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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2210.17152 | Ernie Chu | Ernie Chu, Ju-Ting Chen, Chia-Ping Chen | Audio Time-Scale Modification with Temporal Compressing Networks | null | null | null | null | cs.SD eess.AS | http://creativecommons.org/licenses/by/4.0/ | We propose a novel approach for time-scale modification of audio signals.
Unlike traditional methods that rely on the framing technique or the short-time
Fourier transform to preserve the frequency during temporal stretching, our
neural network model encodes the raw audio into a high-level latent
representation, dubbed Neuralgram, where each vector represents 1024 audio
sample points. Due to a sufficient compression ratio, we are able to apply
arbitrary spatial interpolation of the Neuralgram to perform temporal
stretching. Finally, a learned neural decoder synthesizes the time-scaled audio
samples based on the stretched Neuralgram representation. Both the encoder and
decoder are trained with latent regression losses and adversarial losses in
order to obtain high-fidelity audio samples. Despite its simplicity, our method
has comparable performance compared to the existing baselines and opens a new
possibility in research into modern time-scale modification. Audio samples can
be found at https://tsmnet-mmasia23.github.io
| [
{
"created": "Mon, 31 Oct 2022 09:04:33 GMT",
"version": "v1"
},
{
"created": "Sun, 28 May 2023 10:50:04 GMT",
"version": "v2"
},
{
"created": "Fri, 6 Oct 2023 04:32:16 GMT",
"version": "v3"
}
] | 2023-10-09 | [
[
"Chu",
"Ernie",
""
],
[
"Chen",
"Ju-Ting",
""
],
[
"Chen",
"Chia-Ping",
""
]
] | We propose a novel approach for time-scale modification of audio signals. Unlike traditional methods that rely on the framing technique or the short-time Fourier transform to preserve the frequency during temporal stretching, our neural network model encodes the raw audio into a high-level latent representation, dubbed Neuralgram, where each vector represents 1024 audio sample points. Due to a sufficient compression ratio, we are able to apply arbitrary spatial interpolation of the Neuralgram to perform temporal stretching. Finally, a learned neural decoder synthesizes the time-scaled audio samples based on the stretched Neuralgram representation. Both the encoder and decoder are trained with latent regression losses and adversarial losses in order to obtain high-fidelity audio samples. Despite its simplicity, our method has comparable performance compared to the existing baselines and opens a new possibility in research into modern time-scale modification. Audio samples can be found at https://tsmnet-mmasia23.github.io |
2406.00851 | Ratip Emin Berker | Ratip Emin Berker and Vincent Conitzer | Computing Optimal Equilibria in Repeated Games with Restarts | 13 pages, 2 figures, main body to be published in Proceedings of the
Thirty-Third International Joint Conference on Artificial Intelligence
(IJCAI-24), Jeju, South Korea, 2024 | null | null | null | cs.GT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Infinitely repeated games can support cooperative outcomes that are not
equilibria in the one-shot game. The idea is to make sure that any gains from
deviating will be offset by retaliation in future rounds. However, this model
of cooperation fails in anonymous settings with many strategic agents that
interact in pairs. Here, a player can defect and then avoid penalization by
immediately switching partners. In this paper, we focus on a specific set of
equilibria that avoids this pitfall. In them, agents follow a designated
sequence of actions, and restart if their opponent ever deviates. We show that
the socially-optimal sequence of actions consists of an infinitely repeating
goal value, preceded by a hazing period. We introduce an equivalence relation
on sequences and prove that the computational problem of finding a
representative from the optimal equivalence class is (weakly) NP-hard.
Nevertheless, we present a pseudo-polynomial time dynamic program for this
problem, as well as an integer linear program, and show they are efficient in
practice. Lastly, we introduce a fully polynomial-time approximation scheme
that outputs a hazing sequence with arbitrarily small approximation ratio.
| [
{
"created": "Sun, 2 Jun 2024 20:07:05 GMT",
"version": "v1"
}
] | 2024-06-04 | [
[
"Berker",
"Ratip Emin",
""
],
[
"Conitzer",
"Vincent",
""
]
] | Infinitely repeated games can support cooperative outcomes that are not equilibria in the one-shot game. The idea is to make sure that any gains from deviating will be offset by retaliation in future rounds. However, this model of cooperation fails in anonymous settings with many strategic agents that interact in pairs. Here, a player can defect and then avoid penalization by immediately switching partners. In this paper, we focus on a specific set of equilibria that avoids this pitfall. In them, agents follow a designated sequence of actions, and restart if their opponent ever deviates. We show that the socially-optimal sequence of actions consists of an infinitely repeating goal value, preceded by a hazing period. We introduce an equivalence relation on sequences and prove that the computational problem of finding a representative from the optimal equivalence class is (weakly) NP-hard. Nevertheless, we present a pseudo-polynomial time dynamic program for this problem, as well as an integer linear program, and show they are efficient in practice. Lastly, we introduce a fully polynomial-time approximation scheme that outputs a hazing sequence with arbitrarily small approximation ratio. |
2102.03206 | Veljko Milutinovic Prof | Miroslav Kosanic and Veljko Milutinovic | A Survey on Mathematical Aspects of Machine Learning in GeoPhysics: The
Cases of Weather Forecast, Wind Energy, Wave Energy, Oil and Gas Exploration | 10 pages, 3 figures, review paper | null | null | null | cs.LG physics.geo-ph | http://creativecommons.org/licenses/by/4.0/ | This paper reviews the most notable works applying machine learning
techniques (ML) in the context of geophysics and corresponding subbranches. We
showcase both the progress achieved to date as well as the important future
directions for further research while providing an adequate background in the
fields of weather forecast, wind energy, wave energy, oil and gas exploration.
The objective is to reflect on the previous successes and provide a
comprehensive review of the synergy between these two fields in order to speed
up the novel approaches of machine learning techniques in geophysics. Last but
not least, we would like to point out possible improvements, some of which are
related to the implementation of ML algorithms using DataFlow paradigm as a
means of performance acceleration.
| [
{
"created": "Fri, 5 Feb 2021 14:44:34 GMT",
"version": "v1"
}
] | 2021-02-08 | [
[
"Kosanic",
"Miroslav",
""
],
[
"Milutinovic",
"Veljko",
""
]
] | This paper reviews the most notable works applying machine learning techniques (ML) in the context of geophysics and corresponding subbranches. We showcase both the progress achieved to date as well as the important future directions for further research while providing an adequate background in the fields of weather forecast, wind energy, wave energy, oil and gas exploration. The objective is to reflect on the previous successes and provide a comprehensive review of the synergy between these two fields in order to speed up the novel approaches of machine learning techniques in geophysics. Last but not least, we would like to point out possible improvements, some of which are related to the implementation of ML algorithms using DataFlow paradigm as a means of performance acceleration. |
2406.01698 | Abhimanyu Rajeshkumar Bambhaniya | Abhimanyu Bambhaniya, Ritik Raj, Geonhwa Jeong, Souvik Kundu,
Sudarshan Srinivasan, Midhilesh Elavazhagan, Madhu Kumar and Tushar Krishna | Demystifying Platform Requirements for Diverse LLM Inference Use Cases | 12 Pages, https://github.com/abhibambhaniya/GenZ-LLM-Analyzer | null | null | null | cs.AR cs.AI cs.DC cs.LG | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Large language models (LLMs) have shown remarkable performance across a wide
range of applications, often outperforming human experts. However, deploying
these parameter-heavy models efficiently for diverse inference use cases
requires carefully designed hardware platforms with ample computing, memory,
and network resources. With LLM deployment scenarios and models evolving at
breakneck speed, the hardware requirements to meet SLOs remains an open
research question. In this work, we present an analytical tool, GenZ, to study
the relationship between LLM inference performance and various platform design
parameters. Our analysis provides insights into configuring platforms for
different LLM workloads and use cases. We quantify the platform requirements to
support SOTA LLMs models like LLaMA and GPT-4 under diverse serving settings.
Furthermore, we project the hardware capabilities needed to enable future LLMs
potentially exceeding hundreds of trillions of parameters. The trends and
insights derived from GenZ can guide AI engineers deploying LLMs as well as
computer architects designing next-generation hardware accelerators and
platforms. Ultimately, this work sheds light on the platform design
considerations for unlocking the full potential of large language models across
a spectrum of applications. The source code is available at
https://github.com/abhibambhaniya/GenZ-LLM-Analyzer .
| [
{
"created": "Mon, 3 Jun 2024 18:00:50 GMT",
"version": "v1"
}
] | 2024-06-05 | [
[
"Bambhaniya",
"Abhimanyu",
""
],
[
"Raj",
"Ritik",
""
],
[
"Jeong",
"Geonhwa",
""
],
[
"Kundu",
"Souvik",
""
],
[
"Srinivasan",
"Sudarshan",
""
],
[
"Elavazhagan",
"Midhilesh",
""
],
[
"Kumar",
"Madhu",
""
],
[
"Krishna",
"Tushar",
""
]
] | Large language models (LLMs) have shown remarkable performance across a wide range of applications, often outperforming human experts. However, deploying these parameter-heavy models efficiently for diverse inference use cases requires carefully designed hardware platforms with ample computing, memory, and network resources. With LLM deployment scenarios and models evolving at breakneck speed, the hardware requirements to meet SLOs remains an open research question. In this work, we present an analytical tool, GenZ, to study the relationship between LLM inference performance and various platform design parameters. Our analysis provides insights into configuring platforms for different LLM workloads and use cases. We quantify the platform requirements to support SOTA LLMs models like LLaMA and GPT-4 under diverse serving settings. Furthermore, we project the hardware capabilities needed to enable future LLMs potentially exceeding hundreds of trillions of parameters. The trends and insights derived from GenZ can guide AI engineers deploying LLMs as well as computer architects designing next-generation hardware accelerators and platforms. Ultimately, this work sheds light on the platform design considerations for unlocking the full potential of large language models across a spectrum of applications. The source code is available at https://github.com/abhibambhaniya/GenZ-LLM-Analyzer . |
2307.08347 | Che Liu | Che Liu, Sibo Cheng, Chen Chen, Mengyun Qiao, Weitong Zhang, Anand
Shah, Wenjia Bai, Rossella Arcucci | M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models
and Latent Space Geometry Optimization | Accepted by MICCAI 2023 | null | null | null | cs.CV cs.AI cs.LG | http://creativecommons.org/licenses/by-sa/4.0/ | Medical vision-language models enable co-learning and integrating features
from medical imaging and clinical text. However, these models are not easy to
train and the latent representation space can be complex. Here we propose a
novel way for pre-training and regularising medical vision-language models. The
proposed method, named Medical vision-language pre-training with Frozen
language models and Latent spAce Geometry optimization (M-FLAG), leverages a
frozen language model for training stability and efficiency and introduces a
novel orthogonality loss to harmonize the latent space geometry. We demonstrate
the potential of the pre-trained model on three downstream tasks: medical image
classification, segmentation, and object detection. Extensive experiments
across five public datasets demonstrate that M-FLAG significantly outperforms
existing medical vision-language pre-training approaches and reduces the number
of parameters by 78\%. Notably, M-FLAG achieves outstanding performance on the
segmentation task while using only 1\% of the RSNA dataset, even outperforming
ImageNet pre-trained models that have been fine-tuned using 100\% of the data.
| [
{
"created": "Mon, 17 Jul 2023 09:38:41 GMT",
"version": "v1"
},
{
"created": "Wed, 19 Jul 2023 13:55:32 GMT",
"version": "v2"
}
] | 2023-07-20 | [
[
"Liu",
"Che",
""
],
[
"Cheng",
"Sibo",
""
],
[
"Chen",
"Chen",
""
],
[
"Qiao",
"Mengyun",
""
],
[
"Zhang",
"Weitong",
""
],
[
"Shah",
"Anand",
""
],
[
"Bai",
"Wenjia",
""
],
[
"Arcucci",
"Rossella",
""
]
] | Medical vision-language models enable co-learning and integrating features from medical imaging and clinical text. However, these models are not easy to train and the latent representation space can be complex. Here we propose a novel way for pre-training and regularising medical vision-language models. The proposed method, named Medical vision-language pre-training with Frozen language models and Latent spAce Geometry optimization (M-FLAG), leverages a frozen language model for training stability and efficiency and introduces a novel orthogonality loss to harmonize the latent space geometry. We demonstrate the potential of the pre-trained model on three downstream tasks: medical image classification, segmentation, and object detection. Extensive experiments across five public datasets demonstrate that M-FLAG significantly outperforms existing medical vision-language pre-training approaches and reduces the number of parameters by 78\%. Notably, M-FLAG achieves outstanding performance on the segmentation task while using only 1\% of the RSNA dataset, even outperforming ImageNet pre-trained models that have been fine-tuned using 100\% of the data. |
1905.00851 | Thomas M\"ollenhoff | Thomas M\"ollenhoff, Daniel Cremers | Lifting Vectorial Variational Problems: A Natural Formulation based on
Geometric Measure Theory and Discrete Exterior Calculus | Oral presentation at CVPR 2019 | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Numerous tasks in imaging and vision can be formulated as variational
problems over vector-valued maps. We approach the relaxation and
convexification of such vectorial variational problems via a lifting to the
space of currents. To that end, we recall that functionals with polyconvex
Lagrangians can be reparametrized as convex one-homogeneous functionals on the
graph of the function. This leads to an equivalent shape optimization problem
over oriented surfaces in the product space of domain and codomain. A convex
formulation is then obtained by relaxing the search space from oriented
surfaces to more general currents. We propose a discretization of the resulting
infinite-dimensional optimization problem using Whitney forms, which also
generalizes recent "sublabel-accurate" multilabeling approaches.
| [
{
"created": "Thu, 2 May 2019 16:54:58 GMT",
"version": "v1"
}
] | 2019-05-03 | [
[
"Möllenhoff",
"Thomas",
""
],
[
"Cremers",
"Daniel",
""
]
] | Numerous tasks in imaging and vision can be formulated as variational problems over vector-valued maps. We approach the relaxation and convexification of such vectorial variational problems via a lifting to the space of currents. To that end, we recall that functionals with polyconvex Lagrangians can be reparametrized as convex one-homogeneous functionals on the graph of the function. This leads to an equivalent shape optimization problem over oriented surfaces in the product space of domain and codomain. A convex formulation is then obtained by relaxing the search space from oriented surfaces to more general currents. We propose a discretization of the resulting infinite-dimensional optimization problem using Whitney forms, which also generalizes recent "sublabel-accurate" multilabeling approaches. |
2405.11523 | Youmin Xu | Youmin Xu, Xuanyu Zhang, Jiwen Yu, Chong Mou, Xiandong Meng, Jian
Zhang | Diffusion-Based Hierarchical Image Steganography | arXiv admin note: text overlap with arXiv:2305.16936 | null | null | A-01 | cs.CV | http://creativecommons.org/licenses/by/4.0/ | This paper introduces Hierarchical Image Steganography, a novel method that
enhances the security and capacity of embedding multiple images into a single
container using diffusion models. HIS assigns varying levels of robustness to
images based on their importance, ensuring enhanced protection against
manipulation. It adaptively exploits the robustness of the Diffusion Model
alongside the reversibility of the Flow Model. The integration of Embed-Flow
and Enhance-Flow improves embedding efficiency and image recovery quality,
respectively, setting HIS apart from conventional multi-image steganography
techniques. This innovative structure can autonomously generate a container
image, thereby securely and efficiently concealing multiple images and text.
Rigorous subjective and objective evaluations underscore our advantage in
analytical resistance, robustness, and capacity, illustrating its expansive
applicability in content safeguarding and privacy fortification.
| [
{
"created": "Sun, 19 May 2024 11:29:52 GMT",
"version": "v1"
}
] | 2024-05-21 | [
[
"Xu",
"Youmin",
""
],
[
"Zhang",
"Xuanyu",
""
],
[
"Yu",
"Jiwen",
""
],
[
"Mou",
"Chong",
""
],
[
"Meng",
"Xiandong",
""
],
[
"Zhang",
"Jian",
""
]
] | This paper introduces Hierarchical Image Steganography, a novel method that enhances the security and capacity of embedding multiple images into a single container using diffusion models. HIS assigns varying levels of robustness to images based on their importance, ensuring enhanced protection against manipulation. It adaptively exploits the robustness of the Diffusion Model alongside the reversibility of the Flow Model. The integration of Embed-Flow and Enhance-Flow improves embedding efficiency and image recovery quality, respectively, setting HIS apart from conventional multi-image steganography techniques. This innovative structure can autonomously generate a container image, thereby securely and efficiently concealing multiple images and text. Rigorous subjective and objective evaluations underscore our advantage in analytical resistance, robustness, and capacity, illustrating its expansive applicability in content safeguarding and privacy fortification. |
1912.12616 | Sherif Tarabishy | Sherif Tarabishy, Stamatios Psarras, Marcin Kosicki, Martha Tsigkari | Deep learning surrogate models for spatial and visual connectivity | Accepted manuscript in the International Journal of Architectural
Computing (2019) | null | 10.1177/1478077119894483 | null | cs.LG cs.CV eess.IV stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Spatial and visual connectivity are important metrics when developing
workplace layouts. Calculating those metrics in real-time can be difficult,
depending on the size of the floor plan being analysed and the resolution of
the analyses. This paper investigates the possibility of considerably speeding
up the outcomes of such computationally intensive simulations by using machine
learning to create models capable of identifying the spatial and visual
connectivity potential of a space. To that end we present the entire process of
investigating different machine learning models and a pipeline for training
them on such task, from the incorporation of a bespoke spatial and visual
connectivity analysis engine through a distributed computation pipeline, to the
process of synthesizing training data and evaluating the performance of
different neural networks.
| [
{
"created": "Sun, 29 Dec 2019 09:17:19 GMT",
"version": "v1"
}
] | 2020-01-01 | [
[
"Tarabishy",
"Sherif",
""
],
[
"Psarras",
"Stamatios",
""
],
[
"Kosicki",
"Marcin",
""
],
[
"Tsigkari",
"Martha",
""
]
] | Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real-time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses. This paper investigates the possibility of considerably speeding up the outcomes of such computationally intensive simulations by using machine learning to create models capable of identifying the spatial and visual connectivity potential of a space. To that end we present the entire process of investigating different machine learning models and a pipeline for training them on such task, from the incorporation of a bespoke spatial and visual connectivity analysis engine through a distributed computation pipeline, to the process of synthesizing training data and evaluating the performance of different neural networks. |
2401.12266 | Yawen Zhang | Yawen Zhang | An Exploratory Study of Multimodal Physiological Data in Jazz
Improvisation Using Basic Machine Learning Techniques | Master's thesis | null | null | null | cs.SD eess.AS | http://creativecommons.org/licenses/by/4.0/ | Our study delves into the "Embodied Musicking Dataset," exploring the
intertwined relationships and correlations between physiological and
psychological dimensions during improvisational music performances. The primary
objective is to ascertain the presence of a definitive causal or correlational
relationship between these states and comprehend their manifestation in musical
compositions. This rich dataset provides a perspective on how musicians
coordinate their physicality with sonic events in real-time improvisational
scenarios, emphasizing the concept of "Embodied Musicking."
| [
{
"created": "Mon, 22 Jan 2024 10:32:18 GMT",
"version": "v1"
}
] | 2024-01-24 | [
[
"Zhang",
"Yawen",
""
]
] | Our study delves into the "Embodied Musicking Dataset," exploring the intertwined relationships and correlations between physiological and psychological dimensions during improvisational music performances. The primary objective is to ascertain the presence of a definitive causal or correlational relationship between these states and comprehend their manifestation in musical compositions. This rich dataset provides a perspective on how musicians coordinate their physicality with sonic events in real-time improvisational scenarios, emphasizing the concept of "Embodied Musicking." |
1805.10407 | Sang Michael Xie | Neal Jean, Sang Michael Xie, Stefano Ermon | Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by
Minimizing Predictive Variance | In Proceedings of Neural Information Processing Systems (NeurIPS)
2018 | null | null | null | cs.LG cs.AI stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large amounts of labeled data are typically required to train deep learning
models. For many real-world problems, however, acquiring additional data can be
expensive or even impossible. We present semi-supervised deep kernel learning
(SSDKL), a semi-supervised regression model based on minimizing predictive
variance in the posterior regularization framework. SSDKL combines the
hierarchical representation learning of neural networks with the probabilistic
modeling capabilities of Gaussian processes. By leveraging unlabeled data, we
show improvements on a diverse set of real-world regression tasks over
supervised deep kernel learning and semi-supervised methods such as VAT and
mean teacher adapted for regression.
| [
{
"created": "Sat, 26 May 2018 00:47:14 GMT",
"version": "v1"
},
{
"created": "Mon, 26 Nov 2018 00:36:05 GMT",
"version": "v2"
},
{
"created": "Sat, 5 Jan 2019 18:41:06 GMT",
"version": "v3"
},
{
"created": "Mon, 4 Mar 2019 18:55:13 GMT",
"version": "v4"
}
] | 2019-03-05 | [
[
"Jean",
"Neal",
""
],
[
"Xie",
"Sang Michael",
""
],
[
"Ermon",
"Stefano",
""
]
] | Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularization framework. SSDKL combines the hierarchical representation learning of neural networks with the probabilistic modeling capabilities of Gaussian processes. By leveraging unlabeled data, we show improvements on a diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression. |
1107.4940 | Dejan Kovachev | Dejan Kovachev, Yiwei Cao and Ralf Klamma | Mobile Cloud Computing: A Comparison of Application Models | null | null | null | null | cs.NI cs.DC cs.MM | http://creativecommons.org/licenses/by/3.0/ | Cloud computing is an emerging concept combining many fields of computing.
The foundation of cloud computing is the delivery of services, software and
processing capacity over the Internet, reducing cost, increasing storage,
automating systems, decoupling of service delivery from underlying technology,
and providing flexibility and mobility of information. However, the actual
realization of these benefits is far from being achieved for mobile
applications and open many new research questions. In order to better
understand how to facilitate the building of mobile cloud-based applications,
we have surveyed existing work in mobile computing through the prism of cloud
computing principles. We give a definition of mobile cloud coputing and provide
an overview of the results from this review, in particular, models of mobile
cloud applications. We also highlight research challenges in the area of mobile
cloud computing. We conclude with recommendations for how this better
understanding of mobile cloud computing can help building more powerful mobile
applications.
| [
{
"created": "Mon, 25 Jul 2011 13:17:13 GMT",
"version": "v1"
}
] | 2011-07-26 | [
[
"Kovachev",
"Dejan",
""
],
[
"Cao",
"Yiwei",
""
],
[
"Klamma",
"Ralf",
""
]
] | Cloud computing is an emerging concept combining many fields of computing. The foundation of cloud computing is the delivery of services, software and processing capacity over the Internet, reducing cost, increasing storage, automating systems, decoupling of service delivery from underlying technology, and providing flexibility and mobility of information. However, the actual realization of these benefits is far from being achieved for mobile applications and open many new research questions. In order to better understand how to facilitate the building of mobile cloud-based applications, we have surveyed existing work in mobile computing through the prism of cloud computing principles. We give a definition of mobile cloud coputing and provide an overview of the results from this review, in particular, models of mobile cloud applications. We also highlight research challenges in the area of mobile cloud computing. We conclude with recommendations for how this better understanding of mobile cloud computing can help building more powerful mobile applications. |
2104.08353 | Pablo Barros | Pablo Barros, Alessandra Sciutti | I Only Have Eyes for You: The Impact of Masks On Convolutional-Based
Facial Expression Recognition | Accepted at the LXCV Workshop @ CVPR2021 | null | null | null | cs.CV cs.NE | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The current COVID-19 pandemic has shown us that we are still facing
unpredictable challenges in our society. The necessary constrain on social
interactions affected heavily how we envision and prepare the future of social
robots and artificial agents in general. Adapting current affective perception
models towards constrained perception based on the hard separation between
facial perception and affective understanding would help us to provide robust
systems. In this paper, we perform an in-depth analysis of how recognizing
affect from persons with masks differs from general facial expression
perception. We evaluate how the recently proposed FaceChannel adapts towards
recognizing facial expressions from persons with masks. In Our analysis, we
evaluate different training and fine-tuning schemes to understand better the
impact of masked facial expressions. We also perform specific feature-level
visualization to demonstrate how the inherent capabilities of the FaceChannel
to learn and combine facial features change when in a constrained social
interaction scenario.
| [
{
"created": "Fri, 16 Apr 2021 20:03:30 GMT",
"version": "v1"
}
] | 2021-04-20 | [
[
"Barros",
"Pablo",
""
],
[
"Sciutti",
"Alessandra",
""
]
] | The current COVID-19 pandemic has shown us that we are still facing unpredictable challenges in our society. The necessary constrain on social interactions affected heavily how we envision and prepare the future of social robots and artificial agents in general. Adapting current affective perception models towards constrained perception based on the hard separation between facial perception and affective understanding would help us to provide robust systems. In this paper, we perform an in-depth analysis of how recognizing affect from persons with masks differs from general facial expression perception. We evaluate how the recently proposed FaceChannel adapts towards recognizing facial expressions from persons with masks. In Our analysis, we evaluate different training and fine-tuning schemes to understand better the impact of masked facial expressions. We also perform specific feature-level visualization to demonstrate how the inherent capabilities of the FaceChannel to learn and combine facial features change when in a constrained social interaction scenario. |
2111.14673 | Muhammad Ferjad Naeem | Muhammad Ferjad Naeem, Evin P{\i}nar \"Ornek, Yongqin Xian, Luc Van
Gool, Federico Tombari | 3D Compositional Zero-shot Learning with DeCompositional Consensus | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Parts represent a basic unit of geometric and semantic similarity across
different objects. We argue that part knowledge should be composable beyond the
observed object classes. Towards this, we present 3D Compositional Zero-shot
Learning as a problem of part generalization from seen to unseen object classes
for semantic segmentation. We provide a structured study through benchmarking
the task with the proposed Compositional-PartNet dataset. This dataset is
created by processing the original PartNet to maximize part overlap across
different objects. The existing point cloud part segmentation methods fail to
generalize to unseen object classes in this setting. As a solution, we propose
DeCompositional Consensus, which combines a part segmentation network with a
part scoring network. The key intuition to our approach is that a segmentation
mask over some parts should have a consensus with its part scores when each
part is taken apart. The two networks reason over different part combinations
defined in a per-object part prior to generate the most suitable segmentation
mask. We demonstrate that our method allows compositional zero-shot
segmentation and generalized zero-shot classification, and establishes the
state of the art on both tasks.
| [
{
"created": "Mon, 29 Nov 2021 16:34:53 GMT",
"version": "v1"
},
{
"created": "Fri, 15 Apr 2022 13:38:37 GMT",
"version": "v2"
}
] | 2022-04-18 | [
[
"Naeem",
"Muhammad Ferjad",
""
],
[
"Örnek",
"Evin Pınar",
""
],
[
"Xian",
"Yongqin",
""
],
[
"Van Gool",
"Luc",
""
],
[
"Tombari",
"Federico",
""
]
] | Parts represent a basic unit of geometric and semantic similarity across different objects. We argue that part knowledge should be composable beyond the observed object classes. Towards this, we present 3D Compositional Zero-shot Learning as a problem of part generalization from seen to unseen object classes for semantic segmentation. We provide a structured study through benchmarking the task with the proposed Compositional-PartNet dataset. This dataset is created by processing the original PartNet to maximize part overlap across different objects. The existing point cloud part segmentation methods fail to generalize to unseen object classes in this setting. As a solution, we propose DeCompositional Consensus, which combines a part segmentation network with a part scoring network. The key intuition to our approach is that a segmentation mask over some parts should have a consensus with its part scores when each part is taken apart. The two networks reason over different part combinations defined in a per-object part prior to generate the most suitable segmentation mask. We demonstrate that our method allows compositional zero-shot segmentation and generalized zero-shot classification, and establishes the state of the art on both tasks. |
2406.01435 | Fan He | Fan He, Mingzhen He, Lei Shi, Xiaolin Huang, Johan A.K. Suykens | Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel
Learning | arXiv admin note: text overlap with arXiv:2310.05236 | null | null | null | cs.LG stat.ML | http://creativecommons.org/licenses/by/4.0/ | Ridgeless regression has garnered attention among researchers, particularly
in light of the ``Benign Overfitting'' phenomenon, where models interpolating
noisy samples demonstrate robust generalization. However, kernel ridgeless
regression does not always perform well due to the lack of flexibility. This
paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths
(LAB) RBF kernels, incorporating kernel learning techniques to improve
performance in both experiments and theory. For the first time, we demonstrate
that functions learned from LAB RBF kernels belong to an integral space of
Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit
regularization in the proposed model, its optimization is equivalent to solving
an $\ell_0$-regularized problem in the integral space of RKHSs, elucidating the
origin of its generalization ability. Taking an approximation analysis
viewpoint, we introduce an $l_q$-norm analysis technique (with $0<q<1$) to
derive the learning rate for the proposed model under mild conditions. This
result deepens our theoretical understanding, explaining that our algorithm's
robust approximation ability arises from the large capacity of the integral
space of RKHSs, while its generalization ability is ensured by sparsity,
controlled by the number of support vectors. Experimental results on both
synthetic and real datasets validate our theoretical conclusions.
| [
{
"created": "Mon, 3 Jun 2024 15:28:12 GMT",
"version": "v1"
}
] | 2024-06-04 | [
[
"He",
"Fan",
""
],
[
"He",
"Mingzhen",
""
],
[
"Shi",
"Lei",
""
],
[
"Huang",
"Xiaolin",
""
],
[
"Suykens",
"Johan A. K.",
""
]
] | Ridgeless regression has garnered attention among researchers, particularly in light of the ``Benign Overfitting'' phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless regression does not always perform well due to the lack of flexibility. This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels, incorporating kernel learning techniques to improve performance in both experiments and theory. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit regularization in the proposed model, its optimization is equivalent to solving an $\ell_0$-regularized problem in the integral space of RKHSs, elucidating the origin of its generalization ability. Taking an approximation analysis viewpoint, we introduce an $l_q$-norm analysis technique (with $0<q<1$) to derive the learning rate for the proposed model under mild conditions. This result deepens our theoretical understanding, explaining that our algorithm's robust approximation ability arises from the large capacity of the integral space of RKHSs, while its generalization ability is ensured by sparsity, controlled by the number of support vectors. Experimental results on both synthetic and real datasets validate our theoretical conclusions. |
2303.05455 | Bartosz Minch | Bartosz Minch | In search of the most efficient and memory-saving visualization of high
dimensional data | PhD thesis on searching the most efficient and memory-saving
visualization of high dimensional data. arXiv admin note: substantial text
overlap with arXiv:1902.01108, arXiv:1602.00370 by other authors; text
overlap with arXiv:2109.02508 by other authors | null | null | null | cs.LG cs.HC | http://creativecommons.org/licenses/by/4.0/ | Interactive exploration of large, multidimensional datasets plays a very
important role in various scientific fields. It makes it possible not only to
identify important structural features and forms, such as clusters of vertices
and their connection patterns, but also to evaluate their interrelationships in
terms of position, distance, shape and connection density. We argue that the
visualization of multidimensional data is well approximated by the problem of
two-dimensional embedding of undirected nearest-neighbor graphs. The size of
complex networks is a major challenge for today's computer systems and still
requires more efficient data embedding algorithms. Existing reduction methods
are too slow and do not allow interactive manipulation. We show that
high-quality embeddings are produced with minimal time and memory complexity.
We present very efficient IVHD algorithms (CPU and GPU) and compare them with
the latest and most popular dimensionality reduction methods. We show that the
memory and time requirements are dramatically lower than for base codes. At the
cost of a slight degradation in embedding quality, IVHD preserves the main
structural properties of the data well with a much lower time budget. We also
present a meta-algorithm that allows the use of any unsupervised data embedding
method in a supervised manner.
| [
{
"created": "Mon, 27 Feb 2023 20:56:13 GMT",
"version": "v1"
}
] | 2023-03-10 | [
[
"Minch",
"Bartosz",
""
]
] | Interactive exploration of large, multidimensional datasets plays a very important role in various scientific fields. It makes it possible not only to identify important structural features and forms, such as clusters of vertices and their connection patterns, but also to evaluate their interrelationships in terms of position, distance, shape and connection density. We argue that the visualization of multidimensional data is well approximated by the problem of two-dimensional embedding of undirected nearest-neighbor graphs. The size of complex networks is a major challenge for today's computer systems and still requires more efficient data embedding algorithms. Existing reduction methods are too slow and do not allow interactive manipulation. We show that high-quality embeddings are produced with minimal time and memory complexity. We present very efficient IVHD algorithms (CPU and GPU) and compare them with the latest and most popular dimensionality reduction methods. We show that the memory and time requirements are dramatically lower than for base codes. At the cost of a slight degradation in embedding quality, IVHD preserves the main structural properties of the data well with a much lower time budget. We also present a meta-algorithm that allows the use of any unsupervised data embedding method in a supervised manner. |
2311.17728 | Patrick Lambein-Monette | Bernadette Charron-Bost and Patrick Lambein-Monette | Know your audience | null | null | null | null | cs.DC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Distributed function computation is the problem, for a networked system of
$n$ autonomous agents, to collectively compute the value $f(v_1, \ldots, v_n)$
of some input values, each initially private to one agent in the network. Here,
we study and organize results pertaining to distributed function computation in
anonymous networks, both for the static and the dynamic case, under a
communication model of directed and synchronous message exchanges, but with
varying assumptions in the degree of awareness or control that a single agent
has over its outneighbors.
Our main argument is three-fold. First, in the "blind broadcast" model, where
in each round an agent merely casts out a unique message without any knowledge
or control over its addressees, the computable functions are those that only
depend on the set of the input values, but not on their multiplicities or
relative frequencies in the input. Second, in contrast, when we assume either
that a) in each round, the agents know how many outneighbors they have; b) all
communications links in the network are bidirectional; or c) the agents may
address each of their outneighbors individually, then the set of computable
functions grows to contain all functions that depend on the relative
frequencies of each value in the input - such as the average - but not on their
multiplicities - thus, not the sum. Third, however, if one or several agents
are distinguished as leaders, or if the cardinality of the network is known,
then under any of the above three assumptions it becomes possible to recover
the complete multiset of the input values, and thus compute any function of the
distributed input as long as it is invariant under permutation of its
arguments. In the case of dynamic networks, we also discuss the impact of
multiple connectivity assumptions.
| [
{
"created": "Wed, 29 Nov 2023 15:34:55 GMT",
"version": "v1"
}
] | 2023-11-30 | [
[
"Charron-Bost",
"Bernadette",
""
],
[
"Lambein-Monette",
"Patrick",
""
]
] | Distributed function computation is the problem, for a networked system of $n$ autonomous agents, to collectively compute the value $f(v_1, \ldots, v_n)$ of some input values, each initially private to one agent in the network. Here, we study and organize results pertaining to distributed function computation in anonymous networks, both for the static and the dynamic case, under a communication model of directed and synchronous message exchanges, but with varying assumptions in the degree of awareness or control that a single agent has over its outneighbors. Our main argument is three-fold. First, in the "blind broadcast" model, where in each round an agent merely casts out a unique message without any knowledge or control over its addressees, the computable functions are those that only depend on the set of the input values, but not on their multiplicities or relative frequencies in the input. Second, in contrast, when we assume either that a) in each round, the agents know how many outneighbors they have; b) all communications links in the network are bidirectional; or c) the agents may address each of their outneighbors individually, then the set of computable functions grows to contain all functions that depend on the relative frequencies of each value in the input - such as the average - but not on their multiplicities - thus, not the sum. Third, however, if one or several agents are distinguished as leaders, or if the cardinality of the network is known, then under any of the above three assumptions it becomes possible to recover the complete multiset of the input values, and thus compute any function of the distributed input as long as it is invariant under permutation of its arguments. In the case of dynamic networks, we also discuss the impact of multiple connectivity assumptions. |
1902.02164 | Md Mehedi Hassan Onik | Nasr Al-Zaben, Md Mehedi Hassan Onik, Chul-Soo Kim, Jinhong Yang | Communication Interface Identifier Protocol (CIIP): An Energy Efficient
Protocol for smaller IoT Sensor | Korea Institute of Information and Telecommunication Technology, 2018
Spring General conference, Kongju, South Korea | 2018, Vol.1, Issue No. 1 | null | null | cs.NI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Today we can use technologies like switched Ethernet, TCP/IP, high-speed wide
area networks, and high-performance low-cost computers very easily. However,
protocols designed for those communication are inefficient or not energy
efficient. Smart home, smart grid, blockchain, Internet of Things (IoT) all
these technologies are coming very rapidly with higher communication facilities
demands an energy efficient Ethernet. Due to controller and network equipment
use a huge quantity of energy. Layer to layer communication making our
communication method more complex and costly. In this work, we propose an
architecture, which will make the communication of sensor devices to outside
world easier. Our proposed system removes certain layer from TCP-IP
communication. We used a communication interface identifier protocol (CIIP)
which can be used for smaller IoT sensors.
| [
{
"created": "Fri, 18 Jan 2019 14:47:07 GMT",
"version": "v1"
}
] | 2019-02-07 | [
[
"Al-Zaben",
"Nasr",
""
],
[
"Onik",
"Md Mehedi Hassan",
""
],
[
"Kim",
"Chul-Soo",
""
],
[
"Yang",
"Jinhong",
""
]
] | Today we can use technologies like switched Ethernet, TCP/IP, high-speed wide area networks, and high-performance low-cost computers very easily. However, protocols designed for those communication are inefficient or not energy efficient. Smart home, smart grid, blockchain, Internet of Things (IoT) all these technologies are coming very rapidly with higher communication facilities demands an energy efficient Ethernet. Due to controller and network equipment use a huge quantity of energy. Layer to layer communication making our communication method more complex and costly. In this work, we propose an architecture, which will make the communication of sensor devices to outside world easier. Our proposed system removes certain layer from TCP-IP communication. We used a communication interface identifier protocol (CIIP) which can be used for smaller IoT sensors. |
1512.05486 | Serhii Dyshko | Dyshko Serhii | When the extension property does not hold | 11 pages | null | 10.1142/S0219498817500980 | null | cs.IT math.CO math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A complete extension theorem for linear codes over a module alphabet and the
symmetrized weight composition is proved. It is shown that an extension
property with respect to arbitrary weight function does not hold for module
alphabets with a noncyclic socle.
| [
{
"created": "Thu, 17 Dec 2015 07:54:30 GMT",
"version": "v1"
}
] | 2016-07-19 | [
[
"Serhii",
"Dyshko",
""
]
] | A complete extension theorem for linear codes over a module alphabet and the symmetrized weight composition is proved. It is shown that an extension property with respect to arbitrary weight function does not hold for module alphabets with a noncyclic socle. |
2402.09745 | Weike Fang | Xinyue Liu, Zihe Song, Weike Fang, Wei Yang, Weihang Wang | WEFix: Intelligent Automatic Generation of Explicit Waits for Efficient
Web End-to-End Flaky Tests | 8 pages. Accepted for publication in the proceedings of the ACM Web
Conference 2024 (WWW 24) | null | 10.1145/3589334.3645628 | null | cs.SE | http://creativecommons.org/licenses/by/4.0/ | Web end-to-end (e2e) testing evaluates the workflow of a web application. It
simulates real-world user scenarios to ensure the application flows behave as
expected. However, web e2e tests are notorious for being flaky, i.e., the tests
can produce inconsistent results despite no changes to the code. One common
type of flakiness is caused by nondeterministic execution orders between the
test code and the client-side code under test. In particular, UI-based
flakiness emerges as a notably prevalent and challenging issue to fix because
the test code has limited knowledge about the client-side code execution. In
this paper, we propose WEFix, a technique that can automatically generate fix
code for UI-based flakiness in web e2e testing. The core of our approach is to
leverage browser UI changes to predict the client-side code execution and
generate proper wait oracles. We evaluate the effectiveness and efficiency of
WEFix against 122 web e2e flaky tests from seven popular real-world projects.
Our results show that WEFix dramatically reduces the overhead (from 3.7$\times$
to 1.25$\times$) while achieving a high correctness (98%).
| [
{
"created": "Thu, 15 Feb 2024 06:51:53 GMT",
"version": "v1"
}
] | 2024-05-21 | [
[
"Liu",
"Xinyue",
""
],
[
"Song",
"Zihe",
""
],
[
"Fang",
"Weike",
""
],
[
"Yang",
"Wei",
""
],
[
"Wang",
"Weihang",
""
]
] | Web end-to-end (e2e) testing evaluates the workflow of a web application. It simulates real-world user scenarios to ensure the application flows behave as expected. However, web e2e tests are notorious for being flaky, i.e., the tests can produce inconsistent results despite no changes to the code. One common type of flakiness is caused by nondeterministic execution orders between the test code and the client-side code under test. In particular, UI-based flakiness emerges as a notably prevalent and challenging issue to fix because the test code has limited knowledge about the client-side code execution. In this paper, we propose WEFix, a technique that can automatically generate fix code for UI-based flakiness in web e2e testing. The core of our approach is to leverage browser UI changes to predict the client-side code execution and generate proper wait oracles. We evaluate the effectiveness and efficiency of WEFix against 122 web e2e flaky tests from seven popular real-world projects. Our results show that WEFix dramatically reduces the overhead (from 3.7$\times$ to 1.25$\times$) while achieving a high correctness (98%). |
2302.07344 | Levi Cai | Levi Cai and Nathan E. McGuire and Roger Hanlon and T. Aran Mooney and
Yogesh Girdhar | Semi-Supervised Visual Tracking of Marine Animals using Autonomous
Underwater Vehicles | To appear in IJCV SI: Animal Tracking | Cai, Levi, Nathan E. McGuire, Roger Hanlon, T. Aran Mooney, and
Yogesh Girdhar. "Semi-supervised Visual Tracking of Marine Animals Using
Autonomous Underwater Vehicles." International Journal of Computer Vision
(2023): 1-22 | 10.1007/s11263-023-01762-5 | null | cs.CV cs.LG cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In-situ visual observations of marine organisms is crucial to developing
behavioural understandings and their relations to their surrounding ecosystem.
Typically, these observations are collected via divers, tags, and
remotely-operated or human-piloted vehicles. Recently, however, autonomous
underwater vehicles equipped with cameras and embedded computers with GPU
capabilities are being developed for a variety of applications, and in
particular, can be used to supplement these existing data collection mechanisms
where human operation or tags are more difficult. Existing approaches have
focused on using fully-supervised tracking methods, but labelled data for many
underwater species are severely lacking. Semi-supervised trackers may offer
alternative tracking solutions because they require less data than
fully-supervised counterparts. However, because there are not existing
realistic underwater tracking datasets, the performance of semi-supervised
tracking algorithms in the marine domain is not well understood. To better
evaluate their performance and utility, in this paper we provide (1) a novel
dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2)
an evaluation of state-of-the-art semi-supervised algorithms in the context of
underwater animal tracking, and (3) an evaluation of real-world performance
through demonstrations using a semi-supervised algorithm on-board an autonomous
underwater vehicle to track marine animals in the wild.
| [
{
"created": "Tue, 14 Feb 2023 21:08:52 GMT",
"version": "v1"
}
] | 2023-05-04 | [
[
"Cai",
"Levi",
""
],
[
"McGuire",
"Nathan E.",
""
],
[
"Hanlon",
"Roger",
""
],
[
"Mooney",
"T. Aran",
""
],
[
"Girdhar",
"Yogesh",
""
]
] | In-situ visual observations of marine organisms is crucial to developing behavioural understandings and their relations to their surrounding ecosystem. Typically, these observations are collected via divers, tags, and remotely-operated or human-piloted vehicles. Recently, however, autonomous underwater vehicles equipped with cameras and embedded computers with GPU capabilities are being developed for a variety of applications, and in particular, can be used to supplement these existing data collection mechanisms where human operation or tags are more difficult. Existing approaches have focused on using fully-supervised tracking methods, but labelled data for many underwater species are severely lacking. Semi-supervised trackers may offer alternative tracking solutions because they require less data than fully-supervised counterparts. However, because there are not existing realistic underwater tracking datasets, the performance of semi-supervised tracking algorithms in the marine domain is not well understood. To better evaluate their performance and utility, in this paper we provide (1) a novel dataset specific to marine animals located at http://warp.whoi.edu/vmat/, (2) an evaluation of state-of-the-art semi-supervised algorithms in the context of underwater animal tracking, and (3) an evaluation of real-world performance through demonstrations using a semi-supervised algorithm on-board an autonomous underwater vehicle to track marine animals in the wild. |
2012.03519 | Lin Song | Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Hongbin Sun, Jian Sun,
Nanning Zheng | Fine-Grained Dynamic Head for Object Detection | Accepted by NeurIPS-2020 | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate
the scale variance in object representation by performing instance-level
assignments. Nevertheless, this strategy ignores the distinct characteristics
of different sub-regions in an instance. To this end, we propose a fine-grained
dynamic head to conditionally select a pixel-level combination of FPN features
from different scales for each instance, which further releases the ability of
multi-scale feature representation. Moreover, we design a spatial gate with the
new activation function to reduce computational complexity dramatically through
spatially sparse convolutions. Extensive experiments demonstrate the
effectiveness and efficiency of the proposed method on several state-of-the-art
detection benchmarks. Code is available at
https://github.com/StevenGrove/DynamicHead.
| [
{
"created": "Mon, 7 Dec 2020 08:16:32 GMT",
"version": "v1"
}
] | 2020-12-08 | [
[
"Song",
"Lin",
""
],
[
"Li",
"Yanwei",
""
],
[
"Jiang",
"Zhengkai",
""
],
[
"Li",
"Zeming",
""
],
[
"Sun",
"Hongbin",
""
],
[
"Sun",
"Jian",
""
],
[
"Zheng",
"Nanning",
""
]
] | The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, this strategy ignores the distinct characteristics of different sub-regions in an instance. To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation. Moreover, we design a spatial gate with the new activation function to reduce computational complexity dramatically through spatially sparse convolutions. Extensive experiments demonstrate the effectiveness and efficiency of the proposed method on several state-of-the-art detection benchmarks. Code is available at https://github.com/StevenGrove/DynamicHead. |
1402.2710 | Rodrigo de Lamare | L. Wang, R. C. de Lamare and M. Haardt | Direction Finding Algorithms with Joint Iterative Subspace Optimization | 11 figures, 4 tables. IEEE Transactions on Aerospace and Electronic
Systems, 2014 | null | 10.1109/TAES.2014.120395 | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a reduced-rank scheme with joint iterative optimization is
presented for direction of arrival estimation. A rank-reduction matrix and an
auxiliary reduced-rank parameter vector are jointly optimized to calculate the
output power with respect to each scanning angle. Subspace algorithms to
estimate the rank-reduction matrix and the auxiliary vector are proposed.
Simulations are performed to show that the proposed algorithms achieve an
enhanced performance over existing algorithms in the studied scenarios.
| [
{
"created": "Wed, 12 Feb 2014 01:13:12 GMT",
"version": "v1"
}
] | 2016-11-17 | [
[
"Wang",
"L.",
""
],
[
"de Lamare",
"R. C.",
""
],
[
"Haardt",
"M.",
""
]
] | In this paper, a reduced-rank scheme with joint iterative optimization is presented for direction of arrival estimation. A rank-reduction matrix and an auxiliary reduced-rank parameter vector are jointly optimized to calculate the output power with respect to each scanning angle. Subspace algorithms to estimate the rank-reduction matrix and the auxiliary vector are proposed. Simulations are performed to show that the proposed algorithms achieve an enhanced performance over existing algorithms in the studied scenarios. |
1811.02508 | Jonathan Le Roux | Jonathan Le Roux, Scott Wisdom, Hakan Erdogan, John R. Hershey | SDR - half-baked or well done? | null | null | null | null | cs.SD eess.AS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In speech enhancement and source separation, signal-to-noise ratio is a
ubiquitous objective measure of denoising/separation quality. A decade ago, the
BSS_eval toolkit was developed to give researchers worldwide a way to evaluate
the quality of their algorithms in a simple, fair, and hopefully insightful
way: it attempted to account for channel variations, and to not only evaluate
the total distortion in the estimated signal but also split it in terms of
various factors such as remaining interference, newly added artifacts, and
channel errors. In recent years, hundreds of papers have been relying on this
toolkit to evaluate their proposed methods and compare them to previous works,
often arguing that differences on the order of 0.1 dB proved the effectiveness
of a method over others. We argue here that the signal-to-distortion ratio
(SDR) implemented in the BSS_eval toolkit has generally been improperly used
and abused, especially in the case of single-channel separation, resulting in
misleading results. We propose to use a slightly modified definition, resulting
in a simpler, more robust measure, called scale-invariant SDR (SI-SDR). We
present various examples of critical failure of the original SDR that SI-SDR
overcomes.
| [
{
"created": "Tue, 6 Nov 2018 17:20:05 GMT",
"version": "v1"
}
] | 2018-11-07 | [
[
"Roux",
"Jonathan Le",
""
],
[
"Wisdom",
"Scott",
""
],
[
"Erdogan",
"Hakan",
""
],
[
"Hershey",
"John R.",
""
]
] | In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality. A decade ago, the BSS_eval toolkit was developed to give researchers worldwide a way to evaluate the quality of their algorithms in a simple, fair, and hopefully insightful way: it attempted to account for channel variations, and to not only evaluate the total distortion in the estimated signal but also split it in terms of various factors such as remaining interference, newly added artifacts, and channel errors. In recent years, hundreds of papers have been relying on this toolkit to evaluate their proposed methods and compare them to previous works, often arguing that differences on the order of 0.1 dB proved the effectiveness of a method over others. We argue here that the signal-to-distortion ratio (SDR) implemented in the BSS_eval toolkit has generally been improperly used and abused, especially in the case of single-channel separation, resulting in misleading results. We propose to use a slightly modified definition, resulting in a simpler, more robust measure, called scale-invariant SDR (SI-SDR). We present various examples of critical failure of the original SDR that SI-SDR overcomes. |
2211.10105 | Bicheng Guo | Bicheng Guo, Shuxuan Guo, Miaojing Shi, Peng Chen, Shibo He, Jiming
Chen, Kaicheng Yu | $\alpha$ DARTS Once More: Enhancing Differentiable Architecture Search
by Masked Image Modeling | null | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differentiable architecture search (DARTS) has been a mainstream direction in
automatic machine learning. Since the discovery that original DARTS will
inevitably converge to poor architectures, recent works alleviate this by
either designing rule-based architecture selection techniques or incorporating
complex regularization techniques, abandoning the simplicity of the original
DARTS that selects architectures based on the largest parametric value, namely
$\alpha$. Moreover, we find that all the previous attempts only rely on
classification labels, hence learning only single modal information and
limiting the representation power of the shared network. To this end, we
propose to additionally inject semantic information by formulating a patch
recovery approach. Specifically, we exploit the recent trending masked image
modeling and do not abandon the guidance from the downstream tasks during the
search phase. Our method surpasses all previous DARTS variants and achieves
state-of-the-art results on CIFAR-10, CIFAR-100, and ImageNet without complex
manual-designed strategies.
| [
{
"created": "Fri, 18 Nov 2022 09:07:19 GMT",
"version": "v1"
}
] | 2022-11-21 | [
[
"Guo",
"Bicheng",
""
],
[
"Guo",
"Shuxuan",
""
],
[
"Shi",
"Miaojing",
""
],
[
"Chen",
"Peng",
""
],
[
"He",
"Shibo",
""
],
[
"Chen",
"Jiming",
""
],
[
"Yu",
"Kaicheng",
""
]
] | Differentiable architecture search (DARTS) has been a mainstream direction in automatic machine learning. Since the discovery that original DARTS will inevitably converge to poor architectures, recent works alleviate this by either designing rule-based architecture selection techniques or incorporating complex regularization techniques, abandoning the simplicity of the original DARTS that selects architectures based on the largest parametric value, namely $\alpha$. Moreover, we find that all the previous attempts only rely on classification labels, hence learning only single modal information and limiting the representation power of the shared network. To this end, we propose to additionally inject semantic information by formulating a patch recovery approach. Specifically, we exploit the recent trending masked image modeling and do not abandon the guidance from the downstream tasks during the search phase. Our method surpasses all previous DARTS variants and achieves state-of-the-art results on CIFAR-10, CIFAR-100, and ImageNet without complex manual-designed strategies. |
2404.15386 | Huy Cuong Truong | Andres Tello, Huy Truong, Alexander Lazovik and Victoria Degeler | Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven
Deep Learning Approaches For Water Distribution Networks | Presented at WDSA CCWI, Ferrara, Italy, July 2024 | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Currently, the number of common benchmark datasets that researchers can use
straight away for assessing data-driven deep learning approaches is very
limited. Most studies provide data as configuration files. It is still up to
each practitioner to follow a particular data generation method and run
computationally intensive simulations to obtain usable data for model training
and evaluation. In this work, we provide a collection of datasets that includes
several small and medium size publicly available Water Distribution Networks
(WDNs), including Anytown, Modena, Balerma, C-Town, D-Town, L-Town, Ky1, Ky6,
Ky8, and Ky13. In total 1,394,400 hours of WDNs data operating under normal
conditions is made available to the community.
| [
{
"created": "Tue, 23 Apr 2024 11:58:40 GMT",
"version": "v1"
}
] | 2024-04-25 | [
[
"Tello",
"Andres",
""
],
[
"Truong",
"Huy",
""
],
[
"Lazovik",
"Alexander",
""
],
[
"Degeler",
"Victoria",
""
]
] | Currently, the number of common benchmark datasets that researchers can use straight away for assessing data-driven deep learning approaches is very limited. Most studies provide data as configuration files. It is still up to each practitioner to follow a particular data generation method and run computationally intensive simulations to obtain usable data for model training and evaluation. In this work, we provide a collection of datasets that includes several small and medium size publicly available Water Distribution Networks (WDNs), including Anytown, Modena, Balerma, C-Town, D-Town, L-Town, Ky1, Ky6, Ky8, and Ky13. In total 1,394,400 hours of WDNs data operating under normal conditions is made available to the community. |
1708.05811 | Adi Akavia | Adi Akavia, Dan Feldman, Hayim Shaul | Secure Search on the Cloud via Coresets and Sketches | 25 pages, 2 figures | null | null | null | cs.CR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | \emph{Secure Search} is the problem of retrieving from a database table (or
any unsorted array) the records matching specified attributes, as in SQL SELECT
queries, but where the database and the query are encrypted. Secure search has
been the leading example for practical applications of Fully Homomorphic
Encryption (FHE) starting in Gentry's seminal work; however, to the best of our
knowledge all state-of-the-art secure search algorithms to date are realized by
a polynomial of degree $\Omega(m)$ for $m$ the number of records, which is
typically too slow in practice even for moderate size $m$.
In this work we present the first algorithm for secure search that is
realized by a polynomial of degree polynomial in $\log m$. We implemented our
algorithm in an open source library based on HELib implementation for the
Brakerski-Gentry-Vaikuntanthan's FHE scheme, and ran experiments on Amazon's
EC2 cloud. Our experiments show that we can retrieve the first match in a
database of millions of entries in less than an hour using a single machine;
the time reduced almost linearly with the number of machines.
Our result utilizes a new paradigm of employing coresets and sketches, which
are modern data summarization techniques common in computational geometry and
machine learning, for efficiency enhancement for homomorphic encryption. As a
central tool we design a novel sketch that returns the first positive entry in
a (not necessarily sparse) array; this sketch may be of independent interest.
| [
{
"created": "Sat, 19 Aug 2017 06:36:11 GMT",
"version": "v1"
}
] | 2017-08-22 | [
[
"Akavia",
"Adi",
""
],
[
"Feldman",
"Dan",
""
],
[
"Shaul",
"Hayim",
""
]
] | \emph{Secure Search} is the problem of retrieving from a database table (or any unsorted array) the records matching specified attributes, as in SQL SELECT queries, but where the database and the query are encrypted. Secure search has been the leading example for practical applications of Fully Homomorphic Encryption (FHE) starting in Gentry's seminal work; however, to the best of our knowledge all state-of-the-art secure search algorithms to date are realized by a polynomial of degree $\Omega(m)$ for $m$ the number of records, which is typically too slow in practice even for moderate size $m$. In this work we present the first algorithm for secure search that is realized by a polynomial of degree polynomial in $\log m$. We implemented our algorithm in an open source library based on HELib implementation for the Brakerski-Gentry-Vaikuntanthan's FHE scheme, and ran experiments on Amazon's EC2 cloud. Our experiments show that we can retrieve the first match in a database of millions of entries in less than an hour using a single machine; the time reduced almost linearly with the number of machines. Our result utilizes a new paradigm of employing coresets and sketches, which are modern data summarization techniques common in computational geometry and machine learning, for efficiency enhancement for homomorphic encryption. As a central tool we design a novel sketch that returns the first positive entry in a (not necessarily sparse) array; this sketch may be of independent interest. |
1705.00583 | Thomas Strasser Thomas Strasser | Arjen A. van der Meer and Peter Palensky and Kai Heussen and Daniel
Esteban Morales Bondy and Oliver Gehrke and Cornelius Steinbrink and Marita
Blank and Sebastian Lehnhoff and Edmund Widl and Cyndi Moyo and Thomas I.
Strasser and Van Hoa Nguyen and Nabil Akroud and Mazheruddin H. Syed and
Abdullah Emhemed and Sebastian Rohjans and Ron Brandl and Ata M. Khavari | Cyber-Physical Energy Systems Modeling, Test Specification, and
Co-Simulation Based Testing | 2017 Workshop on Modeling and Simulation of Cyber-Physical Energy
Systems (MSCPES) | null | 10.1109/MSCPES.2017.8064528 | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The gradual deployment of intelligent and coordinated devices in the
electrical power system needs careful investigation of the interactions between
the various domains involved. Especially due to the coupling between ICT and
power systems a holistic approach for testing and validating is required.
Taking existing (quasi-) standardised smart grid system and test specification
methods as a starting point, we are developing a holistic testing and
validation approach that allows a very flexible way of assessing the system
level aspects by various types of experiments (including virtual, real, and
mixed lab settings). This paper describes the formal holistic test case
specification method and applies it to a particular co-simulation experimental
setup. The various building blocks of such a simulation (i.e., FMI, mosaik,
domain-specific simulation federates) are covered in more detail. The presented
method addresses most modeling and specification challenges in cyber-physical
energy systems and is extensible for future additions such as uncertainty
quantification.
| [
{
"created": "Mon, 1 May 2017 16:32:45 GMT",
"version": "v1"
}
] | 2018-12-27 | [
[
"van der Meer",
"Arjen A.",
""
],
[
"Palensky",
"Peter",
""
],
[
"Heussen",
"Kai",
""
],
[
"Bondy",
"Daniel Esteban Morales",
""
],
[
"Gehrke",
"Oliver",
""
],
[
"Steinbrink",
"Cornelius",
""
],
[
"Blank",
"Marita",
""
],
[
"Lehnhoff",
"Sebastian",
""
],
[
"Widl",
"Edmund",
""
],
[
"Moyo",
"Cyndi",
""
],
[
"Strasser",
"Thomas I.",
""
],
[
"Nguyen",
"Van Hoa",
""
],
[
"Akroud",
"Nabil",
""
],
[
"Syed",
"Mazheruddin H.",
""
],
[
"Emhemed",
"Abdullah",
""
],
[
"Rohjans",
"Sebastian",
""
],
[
"Brandl",
"Ron",
""
],
[
"Khavari",
"Ata M.",
""
]
] | The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification. |
2210.01597 | Eleonora Giunchiglia | Eleonora Giunchiglia and Mihaela C\u{a}t\u{a}lina Stoian and Salman
Khan and Fabio Cuzzolin and Thomas Lukasiewicz | ROAD-R: The Autonomous Driving Dataset with Logical Requirements | null | null | 10.1007/s10994-023-06322-z | null | cs.LG cs.AI cs.CV cs.RO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural networks have proven to be very powerful at computer vision tasks.
However, they often exhibit unexpected behaviours, violating known requirements
expressing background knowledge. This calls for models (i) able to learn from
the requirements, and (ii) guaranteed to be compliant with the requirements
themselves. Unfortunately, the development of such models is hampered by the
lack of datasets equipped with formally specified requirements. In this paper,
we introduce the ROad event Awareness Dataset with logical Requirements
(ROAD-R), the first publicly available dataset for autonomous driving with
requirements expressed as logical constraints. Given ROAD-R, we show that
current state-of-the-art models often violate its logical constraints, and that
it is possible to exploit them to create models that (i) have a better
performance, and (ii) are guaranteed to be compliant with the requirements
themselves.
| [
{
"created": "Tue, 4 Oct 2022 13:22:19 GMT",
"version": "v1"
},
{
"created": "Wed, 5 Oct 2022 11:42:42 GMT",
"version": "v2"
}
] | 2023-06-21 | [
[
"Giunchiglia",
"Eleonora",
""
],
[
"Stoian",
"Mihaela Cătălina",
""
],
[
"Khan",
"Salman",
""
],
[
"Cuzzolin",
"Fabio",
""
],
[
"Lukasiewicz",
"Thomas",
""
]
] | Neural networks have proven to be very powerful at computer vision tasks. However, they often exhibit unexpected behaviours, violating known requirements expressing background knowledge. This calls for models (i) able to learn from the requirements, and (ii) guaranteed to be compliant with the requirements themselves. Unfortunately, the development of such models is hampered by the lack of datasets equipped with formally specified requirements. In this paper, we introduce the ROad event Awareness Dataset with logical Requirements (ROAD-R), the first publicly available dataset for autonomous driving with requirements expressed as logical constraints. Given ROAD-R, we show that current state-of-the-art models often violate its logical constraints, and that it is possible to exploit them to create models that (i) have a better performance, and (ii) are guaranteed to be compliant with the requirements themselves. |
2211.16712 | Nan Zhang | Hao Zhang, Nan Zhang, Ruixin Zhang, Lei Shen, Yingyi Zhang, and Meng
Liu | Coordinating Cross-modal Distillation for Molecular Property Prediction | null | null | null | null | cs.LG q-bio.QM | http://creativecommons.org/licenses/by/4.0/ | In recent years, molecular graph representation learning (GRL) has drawn much
more attention in molecular property prediction (MPP) problems. The existing
graph methods have demonstrated that 3D geometric information is significant
for better performance in MPP. However, accurate 3D structures are often costly
and time-consuming to obtain, limiting the large-scale application of GRL. It
is an intuitive solution to train with 3D to 2D knowledge distillation and
predict with only 2D inputs. But some challenging problems remain open for 3D
to 2D distillation. One is that the 3D view is quite distinct from the 2D view,
and the other is that the gradient magnitudes of atoms in distillation are
discrepant and unstable due to the variable molecular size. To address these
challenging problems, we exclusively propose a distillation framework that
contains global molecular distillation and local atom distillation. We also
provide a theoretical insight to justify how to coordinate atom and molecular
information, which tackles the drawback of variable molecular size for atom
information distillation. Experimental results on two popular molecular
datasets demonstrate that our proposed model achieves superior performance over
other methods. Specifically, on the largest MPP dataset PCQM4Mv2 served as an
"ImageNet Large Scale Visual Recognition Challenge" in the field of graph ML,
the proposed method achieved a 6.9% improvement compared with the best works.
And we obtained fourth place with the MAE of 0.0734 on the test-challenge set
for OGB-LSC 2022 Graph Regression Task. We will release the code soon.
| [
{
"created": "Wed, 30 Nov 2022 03:19:34 GMT",
"version": "v1"
}
] | 2022-12-01 | [
[
"Zhang",
"Hao",
""
],
[
"Zhang",
"Nan",
""
],
[
"Zhang",
"Ruixin",
""
],
[
"Shen",
"Lei",
""
],
[
"Zhang",
"Yingyi",
""
],
[
"Liu",
"Meng",
""
]
] | In recent years, molecular graph representation learning (GRL) has drawn much more attention in molecular property prediction (MPP) problems. The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP. However, accurate 3D structures are often costly and time-consuming to obtain, limiting the large-scale application of GRL. It is an intuitive solution to train with 3D to 2D knowledge distillation and predict with only 2D inputs. But some challenging problems remain open for 3D to 2D distillation. One is that the 3D view is quite distinct from the 2D view, and the other is that the gradient magnitudes of atoms in distillation are discrepant and unstable due to the variable molecular size. To address these challenging problems, we exclusively propose a distillation framework that contains global molecular distillation and local atom distillation. We also provide a theoretical insight to justify how to coordinate atom and molecular information, which tackles the drawback of variable molecular size for atom information distillation. Experimental results on two popular molecular datasets demonstrate that our proposed model achieves superior performance over other methods. Specifically, on the largest MPP dataset PCQM4Mv2 served as an "ImageNet Large Scale Visual Recognition Challenge" in the field of graph ML, the proposed method achieved a 6.9% improvement compared with the best works. And we obtained fourth place with the MAE of 0.0734 on the test-challenge set for OGB-LSC 2022 Graph Regression Task. We will release the code soon. |
1402.3067 | Tobias Fritz | John C. Baez and Tobias Fritz | A Bayesian Characterization of Relative Entropy | 32 pages, minor revision | Theory and Applications of Categories, Vol. 29 No. 16 (2014),
421-456 | null | null | cs.IT math-ph math.IT math.MP math.PR quant-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We give a new characterization of relative entropy, also known as the
Kullback-Leibler divergence. We use a number of interesting categories related
to probability theory. In particular, we consider a category FinStat where an
object is a finite set equipped with a probability distribution, while a
morphism is a measure-preserving function $f: X \to Y$ together with a
stochastic right inverse $s: Y \to X$. The function $f$ can be thought of as a
measurement process, while s provides a hypothesis about the state of the
measured system given the result of a measurement. Given this data we can
define the entropy of the probability distribution on $X$ relative to the
"prior" given by pushing the probability distribution on $Y$ forwards along
$s$. We say that $s$ is "optimal" if these distributions agree. We show that
any convex linear, lower semicontinuous functor from FinStat to the additive
monoid $[0,\infty]$ which vanishes when $s$ is optimal must be a scalar
multiple of this relative entropy. Our proof is independent of all earlier
characterizations, but inspired by the work of Petz.
| [
{
"created": "Thu, 13 Feb 2014 09:02:27 GMT",
"version": "v1"
},
{
"created": "Fri, 11 Jul 2014 12:24:57 GMT",
"version": "v2"
}
] | 2017-08-22 | [
[
"Baez",
"John C.",
""
],
[
"Fritz",
"Tobias",
""
]
] | We give a new characterization of relative entropy, also known as the Kullback-Leibler divergence. We use a number of interesting categories related to probability theory. In particular, we consider a category FinStat where an object is a finite set equipped with a probability distribution, while a morphism is a measure-preserving function $f: X \to Y$ together with a stochastic right inverse $s: Y \to X$. The function $f$ can be thought of as a measurement process, while s provides a hypothesis about the state of the measured system given the result of a measurement. Given this data we can define the entropy of the probability distribution on $X$ relative to the "prior" given by pushing the probability distribution on $Y$ forwards along $s$. We say that $s$ is "optimal" if these distributions agree. We show that any convex linear, lower semicontinuous functor from FinStat to the additive monoid $[0,\infty]$ which vanishes when $s$ is optimal must be a scalar multiple of this relative entropy. Our proof is independent of all earlier characterizations, but inspired by the work of Petz. |
1501.00158 | Zhengli Xing | Zhengli Xing, Jie Zhou, Jiangfeng Ye, Jun Yan, Jifeng Zou, Lin Zou,
Qun Wan | Automatic Modulation Recognition of PSK Signals with Sub-Nyquist
Sampling Based on High Order Statistics | 7 pages, 8 figures, submitted to IEEE International Symposium on
Signal Processing and Information Technology | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Sampling rate required in the Nth Power Nonlinear Transformation (NPT) method
is typically much greater than Nyquist rate, which causes heavy burden for the
Analog to Digital Converter (ADC). Taking advantage of the sparse property of
PSK signals' spectrum under NPT, we develop the NPT method for PSK signals with
Sub-Nyquist rate samples. In this paper, combined the NPT method with
Compressive Sensing (CS) theory, frequency spectrum reconstruction of the Nth
power nonlinear transformation of PSK signals is presented, which can be
further used for AMR and rough estimations of unknown carrier frequency and
symbol rate.
| [
{
"created": "Wed, 31 Dec 2014 15:54:32 GMT",
"version": "v1"
}
] | 2015-01-05 | [
[
"Xing",
"Zhengli",
""
],
[
"Zhou",
"Jie",
""
],
[
"Ye",
"Jiangfeng",
""
],
[
"Yan",
"Jun",
""
],
[
"Zou",
"Jifeng",
""
],
[
"Zou",
"Lin",
""
],
[
"Wan",
"Qun",
""
]
] | Sampling rate required in the Nth Power Nonlinear Transformation (NPT) method is typically much greater than Nyquist rate, which causes heavy burden for the Analog to Digital Converter (ADC). Taking advantage of the sparse property of PSK signals' spectrum under NPT, we develop the NPT method for PSK signals with Sub-Nyquist rate samples. In this paper, combined the NPT method with Compressive Sensing (CS) theory, frequency spectrum reconstruction of the Nth power nonlinear transformation of PSK signals is presented, which can be further used for AMR and rough estimations of unknown carrier frequency and symbol rate. |
2111.02058 | Dawei Dai | Dawei Dai and Yutang Li and Huanan Bao and Sy Xia and Guoyin Wang and
Xiaoli Ma | Rethinking the Image Feature Biases Exhibited by Deep CNN Models | 15 pages, 15 figures | null | null | null | cs.CV cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, convolutional neural networks (CNNs) have been applied
successfully in many fields. However, such deep neural models are still
regarded as black box in most tasks. One of the fundamental issues underlying
this problem is understanding which features are most influential in image
recognition tasks and how they are processed by CNNs. It is widely accepted
that CNN models combine low-level features to form complex shapes until the
object can be readily classified, however, several recent studies have argued
that texture features are more important than other features. In this paper, we
assume that the importance of certain features varies depending on specific
tasks, i.e., specific tasks exhibit a feature bias. We designed two
classification tasks based on human intuition to train deep neural models to
identify anticipated biases. We devised experiments comprising many tasks to
test these biases for the ResNet and DenseNet models. From the results, we
conclude that (1) the combined effect of certain features is typically far more
influential than any single feature; (2) in different tasks, neural models can
perform different biases, that is, we can design a specific task to make a
neural model biased toward a specific anticipated feature.
| [
{
"created": "Wed, 3 Nov 2021 08:04:06 GMT",
"version": "v1"
}
] | 2021-11-04 | [
[
"Dai",
"Dawei",
""
],
[
"Li",
"Yutang",
""
],
[
"Bao",
"Huanan",
""
],
[
"Xia",
"Sy",
""
],
[
"Wang",
"Guoyin",
""
],
[
"Ma",
"Xiaoli",
""
]
] | In recent years, convolutional neural networks (CNNs) have been applied successfully in many fields. However, such deep neural models are still regarded as black box in most tasks. One of the fundamental issues underlying this problem is understanding which features are most influential in image recognition tasks and how they are processed by CNNs. It is widely accepted that CNN models combine low-level features to form complex shapes until the object can be readily classified, however, several recent studies have argued that texture features are more important than other features. In this paper, we assume that the importance of certain features varies depending on specific tasks, i.e., specific tasks exhibit a feature bias. We designed two classification tasks based on human intuition to train deep neural models to identify anticipated biases. We devised experiments comprising many tasks to test these biases for the ResNet and DenseNet models. From the results, we conclude that (1) the combined effect of certain features is typically far more influential than any single feature; (2) in different tasks, neural models can perform different biases, that is, we can design a specific task to make a neural model biased toward a specific anticipated feature. |
1802.02562 | David Garc\'ia-Soriano | David Garc\'ia-Soriano and Francesco Bonchi | Fair-by-design matching | null | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Matching algorithms are used routinely to match donors to recipients for
solid organs transplantation, for the assignment of medical residents to
hospitals, record linkage in databases, scheduling jobs on machines, network
switching, online advertising, and image recognition, among others. Although
many optimal solutions may exist to a given matching problem, when the elements
that shall or not be included in a solution correspond to individuals, it
becomes of paramount importance that the solution be selected fairly. In this
paper we study individual fairness in matching problems. Given that many
maximum matchings may exist, each one satisfying a different set of
individuals, the only way to guarantee fairness is through randomization. Hence
we introduce the distributional maxmin fairness framework which provides, for
any given input instance, the strongest guarantee possible simultaneously for
all individuals in terms of satisfaction probability (the probability of being
matched in the solution). Specifically, a probability distribution over
feasible solutions is maxmin-fair if it is not possible to improve the
satisfaction probability of any individual without decreasing it for some other
individual which is no better off. In the special case of matchings in
bipartite graphs, our framework is equivalent to the egalitarian mechanism of
Bogomolnaia and Mouline. Our main contribution is a polynomial-time algorithm
for fair matching building on techniques from minimum cuts, and edge-coloring
algorithms for regular bipartite graphs, and transversal theory. For bipartite
graphs, our algorithm runs in $O((|V|^2 + |E||V|^{2/3}) \cdot (\log |V|)^2)$
expected time and scales to graphs with tens of millions of vertices and
hundreds of millions of edges. To the best of our knowledge, this provides the
first large-scale implementation of the egalitarian mechanism.
| [
{
"created": "Wed, 7 Feb 2018 18:44:43 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Jan 2020 11:09:47 GMT",
"version": "v2"
}
] | 2020-01-09 | [
[
"García-Soriano",
"David",
""
],
[
"Bonchi",
"Francesco",
""
]
] | Matching algorithms are used routinely to match donors to recipients for solid organs transplantation, for the assignment of medical residents to hospitals, record linkage in databases, scheduling jobs on machines, network switching, online advertising, and image recognition, among others. Although many optimal solutions may exist to a given matching problem, when the elements that shall or not be included in a solution correspond to individuals, it becomes of paramount importance that the solution be selected fairly. In this paper we study individual fairness in matching problems. Given that many maximum matchings may exist, each one satisfying a different set of individuals, the only way to guarantee fairness is through randomization. Hence we introduce the distributional maxmin fairness framework which provides, for any given input instance, the strongest guarantee possible simultaneously for all individuals in terms of satisfaction probability (the probability of being matched in the solution). Specifically, a probability distribution over feasible solutions is maxmin-fair if it is not possible to improve the satisfaction probability of any individual without decreasing it for some other individual which is no better off. In the special case of matchings in bipartite graphs, our framework is equivalent to the egalitarian mechanism of Bogomolnaia and Mouline. Our main contribution is a polynomial-time algorithm for fair matching building on techniques from minimum cuts, and edge-coloring algorithms for regular bipartite graphs, and transversal theory. For bipartite graphs, our algorithm runs in $O((|V|^2 + |E||V|^{2/3}) \cdot (\log |V|)^2)$ expected time and scales to graphs with tens of millions of vertices and hundreds of millions of edges. To the best of our knowledge, this provides the first large-scale implementation of the egalitarian mechanism. |
1804.06870 | Hao Tan | Hao Tan, Mohit Bansal | Object Ordering with Bidirectional Matchings for Visual Reasoning | NAACL 2018 (8 pages; added pointer-ordering examples) | null | null | null | cs.CL cs.AI cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual reasoning with compositional natural language instructions, e.g.,
based on the newly-released Cornell Natural Language Visual Reasoning (NLVR)
dataset, is a challenging task, where the model needs to have the ability to
create an accurate mapping between the diverse phrases and the several objects
placed in complex arrangements in the image. Further, this mapping needs to be
processed to answer the question in the statement given the ordering and
relationship of the objects across three similar images. In this paper, we
propose a novel end-to-end neural model for the NLVR task, where we first use
joint bidirectional attention to build a two-way conditioning between the
visual information and the language phrases. Next, we use an RL-based pointer
network to sort and process the varying number of unordered objects (so as to
match the order of the statement phrases) in each of the three images and then
pool over the three decisions. Our model achieves strong improvements (of 4-6%
absolute) over the state-of-the-art on both the structured representation and
raw image versions of the dataset.
| [
{
"created": "Wed, 18 Apr 2018 18:39:17 GMT",
"version": "v1"
},
{
"created": "Thu, 6 Sep 2018 16:56:32 GMT",
"version": "v2"
}
] | 2018-09-07 | [
[
"Tan",
"Hao",
""
],
[
"Bansal",
"Mohit",
""
]
] | Visual reasoning with compositional natural language instructions, e.g., based on the newly-released Cornell Natural Language Visual Reasoning (NLVR) dataset, is a challenging task, where the model needs to have the ability to create an accurate mapping between the diverse phrases and the several objects placed in complex arrangements in the image. Further, this mapping needs to be processed to answer the question in the statement given the ordering and relationship of the objects across three similar images. In this paper, we propose a novel end-to-end neural model for the NLVR task, where we first use joint bidirectional attention to build a two-way conditioning between the visual information and the language phrases. Next, we use an RL-based pointer network to sort and process the varying number of unordered objects (so as to match the order of the statement phrases) in each of the three images and then pool over the three decisions. Our model achieves strong improvements (of 4-6% absolute) over the state-of-the-art on both the structured representation and raw image versions of the dataset. |
2110.05910 | Nathaniel Tye | Nathaniel Tye, Stephan Hofmann, Phillip Stanley-Marbell | Bridging the Band Gap: What Device Physicists Need to Know About Machine
Learning | null | null | null | null | cs.ET physics.app-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This article surveys the landscape of semiconductor materials and devices
research for the acceleration of machine learning (ML) algorithms. We observe a
disconnect between the semiconductor and device physics and engineering
communities, and the digital logic and computer hardware architecture
communities. The article first provides an overview of the principles of
computational complexity and fundamental physical limits to computing and their
relation to physical systems. The article then provides an introduction to ML
by presenting three key components of ML systems: representation, evaluation,
and optimisation. The article then discusses and provides examples of the
application of emerging technologies from the demiconductor and device physics
domains as solutions to computational problems, alongside a brief overview of
emerging devices for computing applications. The article then reviews the
landscape of ML accelerators, comparing fixed-function and reprogrammable
digital logic with novel devices such as memristors, resistive memories,
magnetic memories, and probabilistic bits. We observe broadly lower performance
of ML accelerators based on novel devices and materials when compared to those
based on digital complimentary metal-oxide semiconductor (CMOS) technology,
particularly in the MNIST optical character recognition task, a common ML
benchmark, and also highlight the lack of a trend of progress in approaches
based on novel materials and devices. Lastly, the article proposes figures of
merit for meaningful evaluation and comparison of different ML implementations
in the hope of fostering a dialogue between the materials science, device
physics, digital logic, and computer architecture communities by providing a
common frame of reference for their work.
| [
{
"created": "Tue, 12 Oct 2021 11:43:19 GMT",
"version": "v1"
},
{
"created": "Sat, 16 Oct 2021 19:49:09 GMT",
"version": "v2"
}
] | 2021-10-19 | [
[
"Tye",
"Nathaniel",
""
],
[
"Hofmann",
"Stephan",
""
],
[
"Stanley-Marbell",
"Phillip",
""
]
] | This article surveys the landscape of semiconductor materials and devices research for the acceleration of machine learning (ML) algorithms. We observe a disconnect between the semiconductor and device physics and engineering communities, and the digital logic and computer hardware architecture communities. The article first provides an overview of the principles of computational complexity and fundamental physical limits to computing and their relation to physical systems. The article then provides an introduction to ML by presenting three key components of ML systems: representation, evaluation, and optimisation. The article then discusses and provides examples of the application of emerging technologies from the demiconductor and device physics domains as solutions to computational problems, alongside a brief overview of emerging devices for computing applications. The article then reviews the landscape of ML accelerators, comparing fixed-function and reprogrammable digital logic with novel devices such as memristors, resistive memories, magnetic memories, and probabilistic bits. We observe broadly lower performance of ML accelerators based on novel devices and materials when compared to those based on digital complimentary metal-oxide semiconductor (CMOS) technology, particularly in the MNIST optical character recognition task, a common ML benchmark, and also highlight the lack of a trend of progress in approaches based on novel materials and devices. Lastly, the article proposes figures of merit for meaningful evaluation and comparison of different ML implementations in the hope of fostering a dialogue between the materials science, device physics, digital logic, and computer architecture communities by providing a common frame of reference for their work. |
2203.05918 | Junhua Ma | Junhua Ma, Jiajun Li, Yuxuan Liu, Shangbo Zhou, Xue Li | Integrating Dependency Tree Into Self-attention for Sentence
Representation | ICASSP 2022 | null | null | null | cs.CL cs.LG | http://creativecommons.org/licenses/by/4.0/ | Recent progress on parse tree encoder for sentence representation learning is
notable. However, these works mainly encode tree structures recursively, which
is not conducive to parallelization. On the other hand, these works rarely take
into account the labels of arcs in dependency trees. To address both issues, we
propose Dependency-Transformer, which applies a relation-attention mechanism
that works in concert with the self-attention mechanism. This mechanism aims to
encode the dependency and the spatial positional relations between nodes in the
dependency tree of sentences. By a score-based method, we successfully inject
the syntax information without affecting Transformer's parallelizability. Our
model outperforms or is comparable to the state-of-the-art methods on four
tasks for sentence representation and has obvious advantages in computational
efficiency.
| [
{
"created": "Fri, 11 Mar 2022 13:44:41 GMT",
"version": "v1"
},
{
"created": "Sun, 24 Apr 2022 09:33:25 GMT",
"version": "v2"
},
{
"created": "Sat, 7 May 2022 01:55:59 GMT",
"version": "v3"
}
] | 2022-05-10 | [
[
"Ma",
"Junhua",
""
],
[
"Li",
"Jiajun",
""
],
[
"Liu",
"Yuxuan",
""
],
[
"Zhou",
"Shangbo",
""
],
[
"Li",
"Xue",
""
]
] | Recent progress on parse tree encoder for sentence representation learning is notable. However, these works mainly encode tree structures recursively, which is not conducive to parallelization. On the other hand, these works rarely take into account the labels of arcs in dependency trees. To address both issues, we propose Dependency-Transformer, which applies a relation-attention mechanism that works in concert with the self-attention mechanism. This mechanism aims to encode the dependency and the spatial positional relations between nodes in the dependency tree of sentences. By a score-based method, we successfully inject the syntax information without affecting Transformer's parallelizability. Our model outperforms or is comparable to the state-of-the-art methods on four tasks for sentence representation and has obvious advantages in computational efficiency. |
2102.06924 | Lior Shani | Lior Shani, Tom Zahavy and Shie Mannor | Online Apprenticeship Learning | AAAI 2022 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP)
without access to the cost function. Instead, we observe trajectories sampled
by an expert that acts according to some policy. The goal is to find a policy
that matches the expert's performance on some predefined set of cost functions.
We introduce an online variant of AL (Online Apprenticeship Learning; OAL),
where the agent is expected to perform comparably to the expert while
interacting with the environment. We show that the OAL problem can be
effectively solved by combining two mirror descent based no-regret algorithms:
one for policy optimization and another for learning the worst case cost. By
employing optimistic exploration, we derive a convergent algorithm with
$O(\sqrt{K})$ regret, where $K$ is the number of interactions with the MDP, and
an additional linear error term that depends on the amount of expert
trajectories available. Importantly, our algorithm avoids the need to solve an
MDP at each iteration, making it more practical compared to prior AL methods.
Finally, we implement a deep variant of our algorithm which shares some
similarities to GAIL \cite{ho2016generative}, but where the discriminator is
replaced with the costs learned by the OAL problem. Our simulations suggest
that OAL performs well in high dimensional control problems.
| [
{
"created": "Sat, 13 Feb 2021 12:57:51 GMT",
"version": "v1"
},
{
"created": "Wed, 29 Dec 2021 09:31:02 GMT",
"version": "v2"
}
] | 2021-12-30 | [
[
"Shani",
"Lior",
""
],
[
"Zahavy",
"Tom",
""
],
[
"Mannor",
"Shie",
""
]
] | In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP) without access to the cost function. Instead, we observe trajectories sampled by an expert that acts according to some policy. The goal is to find a policy that matches the expert's performance on some predefined set of cost functions. We introduce an online variant of AL (Online Apprenticeship Learning; OAL), where the agent is expected to perform comparably to the expert while interacting with the environment. We show that the OAL problem can be effectively solved by combining two mirror descent based no-regret algorithms: one for policy optimization and another for learning the worst case cost. By employing optimistic exploration, we derive a convergent algorithm with $O(\sqrt{K})$ regret, where $K$ is the number of interactions with the MDP, and an additional linear error term that depends on the amount of expert trajectories available. Importantly, our algorithm avoids the need to solve an MDP at each iteration, making it more practical compared to prior AL methods. Finally, we implement a deep variant of our algorithm which shares some similarities to GAIL \cite{ho2016generative}, but where the discriminator is replaced with the costs learned by the OAL problem. Our simulations suggest that OAL performs well in high dimensional control problems. |
2403.07532 | Matteo Sodano | Matteo Sodano, Federico Magistri, Lucas Nunes, Jens Behley, Cyrill
Stachniss | Open-World Semantic Segmentation Including Class Similarity | Accepted at CVPR 2024. Code at: https://github.com/PRBonn/ContMAV | null | null | null | cs.CV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Interpreting camera data is key for autonomously acting systems, such as
autonomous vehicles. Vision systems that operate in real-world environments
must be able to understand their surroundings and need the ability to deal with
novel situations. This paper tackles open-world semantic segmentation, i.e.,
the variant of interpreting image data in which objects occur that have not
been seen during training. We propose a novel approach that performs accurate
closed-world semantic segmentation and, at the same time, can identify new
categories without requiring any additional training data. Our approach
additionally provides a similarity measure for every newly discovered class in
an image to a known category, which can be useful information in downstream
tasks such as planning or mapping. Through extensive experiments, we show that
our model achieves state-of-the-art results on classes known from training data
as well as for anomaly segmentation and can distinguish between different
unknown classes.
| [
{
"created": "Tue, 12 Mar 2024 11:11:19 GMT",
"version": "v1"
}
] | 2024-03-13 | [
[
"Sodano",
"Matteo",
""
],
[
"Magistri",
"Federico",
""
],
[
"Nunes",
"Lucas",
""
],
[
"Behley",
"Jens",
""
],
[
"Stachniss",
"Cyrill",
""
]
] | Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles. Vision systems that operate in real-world environments must be able to understand their surroundings and need the ability to deal with novel situations. This paper tackles open-world semantic segmentation, i.e., the variant of interpreting image data in which objects occur that have not been seen during training. We propose a novel approach that performs accurate closed-world semantic segmentation and, at the same time, can identify new categories without requiring any additional training data. Our approach additionally provides a similarity measure for every newly discovered class in an image to a known category, which can be useful information in downstream tasks such as planning or mapping. Through extensive experiments, we show that our model achieves state-of-the-art results on classes known from training data as well as for anomaly segmentation and can distinguish between different unknown classes. |
1705.09886 | Yang Yuan | Yuanzhi Li, Yang Yuan | Convergence Analysis of Two-layer Neural Networks with ReLU Activation | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, stochastic gradient descent (SGD) based techniques has
become the standard tools for training neural networks. However, formal
theoretical understanding of why SGD can train neural networks in practice is
largely missing.
In this paper, we make progress on understanding this mystery by providing a
convergence analysis for SGD on a rich subset of two-layer feedforward networks
with ReLU activations. This subset is characterized by a special structure
called "identity mapping". We prove that, if input follows from Gaussian
distribution, with standard $O(1/\sqrt{d})$ initialization of the weights, SGD
converges to the global minimum in polynomial number of steps. Unlike normal
vanilla networks, the "identity mapping" makes our network asymmetric and thus
the global minimum is unique. To complement our theory, we are also able to
show experimentally that multi-layer networks with this mapping have better
performance compared with normal vanilla networks.
Our convergence theorem differs from traditional non-convex optimization
techniques. We show that SGD converges to optimal in "two phases": In phase I,
the gradient points to the wrong direction, however, a potential function $g$
gradually decreases. Then in phase II, SGD enters a nice one point convex
region and converges. We also show that the identity mapping is necessary for
convergence, as it moves the initial point to a better place for optimization.
Experiment verifies our claims.
| [
{
"created": "Sun, 28 May 2017 02:11:10 GMT",
"version": "v1"
},
{
"created": "Wed, 1 Nov 2017 21:42:23 GMT",
"version": "v2"
}
] | 2017-11-03 | [
[
"Li",
"Yuanzhi",
""
],
[
"Yuan",
"Yang",
""
]
] | In recent years, stochastic gradient descent (SGD) based techniques has become the standard tools for training neural networks. However, formal theoretical understanding of why SGD can train neural networks in practice is largely missing. In this paper, we make progress on understanding this mystery by providing a convergence analysis for SGD on a rich subset of two-layer feedforward networks with ReLU activations. This subset is characterized by a special structure called "identity mapping". We prove that, if input follows from Gaussian distribution, with standard $O(1/\sqrt{d})$ initialization of the weights, SGD converges to the global minimum in polynomial number of steps. Unlike normal vanilla networks, the "identity mapping" makes our network asymmetric and thus the global minimum is unique. To complement our theory, we are also able to show experimentally that multi-layer networks with this mapping have better performance compared with normal vanilla networks. Our convergence theorem differs from traditional non-convex optimization techniques. We show that SGD converges to optimal in "two phases": In phase I, the gradient points to the wrong direction, however, a potential function $g$ gradually decreases. Then in phase II, SGD enters a nice one point convex region and converges. We also show that the identity mapping is necessary for convergence, as it moves the initial point to a better place for optimization. Experiment verifies our claims. |
2306.12768 | Edvin Listo Zec | Marcus Toft{\aa}s, Emilie Klefbom, Edvin Listo Zec, Martin Willbo,
Olof Mogren | Concept-aware clustering for decentralized deep learning under temporal
shift | 4 pages, 2 figures | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | Decentralized deep learning requires dealing with non-iid data across
clients, which may also change over time due to temporal shifts. While non-iid
data has been extensively studied in distributed settings, temporal shifts have
received no attention. To the best of our knowledge, we are first with tackling
the novel and challenging problem of decentralized learning with non-iid and
dynamic data. We propose a novel algorithm that can automatically discover and
adapt to the evolving concepts in the network, without any prior knowledge or
estimation of the number of concepts. We evaluate our algorithm on standard
benchmark datasets and demonstrate that it outperforms previous methods for
decentralized learning.
| [
{
"created": "Thu, 22 Jun 2023 09:45:40 GMT",
"version": "v1"
}
] | 2023-06-23 | [
[
"Toftås",
"Marcus",
""
],
[
"Klefbom",
"Emilie",
""
],
[
"Zec",
"Edvin Listo",
""
],
[
"Willbo",
"Martin",
""
],
[
"Mogren",
"Olof",
""
]
] | Decentralized deep learning requires dealing with non-iid data across clients, which may also change over time due to temporal shifts. While non-iid data has been extensively studied in distributed settings, temporal shifts have received no attention. To the best of our knowledge, we are first with tackling the novel and challenging problem of decentralized learning with non-iid and dynamic data. We propose a novel algorithm that can automatically discover and adapt to the evolving concepts in the network, without any prior knowledge or estimation of the number of concepts. We evaluate our algorithm on standard benchmark datasets and demonstrate that it outperforms previous methods for decentralized learning. |
2305.05389 | John Conroy | John M. Conroy, Neil P Molino, Brian Baughman, Rod Gomez, Ryan
Kaliszewski, and Nicholas A. Lines | Two to Five Truths in Non-Negative Matrix Factorization | null | null | null | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we explore the role of matrix scaling on a matrix of counts
when building a topic model using non-negative matrix factorization. We present
a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly
improve the quality of a non-negative matrix factorization. The results
parallel those in the spectral graph clustering work of \cite{Priebe:2019},
where the authors proved adjacency spectral embedding (ASE) spectral clustering
was more likely to discover core-periphery partitions and Laplacian Spectral
Embedding (LSE) was more likely to discover affinity partitions. In text
analysis non-negative matrix factorization (NMF) is typically used on a matrix
of co-occurrence ``contexts'' and ``terms" counts. The matrix scaling inspired
by LSE gives significant improvement for text topic models in a variety of
datasets. We illustrate the dramatic difference a matrix scalings in NMF can
greatly improve the quality of a topic model on three datasets where human
annotation is available. Using the adjusted Rand index (ARI), a measure cluster
similarity we see an increase of 50\% for Twitter data and over 200\% for a
newsgroup dataset versus using counts, which is the analogue of ASE. For clean
data, such as those from the Document Understanding Conference, NL gives over
40\% improvement over ASE. We conclude with some analysis of this phenomenon
and some connections of this scaling with other matrix scaling methods.
| [
{
"created": "Sat, 6 May 2023 14:40:20 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Sep 2023 16:14:56 GMT",
"version": "v2"
}
] | 2023-09-06 | [
[
"Conroy",
"John M.",
""
],
[
"Molino",
"Neil P",
""
],
[
"Baughman",
"Brian",
""
],
[
"Gomez",
"Rod",
""
],
[
"Kaliszewski",
"Ryan",
""
],
[
"Lines",
"Nicholas A.",
""
]
] | In this paper, we explore the role of matrix scaling on a matrix of counts when building a topic model using non-negative matrix factorization. We present a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel those in the spectral graph clustering work of \cite{Priebe:2019}, where the authors proved adjacency spectral embedding (ASE) spectral clustering was more likely to discover core-periphery partitions and Laplacian Spectral Embedding (LSE) was more likely to discover affinity partitions. In text analysis non-negative matrix factorization (NMF) is typically used on a matrix of co-occurrence ``contexts'' and ``terms" counts. The matrix scaling inspired by LSE gives significant improvement for text topic models in a variety of datasets. We illustrate the dramatic difference a matrix scalings in NMF can greatly improve the quality of a topic model on three datasets where human annotation is available. Using the adjusted Rand index (ARI), a measure cluster similarity we see an increase of 50\% for Twitter data and over 200\% for a newsgroup dataset versus using counts, which is the analogue of ASE. For clean data, such as those from the Document Understanding Conference, NL gives over 40\% improvement over ASE. We conclude with some analysis of this phenomenon and some connections of this scaling with other matrix scaling methods. |
1504.03912 | Lin Jianbiao | Hui Lin, Jianbiao Lin, Ke Ji, Jingjie Wang, Feng Lin | Promote the Industry Standard of Smart Home in China by Intelligent
Router Technology | null | null | null | null | cs.NI | http://creativecommons.org/licenses/by-nc-sa/3.0/ | The reason why smart home remains not popularized lies in bad product user
experience, purchasing cost, and compatibility, and a lack of industry
standard[1]. Echoing problems above, and having relentless devoted to software
and hardware innovation and practice, we have independently developed a set of
solution which is based on innovation and integration of router technology,
mobile Internet technology,Internet of things technology,communication
technology, digital-to-analog conversion and codec technology, and P2P
technology among others. We have also established relevant protocols (without
the application of protocols abroad). By doing this, we managed to establish a
system with low and moderate price, superior performance, all-inclusive
functions, easy installation, convenient portability, real-time reliability,
security encryption, and the capability to manage home furnitures in an
intelligent way. Only a new smart home system like this can inject new idea and
energy into smart home industry and thus vigorously promote the establishment
of smart home industry standard.
| [
{
"created": "Wed, 15 Apr 2015 13:52:40 GMT",
"version": "v1"
}
] | 2015-04-16 | [
[
"Lin",
"Hui",
""
],
[
"Lin",
"Jianbiao",
""
],
[
"Ji",
"Ke",
""
],
[
"Wang",
"Jingjie",
""
],
[
"Lin",
"Feng",
""
]
] | The reason why smart home remains not popularized lies in bad product user experience, purchasing cost, and compatibility, and a lack of industry standard[1]. Echoing problems above, and having relentless devoted to software and hardware innovation and practice, we have independently developed a set of solution which is based on innovation and integration of router technology, mobile Internet technology,Internet of things technology,communication technology, digital-to-analog conversion and codec technology, and P2P technology among others. We have also established relevant protocols (without the application of protocols abroad). By doing this, we managed to establish a system with low and moderate price, superior performance, all-inclusive functions, easy installation, convenient portability, real-time reliability, security encryption, and the capability to manage home furnitures in an intelligent way. Only a new smart home system like this can inject new idea and energy into smart home industry and thus vigorously promote the establishment of smart home industry standard. |
2309.16231 | Hanqing Zhang | Hanqing Zhang, Sun Si, Haiming Wu, Dawei Song | Controllable Text Generation with Residual Memory Transformer | github:https://github.com/littlehacker26/Residual_Memory_Transformer | ACL 2024 | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Large-scale Causal Language Models (CLMs), e.g., GPT3 and ChatGPT, have
brought great success in text generation. However, it is still an open
challenge to control the generation process of CLM while balancing flexibility,
control granularity, and generation efficiency. In this paper, we provide a new
alternative for controllable text generation (CTG), by designing a
non-intrusive, lightweight control plugin to accompany the generation of CLM at
arbitrary time steps. The proposed control plugin, namely Residual Memory
Transformer (RMT), has an encoder-decoder setup, which can accept any types of
control conditions and cooperate with CLM through a residual learning paradigm,
to achieve a more flexible, general, and efficient CTG. Extensive experiments
are carried out on various control tasks, in the form of both automatic and
human evaluations. The results show the superiority of RMT over a range of
state-of-the-art approaches, proving the effectiveness and versatility of our
approach.
| [
{
"created": "Thu, 28 Sep 2023 08:13:33 GMT",
"version": "v1"
}
] | 2024-06-27 | [
[
"Zhang",
"Hanqing",
""
],
[
"Si",
"Sun",
""
],
[
"Wu",
"Haiming",
""
],
[
"Song",
"Dawei",
""
]
] | Large-scale Causal Language Models (CLMs), e.g., GPT3 and ChatGPT, have brought great success in text generation. However, it is still an open challenge to control the generation process of CLM while balancing flexibility, control granularity, and generation efficiency. In this paper, we provide a new alternative for controllable text generation (CTG), by designing a non-intrusive, lightweight control plugin to accompany the generation of CLM at arbitrary time steps. The proposed control plugin, namely Residual Memory Transformer (RMT), has an encoder-decoder setup, which can accept any types of control conditions and cooperate with CLM through a residual learning paradigm, to achieve a more flexible, general, and efficient CTG. Extensive experiments are carried out on various control tasks, in the form of both automatic and human evaluations. The results show the superiority of RMT over a range of state-of-the-art approaches, proving the effectiveness and versatility of our approach. |
1710.07909 | Bing Zhu | Bing Zhu, Kenneth W. Shum, and Hui Li | On the Duality of Fractional Repetition Codes | Accepted by the 2017 IEEE Information Theory Workshop (ITW 2017) | null | 10.1109/ITW.2017.8277971 | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Erasure codes have emerged as an efficient technology for providing data
redundancy in distributed storage systems. However, it is a challenging task to
repair the failed storage nodes in erasure-coded storage systems, which
requires large quantities of network resources. In this paper, we study
fractional repetition (FR) codes, which enable the minimal repair complexity
and also minimum repair bandwidth during node repair. We focus on the duality
of FR codes, and investigate the relationship between the supported file size
of an FR code and its dual code. Furthermore, we present a dual bound on the
supported file size of FR codes.
| [
{
"created": "Sun, 22 Oct 2017 09:03:01 GMT",
"version": "v1"
}
] | 2020-05-15 | [
[
"Zhu",
"Bing",
""
],
[
"Shum",
"Kenneth W.",
""
],
[
"Li",
"Hui",
""
]
] | Erasure codes have emerged as an efficient technology for providing data redundancy in distributed storage systems. However, it is a challenging task to repair the failed storage nodes in erasure-coded storage systems, which requires large quantities of network resources. In this paper, we study fractional repetition (FR) codes, which enable the minimal repair complexity and also minimum repair bandwidth during node repair. We focus on the duality of FR codes, and investigate the relationship between the supported file size of an FR code and its dual code. Furthermore, we present a dual bound on the supported file size of FR codes. |
1111.1797 | Shipra Agrawal | Shipra Agrawal, Navin Goyal | Analysis of Thompson Sampling for the multi-armed bandit problem | This version corrects some minor errors, and reorganizes some content | null | null | null | cs.LG cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The multi-armed bandit problem is a popular model for studying
exploration/exploitation trade-off in sequential decision problems. Many
algorithms are now available for this well-studied problem. One of the earliest
algorithms, given by W. R. Thompson, dates back to 1933. This algorithm,
referred to as Thompson Sampling, is a natural Bayesian algorithm. The basic
idea is to choose an arm to play according to its probability of being the best
arm. Thompson Sampling algorithm has experimentally been shown to be close to
optimal. In addition, it is efficient to implement and exhibits several
desirable properties such as small regret for delayed feedback. However,
theoretical understanding of this algorithm was quite limited. In this paper,
for the first time, we show that Thompson Sampling algorithm achieves
logarithmic expected regret for the multi-armed bandit problem. More precisely,
for the two-armed bandit problem, the expected regret in time $T$ is
$O(\frac{\ln T}{\Delta} + \frac{1}{\Delta^3})$. And, for the $N$-armed bandit
problem, the expected regret in time $T$ is $O([(\sum_{i=2}^N
\frac{1}{\Delta_i^2})^2] \ln T)$. Our bounds are optimal but for the dependence
on $\Delta_i$ and the constant factors in big-Oh.
| [
{
"created": "Tue, 8 Nov 2011 04:27:01 GMT",
"version": "v1"
},
{
"created": "Tue, 27 Dec 2011 08:27:25 GMT",
"version": "v2"
},
{
"created": "Mon, 9 Apr 2012 10:43:05 GMT",
"version": "v3"
}
] | 2012-04-10 | [
[
"Agrawal",
"Shipra",
""
],
[
"Goyal",
"Navin",
""
]
] | The multi-armed bandit problem is a popular model for studying exploration/exploitation trade-off in sequential decision problems. Many algorithms are now available for this well-studied problem. One of the earliest algorithms, given by W. R. Thompson, dates back to 1933. This algorithm, referred to as Thompson Sampling, is a natural Bayesian algorithm. The basic idea is to choose an arm to play according to its probability of being the best arm. Thompson Sampling algorithm has experimentally been shown to be close to optimal. In addition, it is efficient to implement and exhibits several desirable properties such as small regret for delayed feedback. However, theoretical understanding of this algorithm was quite limited. In this paper, for the first time, we show that Thompson Sampling algorithm achieves logarithmic expected regret for the multi-armed bandit problem. More precisely, for the two-armed bandit problem, the expected regret in time $T$ is $O(\frac{\ln T}{\Delta} + \frac{1}{\Delta^3})$. And, for the $N$-armed bandit problem, the expected regret in time $T$ is $O([(\sum_{i=2}^N \frac{1}{\Delta_i^2})^2] \ln T)$. Our bounds are optimal but for the dependence on $\Delta_i$ and the constant factors in big-Oh. |
0710.1254 | Hua Li | Hua Li and Edwin K.P. Chong | A Group Theoretic Model for Information | Submitted to IEEE Transactions on Information Theory | null | null | null | cs.IT math.IT | null | In this paper we formalize the notions of information elements and
information lattices, first proposed by Shannon. Exploiting this formalization,
we identify a comprehensive parallelism between information lattices and
subgroup lattices. Qualitatively, we demonstrate isomorphisms between
information lattices and subgroup lattices. Quantitatively, we establish a
decisive approximation relation between the entropy structures of information
lattices and the log-index structures of the corresponding subgroup lattices.
This approximation extends the approximation for joint entropies carried out
previously by Chan and Yeung. As a consequence of our approximation result, we
show that any continuous law holds in general for the entropies of information
elements if and only if the same law holds in general for the log-indices of
subgroups. As an application, by constructing subgroup counterexamples we find
surprisingly that common information, unlike joint information, obeys neither
the submodularity nor the supermodularity law. We emphasize that the notion of
information elements is conceptually significant--formalizing it helps to
reveal the deep connection between information theory and group theory. The
parallelism established in this paper admits an appealing group-action
explanation and provides useful insights into the intrinsic structure among
information elements from a group-theoretic perspective.
| [
{
"created": "Fri, 5 Oct 2007 18:37:21 GMT",
"version": "v1"
}
] | 2007-10-08 | [
[
"Li",
"Hua",
""
],
[
"Chong",
"Edwin K. P.",
""
]
] | In this paper we formalize the notions of information elements and information lattices, first proposed by Shannon. Exploiting this formalization, we identify a comprehensive parallelism between information lattices and subgroup lattices. Qualitatively, we demonstrate isomorphisms between information lattices and subgroup lattices. Quantitatively, we establish a decisive approximation relation between the entropy structures of information lattices and the log-index structures of the corresponding subgroup lattices. This approximation extends the approximation for joint entropies carried out previously by Chan and Yeung. As a consequence of our approximation result, we show that any continuous law holds in general for the entropies of information elements if and only if the same law holds in general for the log-indices of subgroups. As an application, by constructing subgroup counterexamples we find surprisingly that common information, unlike joint information, obeys neither the submodularity nor the supermodularity law. We emphasize that the notion of information elements is conceptually significant--formalizing it helps to reveal the deep connection between information theory and group theory. The parallelism established in this paper admits an appealing group-action explanation and provides useful insights into the intrinsic structure among information elements from a group-theoretic perspective. |
1901.10050 | Emilia Ciupan | Emilia Ciupan, Mihai Ciupan, Daniela-Corina Jucan | Determining the Mechanical Properties of a New Composite Material Using
Artificial Neural Networks | 6 pages, 4 figures, Published with International Journal of
Engineering Trends and Technology (IJETT) | International Journal of Engineering Trends and Technology 66.2
(2018): 103-108 | 10.14445/22315381/IJETT-V66P218 | null | cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The paper studies the possibility of using artificial neural networks (ANN)
to determine certain mechanical properties of a new composite material. This
new material is obtained by a mixture of hemp and polypropylene fibres. The
material was developed for the industry of upholstered furniture. Specifically,
it is intended for the making of elements of the support structure of some
upholstered goods (chairs, armchairs, sofa sides) with the objective of
replacing wood. The paper aims to calculate the following mechanical
properties: maximum tensile strength and maximum elongation.
| [
{
"created": "Fri, 11 Jan 2019 17:34:30 GMT",
"version": "v1"
}
] | 2019-01-30 | [
[
"Ciupan",
"Emilia",
""
],
[
"Ciupan",
"Mihai",
""
],
[
"Jucan",
"Daniela-Corina",
""
]
] | The paper studies the possibility of using artificial neural networks (ANN) to determine certain mechanical properties of a new composite material. This new material is obtained by a mixture of hemp and polypropylene fibres. The material was developed for the industry of upholstered furniture. Specifically, it is intended for the making of elements of the support structure of some upholstered goods (chairs, armchairs, sofa sides) with the objective of replacing wood. The paper aims to calculate the following mechanical properties: maximum tensile strength and maximum elongation. |
2404.04739 | Robert Schneider | Maxwell Schneider, Cody McCarthy, Michael G. Maxwell, Joshua Pfeffer,
Robert Schneider and Andrew V. Sills | Mathematics of the MML functional quantizer modules for VCV Rack
software synthesizer | 4 pages, published in Infinite Loop: an online journal for
undergraduate research and applied computing projects (2024) | null | null | null | cs.SD eess.AS math.HO | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We detail the mathematical formulation of the line of "functional quantizer"
modules developed by the Mathematics and Music Lab (MML) at Michigan
Technological University, for the VCV Rack software modular synthesizer
platform, which allow synthesizer players to tune oscillators to new musical
scales based on mathematical functions. For example, we describe the
recently-released MML Logarithmic Quantizer (LOG QNT) module that tunes
synthesizer oscillators to the non-Pythagorean musical scale introduced by
indie band The Apples in Stereo.
| [
{
"created": "Sat, 6 Apr 2024 21:56:16 GMT",
"version": "v1"
},
{
"created": "Sat, 20 Apr 2024 00:00:07 GMT",
"version": "v2"
},
{
"created": "Sun, 28 Apr 2024 04:56:45 GMT",
"version": "v3"
}
] | 2024-04-30 | [
[
"Schneider",
"Maxwell",
""
],
[
"McCarthy",
"Cody",
""
],
[
"Maxwell",
"Michael G.",
""
],
[
"Pfeffer",
"Joshua",
""
],
[
"Schneider",
"Robert",
""
],
[
"Sills",
"Andrew V.",
""
]
] | We detail the mathematical formulation of the line of "functional quantizer" modules developed by the Mathematics and Music Lab (MML) at Michigan Technological University, for the VCV Rack software modular synthesizer platform, which allow synthesizer players to tune oscillators to new musical scales based on mathematical functions. For example, we describe the recently-released MML Logarithmic Quantizer (LOG QNT) module that tunes synthesizer oscillators to the non-Pythagorean musical scale introduced by indie band The Apples in Stereo. |
0801.3550 | Uwe Aickelin | Uwe Aickelin and Larry Bull | Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary
Algorithm | null | Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO 2002), pp 263-270, New York, USA, 2002 | null | null | cs.NE cs.AI | null | This paper combines the idea of a hierarchical distributed genetic algorithm
with different inter-agent partnering strategies. Cascading clusters of
sub-populations are built from bottom up, with higher-level sub-populations
optimising larger parts of the problem. Hence higher-level sub-populations
search a larger search space with a lower resolution whilst lower-level
sub-populations search a smaller search space with a higher resolution. The
effects of different partner selection schemes for (sub-)fitness evaluation
purposes are examined for two multiple-choice optimisation problems. It is
shown that random partnering strategies perform best by providing better
sampling and more diversity.
| [
{
"created": "Wed, 23 Jan 2008 11:12:39 GMT",
"version": "v1"
},
{
"created": "Mon, 3 Mar 2008 17:08:00 GMT",
"version": "v2"
}
] | 2010-07-05 | [
[
"Aickelin",
"Uwe",
""
],
[
"Bull",
"Larry",
""
]
] | This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity. |
2303.00458 | Manos Kamarianakis | Manos Kamarianakis, Antonis Protopsaltis, George Papagiannakis | AR-Assisted Surgical Care via 5G networks for First Aid Responders | 3 pages, 2 figures, presented at IEEE International Workshop on
Computer Aided Modeling and Design of Communication Links and Networks
(CAMAD) 2022, 2-3 November 2022 | null | null | null | cs.GR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Surgeons should play a central role in disaster planning and management due
to the overwhelming number of bodily injuries that are typically involved
during most forms of disaster. In fact, various types of surgical procedures
are performed by emergency medical teams after sudden-onset disasters, such as
soft tissue wounds, orthopaedic traumas, abdominal surgeries, etc. HMD-based
Augmented Reality (AR), using state-of-the-art hardware such as the Magic Leap
or the Microsoft HoloLens, have long been foreseen as a key enabler for
clinicians in surgical use cases, especially for procedures performed outside
of the operating room.
This paper describes the Use Case (UC) "AR-assisted emergency surgical care",
identified in the context of the 5G-EPICENTRE EU-funded project. Specifically,
the UC will experiment with holographic AR technology for emergency medical
surgery teams, by overlaying deformable medical models directly on top of the
patient body parts, effectively enabling surgeons to see inside (visualizing
bones, blood vessels, etc.) and perform surgical actions following step-by-step
instructions. The goal is to combine the computational and data-intensive
nature of AR and Computer Vision algorithms with upcoming 5G network
architectures deployed for edge computing so as to satisfy real-time
interaction requirements and provide an efficient and powerful platform for the
pervasive promotion of such applications. By developing the necessary Virtual
Network Functions (VNFs) to manage data-intensive services (e.g., prerendering,
caching, compression) and by exploiting available network resources and
Multi-access Edge Computing (MEC) support, provided by the 5G-EPICENTRE
infrastructure, this UC aims to provide powerful AR-based tools, usable on
site, to first-aid responders.
| [
{
"created": "Wed, 1 Mar 2023 12:33:31 GMT",
"version": "v1"
}
] | 2023-03-02 | [
[
"Kamarianakis",
"Manos",
""
],
[
"Protopsaltis",
"Antonis",
""
],
[
"Papagiannakis",
"George",
""
]
] | Surgeons should play a central role in disaster planning and management due to the overwhelming number of bodily injuries that are typically involved during most forms of disaster. In fact, various types of surgical procedures are performed by emergency medical teams after sudden-onset disasters, such as soft tissue wounds, orthopaedic traumas, abdominal surgeries, etc. HMD-based Augmented Reality (AR), using state-of-the-art hardware such as the Magic Leap or the Microsoft HoloLens, have long been foreseen as a key enabler for clinicians in surgical use cases, especially for procedures performed outside of the operating room. This paper describes the Use Case (UC) "AR-assisted emergency surgical care", identified in the context of the 5G-EPICENTRE EU-funded project. Specifically, the UC will experiment with holographic AR technology for emergency medical surgery teams, by overlaying deformable medical models directly on top of the patient body parts, effectively enabling surgeons to see inside (visualizing bones, blood vessels, etc.) and perform surgical actions following step-by-step instructions. The goal is to combine the computational and data-intensive nature of AR and Computer Vision algorithms with upcoming 5G network architectures deployed for edge computing so as to satisfy real-time interaction requirements and provide an efficient and powerful platform for the pervasive promotion of such applications. By developing the necessary Virtual Network Functions (VNFs) to manage data-intensive services (e.g., prerendering, caching, compression) and by exploiting available network resources and Multi-access Edge Computing (MEC) support, provided by the 5G-EPICENTRE infrastructure, this UC aims to provide powerful AR-based tools, usable on site, to first-aid responders. |
2111.05791 | Xuan Bi | Xuan Bi and Xiaotong Shen | Distribution-Invariant Differential Privacy | null | null | null | null | cs.CR cs.LG stat.ME | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Differential privacy is becoming one gold standard for protecting the privacy
of publicly shared data. It has been widely used in social science, data
science, public health, information technology, and the U.S. decennial census.
Nevertheless, to guarantee differential privacy, existing methods may
unavoidably alter the conclusion of the original data analysis, as
privatization often changes the sample distribution. This phenomenon is known
as the trade-off between privacy protection and statistical accuracy. In this
work, we mitigate this trade-off by developing a distribution-invariant
privatization (DIP) method to reconcile both high statistical accuracy and
strict differential privacy. As a result, any downstream statistical or machine
learning task yields essentially the same conclusion as if one used the
original data. Numerically, under the same strictness of privacy protection,
DIP achieves superior statistical accuracy in a wide range of simulation
studies and real-world benchmarks.
| [
{
"created": "Mon, 8 Nov 2021 22:26:50 GMT",
"version": "v1"
},
{
"created": "Mon, 6 Jun 2022 16:28:56 GMT",
"version": "v2"
}
] | 2022-06-07 | [
[
"Bi",
"Xuan",
""
],
[
"Shen",
"Xiaotong",
""
]
] | Differential privacy is becoming one gold standard for protecting the privacy of publicly shared data. It has been widely used in social science, data science, public health, information technology, and the U.S. decennial census. Nevertheless, to guarantee differential privacy, existing methods may unavoidably alter the conclusion of the original data analysis, as privatization often changes the sample distribution. This phenomenon is known as the trade-off between privacy protection and statistical accuracy. In this work, we mitigate this trade-off by developing a distribution-invariant privatization (DIP) method to reconcile both high statistical accuracy and strict differential privacy. As a result, any downstream statistical or machine learning task yields essentially the same conclusion as if one used the original data. Numerically, under the same strictness of privacy protection, DIP achieves superior statistical accuracy in a wide range of simulation studies and real-world benchmarks. |
2205.05888 | Hang Li | Hang Li and Ahmed Mourad and Bevan Koopman and Guido Zuccon | How does Feedback Signal Quality Impact Effectiveness of Pseudo
Relevance Feedback for Passage Retrieval? | Accepted at SIGIR 2022 | null | null | null | cs.IR | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a
first-stage ranker are relevant to the original query and uses them to improve
the query representation for a second round of retrieval. This assumption
however is often not correct: some or even all of the feedback documents may be
irrelevant. Indeed, the effectiveness of PRF methods may well depend on the
quality of the feedback signal and thus on the effectiveness of the first-stage
ranker. This aspect however has received little attention before.
In this paper we control the quality of the feedback signal and measure its
impact on a range of PRF methods, including traditional bag-of-words methods
(Rocchio), and dense vector-based methods (learnt and not learnt). Our results
show the important role the quality of the feedback signal plays on the
effectiveness of PRF methods. Importantly, and surprisingly, our analysis
reveals that not all PRF methods are the same when dealing with feedback
signals of varying quality. These findings are critical to gain a better
understanding of the PRF methods and of which and when they should be used,
depending on the feedback signal quality, and set the basis for future research
in this area.
| [
{
"created": "Thu, 12 May 2022 05:47:57 GMT",
"version": "v1"
}
] | 2022-05-13 | [
[
"Li",
"Hang",
""
],
[
"Mourad",
"Ahmed",
""
],
[
"Koopman",
"Bevan",
""
],
[
"Zuccon",
"Guido",
""
]
] | Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval. This assumption however is often not correct: some or even all of the feedback documents may be irrelevant. Indeed, the effectiveness of PRF methods may well depend on the quality of the feedback signal and thus on the effectiveness of the first-stage ranker. This aspect however has received little attention before. In this paper we control the quality of the feedback signal and measure its impact on a range of PRF methods, including traditional bag-of-words methods (Rocchio), and dense vector-based methods (learnt and not learnt). Our results show the important role the quality of the feedback signal plays on the effectiveness of PRF methods. Importantly, and surprisingly, our analysis reveals that not all PRF methods are the same when dealing with feedback signals of varying quality. These findings are critical to gain a better understanding of the PRF methods and of which and when they should be used, depending on the feedback signal quality, and set the basis for future research in this area. |
2108.01887 | Machel Reid | Machel Reid, Mikel Artetxe | PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence
Pretraining | Preprint | null | null | null | cs.CL cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the success of multilingual sequence-to-sequence pretraining, most
existing approaches rely on monolingual corpora, and do not make use of the
strong cross-lingual signal contained in parallel data. In this paper, we
present PARADISE (PARAllel & Denoising Integration in SEquence-to-sequence
models), which extends the conventional denoising objective used to train these
models by (i) replacing words in the noised sequence according to a
multilingual dictionary, and (ii) predicting the reference translation
according to a parallel corpus instead of recovering the original sequence. Our
experiments on machine translation and cross-lingual natural language inference
show an average improvement of 2.0 BLEU points and 6.7 accuracy points from
integrating parallel data into pretraining, respectively, obtaining results
that are competitive with several popular models at a fraction of their
computational cost.
| [
{
"created": "Wed, 4 Aug 2021 07:32:56 GMT",
"version": "v1"
}
] | 2021-08-05 | [
[
"Reid",
"Machel",
""
],
[
"Artetxe",
"Mikel",
""
]
] | Despite the success of multilingual sequence-to-sequence pretraining, most existing approaches rely on monolingual corpora, and do not make use of the strong cross-lingual signal contained in parallel data. In this paper, we present PARADISE (PARAllel & Denoising Integration in SEquence-to-sequence models), which extends the conventional denoising objective used to train these models by (i) replacing words in the noised sequence according to a multilingual dictionary, and (ii) predicting the reference translation according to a parallel corpus instead of recovering the original sequence. Our experiments on machine translation and cross-lingual natural language inference show an average improvement of 2.0 BLEU points and 6.7 accuracy points from integrating parallel data into pretraining, respectively, obtaining results that are competitive with several popular models at a fraction of their computational cost. |
2106.02283 | Maximilian Hils | Maximilian Hils, Daniel W. Woods, Rainer B\"ohme (University of
Innsbruck) | Privacy Preference Signals: Past, Present and Future | null | Proceedings on Privacy Enhancing Technologies 2021 | 10.2478/popets-2021-0069 | null | cs.HC cs.CY | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Privacy preference signals are digital representations of how users want
their personal data to be processed. Such signals must be adopted by both the
sender (users) and intended recipients (data processors). Adoption represents a
coordination problem that remains unsolved despite efforts dating back to the
1990s. Browsers implemented standards like the Platform for Privacy Preferences
(P3P) and Do Not Track (DNT), but vendors profiting from personal data faced
few incentives to receive and respect the expressed wishes of data subjects. In
the wake of recent privacy laws, a coalition of AdTech firms published the
Transparency and Consent Framework (TCF), which defines an opt-in consent
signal. This paper integrates post-GDPR developments into the wider history of
privacy preference signals. Our main contribution is a high-frequency
longitudinal study describing how TCF signal gained dominance as of February
2021. We explore which factors correlate with adoption at the website level.
Both the number of third parties on a website and the presence of Google Ads
are associated with higher adoption of TCF. Further, we show that vendors acted
as early adopters of TCF 2.0 and provide two case-studies describing how
Consent Management Providers shifted existing customers to TCF 2.0. We sketch
ways forward for a pro-privacy signal.
| [
{
"created": "Fri, 4 Jun 2021 06:39:20 GMT",
"version": "v1"
},
{
"created": "Wed, 16 Jun 2021 00:22:35 GMT",
"version": "v2"
},
{
"created": "Thu, 17 Jun 2021 08:53:05 GMT",
"version": "v3"
},
{
"created": "Wed, 14 Jul 2021 10:48:17 GMT",
"version": "v4"
}
] | 2021-07-15 | [
[
"Hils",
"Maximilian",
"",
"University of\n Innsbruck"
],
[
"Woods",
"Daniel W.",
"",
"University of\n Innsbruck"
],
[
"Böhme",
"Rainer",
"",
"University of\n Innsbruck"
]
] | Privacy preference signals are digital representations of how users want their personal data to be processed. Such signals must be adopted by both the sender (users) and intended recipients (data processors). Adoption represents a coordination problem that remains unsolved despite efforts dating back to the 1990s. Browsers implemented standards like the Platform for Privacy Preferences (P3P) and Do Not Track (DNT), but vendors profiting from personal data faced few incentives to receive and respect the expressed wishes of data subjects. In the wake of recent privacy laws, a coalition of AdTech firms published the Transparency and Consent Framework (TCF), which defines an opt-in consent signal. This paper integrates post-GDPR developments into the wider history of privacy preference signals. Our main contribution is a high-frequency longitudinal study describing how TCF signal gained dominance as of February 2021. We explore which factors correlate with adoption at the website level. Both the number of third parties on a website and the presence of Google Ads are associated with higher adoption of TCF. Further, we show that vendors acted as early adopters of TCF 2.0 and provide two case-studies describing how Consent Management Providers shifted existing customers to TCF 2.0. We sketch ways forward for a pro-privacy signal. |
2403.14253 | Kyuhee Kim | Kyuhee Kim, Surin Lee and Sangah Lee | K-Act2Emo: Korean Commonsense Knowledge Graph for Indirect Emotional
Expression | 10 pages | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many literary texts, emotions are indirectly conveyed through descriptions
of actions, facial expressions, and appearances, necessitating emotion
inference for narrative understanding. In this paper, we introduce K-Act2Emo, a
Korean commonsense knowledge graph (CSKG) comprising 1,900 indirect emotional
expressions and the emotions inferable from them. We categorize reasoning types
into inferences in positive situations, inferences in negative situations, and
inferences when expressions do not serve as emotional cues. Unlike existing
CSKGs, K-Act2Emo specializes in emotional contexts, and experimental results
validate its effectiveness for training emotion inference models.
Significantly, the BART-based knowledge model fine-tuned with K-Act2Emo
outperforms various existing Korean large language models, achieving
performance levels comparable to GPT-4 Turbo.
| [
{
"created": "Thu, 21 Mar 2024 09:26:04 GMT",
"version": "v1"
},
{
"created": "Sat, 23 Mar 2024 15:53:50 GMT",
"version": "v2"
}
] | 2024-03-26 | [
[
"Kim",
"Kyuhee",
""
],
[
"Lee",
"Surin",
""
],
[
"Lee",
"Sangah",
""
]
] | In many literary texts, emotions are indirectly conveyed through descriptions of actions, facial expressions, and appearances, necessitating emotion inference for narrative understanding. In this paper, we introduce K-Act2Emo, a Korean commonsense knowledge graph (CSKG) comprising 1,900 indirect emotional expressions and the emotions inferable from them. We categorize reasoning types into inferences in positive situations, inferences in negative situations, and inferences when expressions do not serve as emotional cues. Unlike existing CSKGs, K-Act2Emo specializes in emotional contexts, and experimental results validate its effectiveness for training emotion inference models. Significantly, the BART-based knowledge model fine-tuned with K-Act2Emo outperforms various existing Korean large language models, achieving performance levels comparable to GPT-4 Turbo. |
0810.4658 | Keqin Liu | Keqin Liu, Qing Zhao | Indexability of Restless Bandit Problems and Optimality of Whittle's
Index for Dynamic Multichannel Access | submitted to IEEE Transactions on Information Theory | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider a class of restless multi-armed bandit problems (RMBP) that
arises in dynamic multichannel access, user/server scheduling, and optimal
activation in multi-agent systems. For this class of RMBP, we establish the
indexability and obtain Whittle's index in closed-form for both discounted and
average reward criteria. These results lead to a direct implementation of
Whittle's index policy with remarkably low complexity. When these Markov chains
are stochastically identical, we show that Whittle's index policy is optimal
under certain conditions. Furthermore, it has a semi-universal structure that
obviates the need to know the Markov transition probabilities. The optimality
and the semi-universal structure result from the equivalency between Whittle's
index policy and the myopic policy established in this work. For non-identical
channels, we develop efficient algorithms for computing a performance upper
bound given by Lagrangian relaxation. The tightness of the upper bound and the
near-optimal performance of Whittle's index policy are illustrated with
simulation examples.
| [
{
"created": "Sun, 26 Oct 2008 01:58:35 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Nov 2008 16:02:40 GMT",
"version": "v2"
},
{
"created": "Thu, 13 Nov 2008 02:42:59 GMT",
"version": "v3"
}
] | 2008-11-13 | [
[
"Liu",
"Keqin",
""
],
[
"Zhao",
"Qing",
""
]
] | We consider a class of restless multi-armed bandit problems (RMBP) that arises in dynamic multichannel access, user/server scheduling, and optimal activation in multi-agent systems. For this class of RMBP, we establish the indexability and obtain Whittle's index in closed-form for both discounted and average reward criteria. These results lead to a direct implementation of Whittle's index policy with remarkably low complexity. When these Markov chains are stochastically identical, we show that Whittle's index policy is optimal under certain conditions. Furthermore, it has a semi-universal structure that obviates the need to know the Markov transition probabilities. The optimality and the semi-universal structure result from the equivalency between Whittle's index policy and the myopic policy established in this work. For non-identical channels, we develop efficient algorithms for computing a performance upper bound given by Lagrangian relaxation. The tightness of the upper bound and the near-optimal performance of Whittle's index policy are illustrated with simulation examples. |
cs/0606096 | Hendrik Feddes | Lea Cyrus | Building a resource for studying translation shifts | 6 pages, 1 figure | Proc. LREC 2006, Genoa, May 24-26, 2006; pp. 1240-1245 | null | null | cs.CL | null | This paper describes an interdisciplinary approach which brings together the
fields of corpus linguistics and translation studies. It presents ongoing work
on the creation of a corpus resource in which translation shifts are explicitly
annotated. Translation shifts denote departures from formal correspondence
between source and target text, i.e. deviations that have occurred during the
translation process. A resource in which such shifts are annotated in a
systematic way will make it possible to study those phenomena that need to be
addressed if machine translation output is to resemble human translation. The
resource described in this paper contains English source texts (parliamentary
proceedings) and their German translations. The shift annotation is based on
predicate-argument structures and proceeds in two steps: first, predicates and
their arguments are annotated monolingually in a straightforward manner. Then,
the corresponding English and German predicates and arguments are aligned with
each other. Whenever a shift - mainly grammatical or semantic -has occurred,
the alignment is tagged accordingly.
| [
{
"created": "Thu, 22 Jun 2006 13:26:52 GMT",
"version": "v1"
}
] | 2007-05-23 | [
[
"Cyrus",
"Lea",
""
]
] | This paper describes an interdisciplinary approach which brings together the fields of corpus linguistics and translation studies. It presents ongoing work on the creation of a corpus resource in which translation shifts are explicitly annotated. Translation shifts denote departures from formal correspondence between source and target text, i.e. deviations that have occurred during the translation process. A resource in which such shifts are annotated in a systematic way will make it possible to study those phenomena that need to be addressed if machine translation output is to resemble human translation. The resource described in this paper contains English source texts (parliamentary proceedings) and their German translations. The shift annotation is based on predicate-argument structures and proceeds in two steps: first, predicates and their arguments are annotated monolingually in a straightforward manner. Then, the corresponding English and German predicates and arguments are aligned with each other. Whenever a shift - mainly grammatical or semantic -has occurred, the alignment is tagged accordingly. |
1310.7469 | Vanessa Burke | Feng Jiang, Jiemin Wang, Abram Hindle and Mario A. Nascimento | Mining the Temporal Evolution of the Android Bug Reporting Community via
Sliding Windows | null | null | null | TR13-07 | cs.SE | http://creativecommons.org/licenses/by/3.0/ | The open source development community consists of both paid and volunteer
developers as well as new and experienced users. Previous work has applied
social network analysis (SNA) to open source communities and has demonstrated
value in expertise discovery and triaging. One problem with applying SNA
directly to the data of the entire project lifetime is that the impact of local
activities will be drowned out. In this paper we provide a method for
aggregating, analyzing, and visualizing local (small time periods) interactions
of bug reporting participants by using the SNA to measure the betweeness
centrality of these participants. In particular we mined the Android bug
repository by producing social networks from overlapping 30-day windows of bug
reports, each sliding over by day. In this paper we define three patterns of
participant behaviour based on their local centrality. We propose a method of
analyzing the centrality of bug report participants both locally and globally,
then we conduct a thorough case study of the bug reporter's activity within the
Android bug repository. Furthermore, we validate the conclusions of our method
by mining the Android version control system and inspecting the Android release
history. We found that windowed SNA analysis elicited local behaviour that were
invisible during global analysis.
| [
{
"created": "Mon, 28 Oct 2013 15:56:25 GMT",
"version": "v1"
}
] | 2013-10-29 | [
[
"Jiang",
"Feng",
""
],
[
"Wang",
"Jiemin",
""
],
[
"Hindle",
"Abram",
""
],
[
"Nascimento",
"Mario A.",
""
]
] | The open source development community consists of both paid and volunteer developers as well as new and experienced users. Previous work has applied social network analysis (SNA) to open source communities and has demonstrated value in expertise discovery and triaging. One problem with applying SNA directly to the data of the entire project lifetime is that the impact of local activities will be drowned out. In this paper we provide a method for aggregating, analyzing, and visualizing local (small time periods) interactions of bug reporting participants by using the SNA to measure the betweeness centrality of these participants. In particular we mined the Android bug repository by producing social networks from overlapping 30-day windows of bug reports, each sliding over by day. In this paper we define three patterns of participant behaviour based on their local centrality. We propose a method of analyzing the centrality of bug report participants both locally and globally, then we conduct a thorough case study of the bug reporter's activity within the Android bug repository. Furthermore, we validate the conclusions of our method by mining the Android version control system and inspecting the Android release history. We found that windowed SNA analysis elicited local behaviour that were invisible during global analysis. |
2311.08167 | Amit Chaudhary | Naveen Sahu, Mitul Gajera, Amit Chaudhary and Hamish Ivey-Law | SeDe: Balancing Blockchain Privacy and Regulatory Compliance by
Selective De-Anonymization | null | null | null | null | cs.CR | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Privacy is one of the essential pillars for the widespread adoption of
blockchains, but public blockchains are transparent by nature. Modern analytics
techniques can easily subdue the pseudonymity feature of a blockchain user.
Some applications have been able to provide practical privacy protections using
privacy-preserving cryptography techniques. However, malicious actors have
abused them illicitly, discouraging honest actors from using privacy-preserving
applications as "mixing" user interactions and funds with anonymous bad actors,
causing compliance and regulatory concerns.
In this paper, we propose a framework that balances privacy-preserving
features by establishing a regulatory and compliant framework called Selective
De-Anonymization (SeDe). The adoption of this framework allows
privacy-preserving applications on blockchains to de-anonymize illicit
transactions by recursive traversal of subgraphs of linked transactions. Our
technique achieves this without leaving de-anonymization decisions or control
in the hands of a single entity but distributing it among multiple entities
while holding them accountable for their respective actions. To instantiate,
our framework uses threshold encryption schemes and Zero-Knowledge Proofs
(ZKPs).
| [
{
"created": "Tue, 14 Nov 2023 13:49:13 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Nov 2023 12:38:12 GMT",
"version": "v2"
},
{
"created": "Sat, 9 Mar 2024 16:01:27 GMT",
"version": "v3"
},
{
"created": "Fri, 24 May 2024 09:18:10 GMT",
"version": "v4"
}
] | 2024-05-27 | [
[
"Sahu",
"Naveen",
""
],
[
"Gajera",
"Mitul",
""
],
[
"Chaudhary",
"Amit",
""
],
[
"Ivey-Law",
"Hamish",
""
]
] | Privacy is one of the essential pillars for the widespread adoption of blockchains, but public blockchains are transparent by nature. Modern analytics techniques can easily subdue the pseudonymity feature of a blockchain user. Some applications have been able to provide practical privacy protections using privacy-preserving cryptography techniques. However, malicious actors have abused them illicitly, discouraging honest actors from using privacy-preserving applications as "mixing" user interactions and funds with anonymous bad actors, causing compliance and regulatory concerns. In this paper, we propose a framework that balances privacy-preserving features by establishing a regulatory and compliant framework called Selective De-Anonymization (SeDe). The adoption of this framework allows privacy-preserving applications on blockchains to de-anonymize illicit transactions by recursive traversal of subgraphs of linked transactions. Our technique achieves this without leaving de-anonymization decisions or control in the hands of a single entity but distributing it among multiple entities while holding them accountable for their respective actions. To instantiate, our framework uses threshold encryption schemes and Zero-Knowledge Proofs (ZKPs). |
1901.08991 | Luis Armando P\'erez Rey | Luis A. P\'erez Rey, Vlado Menkovski, Jacobus W. Portegies | Diffusion Variational Autoencoders | 10 pages, 8 figures Added an appendix with derivation of asymptotic
expansion of KL divergence for heat kernel on arbitrary Riemannian manifolds,
and an appendix with new experiments on binarized MNIST. Added a previously
missing factor in the asymptotic expansion of the heat kernel and corrected a
coefficient in asymptotic expansion KL divergence; further minor edits | International Joint Conferences on Artificial Intelligence (IJCAI)
2020 | 10.24963/ijcai.2020/375 | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A standard Variational Autoencoder, with a Euclidean latent space, is
structurally incapable of capturing topological properties of certain datasets.
To remove topological obstructions, we introduce Diffusion Variational
Autoencoders with arbitrary manifolds as a latent space. A Diffusion
Variational Autoencoder uses transition kernels of Brownian motion on the
manifold. In particular, it uses properties of the Brownian motion to implement
the reparametrization trick and fast approximations to the KL divergence. We
show that the Diffusion Variational Autoencoder is capable of capturing
topological properties of synthetic datasets. Additionally, we train MNIST on
spheres, tori, projective spaces, SO(3), and a torus embedded in R3. Although a
natural dataset like MNIST does not have latent variables with a clear-cut
topological structure, training it on a manifold can still highlight
topological and geometrical properties.
| [
{
"created": "Fri, 25 Jan 2019 17:10:25 GMT",
"version": "v1"
},
{
"created": "Thu, 28 Mar 2019 09:10:12 GMT",
"version": "v2"
}
] | 2022-04-07 | [
[
"Rey",
"Luis A. Pérez",
""
],
[
"Menkovski",
"Vlado",
""
],
[
"Portegies",
"Jacobus W.",
""
]
] | A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets. To remove topological obstructions, we introduce Diffusion Variational Autoencoders with arbitrary manifolds as a latent space. A Diffusion Variational Autoencoder uses transition kernels of Brownian motion on the manifold. In particular, it uses properties of the Brownian motion to implement the reparametrization trick and fast approximations to the KL divergence. We show that the Diffusion Variational Autoencoder is capable of capturing topological properties of synthetic datasets. Additionally, we train MNIST on spheres, tori, projective spaces, SO(3), and a torus embedded in R3. Although a natural dataset like MNIST does not have latent variables with a clear-cut topological structure, training it on a manifold can still highlight topological and geometrical properties. |
2208.00002 | Zijue Chen | Zijue Chen, Keenan Granland, Rhys Newbury, Chao Chen | HOB-CNN: Hallucination of Occluded Branches with a Convolutional Neural
Network for 2D Fruit Trees | null | null | null | null | cs.CV eess.IV | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Orchard automation has attracted the attention of researchers recently due to
the shortage of global labor force. To automate tasks in orchards such as
pruning, thinning, and harvesting, a detailed understanding of the tree
structure is required. However, occlusions from foliage and fruits can make it
challenging to predict the position of occluded trunks and branches. This work
proposes a regression-based deep learning model, Hallucination of Occluded
Branch Convolutional Neural Network (HOB-CNN), for tree branch position
prediction in varying occluded conditions. We formulate tree branch position
prediction as a regression problem towards the horizontal locations of the
branch along the vertical direction or vice versa. We present comparative
experiments on Y-shaped trees with two state-of-the-art baselines, representing
common approaches to the problem. Experiments show that HOB-CNN outperform the
baselines at predicting branch position and shows robustness against varying
levels of occlusion. We further validated HOB-CNN against two different types
of 2D trees, and HOB-CNN shows generalization across different trees and
robustness under different occluded conditions.
| [
{
"created": "Thu, 28 Jul 2022 06:12:02 GMT",
"version": "v1"
}
] | 2022-08-02 | [
[
"Chen",
"Zijue",
""
],
[
"Granland",
"Keenan",
""
],
[
"Newbury",
"Rhys",
""
],
[
"Chen",
"Chao",
""
]
] | Orchard automation has attracted the attention of researchers recently due to the shortage of global labor force. To automate tasks in orchards such as pruning, thinning, and harvesting, a detailed understanding of the tree structure is required. However, occlusions from foliage and fruits can make it challenging to predict the position of occluded trunks and branches. This work proposes a regression-based deep learning model, Hallucination of Occluded Branch Convolutional Neural Network (HOB-CNN), for tree branch position prediction in varying occluded conditions. We formulate tree branch position prediction as a regression problem towards the horizontal locations of the branch along the vertical direction or vice versa. We present comparative experiments on Y-shaped trees with two state-of-the-art baselines, representing common approaches to the problem. Experiments show that HOB-CNN outperform the baselines at predicting branch position and shows robustness against varying levels of occlusion. We further validated HOB-CNN against two different types of 2D trees, and HOB-CNN shows generalization across different trees and robustness under different occluded conditions. |
2311.10275 | Sandeep Kumar | Alan Nair, Sandeep Kumar, Aravinda Prasad, Andy Rudoff, and Sreenivas
Subramoney | Telescope: Telemetry at Terabyte Scale | null | null | null | null | cs.OS cs.AR cs.DB cs.DC | http://creativecommons.org/licenses/by/4.0/ | Data-hungry applications that require terabytes of memory have become
widespread in recent years. To meet the memory needs of these applications,
data centers are embracing tiered memory architectures with near and far memory
tiers. Precise, efficient, and timely identification of hot and cold data and
their placement in appropriate tiers is critical for performance in such
systems. Unfortunately, the existing state-of-the-art telemetry techniques for
hot and cold data detection are ineffective at the terabyte scale.
We propose Telescope, a novel technique that profiles different levels of the
application's page table tree for fast and efficient identification of hot and
cold data. Telescope is based on the observation that, for a memory- and
TLB-intensive workload, higher levels of a page table tree are also frequently
accessed during a hardware page table walk. Hence, the hotness of the higher
levels of the page table tree essentially captures the hotness of its subtrees
or address space sub-regions at a coarser granularity. We exploit this insight
to quickly converge on even a few megabytes of hot data and efficiently
identify several gigabytes of cold data in terabyte-scale applications.
Importantly, such a technique can seamlessly scale to petabyte-scale
applications.
Telescope's telemetry achieves 90%+ precision and recall at just 0.009%
single CPU utilization for microbenchmarks with a 5 TB memory footprint. Memory
tiering based on Telescope results in 5.6% to 34% throughput improvement for
real-world benchmarks with a 1-2 TB memory footprint compared to other
state-of-the-art telemetry techniques.
| [
{
"created": "Fri, 17 Nov 2023 01:44:14 GMT",
"version": "v1"
},
{
"created": "Thu, 30 Nov 2023 04:14:30 GMT",
"version": "v2"
}
] | 2023-12-01 | [
[
"Nair",
"Alan",
""
],
[
"Kumar",
"Sandeep",
""
],
[
"Prasad",
"Aravinda",
""
],
[
"Rudoff",
"Andy",
""
],
[
"Subramoney",
"Sreenivas",
""
]
] | Data-hungry applications that require terabytes of memory have become widespread in recent years. To meet the memory needs of these applications, data centers are embracing tiered memory architectures with near and far memory tiers. Precise, efficient, and timely identification of hot and cold data and their placement in appropriate tiers is critical for performance in such systems. Unfortunately, the existing state-of-the-art telemetry techniques for hot and cold data detection are ineffective at the terabyte scale. We propose Telescope, a novel technique that profiles different levels of the application's page table tree for fast and efficient identification of hot and cold data. Telescope is based on the observation that, for a memory- and TLB-intensive workload, higher levels of a page table tree are also frequently accessed during a hardware page table walk. Hence, the hotness of the higher levels of the page table tree essentially captures the hotness of its subtrees or address space sub-regions at a coarser granularity. We exploit this insight to quickly converge on even a few megabytes of hot data and efficiently identify several gigabytes of cold data in terabyte-scale applications. Importantly, such a technique can seamlessly scale to petabyte-scale applications. Telescope's telemetry achieves 90%+ precision and recall at just 0.009% single CPU utilization for microbenchmarks with a 5 TB memory footprint. Memory tiering based on Telescope results in 5.6% to 34% throughput improvement for real-world benchmarks with a 1-2 TB memory footprint compared to other state-of-the-art telemetry techniques. |
2009.04441 | Diego Antognini | Kirtan Padh, Diego Antognini, Emma Lejal Glaude, Boi Faltings, Claudiu
Musat | Addressing Fairness in Classification with a Model-Agnostic
Multi-Objective Algorithm | Accepted at UAI 2021. 14 pages, 5 figures, 4 tables | null | null | null | cs.LG cs.AI cs.IR stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The goal of fairness in classification is to learn a classifier that does not
discriminate against groups of individuals based on sensitive attributes, such
as race and gender. One approach to designing fair algorithms is to use
relaxations of fairness notions as regularization terms or in a constrained
optimization problem. We observe that the hyperbolic tangent function can
approximate the indicator function. We leverage this property to define a
differentiable relaxation that approximates fairness notions provably better
than existing relaxations. In addition, we propose a model-agnostic
multi-objective architecture that can simultaneously optimize for multiple
fairness notions and multiple sensitive attributes and supports all statistical
parity-based notions of fairness. We use our relaxation with the
multi-objective architecture to learn fair classifiers. Experiments on public
datasets show that our method suffers a significantly lower loss of accuracy
than current debiasing algorithms relative to the unconstrained model.
| [
{
"created": "Wed, 9 Sep 2020 17:40:24 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Sep 2020 17:17:00 GMT",
"version": "v2"
},
{
"created": "Tue, 8 Jun 2021 12:39:26 GMT",
"version": "v3"
}
] | 2021-06-09 | [
[
"Padh",
"Kirtan",
""
],
[
"Antognini",
"Diego",
""
],
[
"Glaude",
"Emma Lejal",
""
],
[
"Faltings",
"Boi",
""
],
[
"Musat",
"Claudiu",
""
]
] | The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness notions as regularization terms or in a constrained optimization problem. We observe that the hyperbolic tangent function can approximate the indicator function. We leverage this property to define a differentiable relaxation that approximates fairness notions provably better than existing relaxations. In addition, we propose a model-agnostic multi-objective architecture that can simultaneously optimize for multiple fairness notions and multiple sensitive attributes and supports all statistical parity-based notions of fairness. We use our relaxation with the multi-objective architecture to learn fair classifiers. Experiments on public datasets show that our method suffers a significantly lower loss of accuracy than current debiasing algorithms relative to the unconstrained model. |
1410.5370 | Eric Seidel | Eric L. Seidel, Niki Vazou, Ranjit Jhala | Type Targeted Testing | null | null | 10.1007/978-3-662-46669-8_33 | null | cs.PL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present a new technique called type targeted testing, which translates
precise refinement types into comprehensive test-suites. The key insight behind
our approach is that through the lens of SMT solvers, refinement types can also
be viewed as a high-level, declarative, test generation technique, wherein
types are converted to SMT queries whose models can be decoded into concrete
program inputs. Our approach enables the systematic and exhaustive testing of
implementations from high-level declarative specifications, and furthermore,
provides a gradual path from testing to full verification. We have implemented
our approach as a Haskell testing tool called TARGET, and present an evaluation
that shows how TARGET can be used to test a wide variety of properties and how
it compares against state-of-the-art testing approaches.
| [
{
"created": "Mon, 20 Oct 2014 17:48:20 GMT",
"version": "v1"
},
{
"created": "Fri, 16 Jan 2015 03:55:38 GMT",
"version": "v2"
}
] | 2017-08-29 | [
[
"Seidel",
"Eric L.",
""
],
[
"Vazou",
"Niki",
""
],
[
"Jhala",
"Ranjit",
""
]
] | We present a new technique called type targeted testing, which translates precise refinement types into comprehensive test-suites. The key insight behind our approach is that through the lens of SMT solvers, refinement types can also be viewed as a high-level, declarative, test generation technique, wherein types are converted to SMT queries whose models can be decoded into concrete program inputs. Our approach enables the systematic and exhaustive testing of implementations from high-level declarative specifications, and furthermore, provides a gradual path from testing to full verification. We have implemented our approach as a Haskell testing tool called TARGET, and present an evaluation that shows how TARGET can be used to test a wide variety of properties and how it compares against state-of-the-art testing approaches. |
2104.11079 | Tamara Kolda | Aydin Buluc, Tamara G. Kolda, Stefan M. Wild, Mihai Anitescu, Anthony
DeGennaro, John Jakeman, Chandrika Kamath, Ramakrishnan Kannan, Miles E.
Lopes, Per-Gunnar Martinsson, Kary Myers, Jelani Nelson, Juan M. Restrepo, C.
Seshadhri, Draguna Vrabie, Brendt Wohlberg, Stephen J. Wright, Chao Yang,
Peter Zwart | Randomized Algorithms for Scientific Computing (RASC) | null | null | 10.2172/1807223 | null | cs.AI cs.CE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Randomized algorithms have propelled advances in artificial intelligence and
represent a foundational research area in advancing AI for Science. Future
advancements in DOE Office of Science priority areas such as climate science,
astrophysics, fusion, advanced materials, combustion, and quantum computing all
require randomized algorithms for surmounting challenges of complexity,
robustness, and scalability. This report summarizes the outcomes of that
workshop, "Randomized Algorithms for Scientific Computing (RASC)," held
virtually across four days in December 2020 and January 2021.
| [
{
"created": "Mon, 19 Apr 2021 18:59:26 GMT",
"version": "v1"
},
{
"created": "Tue, 28 Sep 2021 15:27:52 GMT",
"version": "v2"
},
{
"created": "Mon, 21 Mar 2022 21:29:54 GMT",
"version": "v3"
}
] | 2022-03-23 | [
[
"Buluc",
"Aydin",
""
],
[
"Kolda",
"Tamara G.",
""
],
[
"Wild",
"Stefan M.",
""
],
[
"Anitescu",
"Mihai",
""
],
[
"DeGennaro",
"Anthony",
""
],
[
"Jakeman",
"John",
""
],
[
"Kamath",
"Chandrika",
""
],
[
"Kannan",
"Ramakrishnan",
""
],
[
"Lopes",
"Miles E.",
""
],
[
"Martinsson",
"Per-Gunnar",
""
],
[
"Myers",
"Kary",
""
],
[
"Nelson",
"Jelani",
""
],
[
"Restrepo",
"Juan M.",
""
],
[
"Seshadhri",
"C.",
""
],
[
"Vrabie",
"Draguna",
""
],
[
"Wohlberg",
"Brendt",
""
],
[
"Wright",
"Stephen J.",
""
],
[
"Yang",
"Chao",
""
],
[
"Zwart",
"Peter",
""
]
] | Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. This report summarizes the outcomes of that workshop, "Randomized Algorithms for Scientific Computing (RASC)," held virtually across four days in December 2020 and January 2021. |
2103.14915 | Shengliang Lu | Shengliang Lu, Shixuan Sun, Johns Paul, Yuchen Li, Bingsheng He | Cache-Efficient Fork-Processing Patterns on Large Graphs | in SIGMOD 2021 | null | 10.1145/3448016.3457253 | null | cs.DB cs.DC | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As large graph processing emerges, we observe a costly fork-processing
pattern (FPP) that is common in many graph algorithms. The unique feature of
the FPP is that it launches many independent queries from different source
vertices on the same graph. For example, an algorithm in analyzing the network
community profile can execute Personalized PageRanks that start from tens of
thousands of source vertices at the same time. We study the efficiency of
handling FPPs in state-of-the-art graph processing systems on multi-core
architectures. We find that those systems suffer from severe cache miss penalty
because of the irregular and uncoordinated memory accesses in processing FPPs.
In this paper, we propose ForkGraph, a cache-efficient FPP processing system
on multi-core architectures. To improve the cache reuse, we divide the graph
into partitions each sized of LLC capacity, and the queries in an FPP are
buffered and executed on the partition basis. We further develop efficient
intra- and inter-partition execution strategies for efficiency. For
intra-partition processing, since the graph partition fits into LLC, we propose
to execute each graph query with efficient sequential algorithms (in contrast
with parallel algorithms in existing parallel graph processing systems) and
present an atomic-free query processing by consolidating contending operations
to cache-resident graph partition. For inter-partition processing, we propose
yielding and priority-based scheduling, to reduce redundant work in processing.
Besides, we theoretically prove that ForkGraph performs the same amount of
work, to within a constant factor, as the fastest known sequential algorithms
in FPP queries processing, which is work efficient. Our evaluations on
real-world graphs show that ForkGraph significantly outperforms
state-of-the-art graph processing systems with two orders of magnitude
speedups.
| [
{
"created": "Sat, 27 Mar 2021 14:29:04 GMT",
"version": "v1"
},
{
"created": "Sun, 11 Apr 2021 01:05:52 GMT",
"version": "v2"
}
] | 2021-04-13 | [
[
"Lu",
"Shengliang",
""
],
[
"Sun",
"Shixuan",
""
],
[
"Paul",
"Johns",
""
],
[
"Li",
"Yuchen",
""
],
[
"He",
"Bingsheng",
""
]
] | As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms. The unique feature of the FPP is that it launches many independent queries from different source vertices on the same graph. For example, an algorithm in analyzing the network community profile can execute Personalized PageRanks that start from tens of thousands of source vertices at the same time. We study the efficiency of handling FPPs in state-of-the-art graph processing systems on multi-core architectures. We find that those systems suffer from severe cache miss penalty because of the irregular and uncoordinated memory accesses in processing FPPs. In this paper, we propose ForkGraph, a cache-efficient FPP processing system on multi-core architectures. To improve the cache reuse, we divide the graph into partitions each sized of LLC capacity, and the queries in an FPP are buffered and executed on the partition basis. We further develop efficient intra- and inter-partition execution strategies for efficiency. For intra-partition processing, since the graph partition fits into LLC, we propose to execute each graph query with efficient sequential algorithms (in contrast with parallel algorithms in existing parallel graph processing systems) and present an atomic-free query processing by consolidating contending operations to cache-resident graph partition. For inter-partition processing, we propose yielding and priority-based scheduling, to reduce redundant work in processing. Besides, we theoretically prove that ForkGraph performs the same amount of work, to within a constant factor, as the fastest known sequential algorithms in FPP queries processing, which is work efficient. Our evaluations on real-world graphs show that ForkGraph significantly outperforms state-of-the-art graph processing systems with two orders of magnitude speedups. |
2405.10153 | Moyi Li | Moyi Li, Dzmitry Katsiuba, Mateusz Dolata and Gerhard Schwabe | Firefighters' Perceptions on Collaboration and Interaction with
Autonomous Drones: Results of a Field Trial | This is authors' copy of the manuscript accepted for ACM CHI
Conference on Human Factors in Computing Systems 2024. Please, refer to the
published article at https://doi.org/10.1145/3613904.3642061 for further
information | CHI '24: Proceedings of the CHI Conference on Human Factors in
Computing Systems (2024), Article No.: 265, 1-19 | 10.1145/3613904.3642061 | null | cs.HC | http://creativecommons.org/licenses/by-sa/4.0/ | Applications of drones in emergency response, like firefighting, have been
promoted in the past decade. As the autonomy of drones continues to improve,
the ways in which they are integrated into firefighting teams and their impact
on crews are changing. This demands more understanding of how firefighters
perceive and interact with autonomous drones. This paper presents a drone-based
system for emergency operations with which firefighters can interact through
sound, lights, and a graphical user interface. We use interviews with
stakeholders collected in two field trials to explore their perceptions of the
interaction and collaboration with drones. Our result shows that firefighters
perceived visual interaction as adequate. However, for audio instructions and
interfaces, information overload emerges as an essential problem. The potential
impact of drones on current work configurations may involve shifting the
position of humans closer to supervisory decision-makers and changing the
training structure and content.
| [
{
"created": "Thu, 16 May 2024 14:48:24 GMT",
"version": "v1"
}
] | 2024-05-17 | [
[
"Li",
"Moyi",
""
],
[
"Katsiuba",
"Dzmitry",
""
],
[
"Dolata",
"Mateusz",
""
],
[
"Schwabe",
"Gerhard",
""
]
] | Applications of drones in emergency response, like firefighting, have been promoted in the past decade. As the autonomy of drones continues to improve, the ways in which they are integrated into firefighting teams and their impact on crews are changing. This demands more understanding of how firefighters perceive and interact with autonomous drones. This paper presents a drone-based system for emergency operations with which firefighters can interact through sound, lights, and a graphical user interface. We use interviews with stakeholders collected in two field trials to explore their perceptions of the interaction and collaboration with drones. Our result shows that firefighters perceived visual interaction as adequate. However, for audio instructions and interfaces, information overload emerges as an essential problem. The potential impact of drones on current work configurations may involve shifting the position of humans closer to supervisory decision-makers and changing the training structure and content. |
2003.00863 | Haotian Zhang | Haotian Zhang, Jianyong Sun and Zongben Xu | Adaptive Structural Hyper-Parameter Configuration by Q-Learning | null | 2020 IEEE Congress on Evolutionary Computation (CEC) | 10.1109/CEC48606.2020.9185665 | null | cs.NE cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Tuning hyper-parameters for evolutionary algorithms is an important issue in
computational intelligence. Performance of an evolutionary algorithm depends
not only on its operation strategy design, but also on its hyper-parameters.
Hyper-parameters can be categorized in two dimensions as structural/numerical
and time-invariant/time-variant. Particularly, structural hyper-parameters in
existing studies are usually tuned in advance for time-invariant parameters, or
with hand-crafted scheduling for time-invariant parameters. In this paper, we
make the first attempt to model the tuning of structural hyper-parameters as a
reinforcement learning problem, and present to tune the structural
hyper-parameter which controls computational resource allocation in the CEC
2018 winner algorithm by Q-learning. Experimental results show favorably
against the winner algorithm on the CEC 2018 test functions.
| [
{
"created": "Mon, 2 Mar 2020 13:10:13 GMT",
"version": "v1"
}
] | 2020-11-24 | [
[
"Zhang",
"Haotian",
""
],
[
"Sun",
"Jianyong",
""
],
[
"Xu",
"Zongben",
""
]
] | Tuning hyper-parameters for evolutionary algorithms is an important issue in computational intelligence. Performance of an evolutionary algorithm depends not only on its operation strategy design, but also on its hyper-parameters. Hyper-parameters can be categorized in two dimensions as structural/numerical and time-invariant/time-variant. Particularly, structural hyper-parameters in existing studies are usually tuned in advance for time-invariant parameters, or with hand-crafted scheduling for time-invariant parameters. In this paper, we make the first attempt to model the tuning of structural hyper-parameters as a reinforcement learning problem, and present to tune the structural hyper-parameter which controls computational resource allocation in the CEC 2018 winner algorithm by Q-learning. Experimental results show favorably against the winner algorithm on the CEC 2018 test functions. |
1707.03186 | Remy Cazabet | Giulio Rossetti and R\'emy Cazabet | Community Discovery in Dynamic Networks: a Survey | null | null | 10.1145/3172867 | null | cs.SI physics.soc-ph | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Networks built to model real world phenomena are characeterised by some
properties that have attracted the attention of the scientific community: (i)
they are organised according to community structure and (ii) their structure
evolves with time. Many researchers have worked on methods that can efficiently
unveil substructures in complex networks, giving birth to the field of
community discovery. A novel and challenging problem started capturing
researcher interest recently: the identification of evolving communities. To
model the evolution of a system, dynamic networks can be used: nodes and edges
are mutable and their presence, or absence, deeply impacts the community
structure that composes them. The aim of this survey is to present the
distinctive features and challenges of dynamic community discovery, and propose
a classification of published approaches. As a "user manual", this work
organizes state of art methodologies into a taxonomy, based on their rationale,
and their specific instanciation. Given a desired definition of network
dynamics, community characteristics and analytical needs, this survey will
support researchers to identify the set of approaches that best fit their
needs. The proposed classification could also help researchers to choose in
which direction should future research be oriented.
| [
{
"created": "Tue, 11 Jul 2017 09:25:20 GMT",
"version": "v1"
},
{
"created": "Wed, 6 Dec 2017 08:14:13 GMT",
"version": "v2"
},
{
"created": "Tue, 3 Sep 2019 12:42:25 GMT",
"version": "v3"
}
] | 2019-09-04 | [
[
"Rossetti",
"Giulio",
""
],
[
"Cazabet",
"Rémy",
""
]
] | Networks built to model real world phenomena are characeterised by some properties that have attracted the attention of the scientific community: (i) they are organised according to community structure and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and challenging problem started capturing researcher interest recently: the identification of evolving communities. To model the evolution of a system, dynamic networks can be used: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches. As a "user manual", this work organizes state of art methodologies into a taxonomy, based on their rationale, and their specific instanciation. Given a desired definition of network dynamics, community characteristics and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers to choose in which direction should future research be oriented. |
2303.09700 | Mihaela Curmei | Han Zhang, Shangen Lu, Yixin Wang, Mihaela Curmei | Delayed and Indirect Impacts of Link Recommendations | null | null | null | null | cs.SI cs.AI cs.LG stat.AP | http://creativecommons.org/licenses/by/4.0/ | The impacts of link recommendations on social networks are challenging to
evaluate, and so far they have been studied in limited settings. Observational
studies are restricted in the kinds of causal questions they can answer and
naive A/B tests often lead to biased evaluations due to unaccounted network
interference. Furthermore, evaluations in simulation settings are often limited
to static network models that do not take into account the potential feedback
loops between link recommendation and organic network evolution. To this end,
we study the impacts of recommendations on social networks in dynamic settings.
Adopting a simulation-based approach, we consider an explicit dynamic formation
model -- an extension of the celebrated Jackson-Rogers model -- and investigate
how link recommendations affect network evolution over time. Empirically, we
find that link recommendations have surprising delayed and indirect effects on
the structural properties of networks. Specifically, we find that link
recommendations can exhibit considerably different impacts in the immediate
term and in the long term. For instance, we observe that friend-of-friend
recommendations can have an immediate effect in decreasing degree inequality,
but in the long term, they can make the degree distribution substantially more
unequal. Moreover, we show that the effects of recommendations can persist in
networks, in part due to their indirect impacts on natural dynamics even after
recommendations are turned off. We show that, in counterfactual simulations,
removing the indirect effects of link recommendations can make the network
trend faster toward what it would have been under natural growth dynamics.
| [
{
"created": "Fri, 17 Mar 2023 00:09:19 GMT",
"version": "v1"
}
] | 2023-03-20 | [
[
"Zhang",
"Han",
""
],
[
"Lu",
"Shangen",
""
],
[
"Wang",
"Yixin",
""
],
[
"Curmei",
"Mihaela",
""
]
] | The impacts of link recommendations on social networks are challenging to evaluate, and so far they have been studied in limited settings. Observational studies are restricted in the kinds of causal questions they can answer and naive A/B tests often lead to biased evaluations due to unaccounted network interference. Furthermore, evaluations in simulation settings are often limited to static network models that do not take into account the potential feedback loops between link recommendation and organic network evolution. To this end, we study the impacts of recommendations on social networks in dynamic settings. Adopting a simulation-based approach, we consider an explicit dynamic formation model -- an extension of the celebrated Jackson-Rogers model -- and investigate how link recommendations affect network evolution over time. Empirically, we find that link recommendations have surprising delayed and indirect effects on the structural properties of networks. Specifically, we find that link recommendations can exhibit considerably different impacts in the immediate term and in the long term. For instance, we observe that friend-of-friend recommendations can have an immediate effect in decreasing degree inequality, but in the long term, they can make the degree distribution substantially more unequal. Moreover, we show that the effects of recommendations can persist in networks, in part due to their indirect impacts on natural dynamics even after recommendations are turned off. We show that, in counterfactual simulations, removing the indirect effects of link recommendations can make the network trend faster toward what it would have been under natural growth dynamics. |
1907.02218 | Ofir Geri | Edith Cohen and Ofir Geri | Sampling Sketches for Concave Sublinear Functions of Frequencies | Full version of a NeurIPS 2019 paper | null | null | null | cs.DS | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We consider massive distributed datasets that consist of elements modeled as
key-value pairs and the task of computing statistics or aggregates where the
contribution of each key is weighted by a function of its frequency (sum of
values of its elements). This fundamental problem has a wealth of applications
in data analytics and machine learning, in particular, with concave sublinear
functions of the frequencies that mitigate the disproportionate effect of keys
with high frequency. The family of concave sublinear functions includes low
frequency moments ($p \leq 1$), capping, logarithms, and their compositions. A
common approach is to sample keys, ideally, proportionally to their
contributions and estimate statistics from the sample. A simple but costly way
to do this is by aggregating the data to produce a table of keys and their
frequencies, apply our function to the frequency values, and then apply a
weighted sampling scheme. Our main contribution is the design of composable
sampling sketches that can be tailored to any concave sublinear function of the
frequencies. Our sketch structure size is very close to the desired sample size
and our samples provide statistical guarantees on the estimation quality that
are very close to that of an ideal sample of the same size computed over
aggregated data. Finally, we demonstrate experimentally the simplicity and
effectiveness of our methods.
| [
{
"created": "Thu, 4 Jul 2019 04:55:21 GMT",
"version": "v1"
},
{
"created": "Fri, 6 Dec 2019 00:12:09 GMT",
"version": "v2"
},
{
"created": "Sun, 22 Dec 2019 16:28:45 GMT",
"version": "v3"
}
] | 2019-12-24 | [
[
"Cohen",
"Edith",
""
],
[
"Geri",
"Ofir",
""
]
] | We consider massive distributed datasets that consist of elements modeled as key-value pairs and the task of computing statistics or aggregates where the contribution of each key is weighted by a function of its frequency (sum of values of its elements). This fundamental problem has a wealth of applications in data analytics and machine learning, in particular, with concave sublinear functions of the frequencies that mitigate the disproportionate effect of keys with high frequency. The family of concave sublinear functions includes low frequency moments ($p \leq 1$), capping, logarithms, and their compositions. A common approach is to sample keys, ideally, proportionally to their contributions and estimate statistics from the sample. A simple but costly way to do this is by aggregating the data to produce a table of keys and their frequencies, apply our function to the frequency values, and then apply a weighted sampling scheme. Our main contribution is the design of composable sampling sketches that can be tailored to any concave sublinear function of the frequencies. Our sketch structure size is very close to the desired sample size and our samples provide statistical guarantees on the estimation quality that are very close to that of an ideal sample of the same size computed over aggregated data. Finally, we demonstrate experimentally the simplicity and effectiveness of our methods. |
2010.10176 | Markus J. Hofmann | Markus J. Hofmann, Lara M\"uller, Andre R\"olke, Ralph Radach and
Chris Biemann | Individual corpora predict fast memory retrieval during reading | Proceedings of the 6th workshop on Cognitive Aspects of the Lexicon
(CogALex-VI), Barcelona, Spain, December 12, 2020; accepted manuscript; 11
pages, 2 figures, 4 Tables | null | null | null | cs.CL cs.IR | http://creativecommons.org/licenses/by/4.0/ | The corpus, from which a predictive language model is trained, can be
considered the experience of a semantic system. We recorded everyday reading of
two participants for two months on a tablet, generating individual corpus
samples of 300/500K tokens. Then we trained word2vec models from individual
corpora and a 70 million-sentence newspaper corpus to obtain individual and
norm-based long-term memory structure. To test whether individual corpora can
make better predictions for a cognitive task of long-term memory retrieval, we
generated stimulus materials consisting of 134 sentences with uncorrelated
individual and norm-based word probabilities. For the subsequent eye tracking
study 1-2 months later, our regression analyses revealed that individual, but
not norm-corpus-based word probabilities can account for first-fixation
duration and first-pass gaze duration. Word length additionally affected gaze
duration and total viewing duration. The results suggest that corpora
representative for an individual's longterm memory structure can better explain
reading performance than a norm corpus, and that recently acquired information
is lexically accessed rapidly.
| [
{
"created": "Tue, 20 Oct 2020 10:18:20 GMT",
"version": "v1"
}
] | 2020-10-21 | [
[
"Hofmann",
"Markus J.",
""
],
[
"Müller",
"Lara",
""
],
[
"Rölke",
"Andre",
""
],
[
"Radach",
"Ralph",
""
],
[
"Biemann",
"Chris",
""
]
] | The corpus, from which a predictive language model is trained, can be considered the experience of a semantic system. We recorded everyday reading of two participants for two months on a tablet, generating individual corpus samples of 300/500K tokens. Then we trained word2vec models from individual corpora and a 70 million-sentence newspaper corpus to obtain individual and norm-based long-term memory structure. To test whether individual corpora can make better predictions for a cognitive task of long-term memory retrieval, we generated stimulus materials consisting of 134 sentences with uncorrelated individual and norm-based word probabilities. For the subsequent eye tracking study 1-2 months later, our regression analyses revealed that individual, but not norm-corpus-based word probabilities can account for first-fixation duration and first-pass gaze duration. Word length additionally affected gaze duration and total viewing duration. The results suggest that corpora representative for an individual's longterm memory structure can better explain reading performance than a norm corpus, and that recently acquired information is lexically accessed rapidly. |
2407.00371 | Linjiang Zhou | Linjiang Zhou, Xiaochuan Shi, Chao Ma, Zepeng Wang | Axiomatization of Gradient Smoothing in Neural Networks | null | null | null | null | cs.LG cs.AI | http://creativecommons.org/licenses/by/4.0/ | Gradients play a pivotal role in neural networks explanation. The inherent
high dimensionality and structural complexity of neural networks result in the
original gradients containing a significant amount of noise. While several
approaches were proposed to reduce noise with smoothing, there is little
discussion of the rationale behind smoothing gradients in neural networks. In
this work, we proposed a gradient smooth theoretical framework for neural
networks based on the function mollification and Monte Carlo integration. The
framework intrinsically axiomatized gradient smoothing and reveals the
rationale of existing methods. Furthermore, we provided an approach to design
new smooth methods derived from the framework. By experimental measurement of
several newly designed smooth methods, we demonstrated the research potential
of our framework.
| [
{
"created": "Sat, 29 Jun 2024 08:43:38 GMT",
"version": "v1"
}
] | 2024-07-02 | [
[
"Zhou",
"Linjiang",
""
],
[
"Shi",
"Xiaochuan",
""
],
[
"Ma",
"Chao",
""
],
[
"Wang",
"Zepeng",
""
]
] | Gradients play a pivotal role in neural networks explanation. The inherent high dimensionality and structural complexity of neural networks result in the original gradients containing a significant amount of noise. While several approaches were proposed to reduce noise with smoothing, there is little discussion of the rationale behind smoothing gradients in neural networks. In this work, we proposed a gradient smooth theoretical framework for neural networks based on the function mollification and Monte Carlo integration. The framework intrinsically axiomatized gradient smoothing and reveals the rationale of existing methods. Furthermore, we provided an approach to design new smooth methods derived from the framework. By experimental measurement of several newly designed smooth methods, we demonstrated the research potential of our framework. |
2302.10975 | Felix Fiedler | Felix Fiedler and Sergio Lucia | Improved uncertainty quantification for neural networks with Bayesian
last layer | This work has been published at IEEE Access with Digital Object
Identifier 10.1109/ACCESS.2023.3329685 under a Creative Commons Attribution
4.0 License | IEEE Access, vol. 11, 2023 | 10.1109/ACCESS.2023.3329685 | null | cs.LG cs.SY eess.SY | http://creativecommons.org/licenses/by/4.0/ | Uncertainty quantification is an important task in machine learning - a task
in which standardneural networks (NNs) have traditionally not excelled. This
can be a limitation for safety-critical applications, where uncertainty-aware
methods like Gaussian processes or Bayesian linear regression are often
preferred. Bayesian neural networks are an approach to address this limitation.
They assume probability distributions for all parameters and yield distributed
predictions. However, training and inference are typically intractable and
approximations must be employed. A promising approximation is NNs with Bayesian
last layer (BLL). They assume distributed weights only in the linear output
layer and yield a normally distributed prediction. To approximate the
intractable Bayesian neural network, point estimates of the distributed weights
in all but the last layer should be obtained by maximizing the marginal
likelihood. This has previously been challenging, as the marginal likelihood is
expensive to evaluate in this setting. We present a reformulation of the
log-marginal likelihood of a NN with BLL which allows for efficient training
using backpropagation. Furthermore, we address the challenge of uncertainty
quantification for extrapolation points. We provide a metric to quantify the
degree of extrapolation and derive a method to improve the uncertainty
quantification for these points. Our methods are derived for the multivariate
case and demonstrated in a simulation study. In comparison to Bayesian linear
regression with fixed features, and a Bayesian neural network trained with
variational inference, our proposed method achieves the highest log-predictive
density on test data.
| [
{
"created": "Tue, 21 Feb 2023 20:23:56 GMT",
"version": "v1"
},
{
"created": "Wed, 12 Jul 2023 07:39:28 GMT",
"version": "v2"
},
{
"created": "Wed, 3 Jan 2024 19:40:07 GMT",
"version": "v3"
}
] | 2024-01-05 | [
[
"Fiedler",
"Felix",
""
],
[
"Lucia",
"Sergio",
""
]
] | Uncertainty quantification is an important task in machine learning - a task in which standardneural networks (NNs) have traditionally not excelled. This can be a limitation for safety-critical applications, where uncertainty-aware methods like Gaussian processes or Bayesian linear regression are often preferred. Bayesian neural networks are an approach to address this limitation. They assume probability distributions for all parameters and yield distributed predictions. However, training and inference are typically intractable and approximations must be employed. A promising approximation is NNs with Bayesian last layer (BLL). They assume distributed weights only in the linear output layer and yield a normally distributed prediction. To approximate the intractable Bayesian neural network, point estimates of the distributed weights in all but the last layer should be obtained by maximizing the marginal likelihood. This has previously been challenging, as the marginal likelihood is expensive to evaluate in this setting. We present a reformulation of the log-marginal likelihood of a NN with BLL which allows for efficient training using backpropagation. Furthermore, we address the challenge of uncertainty quantification for extrapolation points. We provide a metric to quantify the degree of extrapolation and derive a method to improve the uncertainty quantification for these points. Our methods are derived for the multivariate case and demonstrated in a simulation study. In comparison to Bayesian linear regression with fixed features, and a Bayesian neural network trained with variational inference, our proposed method achieves the highest log-predictive density on test data. |
2003.02976 | Dinislam Abdulgalimov | Dinislam Abdulgalimov, Reuben Kirkham, James Nicholson, Vasilis
Vlachokyriakos, Pam Briggs, Patrick Olivier | Designing for Employee Voice | 10 pages, 4 figures, CHI 2020 Proceedings | null | 10.1145/3313831.3376284 | null | cs.HC cs.SI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Employee voice and workplace democracy have a positive impact on employee
wellbeing and the performance of organizations. In this paper, we conducted
interviews with employees to identify facilitators and inhibitors for the voice
within the workplace and a corresponding set of appropriate qualities:
Civility, Validity, Safety and Egalitarianism. We then operationalised these
qualities as a set of design goals - Assured Anonymity, Constructive
Moderation, Adequate Slowness and Controlled Access - in the design and
development of a secure anonymous employee voice system. Our novel take on the
Enterprise Social Network aims to foster good citizenship whilst also promoting
frank yet constructive discussion. We reflect on a two-week deployment of our
system, the diverse range of candid discussions that emerged around important
workplace issues and the potential for change within the host organization. We
conclude by reflecting on the ways in which our approach shaped the discourse
and supported the creation of a trusted environment for employee voice.
| [
{
"created": "Fri, 6 Mar 2020 00:28:59 GMT",
"version": "v1"
}
] | 2020-03-09 | [
[
"Abdulgalimov",
"Dinislam",
""
],
[
"Kirkham",
"Reuben",
""
],
[
"Nicholson",
"James",
""
],
[
"Vlachokyriakos",
"Vasilis",
""
],
[
"Briggs",
"Pam",
""
],
[
"Olivier",
"Patrick",
""
]
] | Employee voice and workplace democracy have a positive impact on employee wellbeing and the performance of organizations. In this paper, we conducted interviews with employees to identify facilitators and inhibitors for the voice within the workplace and a corresponding set of appropriate qualities: Civility, Validity, Safety and Egalitarianism. We then operationalised these qualities as a set of design goals - Assured Anonymity, Constructive Moderation, Adequate Slowness and Controlled Access - in the design and development of a secure anonymous employee voice system. Our novel take on the Enterprise Social Network aims to foster good citizenship whilst also promoting frank yet constructive discussion. We reflect on a two-week deployment of our system, the diverse range of candid discussions that emerged around important workplace issues and the potential for change within the host organization. We conclude by reflecting on the ways in which our approach shaped the discourse and supported the creation of a trusted environment for employee voice. |
2407.20283 | Fuling Chen | Fuling Chen, Kevin Vinsen, Arthur Filoche | Spatial Temporal Approach for High-Resolution Gridded Wind Forecasting
across Southwest Western Australia | null | null | null | null | cs.LG physics.ao-ph | http://creativecommons.org/licenses/by/4.0/ | Accurate wind speed and direction forecasting is paramount across many
sectors, spanning agriculture, renewable energy generation, and bushfire
management. However, conventional forecasting models encounter significant
challenges in precisely predicting wind conditions at high spatial resolutions
for individual locations or small geographical areas (< 20 km2) and capturing
medium to long-range temporal trends and comprehensive spatio-temporal
patterns. This study focuses on a spatial temporal approach for high-resolution
gridded wind forecasting at the height of 3 and 10 metres across large areas of
the Southwest of Western Australia to overcome these challenges. The model
utilises the data that covers a broad geographic area and harnesses a diverse
array of meteorological factors, including terrain characteristics, air
pressure, 10-metre wind forecasts from the European Centre for Medium-Range
Weather Forecasts, and limited observation data from sparsely distributed
weather stations (such as 3-metre wind profiles, humidity, and temperature),
the model demonstrates promising advancements in wind forecasting accuracy and
reliability across the entire region of interest. This paper shows the
potential of our machine learning model for wind forecasts across various
prediction horizons and spatial coverage. It can help facilitate more informed
decision-making and enhance resilience across critical sectors.
| [
{
"created": "Fri, 26 Jul 2024 05:44:27 GMT",
"version": "v1"
}
] | 2024-07-31 | [
[
"Chen",
"Fuling",
""
],
[
"Vinsen",
"Kevin",
""
],
[
"Filoche",
"Arthur",
""
]
] | Accurate wind speed and direction forecasting is paramount across many sectors, spanning agriculture, renewable energy generation, and bushfire management. However, conventional forecasting models encounter significant challenges in precisely predicting wind conditions at high spatial resolutions for individual locations or small geographical areas (< 20 km2) and capturing medium to long-range temporal trends and comprehensive spatio-temporal patterns. This study focuses on a spatial temporal approach for high-resolution gridded wind forecasting at the height of 3 and 10 metres across large areas of the Southwest of Western Australia to overcome these challenges. The model utilises the data that covers a broad geographic area and harnesses a diverse array of meteorological factors, including terrain characteristics, air pressure, 10-metre wind forecasts from the European Centre for Medium-Range Weather Forecasts, and limited observation data from sparsely distributed weather stations (such as 3-metre wind profiles, humidity, and temperature), the model demonstrates promising advancements in wind forecasting accuracy and reliability across the entire region of interest. This paper shows the potential of our machine learning model for wind forecasts across various prediction horizons and spatial coverage. It can help facilitate more informed decision-making and enhance resilience across critical sectors. |
2408.04919 | Chaofan Li | Chaofan Li, Yingxia Shao, Zheng Liu | SEA-SQL: Semantic-Enhanced Text-to-SQL with Adaptive Refinement | null | null | null | null | cs.DB | http://creativecommons.org/licenses/by/4.0/ | Recent advancements in large language models (LLMs) have significantly
contributed to the progress of the Text-to-SQL task. A common requirement in
many of these works is the post-correction of SQL queries. However, the
majority of this process entails analyzing error cases to develop prompts with
rules that eliminate model bias. And there is an absence of execution
verification for SQL queries. In addition, the prevalent techniques primarily
depend on GPT-4 and few-shot prompts, resulting in expensive costs. To
investigate the effective methods for SQL refinement in a cost-efficient
manner, we introduce Semantic-Enhanced Text-to-SQL with Adaptive Refinement
(SEA-SQL), which includes Adaptive Bias Elimination and Dynamic Execution
Adjustment, aims to improve performance while minimizing resource expenditure
with zero-shot prompts. Specifically, SEA-SQL employs a semantic-enhanced
schema to augment database information and optimize SQL queries. During the SQL
query generation, a fine-tuned adaptive bias eliminator is applied to mitigate
inherent biases caused by the LLM. The dynamic execution adjustment is utilized
to guarantee the executability of the bias eliminated SQL query. We conduct
experiments on the Spider and BIRD datasets to demonstrate the effectiveness of
this framework. The results demonstrate that SEA-SQL achieves state-of-the-art
performance in the GPT3.5 scenario with 9%-58% of the generation cost.
Furthermore, SEA-SQL is comparable to GPT-4 with only 0.9%-5.3% of the
generation cost.
| [
{
"created": "Fri, 9 Aug 2024 08:01:37 GMT",
"version": "v1"
}
] | 2024-08-12 | [
[
"Li",
"Chaofan",
""
],
[
"Shao",
"Yingxia",
""
],
[
"Liu",
"Zheng",
""
]
] | Recent advancements in large language models (LLMs) have significantly contributed to the progress of the Text-to-SQL task. A common requirement in many of these works is the post-correction of SQL queries. However, the majority of this process entails analyzing error cases to develop prompts with rules that eliminate model bias. And there is an absence of execution verification for SQL queries. In addition, the prevalent techniques primarily depend on GPT-4 and few-shot prompts, resulting in expensive costs. To investigate the effective methods for SQL refinement in a cost-efficient manner, we introduce Semantic-Enhanced Text-to-SQL with Adaptive Refinement (SEA-SQL), which includes Adaptive Bias Elimination and Dynamic Execution Adjustment, aims to improve performance while minimizing resource expenditure with zero-shot prompts. Specifically, SEA-SQL employs a semantic-enhanced schema to augment database information and optimize SQL queries. During the SQL query generation, a fine-tuned adaptive bias eliminator is applied to mitigate inherent biases caused by the LLM. The dynamic execution adjustment is utilized to guarantee the executability of the bias eliminated SQL query. We conduct experiments on the Spider and BIRD datasets to demonstrate the effectiveness of this framework. The results demonstrate that SEA-SQL achieves state-of-the-art performance in the GPT3.5 scenario with 9%-58% of the generation cost. Furthermore, SEA-SQL is comparable to GPT-4 with only 0.9%-5.3% of the generation cost. |
1808.01614 | Rick Salay | Rick Salay, Krzysztof Czarnecki | Using Machine Learning Safely in Automotive Software: An Assessment and
Adaption of Software Process Requirements in ISO 26262 | null | null | null | null | cs.LG cs.SE stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of machine learning (ML) is on the rise in many sectors of software
development, and automotive software development is no different. In
particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving
Systems (ADS) are two areas where ML plays a significant role. In automotive
development, safety is a critical objective, and the emergence of standards
such as ISO 26262 has helped focus industry practices to address safety in a
systematic and consistent way. Unfortunately, these standards were not designed
to accommodate technologies such as ML or the type of functionality that is
provided by an ADS and this has created a conflict between the need to innovate
and the need to improve safety. In this report, we take steps to address this
conflict by doing a detailed assessment and adaption of ISO 26262 for ML,
specifically in the context of supervised learning. First we analyze the key
factors that are the source of the conflict. Then we assess each software
development process requirement (Part 6 of ISO 26262) for applicability to ML.
Where there are gaps, we propose new requirements to address the gaps. Finally
we discuss the application of this adapted and extended variant of Part 6 to ML
development scenarios.
| [
{
"created": "Sun, 5 Aug 2018 13:40:22 GMT",
"version": "v1"
}
] | 2018-08-07 | [
[
"Salay",
"Rick",
""
],
[
"Czarnecki",
"Krzysztof",
""
]
] | The use of machine learning (ML) is on the rise in many sectors of software development, and automotive software development is no different. In particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are two areas where ML plays a significant role. In automotive development, safety is a critical objective, and the emergence of standards such as ISO 26262 has helped focus industry practices to address safety in a systematic and consistent way. Unfortunately, these standards were not designed to accommodate technologies such as ML or the type of functionality that is provided by an ADS and this has created a conflict between the need to innovate and the need to improve safety. In this report, we take steps to address this conflict by doing a detailed assessment and adaption of ISO 26262 for ML, specifically in the context of supervised learning. First we analyze the key factors that are the source of the conflict. Then we assess each software development process requirement (Part 6 of ISO 26262) for applicability to ML. Where there are gaps, we propose new requirements to address the gaps. Finally we discuss the application of this adapted and extended variant of Part 6 to ML development scenarios. |
2007.14570 | Long Cheng | Song Liao, Christin Wilson, Long Cheng, Hongxin Hu, Huixing Deng | Measuring the Effectiveness of Privacy Policies for Voice Assistant
Applications | null | null | null | null | cs.CR cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Voice Assistants (VA) such as Amazon Alexa and Google Assistant are quickly
and seamlessly integrating into people's daily lives. The increased reliance on
VA services raises privacy concerns such as the leakage of private
conversations and sensitive information. Privacy policies play an important
role in addressing users' privacy concerns and informing them about the data
collection, storage, and sharing practices. VA platforms (both Amazon Alexa and
Google Assistant) allow third-party developers to build new voice-apps and
publish them to the app store. Voice-app developers are required to provide
privacy policies to disclose their apps' data practices. However, little is
known whether these privacy policies are informative and trustworthy or not on
emerging VA platforms. On the other hand, many users invoke voice-apps through
voice and thus there exists a usability challenge for users to access these
privacy policies. In this paper, we conduct the first large-scale data
analytics to systematically measure the effectiveness of privacy policies
provided by voice-app developers on two mainstream VA platforms. We seek to
understand the quality and usability issues of privacy policies provided by
developers in the current app stores. We analyzed 64,720 Amazon Alexa skills
and 2,201 Google Assistant actions. Our work also includes a user study to
understand users' perspectives on VA's privacy policies. Our findings reveal a
worrisome reality of privacy policies in two mainstream voice-app stores, where
there exists a substantial number of problematic privacy policies.
Surprisingly, Google and Amazon even have official voice-apps violating their
own requirements regarding the privacy policy.
| [
{
"created": "Wed, 29 Jul 2020 03:17:51 GMT",
"version": "v1"
}
] | 2020-07-30 | [
[
"Liao",
"Song",
""
],
[
"Wilson",
"Christin",
""
],
[
"Cheng",
"Long",
""
],
[
"Hu",
"Hongxin",
""
],
[
"Deng",
"Huixing",
""
]
] | Voice Assistants (VA) such as Amazon Alexa and Google Assistant are quickly and seamlessly integrating into people's daily lives. The increased reliance on VA services raises privacy concerns such as the leakage of private conversations and sensitive information. Privacy policies play an important role in addressing users' privacy concerns and informing them about the data collection, storage, and sharing practices. VA platforms (both Amazon Alexa and Google Assistant) allow third-party developers to build new voice-apps and publish them to the app store. Voice-app developers are required to provide privacy policies to disclose their apps' data practices. However, little is known whether these privacy policies are informative and trustworthy or not on emerging VA platforms. On the other hand, many users invoke voice-apps through voice and thus there exists a usability challenge for users to access these privacy policies. In this paper, we conduct the first large-scale data analytics to systematically measure the effectiveness of privacy policies provided by voice-app developers on two mainstream VA platforms. We seek to understand the quality and usability issues of privacy policies provided by developers in the current app stores. We analyzed 64,720 Amazon Alexa skills and 2,201 Google Assistant actions. Our work also includes a user study to understand users' perspectives on VA's privacy policies. Our findings reveal a worrisome reality of privacy policies in two mainstream voice-app stores, where there exists a substantial number of problematic privacy policies. Surprisingly, Google and Amazon even have official voice-apps violating their own requirements regarding the privacy policy. |
1907.01739 | Charu Sharma | Charu Sharma, Deepak Nathani, Manohar Kaul | Solving Partial Assignment Problems using Random Clique Complexes | Accepted as a long talk at ICML 2018 | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We present an alternate formulation of the partial assignment problem as
matching random clique complexes, that are higher-order analogues of random
graphs, designed to provide a set of invariants that better detect higher-order
structure. The proposed method creates random clique adjacency matrices for
each k-skeleton of the random clique complexes and matches them, taking into
account each point as the affine combination of its geometric neighbourhood. We
justify our solution theoretically, by analyzing the runtime and storage
complexity of our algorithm along with the asymptotic behaviour of the
quadratic assignment problem (QAP) that is associated with the underlying
random clique adjacency matrices. Experiments on both synthetic and real-world
datasets, containing severe occlusions and distortions, provide insight into
the accuracy, efficiency, and robustness of our approach. We outperform diverse
matching algorithms by a significant margin.
| [
{
"created": "Wed, 3 Jul 2019 04:56:34 GMT",
"version": "v1"
},
{
"created": "Wed, 8 Jul 2020 12:28:06 GMT",
"version": "v2"
},
{
"created": "Wed, 29 Jul 2020 15:12:50 GMT",
"version": "v3"
}
] | 2020-07-30 | [
[
"Sharma",
"Charu",
""
],
[
"Nathani",
"Deepak",
""
],
[
"Kaul",
"Manohar",
""
]
] | We present an alternate formulation of the partial assignment problem as matching random clique complexes, that are higher-order analogues of random graphs, designed to provide a set of invariants that better detect higher-order structure. The proposed method creates random clique adjacency matrices for each k-skeleton of the random clique complexes and matches them, taking into account each point as the affine combination of its geometric neighbourhood. We justify our solution theoretically, by analyzing the runtime and storage complexity of our algorithm along with the asymptotic behaviour of the quadratic assignment problem (QAP) that is associated with the underlying random clique adjacency matrices. Experiments on both synthetic and real-world datasets, containing severe occlusions and distortions, provide insight into the accuracy, efficiency, and robustness of our approach. We outperform diverse matching algorithms by a significant margin. |
2312.13208 | Yingji Zhang | Yingji Zhang, Danilo S. Carvalho, Ian Pratt-Hartmann, Andr\'e Freitas | LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent
Sentence Spaces | null | null | null | null | cs.CL | http://creativecommons.org/licenses/by/4.0/ | Deep generative neural networks, such as Variational AutoEncoders (VAEs),
offer an opportunity to better understand and control language models from the
perspective of sentence-level latent spaces. To combine the controllability of
VAE latent spaces with the state-of-the-art performance of recent large
language models (LLMs), we present in this work LlaMaVAE, which combines
expressive encoder and decoder models (sentenceT5 and LlaMA) with a VAE
architecture, aiming to provide better text generation control to LLMs. In
addition, to conditionally guide the VAE generation, we investigate a new
approach based on flow-based invertible neural networks (INNs) named Invertible
CVAE. Experimental results reveal that LlaMaVAE can outperform the previous
state-of-the-art VAE language model, Optimus, across various tasks, including
language modelling, semantic textual similarity and definition modelling.
Qualitative analysis on interpolation and traversal experiments also indicates
an increased degree of semantic clustering and geometric consistency, which
enables better generation control.
| [
{
"created": "Wed, 20 Dec 2023 17:25:23 GMT",
"version": "v1"
}
] | 2023-12-21 | [
[
"Zhang",
"Yingji",
""
],
[
"Carvalho",
"Danilo S.",
""
],
[
"Pratt-Hartmann",
"Ian",
""
],
[
"Freitas",
"André",
""
]
] | Deep generative neural networks, such as Variational AutoEncoders (VAEs), offer an opportunity to better understand and control language models from the perspective of sentence-level latent spaces. To combine the controllability of VAE latent spaces with the state-of-the-art performance of recent large language models (LLMs), we present in this work LlaMaVAE, which combines expressive encoder and decoder models (sentenceT5 and LlaMA) with a VAE architecture, aiming to provide better text generation control to LLMs. In addition, to conditionally guide the VAE generation, we investigate a new approach based on flow-based invertible neural networks (INNs) named Invertible CVAE. Experimental results reveal that LlaMaVAE can outperform the previous state-of-the-art VAE language model, Optimus, across various tasks, including language modelling, semantic textual similarity and definition modelling. Qualitative analysis on interpolation and traversal experiments also indicates an increased degree of semantic clustering and geometric consistency, which enables better generation control. |
1509.07968 | Takuya Ikeda | Takuya Ikeda, Masaaki Nagahara, Shunsuke Ono | Discrete-Valued Control by Sum-of-Absolute-Values Optimization | submitted to IEEE Transactions on Automatic Control; 11 pages with 2
figures | null | null | null | cs.SY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we propose a new design method of discrete-valued control for
continuous-time linear time-invariant systems based on sum-of-absolute-values
(SOAV) optimization. We first formulate the discrete-valued control design as a
finite-horizon SOAV optimal control, which is an extended version of L1 optimal
control. We then give simple conditions that guarantee the existence,
discreteness, and uniqueness of the SOAV optimal control. Also, we give the
continuity property of the value function, by which we prove the stability of
infinite-horizon model predictive SOAV control systems. We provide a fast
algorithm for the SOAV optimization based on the alternating direction method
of multipliers (ADMM), which has an important advantage in real-time control
computation. A simulation result shows the effectiveness of the proposed
method.
| [
{
"created": "Sat, 26 Sep 2015 12:04:40 GMT",
"version": "v1"
}
] | 2015-09-29 | [
[
"Ikeda",
"Takuya",
""
],
[
"Nagahara",
"Masaaki",
""
],
[
"Ono",
"Shunsuke",
""
]
] | In this paper, we propose a new design method of discrete-valued control for continuous-time linear time-invariant systems based on sum-of-absolute-values (SOAV) optimization. We first formulate the discrete-valued control design as a finite-horizon SOAV optimal control, which is an extended version of L1 optimal control. We then give simple conditions that guarantee the existence, discreteness, and uniqueness of the SOAV optimal control. Also, we give the continuity property of the value function, by which we prove the stability of infinite-horizon model predictive SOAV control systems. We provide a fast algorithm for the SOAV optimization based on the alternating direction method of multipliers (ADMM), which has an important advantage in real-time control computation. A simulation result shows the effectiveness of the proposed method. |
1312.1421 | Mostafa Khoshnevisan | Mostafa Khoshnevisan and J Nicholas Laneman | Intermittent Communication | Submitted to IEEE Trans. Inform. Theory | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formulate a model for intermittent communication that can capture bursty
transmissions or a sporadically available channel, where in either case the
receiver does not know a priori when the transmissions will occur. Focusing on
the point-to-point case, we develop a decoding structure, decoding from pattern
detection, and its achievable rate for such communication scenarios. Decoding
from pattern detection first detects the locations of codeword symbols and then
uses them to decode. We introduce the concept of partial divergence and study
some of its properties in order to obtain stronger achievability results. As
the system becomes more intermittent, the achievable rates decrease due to the
additional uncertainty about the positions of the codeword symbols at the
decoder. Additionally, we provide upper bounds on the capacity of binary
noiseless intermittent communication with the help of a genie-aided encoder and
decoder. The upper bounds imply a tradeoff between the capacity and the
intermittency rate of the communication system, even if the receive window
scales linearly with the codeword length.
| [
{
"created": "Thu, 5 Dec 2013 03:16:08 GMT",
"version": "v1"
},
{
"created": "Fri, 17 Mar 2017 05:01:37 GMT",
"version": "v2"
}
] | 2017-03-20 | [
[
"Khoshnevisan",
"Mostafa",
""
],
[
"Laneman",
"J Nicholas",
""
]
] | We formulate a model for intermittent communication that can capture bursty transmissions or a sporadically available channel, where in either case the receiver does not know a priori when the transmissions will occur. Focusing on the point-to-point case, we develop a decoding structure, decoding from pattern detection, and its achievable rate for such communication scenarios. Decoding from pattern detection first detects the locations of codeword symbols and then uses them to decode. We introduce the concept of partial divergence and study some of its properties in order to obtain stronger achievability results. As the system becomes more intermittent, the achievable rates decrease due to the additional uncertainty about the positions of the codeword symbols at the decoder. Additionally, we provide upper bounds on the capacity of binary noiseless intermittent communication with the help of a genie-aided encoder and decoder. The upper bounds imply a tradeoff between the capacity and the intermittency rate of the communication system, even if the receive window scales linearly with the codeword length. |
2108.02707 | Harrison Rosenberg | Harrison Rosenberg, Brian Tang, Kassem Fawaz, and Somesh Jha | Fairness Properties of Face Recognition and Obfuscation Systems | null | null | null | null | cs.CV cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The proliferation of automated face recognition in the commercial and
government sectors has caused significant privacy concerns for individuals. One
approach to address these privacy concerns is to employ evasion attacks against
the metric embedding networks powering face recognition systems: Face
obfuscation systems generate imperceptibly perturbed images that cause face
recognition systems to misidentify the user. Perturbed faces are generated on
metric embedding networks, which are known to be unfair in the context of face
recognition. A question of demographic fairness naturally follows: are there
demographic disparities in face obfuscation system performance? We answer this
question with an analytical and empirical exploration of recent face
obfuscation systems. Metric embedding networks are found to be demographically
aware: face embeddings are clustered by demographic. We show how this
clustering behavior leads to reduced face obfuscation utility for faces in
minority groups. An intuitive analytical model yields insight into these
phenomena.
| [
{
"created": "Thu, 5 Aug 2021 16:18:15 GMT",
"version": "v1"
},
{
"created": "Tue, 19 Oct 2021 13:18:21 GMT",
"version": "v2"
},
{
"created": "Fri, 16 Sep 2022 17:46:37 GMT",
"version": "v3"
}
] | 2022-09-19 | [
[
"Rosenberg",
"Harrison",
""
],
[
"Tang",
"Brian",
""
],
[
"Fawaz",
"Kassem",
""
],
[
"Jha",
"Somesh",
""
]
] | The proliferation of automated face recognition in the commercial and government sectors has caused significant privacy concerns for individuals. One approach to address these privacy concerns is to employ evasion attacks against the metric embedding networks powering face recognition systems: Face obfuscation systems generate imperceptibly perturbed images that cause face recognition systems to misidentify the user. Perturbed faces are generated on metric embedding networks, which are known to be unfair in the context of face recognition. A question of demographic fairness naturally follows: are there demographic disparities in face obfuscation system performance? We answer this question with an analytical and empirical exploration of recent face obfuscation systems. Metric embedding networks are found to be demographically aware: face embeddings are clustered by demographic. We show how this clustering behavior leads to reduced face obfuscation utility for faces in minority groups. An intuitive analytical model yields insight into these phenomena. |
2212.08985 | Ning Wang | Ning Wang, Jiangrong Xie, Hang Luo, Qinglin Cheng, Jihao Wu, Mingbo
Jia, Linlin Li | Efficient Image Captioning for Edge Devices | To appear in AAAI 2023 | null | null | null | cs.CV | http://creativecommons.org/licenses/by/4.0/ | Recent years have witnessed the rapid progress of image captioning. However,
the demands for large memory storage and heavy computational burden prevent
these captioning models from being deployed on mobile devices. The main
obstacles lie in the heavyweight visual feature extractors (i.e., object
detectors) and complicated cross-modal fusion networks. To this end, we propose
LightCap, a lightweight image captioner for resource-limited devices. The core
design is built on the recent CLIP model for efficient image captioning. To be
specific, on the one hand, we leverage the CLIP model to extract the compact
grid features without relying on the time-consuming object detectors. On the
other hand, we transfer the image-text retrieval design of CLIP to image
captioning scenarios by devising a novel visual concept extractor and a
cross-modal modulator. We further optimize the cross-modal fusion model and
parallel prediction heads via sequential and ensemble distillations. With the
carefully designed architecture, our model merely contains 40M parameters,
saving the model size by more than 75% and the FLOPs by more than 98% in
comparison with the current state-of-the-art methods. In spite of the low
capacity, our model still exhibits state-of-the-art performance on prevalent
datasets, e.g., 136.6 CIDEr on COCO Karpathy test split. Testing on the
smartphone with only a single CPU, the proposed LightCap exhibits a fast
inference speed of 188ms per image, which is ready for practical applications.
| [
{
"created": "Sun, 18 Dec 2022 01:56:33 GMT",
"version": "v1"
}
] | 2022-12-20 | [
[
"Wang",
"Ning",
""
],
[
"Xie",
"Jiangrong",
""
],
[
"Luo",
"Hang",
""
],
[
"Cheng",
"Qinglin",
""
],
[
"Wu",
"Jihao",
""
],
[
"Jia",
"Mingbo",
""
],
[
"Li",
"Linlin",
""
]
] | Recent years have witnessed the rapid progress of image captioning. However, the demands for large memory storage and heavy computational burden prevent these captioning models from being deployed on mobile devices. The main obstacles lie in the heavyweight visual feature extractors (i.e., object detectors) and complicated cross-modal fusion networks. To this end, we propose LightCap, a lightweight image captioner for resource-limited devices. The core design is built on the recent CLIP model for efficient image captioning. To be specific, on the one hand, we leverage the CLIP model to extract the compact grid features without relying on the time-consuming object detectors. On the other hand, we transfer the image-text retrieval design of CLIP to image captioning scenarios by devising a novel visual concept extractor and a cross-modal modulator. We further optimize the cross-modal fusion model and parallel prediction heads via sequential and ensemble distillations. With the carefully designed architecture, our model merely contains 40M parameters, saving the model size by more than 75% and the FLOPs by more than 98% in comparison with the current state-of-the-art methods. In spite of the low capacity, our model still exhibits state-of-the-art performance on prevalent datasets, e.g., 136.6 CIDEr on COCO Karpathy test split. Testing on the smartphone with only a single CPU, the proposed LightCap exhibits a fast inference speed of 188ms per image, which is ready for practical applications. |
1906.04586 | Ons Khemiri | Ons Khemiri | Proposition d'une nouvelle approche d'extraction des motifs ferm\'es
fr\'equents | in French. arXiv admin note: substantial text overlap with
arXiv:1810.07116, arXiv:1312.1558 by other authors | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This work is done as part of a master's thesis project. The increase in the
volume of data has given rise to various issues related to the collection,
storage, analysis and exploitation of these data in order to create an added
value. In this master, we are interested in the search of frequent closed
patterns in the transaction bases. One way to process data is to partition the
search space into subcontexts, and then explore the subcontexts simultaneously.
In this context, we have proposed a new approach for extracting frequent closed
itemsets. The main idea is to update frequent closed patterns with their
minimal generators by applying a strategy of partitioning of the initial
extraction context. Our new approach called UFCIGs-DAC was designed and
implemented to perform a search in the test bases. The main originality of this
approach is the simultaneous exploration of the research space by the update of
the frequent closed patterns and the minimal generators. Moreover, our approach
can be adapted to any algorithm of extraction of the frequent closed patterns
with their minimal generators.
| [
{
"created": "Sun, 9 Jun 2019 19:07:37 GMT",
"version": "v1"
}
] | 2019-06-12 | [
[
"Khemiri",
"Ons",
""
]
] | This work is done as part of a master's thesis project. The increase in the volume of data has given rise to various issues related to the collection, storage, analysis and exploitation of these data in order to create an added value. In this master, we are interested in the search of frequent closed patterns in the transaction bases. One way to process data is to partition the search space into subcontexts, and then explore the subcontexts simultaneously. In this context, we have proposed a new approach for extracting frequent closed itemsets. The main idea is to update frequent closed patterns with their minimal generators by applying a strategy of partitioning of the initial extraction context. Our new approach called UFCIGs-DAC was designed and implemented to perform a search in the test bases. The main originality of this approach is the simultaneous exploration of the research space by the update of the frequent closed patterns and the minimal generators. Moreover, our approach can be adapted to any algorithm of extraction of the frequent closed patterns with their minimal generators. |
0710.4727 | EDA Publishing Association | Paul Muller, Armin Tajalli, Mojtaba Atarodi, Yusuf Leblebici | Top-Down Design of a Low-Power Multi-Channel 2.5-Gbit/s/Channel Gated
Oscillator Clock-Recovery Circuit | Submitted on behalf of EDAA (http://www.edaa.com/) | Dans Design, Automation and Test in Europe - DATE'05, Munich :
Allemagne (2005) | null | null | cs.AR | null | We present a complete top-down design of a low-power multi-channel clock
recovery circuit based on gated current-controlled oscillators. The flow
includes several tools and methods used to specify block constraints, to design
and verify the topology down to the transistor level, as well as to achieve a
power consumption as low as 5mW/Gbit/s. Statistical simulation is used to
estimate the achievable bit error rate in presence of phase and frequency
errors and to prove the feasibility of the concept. VHDL modeling provides
extensive verification of the topology. Thermal noise modeling based on
well-known concepts delivers design parameters for the device sizing and
biasing. We present two practical examples of possible design improvements
analyzed and implemented with this methodology.
| [
{
"created": "Thu, 25 Oct 2007 09:38:14 GMT",
"version": "v1"
}
] | 2011-11-09 | [
[
"Muller",
"Paul",
""
],
[
"Tajalli",
"Armin",
""
],
[
"Atarodi",
"Mojtaba",
""
],
[
"Leblebici",
"Yusuf",
""
]
] | We present a complete top-down design of a low-power multi-channel clock recovery circuit based on gated current-controlled oscillators. The flow includes several tools and methods used to specify block constraints, to design and verify the topology down to the transistor level, as well as to achieve a power consumption as low as 5mW/Gbit/s. Statistical simulation is used to estimate the achievable bit error rate in presence of phase and frequency errors and to prove the feasibility of the concept. VHDL modeling provides extensive verification of the topology. Thermal noise modeling based on well-known concepts delivers design parameters for the device sizing and biasing. We present two practical examples of possible design improvements analyzed and implemented with this methodology. |
2010.12718 | Tanmay Gangwani | Tanmay Gangwani, Yuan Zhou, Jian Peng | Learning Guidance Rewards with Trajectory-space Smoothing | NeurIPS 2020 camera-ready | null | null | null | cs.LG stat.ML | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Long-term temporal credit assignment is an important challenge in deep
reinforcement learning (RL). It refers to the ability of the agent to attribute
actions to consequences that may occur after a long time interval. Existing
policy-gradient and Q-learning algorithms typically rely on dense environmental
rewards that provide rich short-term supervision and help with credit
assignment. However, they struggle to solve tasks with delays between an action
and the corresponding rewarding feedback. To make credit assignment easier,
recent works have proposed algorithms to learn dense "guidance" rewards that
could be used in place of the sparse or delayed environmental rewards. This
paper is in the same vein -- starting with a surrogate RL objective that
involves smoothing in the trajectory-space, we arrive at a new algorithm for
learning guidance rewards. We show that the guidance rewards have an intuitive
interpretation, and can be obtained without training any additional neural
networks. Due to the ease of integration, we use the guidance rewards in a few
popular algorithms (Q-learning, Actor-Critic, Distributional-RL) and present
results in single-agent and multi-agent tasks that elucidate the benefit of our
approach when the environmental rewards are sparse or delayed.
| [
{
"created": "Fri, 23 Oct 2020 23:55:06 GMT",
"version": "v1"
}
] | 2020-10-27 | [
[
"Gangwani",
"Tanmay",
""
],
[
"Zhou",
"Yuan",
""
],
[
"Peng",
"Jian",
""
]
] | Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that may occur after a long time interval. Existing policy-gradient and Q-learning algorithms typically rely on dense environmental rewards that provide rich short-term supervision and help with credit assignment. However, they struggle to solve tasks with delays between an action and the corresponding rewarding feedback. To make credit assignment easier, recent works have proposed algorithms to learn dense "guidance" rewards that could be used in place of the sparse or delayed environmental rewards. This paper is in the same vein -- starting with a surrogate RL objective that involves smoothing in the trajectory-space, we arrive at a new algorithm for learning guidance rewards. We show that the guidance rewards have an intuitive interpretation, and can be obtained without training any additional neural networks. Due to the ease of integration, we use the guidance rewards in a few popular algorithms (Q-learning, Actor-Critic, Distributional-RL) and present results in single-agent and multi-agent tasks that elucidate the benefit of our approach when the environmental rewards are sparse or delayed. |
1705.07706 | Armand Vilalta | Dario Garcia-Gasulla, Armand Vilalta, Ferran Par\'es, Jonatan Moreno,
Eduard Ayguad\'e, Jesus Labarta, Ulises Cort\'es and Toyotaro Suzumura | An Out-of-the-box Full-network Embedding for Convolutional Neural
Networks | null | null | null | null | cs.LG cs.NE | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Transfer learning for feature extraction can be used to exploit deep
representations in contexts where there is very few training data, where there
are limited computational resources, or when tuning the hyper-parameters needed
for training is not an option. While previous contributions to feature
extraction propose embeddings based on a single layer of the network, in this
paper we propose a full-network embedding which successfully integrates
convolutional and fully connected features, coming from all layers of a deep
convolutional neural network. To do so, the embedding normalizes features in
the context of the problem, and discretizes their values to reduce noise and
regularize the embedding space. Significantly, this also reduces the
computational cost of processing the resultant representations. The proposed
method is shown to outperform single layer embeddings on several image
classification tasks, while also being more robust to the choice of the
pre-trained model used for obtaining the initial features. The performance gap
in classification accuracy between thoroughly tuned solutions and the
full-network embedding is also reduced, which makes of the proposed approach a
competitive solution for a large set of applications.
| [
{
"created": "Mon, 22 May 2017 13:14:11 GMT",
"version": "v1"
}
] | 2017-05-23 | [
[
"Garcia-Gasulla",
"Dario",
""
],
[
"Vilalta",
"Armand",
""
],
[
"Parés",
"Ferran",
""
],
[
"Moreno",
"Jonatan",
""
],
[
"Ayguadé",
"Eduard",
""
],
[
"Labarta",
"Jesus",
""
],
[
"Cortés",
"Ulises",
""
],
[
"Suzumura",
"Toyotaro",
""
]
] | Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option. While previous contributions to feature extraction propose embeddings based on a single layer of the network, in this paper we propose a full-network embedding which successfully integrates convolutional and fully connected features, coming from all layers of a deep convolutional neural network. To do so, the embedding normalizes features in the context of the problem, and discretizes their values to reduce noise and regularize the embedding space. Significantly, this also reduces the computational cost of processing the resultant representations. The proposed method is shown to outperform single layer embeddings on several image classification tasks, while also being more robust to the choice of the pre-trained model used for obtaining the initial features. The performance gap in classification accuracy between thoroughly tuned solutions and the full-network embedding is also reduced, which makes of the proposed approach a competitive solution for a large set of applications. |
2008.07644 | Ohad Ben-Shahar | Peleg Harel and Ohad Ben-Shahar | Pictorial and apictorial polygonal jigsaw puzzles: The lazy caterer
model, properties, and solvers | null | null | null | null | cs.CV cs.AI cs.CG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Jigsaw puzzle solving, the problem of constructing a coherent whole from a
set of non-overlapping unordered visual fragments, is fundamental to numerous
applications and yet most of the literature of the last two decades has focused
thus far on less realistic puzzles whose pieces are identical squares. Here we
formalize a new type of jigsaw puzzle where the pieces are general convex
polygons generated by cutting through a global polygonal shape/image with an
arbitrary number of straight cuts, a generation model inspired by the
celebrated Lazy caterer's sequence. We analyze the theoretical properties of
such puzzles, including the inherent challenges in solving them once pieces are
contaminated with geometrical noise. To cope with such difficulties and obtain
tractable solutions, we abstract the problem as a multi-body spring-mass
dynamical system endowed with hierarchical loop constraints and a layered
reconstruction process. We define evaluation metrics and present experimental
results on both apictorial and pictorial puzzles to show that they are solvable
completely automatically.
| [
{
"created": "Mon, 17 Aug 2020 22:07:40 GMT",
"version": "v1"
},
{
"created": "Thu, 16 Dec 2021 15:32:53 GMT",
"version": "v2"
}
] | 2021-12-17 | [
[
"Harel",
"Peleg",
""
],
[
"Ben-Shahar",
"Ohad",
""
]
] | Jigsaw puzzle solving, the problem of constructing a coherent whole from a set of non-overlapping unordered visual fragments, is fundamental to numerous applications and yet most of the literature of the last two decades has focused thus far on less realistic puzzles whose pieces are identical squares. Here we formalize a new type of jigsaw puzzle where the pieces are general convex polygons generated by cutting through a global polygonal shape/image with an arbitrary number of straight cuts, a generation model inspired by the celebrated Lazy caterer's sequence. We analyze the theoretical properties of such puzzles, including the inherent challenges in solving them once pieces are contaminated with geometrical noise. To cope with such difficulties and obtain tractable solutions, we abstract the problem as a multi-body spring-mass dynamical system endowed with hierarchical loop constraints and a layered reconstruction process. We define evaluation metrics and present experimental results on both apictorial and pictorial puzzles to show that they are solvable completely automatically. |
1402.6016 | Suayb Arslan | Suayb S. Arslan | Incremental Redundancy, Fountain Codes and Advanced Topics | 57 pages, 22 figures, Version 0.2 | null | null | null | cs.IT math.IT | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This document is written in order to establish a common base ground on which
the majority of the relevant research about linear fountain codes can be
analyzed and compared. As far as I am concerned, there is no unified approach
that outlines and compares most of the published linear fountain codes in a
single and self-contained framework. This written document has not only
resulted in the review of theoretical fundamentals of efficient coding
techniques for incremental redundancy and linear fountain coding, but also
helped me have a comprehensive reference document and hopefully for many other
graduate students who would like to have some background to pursue a research
career regarding fountain codes and their various applications. Some background
in information, coding, graph and probability theory is expected. Although
various aspects of this topic and many other relevant research are deliberately
left out, I still hope that this document shall serve researchers' need well. I
have also included several exercises to warm up. The presentation style is
usually informal and the presented material is not necessarily rigorous. There
are many spots in the text that are product of my coauthors and myself,
although some of which have not been published yet.
| [
{
"created": "Mon, 24 Feb 2014 23:41:50 GMT",
"version": "v1"
},
{
"created": "Mon, 14 Jul 2014 22:40:31 GMT",
"version": "v2"
}
] | 2014-07-16 | [
[
"Arslan",
"Suayb S.",
""
]
] | This document is written in order to establish a common base ground on which the majority of the relevant research about linear fountain codes can be analyzed and compared. As far as I am concerned, there is no unified approach that outlines and compares most of the published linear fountain codes in a single and self-contained framework. This written document has not only resulted in the review of theoretical fundamentals of efficient coding techniques for incremental redundancy and linear fountain coding, but also helped me have a comprehensive reference document and hopefully for many other graduate students who would like to have some background to pursue a research career regarding fountain codes and their various applications. Some background in information, coding, graph and probability theory is expected. Although various aspects of this topic and many other relevant research are deliberately left out, I still hope that this document shall serve researchers' need well. I have also included several exercises to warm up. The presentation style is usually informal and the presented material is not necessarily rigorous. There are many spots in the text that are product of my coauthors and myself, although some of which have not been published yet. |
2104.10864 | Karandeep Singh | Karandeep Singh, Gabriel Lima, Meeyoung Cha, Chiyoung Cha, Juhi
Kulshrestha, Yong-Yeol Ahn, Onur Varol | Misinformation, Believability, and Vaccine Acceptance Over 40 Countries:
Takeaways From the Initial Phase of The COVID-19 Infodemic | null | null | 10.1371/journal.pone.0263381 | null | cs.SI cs.CY | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The COVID-19 pandemic has been damaging to the lives of people all around the
world. Accompanied by the pandemic is an infodemic, an abundant and
uncontrolled spreading of potentially harmful misinformation. The infodemic may
severely change the pandemic's course by interfering with public health
interventions such as wearing masks, social distancing, and vaccination. In
particular, the impact of the infodemic on vaccination is critical because it
holds the key to reverting to pre-pandemic normalcy. This paper presents
findings from a global survey on the extent of worldwide exposure to the
COVID-19 infodemic, assesses different populations' susceptibility to false
claims, and analyzes its association with vaccine acceptance. Based on
responses gathered from over 18,400 individuals from 40 countries, we find a
strong association between perceived believability of misinformation and
vaccination hesitancy. Additionally, our study shows that only half of the
online users exposed to rumors might have seen the fact-checked information.
Moreover, depending on the country, between 6% and 37% of individuals
considered these rumors believable. Our survey also shows that poorer regions
are more susceptible to encountering and believing COVID-19 misinformation. We
discuss implications of our findings on public campaigns that proactively
spread accurate information to countries that are more susceptible to the
infodemic. We also highlight fact-checking platforms' role in better
identifying and prioritizing claims that are perceived to be believable and
have wide exposure. Our findings give insights into better handling of risk
communication during the initial phase of a future pandemic.
| [
{
"created": "Thu, 22 Apr 2021 05:09:25 GMT",
"version": "v1"
}
] | 2022-04-06 | [
[
"Singh",
"Karandeep",
""
],
[
"Lima",
"Gabriel",
""
],
[
"Cha",
"Meeyoung",
""
],
[
"Cha",
"Chiyoung",
""
],
[
"Kulshrestha",
"Juhi",
""
],
[
"Ahn",
"Yong-Yeol",
""
],
[
"Varol",
"Onur",
""
]
] | The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spreading of potentially harmful misinformation. The infodemic may severely change the pandemic's course by interfering with public health interventions such as wearing masks, social distancing, and vaccination. In particular, the impact of the infodemic on vaccination is critical because it holds the key to reverting to pre-pandemic normalcy. This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic, assesses different populations' susceptibility to false claims, and analyzes its association with vaccine acceptance. Based on responses gathered from over 18,400 individuals from 40 countries, we find a strong association between perceived believability of misinformation and vaccination hesitancy. Additionally, our study shows that only half of the online users exposed to rumors might have seen the fact-checked information. Moreover, depending on the country, between 6% and 37% of individuals considered these rumors believable. Our survey also shows that poorer regions are more susceptible to encountering and believing COVID-19 misinformation. We discuss implications of our findings on public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic. We also highlight fact-checking platforms' role in better identifying and prioritizing claims that are perceived to be believable and have wide exposure. Our findings give insights into better handling of risk communication during the initial phase of a future pandemic. |
2301.06323 | Rui Sun | Rui Sun, Xiuyu Wu, Yunfang Wu | An Error-Guided Correction Model for Chinese Spelling Error Correction | null | null | null | null | cs.CL cs.AI | http://creativecommons.org/licenses/by/4.0/ | Although existing neural network approaches have achieved great success on
Chinese spelling correction, there is still room to improve. The model is
required to avoid over-correction and to distinguish a correct token from its
phonological and visually similar ones. In this paper, we propose an
error-guided correction model (EGCM) to improve Chinese spelling correction. By
borrowing the powerful ability of BERT, we propose a novel zero-shot error
detection method to do a preliminary detection, which guides our model to
attend more on the probably wrong tokens in encoding and to avoid modifying the
correct tokens in generating. Furthermore, we introduce a new loss function to
integrate the error confusion set, which enables our model to distinguish
easily misused tokens. Moreover, our model supports highly parallel decoding to
meet real application requirements. Experiments are conducted on widely used
benchmarks. Our model achieves superior performance against state-of-the-art
approaches by a remarkable margin, on both the correction quality and
computation speed.
| [
{
"created": "Mon, 16 Jan 2023 09:27:45 GMT",
"version": "v1"
},
{
"created": "Mon, 20 Mar 2023 08:37:45 GMT",
"version": "v2"
}
] | 2023-03-21 | [
[
"Sun",
"Rui",
""
],
[
"Wu",
"Xiuyu",
""
],
[
"Wu",
"Yunfang",
""
]
] | Although existing neural network approaches have achieved great success on Chinese spelling correction, there is still room to improve. The model is required to avoid over-correction and to distinguish a correct token from its phonological and visually similar ones. In this paper, we propose an error-guided correction model (EGCM) to improve Chinese spelling correction. By borrowing the powerful ability of BERT, we propose a novel zero-shot error detection method to do a preliminary detection, which guides our model to attend more on the probably wrong tokens in encoding and to avoid modifying the correct tokens in generating. Furthermore, we introduce a new loss function to integrate the error confusion set, which enables our model to distinguish easily misused tokens. Moreover, our model supports highly parallel decoding to meet real application requirements. Experiments are conducted on widely used benchmarks. Our model achieves superior performance against state-of-the-art approaches by a remarkable margin, on both the correction quality and computation speed. |
1907.09236 | Isaac Ronald Ward | Isaac Ronald Ward, Hamid Laga, Mohammed Bennamoun | RGB-D image-based Object Detection: from Traditional Methods to Deep
Learning Techniques | Chapter in the book 'RGB-D Image Analysis and Processing' (Paul
Rosin) | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Object detection from RGB images is a long-standing problem in image
processing and computer vision. It has applications in various domains
including robotics, surveillance, human-computer interaction, and medical
diagnosis. With the availability of low cost 3D scanners, a large number of
RGB-D object detection approaches have been proposed in the past years. This
chapter provides a comprehensive survey of the recent developments in this
field. We structure the chapter into two parts; the focus of the first part is
on techniques that are based on hand-crafted features combined with machine
learning algorithms. The focus of the second part is on the more recent work,
which is based on deep learning. Deep learning techniques, coupled with the
availability of large training datasets, have now revolutionized the field of
computer vision, including RGB-D object detection, achieving an unprecedented
level of performance. We survey the key contributions, summarize the most
commonly used pipelines, discuss their benefits and limitations, and highlight
some important directions for future research.
| [
{
"created": "Mon, 22 Jul 2019 11:18:01 GMT",
"version": "v1"
}
] | 2019-07-23 | [
[
"Ward",
"Isaac Ronald",
""
],
[
"Laga",
"Hamid",
""
],
[
"Bennamoun",
"Mohammed",
""
]
] | Object detection from RGB images is a long-standing problem in image processing and computer vision. It has applications in various domains including robotics, surveillance, human-computer interaction, and medical diagnosis. With the availability of low cost 3D scanners, a large number of RGB-D object detection approaches have been proposed in the past years. This chapter provides a comprehensive survey of the recent developments in this field. We structure the chapter into two parts; the focus of the first part is on techniques that are based on hand-crafted features combined with machine learning algorithms. The focus of the second part is on the more recent work, which is based on deep learning. Deep learning techniques, coupled with the availability of large training datasets, have now revolutionized the field of computer vision, including RGB-D object detection, achieving an unprecedented level of performance. We survey the key contributions, summarize the most commonly used pipelines, discuss their benefits and limitations, and highlight some important directions for future research. |
2402.18774 | Anna Kawakami | Anna Kawakami, Amanda Coston, Haiyi Zhu, Hoda Heidari, Kenneth
Holstein | The Situate AI Guidebook: Co-Designing a Toolkit to Support
Multi-Stakeholder Early-stage Deliberations Around Public Sector AI Proposals | null | null | 10.1145/3613904.3642849 | null | cs.HC | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Public sector agencies are rapidly deploying AI systems to augment or
automate critical decisions in real-world contexts like child welfare, criminal
justice, and public health. A growing body of work documents how these AI
systems often fail to improve services in practice. These failures can often be
traced to decisions made during the early stages of AI ideation and design,
such as problem formulation. However, today, we lack systematic processes to
support effective, early-stage decision-making about whether and under what
conditions to move forward with a proposed AI project. To understand how to
scaffold such processes in real-world settings, we worked with public sector
agency leaders, AI developers, frontline workers, and community advocates
across four public sector agencies and three community advocacy groups in the
United States. Through an iterative co-design process, we created the Situate
AI Guidebook: a structured process centered around a set of deliberation
questions to scaffold conversations around (1) goals and intended use or a
proposed AI system, (2) societal and legal considerations, (3) data and
modeling constraints, and (4) organizational governance factors. We discuss how
the guidebook's design is informed by participants' challenges, needs, and
desires for improved deliberation processes. We further elaborate on
implications for designing responsible AI toolkits in collaboration with public
sector agency stakeholders and opportunities for future work to expand upon the
guidebook. This design approach can be more broadly adopted to support the
co-creation of responsible AI toolkits that scaffold key decision-making
processes surrounding the use of AI in the public sector and beyond.
| [
{
"created": "Thu, 29 Feb 2024 00:31:26 GMT",
"version": "v1"
},
{
"created": "Tue, 5 Mar 2024 14:42:01 GMT",
"version": "v2"
}
] | 2024-03-06 | [
[
"Kawakami",
"Anna",
""
],
[
"Coston",
"Amanda",
""
],
[
"Zhu",
"Haiyi",
""
],
[
"Heidari",
"Hoda",
""
],
[
"Holstein",
"Kenneth",
""
]
] | Public sector agencies are rapidly deploying AI systems to augment or automate critical decisions in real-world contexts like child welfare, criminal justice, and public health. A growing body of work documents how these AI systems often fail to improve services in practice. These failures can often be traced to decisions made during the early stages of AI ideation and design, such as problem formulation. However, today, we lack systematic processes to support effective, early-stage decision-making about whether and under what conditions to move forward with a proposed AI project. To understand how to scaffold such processes in real-world settings, we worked with public sector agency leaders, AI developers, frontline workers, and community advocates across four public sector agencies and three community advocacy groups in the United States. Through an iterative co-design process, we created the Situate AI Guidebook: a structured process centered around a set of deliberation questions to scaffold conversations around (1) goals and intended use or a proposed AI system, (2) societal and legal considerations, (3) data and modeling constraints, and (4) organizational governance factors. We discuss how the guidebook's design is informed by participants' challenges, needs, and desires for improved deliberation processes. We further elaborate on implications for designing responsible AI toolkits in collaboration with public sector agency stakeholders and opportunities for future work to expand upon the guidebook. This design approach can be more broadly adopted to support the co-creation of responsible AI toolkits that scaffold key decision-making processes surrounding the use of AI in the public sector and beyond. |
1904.02181 | Qiao Jin | Qiao Jin, Bhuwan Dhingra, William W. Cohen, Xinghua Lu | Probing Biomedical Embeddings from Language Models | NAACL-HLT 2019 Workshop on Evaluating Vector Space Representations
for NLP (RepEval) | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Contextualized word embeddings derived from pre-trained language models (LMs)
show significant improvements on downstream NLP tasks. Pre-training on
domain-specific corpora, such as biomedical articles, further improves their
performance. In this paper, we conduct probing experiments to determine what
additional information is carried intrinsically by the in-domain trained
contextualized embeddings. For this we use the pre-trained LMs as fixed feature
extractors and restrict the downstream task models to not have additional
sequence modeling layers. We compare BERT, ELMo, BioBERT and BioELMo, a
biomedical version of ELMo trained on 10M PubMed abstracts. Surprisingly, while
fine-tuned BioBERT is better than BioELMo in biomedical NER and NLI tasks, as a
fixed feature extractor BioELMo outperforms BioBERT in our probing tasks. We
use visualization and nearest neighbor analysis to show that better encoding of
entity-type and relational information leads to this superiority.
| [
{
"created": "Wed, 3 Apr 2019 18:05:02 GMT",
"version": "v1"
}
] | 2019-04-05 | [
[
"Jin",
"Qiao",
""
],
[
"Dhingra",
"Bhuwan",
""
],
[
"Cohen",
"William W.",
""
],
[
"Lu",
"Xinghua",
""
]
] | Contextualized word embeddings derived from pre-trained language models (LMs) show significant improvements on downstream NLP tasks. Pre-training on domain-specific corpora, such as biomedical articles, further improves their performance. In this paper, we conduct probing experiments to determine what additional information is carried intrinsically by the in-domain trained contextualized embeddings. For this we use the pre-trained LMs as fixed feature extractors and restrict the downstream task models to not have additional sequence modeling layers. We compare BERT, ELMo, BioBERT and BioELMo, a biomedical version of ELMo trained on 10M PubMed abstracts. Surprisingly, while fine-tuned BioBERT is better than BioELMo in biomedical NER and NLI tasks, as a fixed feature extractor BioELMo outperforms BioBERT in our probing tasks. We use visualization and nearest neighbor analysis to show that better encoding of entity-type and relational information leads to this superiority. |
1810.08237 | Nikola Nikolov | Nikola I. Nikolov, Richard H.R. Hahnloser | Large-scale Hierarchical Alignment for Data-driven Text Rewriting | RANLP 2019 | null | null | null | cs.CL | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a simple unsupervised method for extracting pseudo-parallel
monolingual sentence pairs from comparable corpora representative of two
different text styles, such as news articles and scientific papers. Our
approach does not require a seed parallel corpus, but instead relies solely on
hierarchical search over pre-trained embeddings of documents and sentences. We
demonstrate the effectiveness of our method through automatic and extrinsic
evaluation on text simplification from the normal to the Simple Wikipedia. We
show that pseudo-parallel sentences extracted with our method not only
supplement existing parallel data, but can even lead to competitive performance
on their own.
| [
{
"created": "Thu, 18 Oct 2018 18:51:43 GMT",
"version": "v1"
},
{
"created": "Thu, 25 Jul 2019 07:25:09 GMT",
"version": "v2"
}
] | 2019-07-26 | [
[
"Nikolov",
"Nikola I.",
""
],
[
"Hahnloser",
"Richard H. R.",
""
]
] | We propose a simple unsupervised method for extracting pseudo-parallel monolingual sentence pairs from comparable corpora representative of two different text styles, such as news articles and scientific papers. Our approach does not require a seed parallel corpus, but instead relies solely on hierarchical search over pre-trained embeddings of documents and sentences. We demonstrate the effectiveness of our method through automatic and extrinsic evaluation on text simplification from the normal to the Simple Wikipedia. We show that pseudo-parallel sentences extracted with our method not only supplement existing parallel data, but can even lead to competitive performance on their own. |
2010.06425 | Esther Rodrigo Bonet | Esther Rodrigo Bonet, Duc Minh Nguyen and Nikos Deligiannis | Temporal Collaborative Filtering with Graph Convolutional Neural
Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal collaborative filtering (TCF) methods aim at modelling non-static
aspects behind recommender systems, such as the dynamics in users' preferences
and social trends around items. State-of-the-art TCF methods employ recurrent
neural networks (RNNs) to model such aspects. These methods deploy
matrix-factorization-based (MF-based) approaches to learn the user and item
representations. Recently, graph-neural-network-based (GNN-based) approaches
have shown improved performance in providing accurate recommendations over
traditional MF-based approaches in non-temporal CF settings. Motivated by this,
we propose a novel TCF method that leverages GNNs to learn user and item
representations, and RNNs to model their temporal dynamics. A challenge with
this method lies in the increased data sparsity, which negatively impacts
obtaining meaningful quality representations with GNNs. To overcome this
challenge, we train a GNN model at each time step using a set of observed
interactions accumulated time-wise. Comprehensive experiments on real-world
data show the improved performance obtained by our method over several
state-of-the-art temporal and non-temporal CF models.
| [
{
"created": "Tue, 13 Oct 2020 14:38:40 GMT",
"version": "v1"
}
] | 2020-10-14 | [
[
"Bonet",
"Esther Rodrigo",
""
],
[
"Nguyen",
"Duc Minh",
""
],
[
"Deligiannis",
"Nikos",
""
]
] | Temporal collaborative filtering (TCF) methods aim at modelling non-static aspects behind recommender systems, such as the dynamics in users' preferences and social trends around items. State-of-the-art TCF methods employ recurrent neural networks (RNNs) to model such aspects. These methods deploy matrix-factorization-based (MF-based) approaches to learn the user and item representations. Recently, graph-neural-network-based (GNN-based) approaches have shown improved performance in providing accurate recommendations over traditional MF-based approaches in non-temporal CF settings. Motivated by this, we propose a novel TCF method that leverages GNNs to learn user and item representations, and RNNs to model their temporal dynamics. A challenge with this method lies in the increased data sparsity, which negatively impacts obtaining meaningful quality representations with GNNs. To overcome this challenge, we train a GNN model at each time step using a set of observed interactions accumulated time-wise. Comprehensive experiments on real-world data show the improved performance obtained by our method over several state-of-the-art temporal and non-temporal CF models. |
2103.13629 | Wanhua Li | Wanhua Li, Xiaoke Huang, Jiwen Lu, Jianjiang Feng, Jie Zhou | Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware
Regression | Accepted by CVPR2021. Code is available at
https://github.com/Li-Wanhua/POEs | null | null | null | cs.CV cs.LG | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Uncertainty is the only certainty there is. Modeling data uncertainty is
essential for regression, especially in unconstrained settings. Traditionally
the direct regression formulation is considered and the uncertainty is modeled
by modifying the output space to a certain family of probabilistic
distributions. On the other hand, classification based regression and ranking
based solutions are more popular in practice while the direct regression
methods suffer from the limited performance. How to model the uncertainty
within the present-day technologies for regression remains an open issue. In
this paper, we propose to learn probabilistic ordinal embeddings which
represent each data as a multivariate Gaussian distribution rather than a
deterministic point in the latent space. An ordinal distribution constraint is
proposed to exploit the ordinal nature of regression. Our probabilistic ordinal
embeddings can be integrated into popular regression approaches and empower
them with the ability of uncertainty estimation. Experimental results show that
our approach achieves competitive performance. Code is available at
https://github.com/Li-Wanhua/POEs.
| [
{
"created": "Thu, 25 Mar 2021 06:56:09 GMT",
"version": "v1"
}
] | 2021-03-26 | [
[
"Li",
"Wanhua",
""
],
[
"Huang",
"Xiaoke",
""
],
[
"Lu",
"Jiwen",
""
],
[
"Feng",
"Jianjiang",
""
],
[
"Zhou",
"Jie",
""
]
] | Uncertainty is the only certainty there is. Modeling data uncertainty is essential for regression, especially in unconstrained settings. Traditionally the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. On the other hand, classification based regression and ranking based solutions are more popular in practice while the direct regression methods suffer from the limited performance. How to model the uncertainty within the present-day technologies for regression remains an open issue. In this paper, we propose to learn probabilistic ordinal embeddings which represent each data as a multivariate Gaussian distribution rather than a deterministic point in the latent space. An ordinal distribution constraint is proposed to exploit the ordinal nature of regression. Our probabilistic ordinal embeddings can be integrated into popular regression approaches and empower them with the ability of uncertainty estimation. Experimental results show that our approach achieves competitive performance. Code is available at https://github.com/Li-Wanhua/POEs. |
2209.08335 | Louis Mahon | Louis Mahon and Thomas Lukasiewicz | Efficient Deep Clustering of Human Activities and How to Improve
Evaluation | null | null | null | null | cs.LG | http://creativecommons.org/licenses/by/4.0/ | There has been much recent research on human activity re\-cog\-ni\-tion
(HAR), due to the proliferation of wearable sensors in watches and phones, and
the advances of deep learning methods, which avoid the need to manually extract
features from raw sensor signals. A significant disadvantage of deep learning
applied to HAR is the need for manually labelled training data, which is
especially difficult to obtain for HAR datasets. Progress is starting to be
made in the unsupervised setting, in the form of deep HAR clustering models,
which can assign labels to data without having been given any labels to train
on, but there are problems with evaluating deep HAR clustering models, which
makes assessing the field and devising new methods difficult. In this paper, we
highlight several distinct problems with how deep HAR clustering models are
evaluated, describing these problems in detail and conducting careful
experiments to explicate the effect that they can have on results. We then
discuss solutions to these problems, and suggest standard evaluation settings
for future deep HAR clustering models. Additionally, we present a new deep
clustering model for HAR. When tested under our proposed settings, our model
performs better than (or on par with) existing models, while also being more
efficient and better able to scale to more complex datasets by avoiding the
need for an autoencoder.
| [
{
"created": "Sat, 17 Sep 2022 14:12:42 GMT",
"version": "v1"
}
] | 2022-09-20 | [
[
"Mahon",
"Louis",
""
],
[
"Lukasiewicz",
"Thomas",
""
]
] | There has been much recent research on human activity re\-cog\-ni\-tion (HAR), due to the proliferation of wearable sensors in watches and phones, and the advances of deep learning methods, which avoid the need to manually extract features from raw sensor signals. A significant disadvantage of deep learning applied to HAR is the need for manually labelled training data, which is especially difficult to obtain for HAR datasets. Progress is starting to be made in the unsupervised setting, in the form of deep HAR clustering models, which can assign labels to data without having been given any labels to train on, but there are problems with evaluating deep HAR clustering models, which makes assessing the field and devising new methods difficult. In this paper, we highlight several distinct problems with how deep HAR clustering models are evaluated, describing these problems in detail and conducting careful experiments to explicate the effect that they can have on results. We then discuss solutions to these problems, and suggest standard evaluation settings for future deep HAR clustering models. Additionally, we present a new deep clustering model for HAR. When tested under our proposed settings, our model performs better than (or on par with) existing models, while also being more efficient and better able to scale to more complex datasets by avoiding the need for an autoencoder. |
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