of GPR inversion when faced with complex GPR B-Scan
data, as well as addressing the spatial alignment issues between time-series
B-Scan data and spatial permittivity maps. It displayed the ability to fuse
features from several adjacent traces on the B-Scan data to enhance each trace,
and then further condense the features of each trace separately. As a result,
the sensitive zones on the permittivity maps spatially aligned to the enhanced
trace could be reconstructed accurately. The GPRInvNet has been utilized to
reconstruct the permittivity map of tunnel linings. A diverse range of
dielectric models of tunnel linings containing complex defects has been
reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet
is capable of effectively reconstructing complex tunnel lining defects with
clear boundaries. Comparative results with existing baseline methods also
demonstrated the superiority of the GPRInvNet. For the purpose of generalizing
the GPRInvNet to real GPR data, some background noise patches recorded from
practical model testing were integrated into the synthetic GPR data to retrain
the GPRInvNet. The model testing has been conducted for validation, and
experimental results revealed that the GPRInvNet had also achieved satisfactory
results with regard to the real data.
| [
{
"created": "Thu, 12 Dec 2019 03:43:09 GMT",
"version": "v1"
},
{
"created": "Fri, 13 Dec 2019 03:35:45 GMT",
"version": "v2"
},
{
"created": "Sun, 26 Sep 2021 08:15:44 GMT",
"version": "v3"
}
] | 2021-09-28 | [
[
"Liu",
"Bin",
""
],
[
"Ren",
"Yuxiao",
""
],
[
"Liu",
"Hanchi",
""
],
[
"Xu",
"Hui",
""
],
[
"Wang",
"Zhengfang",
""
],
[
"Cohn",
"Anthony G.",
""
],
[
"Jiang",
"Peng",
""
]
] | A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data. |
2203.03216 | Beiduo Chen | Beiduo Chen, Jun-Yu Ma, Jiajun Qi, Wu Guo, Zhen-Hua Ling, Quan Liu | USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration
Network for Multilingual Complex Named Entity Recognition | Winner system (USTC-NELSLIP) of SemEval 2022 MultiCoNER shared task
on 3 tracks (Chinese, Bangla, Code-mixed) | null | 10.18653/v1/2022.semeval-1.223 | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper describes the system developed by the USTC-NELSLIP team for
SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition
(MultiCoNER). We propose a gazetteer-adapted integration network (GAIN) to
improve the performance of language models for recognizing complex named
entities. The method first adapts the representations of gazetteer networks to
those of language models by minimizing the KL divergence between them. After
adaptation, these two networks are then integrated for backend supervised named
entity recognition (NER) training. The proposed method is applied to several
state-of-the-art Transformer-based NER models with a gazetteer built from
Wikidata, and shows great generalization ability across them. The final
predictions are derived from an ensemble of these trained models. Experimental
results and detailed analysis verify the effectiveness of the proposed method.
The official results show that our system ranked 1st on three tracks (Chinese,
Code-mixed and Bangla) and 2nd on the other ten tracks in this task.
| [
{
"created": "Mon, 7 Mar 2022 09:05:37 GMT",
"version": "v1"
},
{
"created": "Mon, 18 Apr 2022 04:46:07 GMT",
"version": "v2"
}
] | 2023-05-09 | [
[
"Chen",
"Beiduo",
""
],
[
"Ma",
"Jun-Yu",
""
],
[
"Qi",
"Jiajun",
""
],
[
"Guo",
"Wu",
""
],
[
"Ling",
"Zhen-Hua",
""
],
[
"Liu",
"Quan",
""
]
] | This paper describes the system developed by the USTC-NELSLIP team for SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition (MultiCoNER). We propose a gazetteer-adapted integration network (GAIN) to improve the performance of language models for recognizing complex named entities. The method first adapts the representations of gazetteer networks to those of language models by minimizing the KL divergence between them. After adaptation, these two networks are then integrated for backend supervised named entity recognition (NER) training. The proposed method is applied to several state-of-the-art Transformer-based NER models with a gazetteer built from Wikidata, and shows great generalization ability across them. The final predictions are derived from an ensemble of these trained models. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on three tracks (Chinese, Code-mixed and Bangla) and 2nd on the other ten tracks in this task. |
2306.07699 | Haozhen Zhang | Haozhen Zhang, Xueting Han, Xi Xiao, Jing Bai | Time-aware Graph Structure Learning via Sequence Prediction on Temporal
Graphs | Accepted by CIKM 2023. The code is available at
https://github.com/ViktorAxelsen/TGSL | null | null | null | cs.LG cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal Graph Learning, which aims to model the time-evolving nature of
graphs, has gained increasing attention and achieved remarkable performance
recently. However, in reality, graph structures are often incomplete and noisy,
which hinders temporal graph networks (TGNs) from learning informative
representations. Graph contrastive learning uses data augmentation to generate
plausible variations of existing data and learn robust representations.
However, rule-based augmentation approaches may be suboptimal as they lack
learnability and fail to leverage rich information from downstream tasks. To
address these issues, we propose a Time-aware Graph Structure Learning (TGSL)
approach via sequence prediction on temporal graphs, which learns better graph
structures for downstream tasks through adding potential temporal edges. In
particular, it predicts time-aware context embedding based on previously
observed interactions and uses the Gumble-Top-K to select the closest candidate
edges to this context embedding. Additionally, several candidate sampling
strategies are proposed to ensure both efficiency and diversity. Furthermore,
we jointly learn the graph structure and TGNs in an end-to-end manner and
perform inference on the refined graph. Extensive experiments on temporal link
prediction benchmarks demonstrate that TGSL yields significant gains for the
popular TGNs such as TGAT and GraphMixer, and it outperforms other contrastive
learning methods on temporal graphs. We release the code at
https://github.com/ViktorAxelsen/TGSL.
| [
{
"created": "Tue, 13 Jun 2023 11:34:36 GMT",
"version": "v1"
},
{
"created": "Tue, 15 Aug 2023 09:03:39 GMT",
"version": "v2"
}
] | 2023-08-16 | [
[
"Zhang",
"Haozhen",
""
],
[
"Han",
"Xueting",
""
],
[
"Xiao",
"Xi",
""
],
[
"Bai",
"Jing",
""
]
] | Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality, graph structures are often incomplete and noisy, which hinders temporal graph networks (TGNs) from learning informative representations. Graph contrastive learning uses data augmentation to generate plausible variations of existing data and learn robust representations. However, rule-based augmentation approaches may be suboptimal as they lack learnability and fail to leverage rich information from downstream tasks. To address these issues, we propose a Time-aware Graph Structure Learning (TGSL) approach via sequence prediction on temporal graphs, which learns better graph structures for downstream tasks through adding potential temporal edges. In particular, it predicts time-aware context embedding based on previously observed interactions and uses the Gumble-Top-K to select the closest candidate edges to this context embedding. Additionally, several candidate sampling strategies are proposed to ensure both efficiency and diversity. Furthermore, we jointly learn the graph structure and TGNs in an end-to-end manner and perform inference on the refined graph. Extensive experiments on temporal link prediction benchmarks demonstrate that TGSL yields significant gains for the popular TGNs such as TGAT and GraphMixer, and it outperforms other contrastive learning methods on temporal graphs. We release the code at https://github.com/ViktorAxelsen/TGSL. |
2210.09220 | David Sinclair D.Phil Oxon | Willem.T.Pye, David.A.Sinclair | A Saccaded Visual Transformer for General Object Spotting | 11 pages mostly figure, central idea is to train on distance a patch
is form a labelled feature | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the novel combination of a visual transformer style patch
classifier with saccaded local attention. A novel optimisation paradigm for
training object models is also presented, rather than the optimisation function
minimising class membership probability error the network is trained to
estimate the normalised distance to the centroid of labelled objects. This
approach builds a degree of transnational invariance directly into the model
and allows fast saccaded search with gradient ascent to find object centroids.
The resulting saccaded visual transformer is demonstrated on human faces.
| [
{
"created": "Mon, 17 Oct 2022 16:17:02 GMT",
"version": "v1"
}
] | 2022-10-18 | [
[
"Pye",
"Willem. T.",
""
],
[
"Sinclair",
"David. A.",
""
]
] | This paper presents the novel combination of a visual transformer style patch classifier with saccaded local attention. A novel optimisation paradigm for training object models is also presented, rather than the optimisation function minimising class membership probability error the network is trained to estimate the normalised distance to the centroid of labelled objects. This approach builds a degree of transnational invariance directly into the model and allows fast saccaded search with gradient ascent to find object centroids. The resulting saccaded visual transformer is demonstrated on human faces. |
2406.06385 | Yelysei Bondarenko | Yelysei Bondarenko, Riccardo Del Chiaro, Markus Nagel | Low-Rank Quantization-Aware Training for LLMs | null | null | null | null | cs.LG cs.AI cs.CL | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Large language models (LLMs) are omnipresent, however their practical
deployment is challenging due to their ever increasing computational and memory
demands. Quantization is one of the most effective ways to make them more
compute and memory efficient. Quantization-aware training (QAT) methods,
generally produce the best quantized performance, however it comes at the cost
of potentially long training time and excessive memory usage, making it
impractical when applying for LLMs. Inspired by parameter-efficient fine-tuning
(PEFT) and low-rank adaptation (LoRA) literature, we propose LR-QAT -- a
lightweight and memory-efficient QAT algorithm for LLMs. LR-QAT employs several
components to save memory without sacrificing predictive performance: (a)
low-rank auxiliary weights that are aware of the quantization grid; (b) a
downcasting operator using fixed-point or double-packed integers and (c)
checkpointing. Unlike most related work, our method (i) is inference-efficient,
leading to no additional overhead compared to traditional PTQ; (ii) can be seen
as a general extended pretraining framework, meaning that the resulting model
can still be utilized for any downstream task afterwards; (iii) can be applied
across a wide range of quantization settings, such as different choices
quantization granularity, activation quantization, and seamlessly combined with
many PTQ techniques. We apply LR-QAT to LLaMA-2/3 and Mistral model families
and validate its effectiveness on several downstream tasks. Our method
outperforms common post-training quantization (PTQ) approaches and reaches the
same model performance as full-model QAT at the fraction of its memory usage.
Specifically, we can train a 7B LLM on a single consumer grade GPU with 24GB of
memory.
| [
{
"created": "Mon, 10 Jun 2024 15:44:22 GMT",
"version": "v1"
},
{
"created": "Thu, 20 Jun 2024 15:18:50 GMT",
"version": "v2"
}
] | 2024-06-21 | [
[
"Bondarenko",
"Yelysei",
""
],
[
"Del Chiaro",
"Riccardo",
""
],
[
"Nagel",
"Markus",
""
]
] | Large language models (LLMs) are omnipresent, however their practical deployment is challenging due to their ever increasing computational and memory demands. Quantization is one of the most effective ways to make them more compute and memory efficient. Quantization-aware training (QAT) methods, generally produce the best quantized performance, however it comes at the cost of potentially long training time and excessive memory usage, making it impractical when applying for LLMs. Inspired by parameter-efficient fine-tuning (PEFT) and low-rank adaptation (LoRA) literature, we propose LR-QAT -- a lightweight and memory-efficient QAT algorithm for LLMs. LR-QAT employs several components to save memory without sacrificing predictive performance: (a) low-rank auxiliary weights that are aware of the quantization grid; (b) a downcasting operator using fixed-point or double-packed integers and (c) checkpointing. Unlike most related work, our method (i) is inference-efficient, leading to no additional overhead compared to traditional PTQ; (ii) can be seen as a general extended pretraining framework, meaning that the resulting model can still be utilized for any downstream task afterwards; (iii) can be applied across a wide range of quantization settings, such as different choices quantization granularity, activation quantization, and seamlessly combined with many PTQ techniques. We apply LR-QAT to LLaMA-2/3 and Mistral model families and validate its effectiveness on several downstream tasks. Our method outperforms common post-training quantization (PTQ) approaches and reaches the same model performance as full-model QAT at the fraction of its memory usage. Specifically, we can train a 7B LLM on a single consumer grade GPU with 24GB of memory. |
2303.13712 | Ruqing Xu | Ruqing Xu, Sarah Dean | Decision-aid or Controller? Steering Human Decision Makers with
Algorithms | null | null | null | null | cs.AI cs.CY cs.HC cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Algorithms are used to aid human decision makers by making predictions and
recommending decisions. Currently, these algorithms are trained to optimize
prediction accuracy. What if they were optimized to control final decisions? In
this paper, we study a decision-aid algorithm that learns about the human
decision maker and provides ''personalized recommendations'' to influence final
decisions. We first consider fixed human decision functions which map
observable features and the algorithm's recommendations to final decisions. We
characterize the conditions under which perfect control over final decisions is
attainable. Under fairly general assumptions, the parameters of the human
decision function can be identified from past interactions between the
algorithm and the human decision maker, even when the algorithm was constrained
to make truthful recommendations. We then consider a decision maker who is
aware of the algorithm's manipulation and responds strategically. By posing the
setting as a variation of the cheap talk game [Crawford and Sobel, 1982], we
show that all equilibria are partition equilibria where only coarse information
is shared: the algorithm recommends an interval containing the ideal decision.
We discuss the potential applications of such algorithms and their social
implications.
| [
{
"created": "Thu, 23 Mar 2023 23:24:26 GMT",
"version": "v1"
}
] | 2023-03-27 | [
[
"Xu",
"Ruqing",
""
],
[
"Dean",
"Sarah",
""
]
] | Algorithms are used to aid human decision makers by making predictions and recommending decisions. Currently, these algorithms are trained to optimize prediction accuracy. What if they were optimized to control final decisions? In this paper, we study a decision-aid algorithm that learns about the human decision maker and provides ''personalized recommendations'' to influence final decisions. We first consider fixed human decision functions which map observable features and the algorithm's recommendations to final decisions. We characterize the conditions under which perfect control over final decisions is attainable. Under fairly general assumptions, the parameters of the human decision function can be identified from past interactions between the algorithm and the human decision maker, even when the algorithm was constrained to make truthful recommendations. We then consider a decision maker who is aware of the algorithm's manipulation and responds strategically. By posing the setting as a variation of the cheap talk game [Crawford and Sobel, 1982], we show that all equilibria are partition equilibria where only coarse information is shared: the algorithm recommends an interval containing the ideal decision. We discuss the potential applications of such algorithms and their social implications. |
1612.05156 | Emil Solsb{\ae}k Ottosen M.Sc. | Emil Solsb{\ae}k Ottosen and Monika D\"orfler | A Phase Vocoder based on Nonstationary Gabor Frames | 10 pages, 6 figures | null | 10.1109/TASLP.2017.2750767 | null | cs.SD | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new algorithm for time stretching music signals based on the
theory of nonstationary Gabor frames (NSGFs). The algorithm extends the
techniques of the classical phase vocoder (PV) by incorporating adaptive
time-frequency (TF) representations and adaptive phase locking. The adaptive TF
representations imply good time resolution for the onsets of attack transients
and good frequency resolution for the sinusoidal components. We estimate the
phase values only at peak channels and the remaining phases are then locked to
the values of the peaks in an adaptive manner. During attack transients we keep
the stretch factor equal to one and we propose a new strategy for determining
which channels are relevant for reinitializing the corresponding phase values.
In contrast to previously published algorithms we use a non-uniform NSGF to
obtain a low redundancy of the corresponding TF representation. We show that
with just three times as many TF coefficients as signal samples, artifacts such
as phasiness and transient smearing can be greatly reduced compared to the
classical PV. The proposed algorithm is tested on both synthetic and real world
signals and compared with state of the art algorithms in a reproducible manner.
| [
{
"created": "Thu, 15 Dec 2016 19:43:54 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Sep 2017 11:35:59 GMT",
"version": "v2"
}
] | 2017-09-14 | [
[
"Ottosen",
"Emil Solsbæk",
""
],
[
"Dörfler",
"Monika",
""
]
] | We propose a new algorithm for time stretching music signals based on the theory of nonstationary Gabor frames (NSGFs). The algorithm extends the techniques of the classical phase vocoder (PV) by incorporating adaptive time-frequency (TF) representations and adaptive phase locking. The adaptive TF representations imply good time resolution for the onsets of attack transients and good frequency resolution for the sinusoidal components. We estimate the phase values only at peak channels and the remaining phases are then locked to the values of the peaks in an adaptive manner. During attack transients we keep the stretch factor equal to one and we propose a new strategy for determining which channels are relevant for reinitializing the corresponding phase values. In contrast to previously published algorithms we use a non-uniform NSGF to obtain a low redundancy of the corresponding TF representation. We show that with just three times as many TF coefficients as signal samples, artifacts such as phasiness and transient smearing can be greatly reduced compared to the classical PV. The proposed algorithm is tested on both synthetic and real world signals and compared with state of the art algorithms in a reproducible manner. |
2407.16485 | Baiyu Peng | Baiyu Peng, Aude Billard | Learning General Continuous Constraint from Demonstrations via
Positive-Unlabeled Learning | null | null | null | null | cs.LG cs.AI cs.RO | http://creativecommons.org/licenses/by/4.0/ | Planning for a wide range of real-world tasks necessitates to know and write
all constraints. However, instances exist where these constraints are either
unknown or challenging to specify accurately. A possible solution is to infer
the unknown constraints from expert demonstration. The majority of prior works
limit themselves to learning simple linear constraints, or require strong
knowledge of the true constraint parameterization or environmental model. To
mitigate these problems, this paper presents a positive-unlabeled (PU) learning
approach to infer a continuous, arbitrary and possibly nonlinear, constraint
from demonstration. From a PU learning view, We treat all data in
demonstrations as positive (feasible) data, and learn a (sub)-optimal policy to
generate high-reward-winning but potentially infeasible trajectories, which
serve as unlabeled data containing both feasible and infeasible states. Under
an assumption on data distribution, a feasible-infeasible classifier (i.e.,
constraint model) is learned from the two datasets through a postprocessing PU
learning technique. The entire method employs an iterative framework
alternating between updating the policy, which generates and selects
higher-reward policies, and updating the constraint model. Additionally, a
memory buffer is introduced to record and reuse samples from previous
iterations to prevent forgetting. The effectiveness of the proposed method is
validated in two Mujoco environments, successfully inferring continuous
nonlinear constraints and outperforming a baseline method in terms of
constraint accuracy and policy safety.
| [
{
"created": "Tue, 23 Jul 2024 14:00:18 GMT",
"version": "v1"
}
] | 2024-07-24 | [
[
"Peng",
"Baiyu",
""
],
[
"Billard",
"Aude",
""
]
] | Planning for a wide range of real-world tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to specify accurately. A possible solution is to infer the unknown constraints from expert demonstration. The majority of prior works limit themselves to learning simple linear constraints, or require strong knowledge of the true constraint parameterization or environmental model. To mitigate these problems, this paper presents a positive-unlabeled (PU) learning approach to infer a continuous, arbitrary and possibly nonlinear, constraint from demonstration. From a PU learning view, We treat all data in demonstrations as positive (feasible) data, and learn a (sub)-optimal policy to generate high-reward-winning but potentially infeasible trajectories, which serve as unlabeled data containing both feasible and infeasible states. Under an assumption on data distribution, a feasible-infeasible classifier (i.e., constraint model) is learned from the two datasets through a postprocessing PU learning technique. The entire method employs an iterative framework alternating between updating the policy, which generates and selects higher-reward policies, and updating the constraint model. Additionally, a memory buffer is introduced to record and reuse samples from previous iterations to prevent forgetting. The effectiveness of the proposed method is validated in two Mujoco environments, successfully inferring continuous nonlinear constraints and outperforming a baseline method in terms of constraint accuracy and policy safety. |
2010.04363 | Zhiwei Xu | Zhiwei Xu, Thalaiyasingam Ajanthan, Richard Hartley | Refining Semantic Segmentation with Superpixel by Transparent
Initialization and Sparse Encoder | null | null | null | null | cs.CV cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although deep learning greatly improves the performance of semantic
segmentation, its success mainly lies in object central areas without accurate
edges. As superpixels are a popular and effective auxiliary to preserve object
edges, in this paper, we jointly learn semantic segmentation with trainable
superpixels. We achieve it with fully-connected layers with Transparent
Initialization (TI) and efficient logit consistency using a sparse encoder. The
proposed TI preserves the effects of learned parameters of pretrained networks.
This avoids a significant increase of the loss of pretrained networks, which
otherwise may be caused by inappropriate parameter initialization of the
additional layers. Meanwhile, consistent pixel labels in each superpixel are
guaranteed by logit consistency. The sparse encoder with sparse matrix
operations substantially reduces both the memory requirement and the
computational complexity. We demonstrated the superiority of TI over other
parameter initialization methods and tested its numerical stability. The
effectiveness of our proposal was validated on PASCAL VOC 2012, ADE20K, and
PASCAL Context showing enhanced semantic segmentation edges. With quantitative
evaluations on segmentation edges using performance ratio and F-measure, our
method outperforms the state-of-the-art.
| [
{
"created": "Fri, 9 Oct 2020 04:20:54 GMT",
"version": "v1"
},
{
"created": "Sat, 7 Nov 2020 23:36:29 GMT",
"version": "v2"
},
{
"created": "Tue, 24 Nov 2020 10:14:58 GMT",
"version": "v3"
}
] | 2020-11-25 | [
[
"Xu",
"Zhiwei",
""
],
[
"Ajanthan",
"Thalaiyasingam",
""
],
[
"Hartley",
"Richard",
""
]
] | Although deep learning greatly improves the performance of semantic segmentation, its success mainly lies in object central areas without accurate edges. As superpixels are a popular and effective auxiliary to preserve object edges, in this paper, we jointly learn semantic segmentation with trainable superpixels. We achieve it with fully-connected layers with Transparent Initialization (TI) and efficient logit consistency using a sparse encoder. The proposed TI preserves the effects of learned parameters of pretrained networks. This avoids a significant increase of the loss of pretrained networks, which otherwise may be caused by inappropriate parameter initialization of the additional layers. Meanwhile, consistent pixel labels in each superpixel are guaranteed by logit consistency. The sparse encoder with sparse matrix operations substantially reduces both the memory requirement and the computational complexity. We demonstrated the superiority of TI over other parameter initialization methods and tested its numerical stability. The effectiveness of our proposal was validated on PASCAL VOC 2012, ADE20K, and PASCAL Context showing enhanced semantic segmentation edges. With quantitative evaluations on segmentation edges using performance ratio and F-measure, our method outperforms the state-of-the-art. |
2310.17894 | Haiqin Yang | Weixu Zhang, Yifei Wang, Yuanfeng Song, Victor Junqiu Wei, Yuxing
Tian, Yiyan Qi, Jonathan H. Chan, Raymond Chi-Wing Wong, Haiqin Yang | Natural Language Interfaces for Tabular Data Querying and Visualization:
A Survey | 20 pages, 4 figures, 5 tables. Accepted by IEEE TKDE | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The emergence of natural language processing has revolutionized the way users
interact with tabular data, enabling a shift from traditional query languages
and manual plotting to more intuitive, language-based interfaces. The rise of
large language models (LLMs) such as ChatGPT and its successors has further
advanced this field, opening new avenues for natural language processing
techniques. This survey presents a comprehensive overview of natural language
interfaces for tabular data querying and visualization, which allow users to
interact with data using natural language queries. We introduce the fundamental
concepts and techniques underlying these interfaces with a particular emphasis
on semantic parsing, the key technology facilitating the translation from
natural language to SQL queries or data visualization commands. We then delve
into the recent advancements in Text-to-SQL and Text-to-Vis problems from the
perspectives of datasets, methodologies, metrics, and system designs. This
includes a deep dive into the influence of LLMs, highlighting their strengths,
limitations, and potential for future improvements. Through this survey, we aim
to provide a roadmap for researchers and practitioners interested in developing
and applying natural language interfaces for data interaction in the era of
large language models.
| [
{
"created": "Fri, 27 Oct 2023 05:01:20 GMT",
"version": "v1"
},
{
"created": "Sat, 11 May 2024 09:44:35 GMT",
"version": "v2"
},
{
"created": "Mon, 20 May 2024 02:45:37 GMT",
"version": "v3"
}
] | 2024-05-21 | [
[
"Zhang",
"Weixu",
""
],
[
"Wang",
"Yifei",
""
],
[
"Song",
"Yuanfeng",
""
],
[
"Wei",
"Victor Junqiu",
""
],
[
"Tian",
"Yuxing",
""
],
[
"Qi",
"Yiyan",
""
],
[
"Chan",
"Jonathan H.",
""
],
[
"Wong",
"Raymond Chi-Wing",
""
],
[
"Yang",
"Haiqin",
""
]
] | The emergence of natural language processing has revolutionized the way users interact with tabular data, enabling a shift from traditional query languages and manual plotting to more intuitive, language-based interfaces. The rise of large language models (LLMs) such as ChatGPT and its successors has further advanced this field, opening new avenues for natural language processing techniques. This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing, the key technology facilitating the translation from natural language to SQL queries or data visualization commands. We then delve into the recent advancements in Text-to-SQL and Text-to-Vis problems from the perspectives of datasets, methodologies, metrics, and system designs. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements. Through this survey, we aim to provide a roadmap for researchers and practitioners interested in developing and applying natural language interfaces for data interaction in the era of large language models. |
1903.00100 | Shaojie Xu | Shaojie Xu, Anvesha Amaravati, Justin Romberg, Arijit Raychowdhury | Appearance-based Gesture recognition in the compressed domain | arXiv admin note: text overlap with arXiv:1605.08313 | 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), New Orleans, LA, 2017, pp. 1722-1726 | 10.1109/ICASSP.2017.7952451 | null | cs.CV cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a novel appearance-based gesture recognition algorithm using
compressed domain signal processing techniques. Gesture features are extracted
directly from the compressed measurements, which are the block averages and the
coded linear combinations of the image sensor's pixel values. We also improve
both the computational efficiency and the memory requirement of the previous
DTW-based K-NN gesture classifiers. Both simulation testing and hardware
implementation strongly support the proposed algorithm.
| [
{
"created": "Tue, 19 Feb 2019 06:05:12 GMT",
"version": "v1"
}
] | 2019-03-04 | [
[
"Xu",
"Shaojie",
""
],
[
"Amaravati",
"Anvesha",
""
],
[
"Romberg",
"Justin",
""
],
[
"Raychowdhury",
"Arijit",
""
]
] | We propose a novel appearance-based gesture recognition algorithm using compressed domain signal processing techniques. Gesture features are extracted directly from the compressed measurements, which are the block averages and the coded linear combinations of the image sensor's pixel values. We also improve both the computational efficiency and the memory requirement of the previous DTW-based K-NN gesture classifiers. Both simulation testing and hardware implementation strongly support the proposed algorithm. |
2106.05633 | Arthur Brack | Arthur Brack and Anett Hoppe and Ralph Ewerth | Citation Recommendation for Research Papers via Knowledge Graphs | Accepted for publication in 25th International Conference on Theory
and Practice of Digital Libraries (TPDL), 2021 | null | null | null | cs.DL cs.IR | http://creativecommons.org/licenses/by/4.0/ | Citation recommendation for research papers is a valuable task that can help
researchers improve the quality of their work by suggesting relevant related
work. Current approaches for this task rely primarily on the text of the papers
and the citation network. In this paper, we propose to exploit an additional
source of information, namely research knowledge graphs (KG) that interlink
research papers based on mentioned scientific concepts. Our experimental
results demonstrate that the combination of information from research KGs with
existing state-of-the-art approaches is beneficial. Experimental results are
presented for the STM-KG (STM: Science, Technology, Medicine), which is an
automatically populated knowledge graph based on the scientific concepts
extracted from papers of ten domains. The proposed approach outperforms the
state of the art with a mean average precision of 20.6% (+0.8) for the top-50
retrieved results.
| [
{
"created": "Thu, 10 Jun 2021 10:16:51 GMT",
"version": "v1"
}
] | 2021-06-11 | [
[
"Brack",
"Arthur",
""
],
[
"Hoppe",
"Anett",
""
],
[
"Ewerth",
"Ralph",
""
]
] | Citation recommendation for research papers is a valuable task that can help researchers improve the quality of their work by suggesting relevant related work. Current approaches for this task rely primarily on the text of the papers and the citation network. In this paper, we propose to exploit an additional source of information, namely research knowledge graphs (KG) that interlink research papers based on mentioned scientific concepts. Our experimental results demonstrate that the combination of information from research KGs with existing state-of-the-art approaches is beneficial. Experimental results are presented for the STM-KG (STM: Science, Technology, Medicine), which is an automatically populated knowledge graph based on the scientific concepts extracted from papers of ten domains. The proposed approach outperforms the state of the art with a mean average precision of 20.6% (+0.8) for the top-50 retrieved results. |
2403.18416 | Thomas Leyssens | Thomas Leyssens, Michel Henry, Jonathan Lambrechts, Jean-Francois
Remacle | A Delaunay Refinement Algorithm for the Particle Finite Element Method
applied to Free Surface Flows | null | null | null | null | cs.CE | http://creativecommons.org/licenses/by/4.0/ | This paper proposes two contributions to the calculation of free surface
flows using the particle finite element method (PFEM). The PFEM is based on a
Lagrangian approach: a set of particles defines the fluid. Then, unlike a pure
Lagrangian method, all the particles are connected by a triangular mesh. The
difficulty lies in locating the free surface from this mesh. It is a matter of
deciding which of the elements in the mesh are part of the fluid domain, and to
define a boundary - the free surface. Then, the incompressible Navier-Stokes
equations are solved on the fluid domain and the particles' position is updated
using the resulting velocity vector. Our first contribution is to propose an
approach to adapt the mesh with theoretical guarantees of quality: the mesh
generation community has acquired a lot of experience and understanding about
mesh adaptation approaches with guarantees of quality on the final mesh. We use
here a Delaunay refinement strategy, allowing to insert and remove nodes while
gradually improving mesh quality. We show that this allows to create stable and
smooth free surface geometries. Our PFEM approach models the topological
evolution of one fluid. It is nevertheless necessary to apply conditions on the
domain boundaries. When a boundary is a free surface, the flow on the other
side is not modelled, it is represented by an external pressure. On the
external free surface boundary, atmospheric pressure can be imposed.
Nevertheless, there may be internal free surfaces: the fluid can fully
encapsulate cavities to form bubbles. The pressure required to maintain the
volume of those bubbles is a priori unknown. We propose a multi-point
constraint approach to enforce global incompressibility of those empty bubbles.
This approach allows to accurately model bubbly flows that involve two fluids
with large density differences, while only modelling the heavier fluid.
| [
{
"created": "Wed, 27 Mar 2024 10:08:48 GMT",
"version": "v1"
}
] | 2024-03-28 | [
[
"Leyssens",
"Thomas",
""
],
[
"Henry",
"Michel",
""
],
[
"Lambrechts",
"Jonathan",
""
],
[
"Remacle",
"Jean-Francois",
""
]
] | This paper proposes two contributions to the calculation of free surface flows using the particle finite element method (PFEM). The PFEM is based on a Lagrangian approach: a set of particles defines the fluid. Then, unlike a pure Lagrangian method, all the particles are connected by a triangular mesh. The difficulty lies in locating the free surface from this mesh. It is a matter of deciding which of the elements in the mesh are part of the fluid domain, and to define a boundary - the free surface. Then, the incompressible Navier-Stokes equations are solved on the fluid domain and the particles' position is updated using the resulting velocity vector. Our first contribution is to propose an approach to adapt the mesh with theoretical guarantees of quality: the mesh generation community has acquired a lot of experience and understanding about mesh adaptation approaches with guarantees of quality on the final mesh. We use here a Delaunay refinement strategy, allowing to insert and remove nodes while gradually improving mesh quality. We show that this allows to create stable and smooth free surface geometries. Our PFEM approach models the topological evolution of one fluid. It is nevertheless necessary to apply conditions on the domain boundaries. When a boundary is a free surface, the flow on the other side is not modelled, it is represented by an external pressure. On the external free surface boundary, atmospheric pressure can be imposed. Nevertheless, there may be internal free surfaces: the fluid can fully encapsulate cavities to form bubbles. The pressure required to maintain the volume of those bubbles is a priori unknown. We propose a multi-point constraint approach to enforce global incompressibility of those empty bubbles. This approach allows to accurately model bubbly flows that involve two fluids with large density differences, while only modelling the heavier fluid. |
2301.07068 | Davide Corsi | Luca Marzari, Davide Corsi, Ferdinando Cicalese and Alessandro
Farinelli | The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural
Networks | Accepted in the International Joint Conference on Artificial
Intelligence (IJCAI), 2023. [Marzari and Corsi contributed equally] | null | null | null | cs.AI cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep Neural Networks are increasingly adopted in critical tasks that require
a high level of safety, e.g., autonomous driving. While state-of-the-art
verifiers can be employed to check whether a DNN is unsafe w.r.t. some given
property (i.e., whether there is at least one unsafe input configuration),
their yes/no output is not informative enough for other purposes, such as
shielding, model selection, or training improvements. In this paper, we
introduce the #DNN-Verification problem, which involves counting the number of
input configurations of a DNN that result in a violation of a particular safety
property. We analyze the complexity of this problem and propose a novel
approach that returns the exact count of violations. Due to the #P-completeness
of the problem, we also propose a randomized, approximate method that provides
a provable probabilistic bound of the correct count while significantly
reducing computational requirements. We present experimental results on a set
of safety-critical benchmarks that demonstrate the effectiveness of our
approximate method and evaluate the tightness of the bound.
| [
{
"created": "Tue, 17 Jan 2023 18:32:01 GMT",
"version": "v1"
},
{
"created": "Tue, 9 May 2023 09:02:59 GMT",
"version": "v2"
},
{
"created": "Mon, 22 May 2023 07:58:42 GMT",
"version": "v3"
},
{
"created": "Mon, 19 Jun 2023 13:13:38 GMT",
"version": "v4"
}
] | 2023-06-21 | [
[
"Marzari",
"Luca",
""
],
[
"Corsi",
"Davide",
""
],
[
"Cicalese",
"Ferdinando",
""
],
[
"Farinelli",
"Alessandro",
""
]
] | Deep Neural Networks are increasingly adopted in critical tasks that require a high level of safety, e.g., autonomous driving. While state-of-the-art verifiers can be employed to check whether a DNN is unsafe w.r.t. some given property (i.e., whether there is at least one unsafe input configuration), their yes/no output is not informative enough for other purposes, such as shielding, model selection, or training improvements. In this paper, we introduce the #DNN-Verification problem, which involves counting the number of input configurations of a DNN that result in a violation of a particular safety property. We analyze the complexity of this problem and propose a novel approach that returns the exact count of violations. Due to the #P-completeness of the problem, we also propose a randomized, approximate method that provides a provable probabilistic bound of the correct count while significantly reducing computational requirements. We present experimental results on a set of safety-critical benchmarks that demonstrate the effectiveness of our approximate method and evaluate the tightness of the bound. |
2304.12652 | Peng Dai | Peng Dai, Yinda Zhang, Xin Yu, Xiaoyang Lyu, Xiaojuan Qi | Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rendering novel view images is highly desirable for many applications.
Despite recent progress, it remains challenging to render high-fidelity and
view-consistent novel views of large-scale scenes from in-the-wild images with
inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid
neural rendering model that makes image-based representation and neural 3D
representation join forces to render high-quality, view-consistent images.
Besides, images captured in the wild inevitably contain artifacts, such as
motion blur, which deteriorates the quality of rendered images. Accordingly, we
propose strategies to simulate blur effects on the rendered images to mitigate
the negative influence of blurriness images and reduce their importance during
training based on precomputed quality-aware weights. Extensive experiments on
real and synthetic data demonstrate our model surpasses state-of-the-art
point-based methods for novel view synthesis. The code is available at
https://daipengwa.github.io/Hybrid-Rendering-ProjectPage.
| [
{
"created": "Tue, 25 Apr 2023 08:36:33 GMT",
"version": "v1"
},
{
"created": "Sun, 9 Jul 2023 13:45:44 GMT",
"version": "v2"
}
] | 2023-07-11 | [
[
"Dai",
"Peng",
""
],
[
"Zhang",
"Yinda",
""
],
[
"Yu",
"Xin",
""
],
[
"Lyu",
"Xiaoyang",
""
],
[
"Qi",
"Xiaojuan",
""
]
] | Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed quality-aware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-the-art point-based methods for novel view synthesis. The code is available at https://daipengwa.github.io/Hybrid-Rendering-ProjectPage. |
1201.6567 | Ravi Kumar | Bahman Bahmani, Ravi Kumar, Sergei Vassilvitskii | Densest Subgraph in Streaming and MapReduce | VLDB2012 | Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 5, pp.
454-465 (2012) | null | null | cs.DB | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The problem of finding locally dense components of a graph is an important
primitive in data analysis, with wide-ranging applications from community
mining to spam detection and the discovery of biological network modules. In
this paper we present new algorithms for finding the densest subgraph in the
streaming model. For any epsilon>0, our algorithms make O((log n)/log
(1+epsilon)) passes over the input and find a subgraph whose density is
guaranteed to be within a factor 2(1+epsilon) of the optimum. Our algorithms
are also easily parallelizable and we illustrate this by realizing them in the
MapReduce model. In addition we perform extensive experimental evaluation on
massive real-world graphs showing the performance and scalability of our
algorithms in practice.
| [
{
"created": "Tue, 31 Jan 2012 15:10:03 GMT",
"version": "v1"
}
] | 2012-02-01 | [
[
"Bahmani",
"Bahman",
""
],
[
"Kumar",
"Ravi",
""
],
[
"Vassilvitskii",
"Sergei",
""
]
] | The problem of finding locally dense components of a graph is an important primitive in data analysis, with wide-ranging applications from community mining to spam detection and the discovery of biological network modules. In this paper we present new algorithms for finding the densest subgraph in the streaming model. For any epsilon>0, our algorithms make O((log n)/log (1+epsilon)) passes over the input and find a subgraph whose density is guaranteed to be within a factor 2(1+epsilon) of the optimum. Our algorithms are also easily parallelizable and we illustrate this by realizing them in the MapReduce model. In addition we perform extensive experimental evaluation on massive real-world graphs showing the performance and scalability of our algorithms in practice. |
1603.02655 | Varun Nagpal | Varun Nagpal | Study and evaluation of an Irregular Graph Algorithm on Multicore and
GPU Processor Architectures | null | null | null | 1115-1213Nagpal | cs.DC cs.PF | http://creativecommons.org/licenses/by-nc-sa/4.0/ | One area of Computing applications which poses significant challenge of
performance scalability on Chip Multiprocessors(CMP's) are Irregular
applications. Such applications have very little computation and unpredictable
memory access patterns making them memory-bound in contrast to compute-bound
applications. Since the gap between processor and memory performance continues
to exist, difficulty to hide and decrease this gap is one of the important
factors which results in poor performance of these applications on CMP's.
The goal of this thesis is to overcome many such challenges posed during
performance acceleration of an irregular graph algorithm called Triad Census.
We accelerated the Triad Census algorithm on two significantly different Chip
Multiprocessors: Dual-socket Intel Xeon Multicore (8 hardware threads per
socket) and 240-processor core NVIDIA Tesla C1060 GPGPU(128 hardware threads
per core).
The experimental results obtained on Intel Multicore Xeon system shows
performance speedups (w.r.t baseline sequential) of maximum 56x , average 33x
and minimum 8.3x for real world graph data sets. On NVIDIA Tesla C1060 GPGPU,
we were able to match almost equally the Multicore results - 58.4x maximum,
32.8x average and 4.2x minimum speedups w.r.t baseline sequential. In terms of
raw performance, for the graph data set called Patents network, our results on
Intel Xeon Multicore(16 hw threads) were 1.27x times faster than previous
results on Cray XMT(16 hw threads) while results achieved on GPGPU were
comparatively slower(0.72x). To the best of our knowledge, this algorithm has
only been accelerated on supercomputer class computer named Cray XMT and no
work exists that demonstrates performance evaluation and comparison of this
algorithm on relatively lower-cost Multicore and GPGPU based platforms.
| [
{
"created": "Tue, 8 Mar 2016 20:07:31 GMT",
"version": "v1"
}
] | 2016-03-09 | [
[
"Nagpal",
"Varun",
""
]
] | One area of Computing applications which poses significant challenge of performance scalability on Chip Multiprocessors(CMP's) are Irregular applications. Such applications have very little computation and unpredictable memory access patterns making them memory-bound in contrast to compute-bound applications. Since the gap between processor and memory performance continues to exist, difficulty to hide and decrease this gap is one of the important factors which results in poor performance of these applications on CMP's. The goal of this thesis is to overcome many such challenges posed during performance acceleration of an irregular graph algorithm called Triad Census. We accelerated the Triad Census algorithm on two significantly different Chip Multiprocessors: Dual-socket Intel Xeon Multicore (8 hardware threads per socket) and 240-processor core NVIDIA Tesla C1060 GPGPU(128 hardware threads per core). The experimental results obtained on Intel Multicore Xeon system shows performance speedups (w.r.t baseline sequential) of maximum 56x , average 33x and minimum 8.3x for real world graph data sets. On NVIDIA Tesla C1060 GPGPU, we were able to match almost equally the Multicore results - 58.4x maximum, 32.8x average and 4.2x minimum speedups w.r.t baseline sequential. In terms of raw performance, for the graph data set called Patents network, our results on Intel Xeon Multicore(16 hw threads) were 1.27x times faster than previous results on Cray XMT(16 hw threads) while results achieved on GPGPU were comparatively slower(0.72x). To the best of our knowledge, this algorithm has only been accelerated on supercomputer class computer named Cray XMT and no work exists that demonstrates performance evaluation and comparison of this algorithm on relatively lower-cost Multicore and GPGPU based platforms. |
0710.4819 | EDA Publishing Association | S. Lopez, G. M. Callico, J. F. Lopez, R. Sarmiento | A High Quality/Low Computational Cost Technique for Block Matching
Motion Estimation | Submitted on behalf of EDAA (http://www.edaa.com/) | Dans Design, Automation and Test in Europe | Designers'Forum -
DATE'05, Munich : Allemagne (2005) | null | null | cs.MM | null | Motion estimation is the most critical process in video coding systems. First
of all, it has a definitive impact on the rate-distortion performance given by
the video encoder. Secondly, it is the most computationally intensive process
within the encoding loop. For these reasons, the design of high-performance
low-cost motion estimators is a crucial task in the video compression field. An
adaptive cost block matching (ACBM) motion estimation technique is presented in
this paper, featuring an excellent tradeoff between the quality of the
reconstructed video sequences and the computational effort. Simulation results
demonstrate that the ACBM algorithm achieves a slight better rate-distortion
performance than the one given by the well-known full search algorithm block
matching algorithm with reductions of up to 95% in the computational load.
| [
{
"created": "Thu, 25 Oct 2007 12:03:15 GMT",
"version": "v1"
}
] | 2011-11-09 | [
[
"Lopez",
"S.",
""
],
[
"Callico",
"G. M.",
""
],
[
"Lopez",
"J. F.",
""
],
[
"Sarmiento",
"R.",
""
]
] | Motion estimation is the most critical process in video coding systems. First of all, it has a definitive impact on the rate-distortion performance given by the video encoder. Secondly, it is the most computationally intensive process within the encoding loop. For these reasons, the design of high-performance low-cost motion estimators is a crucial task in the video compression field. An adaptive cost block matching (ACBM) motion estimation technique is presented in this paper, featuring an excellent tradeoff between the quality of the reconstructed video sequences and the computational effort. Simulation results demonstrate that the ACBM algorithm achieves a slight better rate-distortion performance than the one given by the well-known full search algorithm block matching algorithm with reductions of up to 95% in the computational load. |
2302.05499 | Sumyeong Ahn | Sumyeong Ahn, Jongwoo Ko, Se-Young Yun | CUDA: Curriculum of Data Augmentation for Long-Tailed Recognition | ICLR'23 Spotlight, 23 pages | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Class imbalance problems frequently occur in real-world tasks, and
conventional deep learning algorithms are well known for performance
degradation on imbalanced training datasets. To mitigate this problem, many
approaches have aimed to balance among given classes by re-weighting or
re-sampling training samples. These re-balancing methods increase the impact of
minority classes and reduce the influence of majority classes on the output of
models. However, the extracted representations may be of poor quality owing to
the limited number of minority samples. To handle this restriction, several
methods have been developed that increase the representations of minority
samples by leveraging the features of the majority samples. Despite extensive
recent studies, no deep analysis has been conducted on determination of classes
to be augmented and strength of augmentation has been conducted. In this study,
we first investigate the correlation between the degree of augmentation and
class-wise performance, and find that the proper degree of augmentation must be
allocated for each class to mitigate class imbalance problems. Motivated by
this finding, we propose a simple and efficient novel curriculum, which is
designed to find the appropriate per-class strength of data augmentation,
called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA
can simply be integrated into existing long-tailed recognition methods. We
present the results of experiments showing that CUDA effectively achieves
better generalization performance compared to the state-of-the-art method on
various imbalanced datasets such as CIFAR-100-LT, ImageNet-LT, and iNaturalist
2018.
| [
{
"created": "Fri, 10 Feb 2023 20:30:22 GMT",
"version": "v1"
}
] | 2023-02-14 | [
[
"Ahn",
"Sumyeong",
""
],
[
"Ko",
"Jongwoo",
""
],
[
"Yun",
"Se-Young",
""
]
] | Class imbalance problems frequently occur in real-world tasks, and conventional deep learning algorithms are well known for performance degradation on imbalanced training datasets. To mitigate this problem, many approaches have aimed to balance among given classes by re-weighting or re-sampling training samples. These re-balancing methods increase the impact of minority classes and reduce the influence of majority classes on the output of models. However, the extracted representations may be of poor quality owing to the limited number of minority samples. To handle this restriction, several methods have been developed that increase the representations of minority samples by leveraging the features of the majority samples. Despite extensive recent studies, no deep analysis has been conducted on determination of classes to be augmented and strength of augmentation has been conducted. In this study, we first investigate the correlation between the degree of augmentation and class-wise performance, and find that the proper degree of augmentation must be allocated for each class to mitigate class imbalance problems. Motivated by this finding, we propose a simple and efficient novel curriculum, which is designed to find the appropriate per-class strength of data augmentation, called CUDA: CUrriculum of Data Augmentation for long-tailed recognition. CUDA can simply be integrated into existing long-tailed recognition methods. We present the results of experiments showing that CUDA effectively achieves better generalization performance compared to the state-of-the-art method on various imbalanced datasets such as CIFAR-100-LT, ImageNet-LT, and iNaturalist 2018. |
2105.01861 | Pawe{\l} Parys | Pawe{\l} Parys | Higher-Order Model Checking Step by Step | This is an extended version of a paper published on the ICALP 2021
conference | null | null | null | cs.LO cs.FL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We show a new simple algorithm that solves the model-checking problem for
recursion schemes: check whether the tree generated by a given higher-order
recursion scheme is accepted by a given alternating parity automaton. The
algorithm amounts to a procedure that transforms a recursion scheme of order
$n$ to a recursion scheme of order $n-1$, preserving acceptance, and increasing
the size only exponentially. After repeating the procedure $n$ times, we obtain
a recursion scheme of order $0$, for which the problem boils down to solving a
finite parity game. Since the size grows exponentially at each step, the
overall complexity is $n$-EXPTIME, which is known to be optimal. More
precisely, the transformation is linear in the size of the recursion scheme,
assuming that the arity of employed nonterminals and the size of the automaton
are bounded by a constant; this results in an FPT algorithm for the
model-checking problem.
Our transformation is a generalization of a previous transformation of the
author (2020), working for reachability automata in place of parity automata.
The step-by-step approach can be opposed to previous algorithms solving the
considered problem "in one step", being compulsorily more complicated.
| [
{
"created": "Wed, 5 May 2021 04:21:31 GMT",
"version": "v1"
}
] | 2021-05-06 | [
[
"Parys",
"Paweł",
""
]
] | We show a new simple algorithm that solves the model-checking problem for recursion schemes: check whether the tree generated by a given higher-order recursion scheme is accepted by a given alternating parity automaton. The algorithm amounts to a procedure that transforms a recursion scheme of order $n$ to a recursion scheme of order $n-1$, preserving acceptance, and increasing the size only exponentially. After repeating the procedure $n$ times, we obtain a recursion scheme of order $0$, for which the problem boils down to solving a finite parity game. Since the size grows exponentially at each step, the overall complexity is $n$-EXPTIME, which is known to be optimal. More precisely, the transformation is linear in the size of the recursion scheme, assuming that the arity of employed nonterminals and the size of the automaton are bounded by a constant; this results in an FPT algorithm for the model-checking problem. Our transformation is a generalization of a previous transformation of the author (2020), working for reachability automata in place of parity automata. The step-by-step approach can be opposed to previous algorithms solving the considered problem "in one step", being compulsorily more complicated. |
2404.00358 | Duosheng Chen | Duosheng Chen, Shihao Zhou, Jinshan Pan, Jinglei Shi, Lishen Qu and
Jufeng Yang | Spread Your Wings: A Radial Strip Transformer for Image Deblurring | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Exploring motion information is important for the motion deblurring task.
Recent the window-based transformer approaches have achieved decent performance
in image deblurring. Note that the motion causing blurry results is usually
composed of translation and rotation movements and the window-shift operation
in the Cartesian coordinate system by the window-based transformer approaches
only directly explores translation motion in orthogonal directions. Thus, these
methods have the limitation of modeling the rotation part. To alleviate this
problem, we introduce the polar coordinate-based transformer, which has the
angles and distance to explore rotation motion and translation information
together. In this paper, we propose a Radial Strip Transformer (RST), which is
a transformer-based architecture that restores the blur images in a polar
coordinate system instead of a Cartesian one. RST contains a dynamic radial
embedding module (DRE) to extract the shallow feature by a radial deformable
convolution. We design a polar mask layer to generate the offsets for the
deformable convolution, which can reshape the convolution kernel along the
radius to better capture the rotation motion information. Furthermore, we
proposed a radial strip attention solver (RSAS) as deep feature extraction,
where the relationship of windows is organized by azimuth and radius. This
attention module contains radial strip windows to reweight image features in
the polar coordinate, which preserves more useful information in rotation and
translation motion together for better recovering the sharp images.
Experimental results on six synthesis and real-world datasets prove that our
method performs favorably against other SOTA methods for the image deblurring
task.
| [
{
"created": "Sat, 30 Mar 2024 13:20:04 GMT",
"version": "v1"
},
{
"created": "Sun, 19 May 2024 03:19:52 GMT",
"version": "v2"
},
{
"created": "Wed, 22 May 2024 02:50:58 GMT",
"version": "v3"
}
] | 2024-05-24 | [
[
"Chen",
"Duosheng",
""
],
[
"Zhou",
"Shihao",
""
],
[
"Pan",
"Jinshan",
""
],
[
"Shi",
"Jinglei",
""
],
[
"Qu",
"Lishen",
""
],
[
"Yang",
"Jufeng",
""
]
] | Exploring motion information is important for the motion deblurring task. Recent the window-based transformer approaches have achieved decent performance in image deblurring. Note that the motion causing blurry results is usually composed of translation and rotation movements and the window-shift operation in the Cartesian coordinate system by the window-based transformer approaches only directly explores translation motion in orthogonal directions. Thus, these methods have the limitation of modeling the rotation part. To alleviate this problem, we introduce the polar coordinate-based transformer, which has the angles and distance to explore rotation motion and translation information together. In this paper, we propose a Radial Strip Transformer (RST), which is a transformer-based architecture that restores the blur images in a polar coordinate system instead of a Cartesian one. RST contains a dynamic radial embedding module (DRE) to extract the shallow feature by a radial deformable convolution. We design a polar mask layer to generate the offsets for the deformable convolution, which can reshape the convolution kernel along the radius to better capture the rotation motion information. Furthermore, we proposed a radial strip attention solver (RSAS) as deep feature extraction, where the relationship of windows is organized by azimuth and radius. This attention module contains radial strip windows to reweight image features in the polar coordinate, which preserves more useful information in rotation and translation motion together for better recovering the sharp images. Experimental results on six synthesis and real-world datasets prove that our method performs favorably against other SOTA methods for the image deblurring task. |
2205.15172 | Guilherme Moraes Rosa | Guilherme Moraes Rosa and Luiz Bonifacio and Vitor Jeronymo and Hugo
Abonizio and Roberto Lotufo and Rodrigo Nogueira | Billions of Parameters Are Worth More Than In-domain Training Data: A
case study in the Legal Case Entailment Task | null | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work has shown that language models scaled to billions of parameters,
such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In
this work, we experiment with zero-shot models in the legal case entailment
task of the COLIEE 2022 competition. Our experiments show that scaling the
number of parameters in a language model improves the F1 score of our previous
zero-shot result by more than 6 points, suggesting that stronger zero-shot
capability may be a characteristic of larger models, at least for this task.
Our 3B-parameter zero-shot model outperforms all models, including ensembles,
in the COLIEE 2021 test set and also achieves the best performance of a single
model in the COLIEE 2022 competition, second only to the ensemble composed of
the 3B model itself and a smaller version of the same model. Despite the
challenges posed by large language models, mainly due to latency constraints in
real-time applications, we provide a demonstration of our zero-shot monoT5-3b
model being used in production as a search engine, including for legal
documents. The code for our submission and the demo of our system are available
at https://github.com/neuralmind-ai/coliee and
https://neuralsearchx.neuralmind.ai, respectively.
| [
{
"created": "Mon, 30 May 2022 15:21:26 GMT",
"version": "v1"
}
] | 2022-05-31 | [
[
"Rosa",
"Guilherme Moraes",
""
],
[
"Bonifacio",
"Luiz",
""
],
[
"Jeronymo",
"Vitor",
""
],
[
"Abonizio",
"Hugo",
""
],
[
"Lotufo",
"Roberto",
""
],
[
"Nogueira",
"Rodrigo",
""
]
] | Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. In this work, we experiment with zero-shot models in the legal case entailment task of the COLIEE 2022 competition. Our experiments show that scaling the number of parameters in a language model improves the F1 score of our previous zero-shot result by more than 6 points, suggesting that stronger zero-shot capability may be a characteristic of larger models, at least for this task. Our 3B-parameter zero-shot model outperforms all models, including ensembles, in the COLIEE 2021 test set and also achieves the best performance of a single model in the COLIEE 2022 competition, second only to the ensemble composed of the 3B model itself and a smaller version of the same model. Despite the challenges posed by large language models, mainly due to latency constraints in real-time applications, we provide a demonstration of our zero-shot monoT5-3b model being used in production as a search engine, including for legal documents. The code for our submission and the demo of our system are available at https://github.com/neuralmind-ai/coliee and https://neuralsearchx.neuralmind.ai, respectively. |
1912.08776 | Byungsoo Kim | Simon Biland, Vinicius C. Azevedo, Byungsoo Kim and Barbara
Solenthaler | Frequency-Aware Reconstruction of Fluid Simulations with Generative
Networks | Submitted to Eurographics2020 | Eurographics 2020 - Short Papers | 10.2312/egs.20201019 | null | cs.LG cs.GR physics.comp-ph stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Convolutional neural networks were recently employed to fully reconstruct
fluid simulation data from a set of reduced parameters. However, since
(de-)convolutions traditionally trained with supervised L1-loss functions do
not discriminate between low and high frequencies in the data, the error is not
minimized efficiently for higher bands. This directly correlates with the
quality of the perceived results, since missing high frequency details are
easily noticeable. In this paper, we analyze the reconstruction quality of
generative networks and present a frequency-aware loss function that is able to
focus on specific bands of the dataset during training time. We show that our
approach improves reconstruction quality of fluid simulation data in
mid-frequency bands, yielding perceptually better results while requiring
comparable training time.
| [
{
"created": "Wed, 18 Dec 2019 18:13:22 GMT",
"version": "v1"
}
] | 2020-05-29 | [
[
"Biland",
"Simon",
""
],
[
"Azevedo",
"Vinicius C.",
""
],
[
"Kim",
"Byungsoo",
""
],
[
"Solenthaler",
"Barbara",
""
]
] | Convolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters. However, since (de-)convolutions traditionally trained with supervised L1-loss functions do not discriminate between low and high frequencies in the data, the error is not minimized efficiently for higher bands. This directly correlates with the quality of the perceived results, since missing high frequency details are easily noticeable. In this paper, we analyze the reconstruction quality of generative networks and present a frequency-aware loss function that is able to focus on specific bands of the dataset during training time. We show that our approach improves reconstruction quality of fluid simulation data in mid-frequency bands, yielding perceptually better results while requiring comparable training time. |
1706.08106 | Christophe Guyeux | Wiem Elghazel, Kamal Medjaher, Nourredine Zerhouni, Jacques Bahi,
Ahamd Farhat, Christophe Guyeux, and Mourad Hakem | Random Forests for Industrial Device Functioning Diagnostics Using
Wireless Sensor Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, random forests are proposed for operating devices diagnostics
in the presence of a variable number of features. In various contexts, like
large or difficult-to-access monitored areas, wired sensor networks providing
features to achieve diagnostics are either very costly to use or totally
impossible to spread out. Using a wireless sensor network can solve this
problem, but this latter is more subjected to flaws. Furthermore, the networks'
topology often changes, leading to a variability in quality of coverage in the
targeted area. Diagnostics at the sink level must take into consideration that
both the number and the quality of the provided features are not constant, and
that some politics like scheduling or data aggregation may be developed across
the network. The aim of this article is ($1$) to show that random forests are
relevant in this context, due to their flexibility and robustness, and ($2$) to
provide first examples of use of this method for diagnostics based on data
provided by a wireless sensor network.
| [
{
"created": "Sun, 25 Jun 2017 13:54:33 GMT",
"version": "v1"
}
] | 2017-06-27 | [
[
"Elghazel",
"Wiem",
""
],
[
"Medjaher",
"Kamal",
""
],
[
"Zerhouni",
"Nourredine",
""
],
[
"Bahi",
"Jacques",
""
],
[
"Farhat",
"Ahamd",
""
],
[
"Guyeux",
"Christophe",
""
],
[
"Hakem",
"Mourad",
""
]
] | In this paper, random forests are proposed for operating devices diagnostics in the presence of a variable number of features. In various contexts, like large or difficult-to-access monitored areas, wired sensor networks providing features to achieve diagnostics are either very costly to use or totally impossible to spread out. Using a wireless sensor network can solve this problem, but this latter is more subjected to flaws. Furthermore, the networks' topology often changes, leading to a variability in quality of coverage in the targeted area. Diagnostics at the sink level must take into consideration that both the number and the quality of the provided features are not constant, and that some politics like scheduling or data aggregation may be developed across the network. The aim of this article is ($1$) to show that random forests are relevant in this context, due to their flexibility and robustness, and ($2$) to provide first examples of use of this method for diagnostics based on data provided by a wireless sensor network. |
1911.09996 | Vacit Oguz Yazici | Vacit Oguz Yazici, Abel Gonzalez-Garcia, Arnau Ramisa, Bartlomiej
Twardowski, Joost van de Weijer | Orderless Recurrent Models for Multi-label Classification | Accepted to CVPR 2020 | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recurrent neural networks (RNN) are popular for many computer vision tasks,
including multi-label classification. Since RNNs produce sequential outputs,
labels need to be ordered for the multi-label classification task. Current
approaches sort labels according to their frequency, typically ordering them in
either rare-first or frequent-first. These imposed orderings do not take into
account that the natural order to generate the labels can change for each
image, e.g.\ first the dominant object before summing up the smaller objects in
the image. Therefore, in this paper, we propose ways to dynamically order the
ground truth labels with the predicted label sequence. This allows for the
faster training of more optimal LSTM models for multi-label classification.
Analysis evidences that our method does not suffer from duplicate generation,
something which is common for other models. Furthermore, it outperforms other
CNN-RNN models, and we show that a standard architecture of an image encoder
and language decoder trained with our proposed loss obtains the
state-of-the-art results on the challenging MS-COCO, WIDER Attribute and
PA-100K and competitive results on NUS-WIDE.
| [
{
"created": "Fri, 22 Nov 2019 12:25:14 GMT",
"version": "v1"
},
{
"created": "Mon, 25 Nov 2019 11:16:41 GMT",
"version": "v2"
},
{
"created": "Thu, 12 Mar 2020 17:10:18 GMT",
"version": "v3"
}
] | 2020-03-13 | [
[
"Yazici",
"Vacit Oguz",
""
],
[
"Gonzalez-Garcia",
"Abel",
""
],
[
"Ramisa",
"Arnau",
""
],
[
"Twardowski",
"Bartlomiej",
""
],
[
"van de Weijer",
"Joost",
""
]
] | Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels according to their frequency, typically ordering them in either rare-first or frequent-first. These imposed orderings do not take into account that the natural order to generate the labels can change for each image, e.g.\ first the dominant object before summing up the smaller objects in the image. Therefore, in this paper, we propose ways to dynamically order the ground truth labels with the predicted label sequence. This allows for the faster training of more optimal LSTM models for multi-label classification. Analysis evidences that our method does not suffer from duplicate generation, something which is common for other models. Furthermore, it outperforms other CNN-RNN models, and we show that a standard architecture of an image encoder and language decoder trained with our proposed loss obtains the state-of-the-art results on the challenging MS-COCO, WIDER Attribute and PA-100K and competitive results on NUS-WIDE. |
2302.00762 | Chaitanya Malaviya | Yuewei Yuan, Chaitanya Malaviya, Mark Yatskar | AmbiCoref: Evaluating Human and Model Sensitivity to Ambiguous
Coreference | EACL 2023 Findings | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Given a sentence "Abby told Brittney that she upset Courtney", one would
struggle to understand who "she" refers to, and ask for clarification. However,
if the word "upset" were replaced with "hugged", "she" unambiguously refers to
Abby. We study if modern coreference resolution models are sensitive to such
pronominal ambiguity. To this end, we construct AmbiCoref, a diagnostic corpus
of minimal sentence pairs with ambiguous and unambiguous referents. Our
examples generalize psycholinguistic studies of human perception of ambiguity
around particular arrangements of verbs and their arguments. Analysis shows
that (1) humans are less sure of referents in ambiguous AmbiCoref examples than
unambiguous ones, and (2) most coreference models show little difference in
output between ambiguous and unambiguous pairs. We release AmbiCoref as a
diagnostic corpus for testing whether models treat ambiguity similarly to
humans.
| [
{
"created": "Wed, 1 Feb 2023 21:25:34 GMT",
"version": "v1"
},
{
"created": "Fri, 3 Feb 2023 16:07:53 GMT",
"version": "v2"
}
] | 2023-02-06 | [
[
"Yuan",
"Yuewei",
""
],
[
"Malaviya",
"Chaitanya",
""
],
[
"Yatskar",
"Mark",
""
]
] | Given a sentence "Abby told Brittney that she upset Courtney", one would struggle to understand who "she" refers to, and ask for clarification. However, if the word "upset" were replaced with "hugged", "she" unambiguously refers to Abby. We study if modern coreference resolution models are sensitive to such pronominal ambiguity. To this end, we construct AmbiCoref, a diagnostic corpus of minimal sentence pairs with ambiguous and unambiguous referents. Our examples generalize psycholinguistic studies of human perception of ambiguity around particular arrangements of verbs and their arguments. Analysis shows that (1) humans are less sure of referents in ambiguous AmbiCoref examples than unambiguous ones, and (2) most coreference models show little difference in output between ambiguous and unambiguous pairs. We release AmbiCoref as a diagnostic corpus for testing whether models treat ambiguity similarly to humans. |
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