Tootfinder

An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning
Zhen Li, Kaixiang Zhu, Xuegong Zhou, Lingli Wang
https://arxiv.org/abs/2403.17395

An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning
We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning.

A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites
Betim Bahtiri, Behrouz Arash, Sven Scheffler, Maximilian Jux, Raimund Rolfes
https://arxiv.org/abs/2403.18310

A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites
This work proposes a physics-informed deep learning (PIDL)-based constitutive model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticle-filled epoxies under various ambient conditions. The deep-learning model is trained to enforce thermodynamic principles, leading to a thermodynamically consistent constitutive model. To accomplish this, a long short-term memory network is combined with a feed-forward neural network to predict internal variables requir…

If you’re a [physics] theorist interested in getting funding, obviously the thing to do was to pivot quickly from quantum computing to machine learning and AI,
and get to work on the people at Quanta [magazine] to provide suitable PR.
Today Quanta features an article explaining how “Using machine learning, string theorists are finally showing how microscopic configurations of extra dimensions translate into sets of elementary particles.”
Looking at these new neural network c…

Union: An Automatic Workload Manager for Accelerating Network Simulation
Xin Wang, Misbah Mubarak, Yao Kang, Robert B. Ross, Zhiling Lan
https://arxiv.org/abs/2403.17036

Union: An Automatic Workload Manager for Accelerating Network Simulation
With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hy…

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
Hamza Saleem, Amir Ziashahabi, Muhammad Naveed, Salman Avestimehr
https://arxiv.org/abs/2403.17296

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
Training machine learning models on data from multiple entities without direct data sharing can unlock applications otherwise hindered by business, legal, or ethical constraints. In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models. We adopt a two-server model where data owners secret-share their data between two servers that train and evaluate the model on the joint data. A significant source of inefficiency a…

Multi-Objective Learning for Deformable Image Registration
Monika Grewal, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten
https://arxiv.org/abs/2402.16658

Multi-Objective Learning for Deformable Image Registration
Deformable image registration (DIR) involves optimization of multiple conflicting objectives, however, not many existing DIR algorithms are multi-objective (MO). Further, while there has been progress in the design of deep learning algorithms for DIR, there is no work in the direction of MO DIR using deep learning. In this paper, we fill this gap by combining a recently proposed approach for MO training of neural networks with a well-known deep neural network for DIR and create a deep learning …

The US corporate media, led by NBC, quietly rebuilds its ties to Donald Trump, as the reality sinks in that they may soon be dealing with a Trump administration (Max Tani/Semafor)
https://www.semafor.com/article/03/24/2024/le…

Led by NBC, U.S. corporate media is learning to live with Trump | Semafor
MSNBC hired former Republican National Committee chair Ronna McDaniel as an on-air commentator, despite her previous public criticism of the network's coverage of Republican politics.

New unit now available in the GÉANT #Network #eAcademy: #Nagios!
In this #learning unit we show how to instal…

New learning unit in the Network eAcademy: Nagios | GÉANT CONNECT Online
A new unit is now available in the GÉANT Network eAcademy as part of the Intelligence Management track: Nagios. Nagios is a monitoring and measuring framework for both physical and virtual servers, for the services which run on them and for network devices of all kinds. In this learning unit we show how to install

This https://arxiv.org/abs/2309.08849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critic…

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Natalie Lang, Alejandro Cohen, Nir Shlezinger
https://arxiv.org/abs/2403.18375

Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN) models in parallel with periodic centralized aggregations. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. Conventional approaches discard incomplete intra-model updates done by stragglers, alter the amount of local workload and a…

MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
Nguyen Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai
https://arxiv.org/abs/2402.16898

MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization
Multiplex influence maximization (MIM) asks us to identify a set of seed users such as to maximize the expected number of influenced users in a multiplex network. MIM has been one of central research topics, especially in nowadays social networking landscape where users participate in multiple online social networks (OSNs) and their influences can propagate among several OSNs simultaneously. Although there exist a couple combinatorial algorithms to MIM, learning-based solutions have been desire…

Training Classical Neural Networks by Quantum Machine Learning
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin, Sean Chen, Jason Gemsun Young, Yeong-Jar Chang, Min-Hsiu Hsieh
https://arxiv.org/abs/2402.16465

Training Classical Neural Networks by Quantum Machine Learning
In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with $M$ parameters to a quantum neural network (QNN) with $O(\text{polylog} (M))$ rotational gate angles, …

Digital Twin Assisted Intelligent Network Management for Vehicular Applications
Kaige Qu, Weihua Zhuang
https://arxiv.org/abs/2403.16021 https://

Digital Twin Assisted Intelligent Network Management for Vehicular Applications
The emerging data-driven methods based on artificial intelligence (AI) have paved the way for intelligent, flexible, and adaptive network management in vehicular applications. To enhance network management towards network automation, this article presents a digital twin (DT) assisted two-tier learning framework, which facilitates the automated life-cycle management of machine learning based intelligent network management functions (INMFs). Specifically, at a high tier, meta learning is employed…

Multi-View Deep Learning for Imaging Atmospheric Cherenkov Telescopes
Hannes Warnhofer, Samuel T. Spencer, Alison M. W. Mitchell
https://arxiv.org/abs/2403.18516

Multi-View Deep Learning for Imaging Atmospheric Cherenkov Telescopes
This research note concerns the application of deep-learning-based multi-view-imaging techniques to data from the H.E.S.S. Imaging Atmospheric Cherenkov Telescope array. We find that the earlier the fusion of layer information from different views takes place in the neural network, the better our model performs with this data. Our analysis shows that the point in the network where the information from the different views is combined is far more important for the model performance than the metho…

This https://arxiv.org/abs/2402.11743 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Hybrid Online-Offline Learning for Task Offloading in Mobile Edge Computing Systems
We consider a multi-user multi-server mobile edge computing (MEC) system, in which users arrive on a network randomly over time and generate computation tasks, which will be computed either locally on their own computing devices or be offloaded to one of the MEC servers. Under such a dynamic network environment, we propose a novel task offloading policy based on hybrid online-offline learning, which can efficiently reduce the overall computation delay and energy consumption only with informatio…

On the Stability of Learning in Network Games with Many Players
Aamal Hussain, Dan Leonte, Francesco Belardinelli, Georgios Piliouras
https://arxiv.org/abs/2403.15848

On the Stability of Learning in Network Games with Many Players
Multi-agent learning algorithms have been shown to display complex, unstable behaviours in a wide array of games. In fact, previous works indicate that convergent behaviours are less likely to occur as the total number of agents increases. This seemingly prohibits convergence to stable strategies, such as Nash Equilibria, in games with many players. To make progress towards addressing this challenge we study the Q-Learning Dynamics, a classical model for exploration and exploitation in multi-…

Led by NBC, U.S. corporate media is learning to cower before Trump
Like what happened to the Republican Party --- Fears of retaliation during a possible second Trump term are shifting the media center of gravity toward authoritarianism
https://www.s…

Led by NBC, U.S. corporate media is learning to live with Trump | Semafor
MSNBC hired former Republican National Committee chair Ronna McDaniel as an on-air commentator, despite her previous public criticism of the network's coverage of Republican politics.

NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification
Hanna Abi Akl
https://arxiv.org/abs/2402.16910

NeSy is alive and well: A LLM-driven symbolic approach for better code comment data generation and classification
We present a neuro-symbolic (NeSy) workflow combining a symbolic-based learning technique with a large language model (LLM) agent to generate synthetic data for code comment classification in the C programming language. We also show how generating controlled synthetic data using this workflow fixes some of the notable weaknesses of LLM-based generation and increases the performance of classical machine learning models on the code comment classification task. Our best model, a Neural Network, ac…

Deep learning-based predictive modelling of transonic flow over an aerofoil
Li-Wei Chen, Nils Thuerey
https://arxiv.org/abs/2403.17131 https://

Deep learning-based predictive modelling of transonic flow over an aerofoil
Effectively predicting transonic unsteady flow over an aerofoil poses inherent challenges. In this study, we harness the power of deep neural network (DNN) models using the attention U-Net architecture. Through efficient training of these models, we achieve the capability to capture the complexities of transonic and unsteady flow dynamics at high resolution, even when faced with previously unseen conditions. We demonstrate that by leveraging the differentiability inherent in neural network repr…

FetchAid: Making Parcel Lockers More Accessible to Blind and Low Vision People With Deep-learning Enhanced Touchscreen Guidance, Error-Recovery Mechanism, and AR-based Search Support
Zhitong Guan, Zeyu Xiong, Mingming Fan
https://arxiv.org/abs/2402.15723

FetchAid: Making Parcel Lockers More Accessible to Blind and Low Vision People With Deep-learning Enhanced Touchscreen Guidance, Error-Recovery Mechanism, and AR-based Search Support
Parcel lockers have become an increasingly prevalent last-mile delivery method. Yet, a recent study revealed its accessibility challenges to blind and low-vision people (BLV). Informed by the study, we designed FetchAid, a standalone intelligent mobile app assisting BLV in using a parcel locker in real-time by integrating computer vision and augmented reality (AR) technologies. FetchAid first uses a deep network to detect the user's fingertip and relevant buttons on the touch screen of the parc…

Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
Hanzhe Li, Xiangxiang Wang, Yuan Feng, Yaqian Qi, Jingxiao Tian
https://arxiv.org/abs/2403.18100

Driving Intelligent IoT Monitoring and Control through Cloud Computing and Machine Learning
This article explores how to drive intelligent iot monitoring and control through cloud computing and machine learning. As iot and the cloud continue to generate large and diverse amounts of data as sensor devices in the network, the collected data is sent to the cloud for statistical analysis, prediction, and data analysis to achieve business objectives. However, because the cloud computing model is limited by distance, it can be problematic in environments where the quality of the Internet co…

A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements
Darwin Alexander O. Co, Vince Angelo A. Chavez, Denny Lane B. Sombillo
https://arxiv.org/abs/2403.18265

A Deep Learning Framework for Disentangling Triangle Singularity and Pole-Based Enhancements
Enhancements in the invariant mass distribution or scattering cross-section are usually associated with resonances. However, the nature of exotic signals found near hadron-hadron thresholds remain a puzzle today. In fact, a purely kinematic triangle mechanism is also capable of producing similar structures, but do not correspond to any unstable quantum state. In this paper, we report for the first time, that a deep neural network can be trained to distinguish triangle singularity from pole-base…

Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
Christian Nauck, Michael Lindner, Nora Molkenthin, J\"urgen Kurths, Eckehard Sch\"oll, J\"org Raisch, Frank Hellmann
https://arxiv.org/abs/2402.17500 …

Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
A central question of network science is how functional properties of systems arise from their structure. For networked dynamical systems, structure is typically quantified with network measures. A functional property that is of theoretical and practical interest for oscillatory systems is the stability of synchrony to localized perturbations. Recently, Graph Neural Networks (GNNs) have been shown to predict this stability successfully; at the same time, network measures have struggled to paint…

EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
Lijin Wu, Shanshan Lei, Feilong Liao, Yuanjun Zheng, Yuxin Liu, Wentao Fu, Hao Song, Jiajun Zhou
https://arxiv.org/abs/2403.17980

EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
As the number of IoT devices increases, security concerns become more prominent. The impact of threats can be minimized by deploying Network Intrusion Detection System (NIDS) by monitoring network traffic, detecting and discovering intrusions, and issuing security alerts promptly. Most intrusion detection research in recent years has been directed towards the pair of traffic itself without considering the interrelationships among them, thus limiting the monitoring of complex IoT network attack …

Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning
Matteo Caldana, Paola F. Antonietti, Luca Dede'
https://arxiv.org/abs/2402.16517

Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning
Finite element-based high-order solvers of conservation laws offer large accuracy but face challenges near discontinuities due to the Gibbs phenomenon. Artificial viscosity is a popular and effective solution to this problem based on physical insight. In this work, we present a physics-informed machine learning algorithm to automate the discovery of artificial viscosity models in a non-supervised paradigm. The algorithm is inspired by reinforcement learning and trains a neural network acting ce…

This https://arxiv.org/abs/2203.13847 has been replaced.
link: https://scholar.google.com/scholar?q=a

Cluster Algebras: Network Science and Machine Learning
Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine learning. Network analysis methods are applied to the exchange graphs for cluster algebras of varying mutation types. The analysis indicates that when the graphs are represented without identifying by permutation equivalence between clusters an elegant symmetry emerges in the…

Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part I: Forecasting the Growth-Geometry split
Kunhao Zhong, Evan Saraivanov, James Caputi, Vivian Miranda, Supranta S. Boruah, Tim Eifler, Elisabeth Krause
https://arxiv.org/abs/2402.17716

Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part I: Forecasting the Growth-Geometry split
We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its capabilities in forecasting models beyond the standard $Λ$CDM, we forecast how well LSST Year 1 data will be able to probe the consistency between geometry $Ω^{\rm geo}_\mathrm{m}$ and growth $Ω^{\rm growth}_\mathrm{m}$ dark matter densities in t…

This https://arxiv.org/abs/2403.12820 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

A Physics-embedded Deep Learning Framework for Cloth Simulation
Delicate cloth simulations have long been desired in computer graphics. Various methods were proposed to improve engaged force interactions, collision handling, and numerical integrations. Deep learning has the potential to achieve fast and real-time simulation, but common neural network structures often demand many parameters to capture cloth dynamics. This paper proposes a physics-embedded learning framework that directly encodes physical features of cloth simulation. The convolutional neural…

This https://arxiv.org/abs/2311.10351 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Galaxy stellar and total mass estimation using machine learning
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions for the distributions of stellar and dark matter. In this work, we use a general sample of galaxies from the TNG100 simulation to investigate the ability of multi-branch convolutional neural network (CNN) based machine learning methods to predict the central (…

Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
Michal Bou\v{s}ka, P\v{r}emysl \v{S}\r{u}cha, Anton\'in Nov\'ak, Zden\v{e}k Hanz\'alek
https://arxiv.org/abs/2402.14847

Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
In this paper, we investigate the use of the deep learning method for solving a well-known NP-hard single machine scheduling problem with the objective of minimizing the total tardiness. We propose a deep neural network that acts as a polynomial-time estimator of the criterion value used in a single-pass scheduling algorithm based on Lawler's decomposition and symmetric decomposition proposed by Della Croce et al. Essentially, the neural network guides the algorithm by estimating the best split…

Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network
Dominik Panek, Carina Rz\k{a}ca, Maksymilian Szczypior, Joanna Sorysz, Krzysztof Misztal, Zbigniew Baster, Zenon Rajfur
https://arxiv.org/abs/2403.18026…

Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network
High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial netwo…

On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency
Georgios Tzolopoulos, Christos Korgialas, Constantine Kotropoulos
https://arxiv.org/abs/2403.18402

On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency
The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems. Here, a novel approach for power grid classification is developed, leveraging ENF. Spectrograms are generated from audio and power recordings across different grids, revealing distinctive ENF patterns that aid in grid classification through a fusion of classifiers. Four traditional machine learning classifiers plus a Convolutional Neural Network (CNN), optimized using Neural Architecture Se…

This https://arxiv.org/abs/2311.06144 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Multi-Agent Reinforcement Learning for the Low-Level Control of a Quadrotor UAV
By leveraging the underlying structures of the quadrotor dynamics, we propose multi-agent reinforcement learning frameworks to innovate the low-level control of a quadrotor, where independent agents operate cooperatively to achieve a common goal. While single-agent reinforcement learning has been successfully applied in quadrotor controls, training a large monolithic network is often data-intensive and time-consuming. Moreover, achieving agile yawing control remains a significant challenge due …

Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network
Jianhong Wang
https://arxiv.org/abs/2402.15324

Shapley Value Based Multi-Agent Reinforcement Learning: Theory, Method and Its Application to Energy Network
Multi-agent reinforcement learning is an area of rapid advancement in artificial intelligence and machine learning. One of the important questions to be answered is how to conduct credit assignment in a multi-agent system. There have been many schemes designed to conduct credit assignment by multi-agent reinforcement learning algorithms. Although these credit assignment schemes have been proved useful in improving the performance of multi-agent reinforcement learning, most of them are designed …

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://

This https://arxiv.org/abs/2306.11376 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Coevolution of cognition and cooperation in structured populations under reinforcement learning
We study the evolution of behavior under reinforcement learning in a Prisoner's Dilemma where agents interact in a regular network and can learn about whether they play one-shot or repeatedly by incurring a cost of deliberation. With respect to other behavioral rules used in the literature, (i) we confirm the existence of a threshold value of the probability of repeated interaction, switching the emergent behavior from intuitive defector to dual-process cooperator; (ii) we find a different role…

This https://arxiv.org/abs/2402.12532 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Scalable Human-Machine Point Cloud Compression
Due to the limited computational capabilities of edge devices, deep learning inference can be quite expensive. One remedy is to compress and transmit point cloud data over the network for server-side processing. Unfortunately, this approach can be sensitive to network factors, including available bitrate. Luckily, the bitrate requirements can be reduced without sacrificing inference accuracy by using a machine task-specialized codec. In this paper, we present a scalable codec for point-cloud da…

This https://arxiv.org/abs/2304.08061 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Provable local learning rule by expert aggregation for a Hawkes network
We propose a simple network of Hawkes processes as a cognitive model capable of learning to classify objects. Our learning algorithm, named HAN for Hawkes Aggregation of Neurons, is based on a local synaptic learning rule based on spiking probabilities at each output node. We were able to use local regret bounds to prove mathematically that the network is able to learn on average and even asymptotically under more restrictive assumptions.

This https://arxiv.org/abs/2401.16321 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Optimal Control of Renewable Energy Communities subject to Network Peak Fees with Model Predictive Control and Reinforcement Learning Algorithms
We propose in this paper an optimal control framework for renewable energy communities (RECs) equipped with controllable assets. Such RECs allow its members to exchange production surplus through an internal market. The objective is to control their assets in order to minimise the sum of individual electricity bills. These bills account for the electricity exchanged through the REC and with the retailers. Typically, for large companies, another important part of the bills are the costs related …

This https://arxiv.org/abs/2309.09814 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Convolutional Deep Kernel Machines
Standard infinite-width limits of neural networks sacrifice the ability for intermediate layers to learn representations from data. Recent work (A theory of representation learning gives a deep generalisation of kernel methods, Yang et al. 2023) modified the Neural Network Gaussian Process (NNGP) limit of Bayesian neural networks so that representation learning is retained. Furthermore, they found that applying this modified limit to a deep Gaussian process gives a practical learning algorithm …

Training Classical Neural Networks by Quantum Machine Learning
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin, Sean Chen, Jason Gemsun Young, Yeong-Jar Chang, Min-Hsiu Hsieh
https://arxiv.org/abs/2402.16465

Training Classical Neural Networks by Quantum Machine Learning
In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with $M$ parameters to a quantum neural network (QNN) with $O(\text{polylog} (M))$ rotational gate angles, …

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification
Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing
https://arxiv.org/abs/2402.17196

Prediction of the SYM-H Index Using a Bayesian Deep Learning Method with Uncertainty Quantification
We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1-minute and 5-minute resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index v…

Deep Learning Approach to Forecasting COVID-19 Cases in Residential Buildings of Hong Kong Public Housing Estates: The Role of Environment and Sociodemographics
E. Leung (JC School of Public Health and Primary Care, The Chinese University of Hong Kong), J. Guan (JC School of Public Health and Primary Care, The Chinese University of Hong Kong), KO. Kwok (JC School of Public Health and Primary Care, The Chinese University of Hong Kong), CT. Hung (JC School of Public Health and Primary Ca…

Deep Learning Approach to Forecasting COVID-19 Cases in Residential Buildings of Hong Kong Public Housing Estates: The Role of Environment and Sociodemographics
Introduction: The current study investigates the complex association between COVID-19 and the studied districts' socioecology (e.g. internal and external built environment, sociodemographic profiles, etc.) to quantify their contributions to the early outbreaks and epidemic resurgence of COVID-19. Methods: We aligned the analytic model's architecture with the hierarchical structure of the resident's socioecology using a multi-headed hierarchical convolutional neural network to structure the vast…

Deep learning-based predictive modelling of transonic flow over an aerofoil
Li-Wei Chen, Nils Thuerey
https://arxiv.org/abs/2403.17131 https://

Deep learning-based predictive modelling of transonic flow over an aerofoil
Effectively predicting transonic unsteady flow over an aerofoil poses inherent challenges. In this study, we harness the power of deep neural network (DNN) models using the attention U-Net architecture. Through efficient training of these models, we achieve the capability to capture the complexities of transonic and unsteady flow dynamics at high resolution, even when faced with previously unseen conditions. We demonstrate that by leveraging the differentiability inherent in neural network repr…

This https://arxiv.org/abs/2310.12541 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Large Language Model for Multi-objective Evolutionary Optimization
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully handcrafted design with domain knowledge. Recently, some attempts have been made to replace the manually designed operators in MOEAs with learning-based operators (e.g., neural network models). However, much effort is still required for designing and training such models, and th…

This https://arxiv.org/abs/2401.00428 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Training towards significance with the decorrelated event classifier transformer neural network
Experimental particle physics uses machine learning for many of tasks, where one application is to classify signal and background events. The classification can be used to bin an analysis region to enhance the expected significance for a mass resonance search. In natural language processing, one of the leading neural network architectures is the transformer. In this work, an event classifier transformer is proposed to bin an analysis region, in which the network is trained with special techniqu…

new #library #book #talk @ "New Books Network": "Refocusing Academic Libraries Through Learning and Discourse – The Idea of a Library" by Mary K. Bolin:

This https://arxiv.org/abs/2404.12633 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Gene…

PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction
Yuxiang Zhao, Zhuomin Chai, Xun Jiang, Yibo Lin, Runsheng Wang, Ru Huang
https://arxiv.org/abs/2403.18569

PDNNet: PDN-Aware GNN-CNN Heterogeneous Network for Dynamic IR Drop Prediction
IR drop on the power delivery network (PDN) is closely related to PDN's configuration and cell current consumption. As the integrated circuit (IC) design is growing larger, dynamic IR drop simulation becomes computationally unaffordable and machine learning based IR drop prediction has been explored as a promising solution. Although CNN-based methods have been adapted to IR drop prediction task in several works, the shortcomings of overlooking PDN configuration is non-negligible. In this paper,…

Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
Mingjin Zhang, Jiannong Cao, Yuvraj Sahni, Xiangchun Chen, Shan Jiang
https://arxiv.org/abs/2403.15815 <…

Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural Network (DNN) models in proximity to the sources of data. To enable the training of large models on resource-constraint edge devices and protect data privacy, parallel split learning is becoming a practical and popular approach. However, current parallel split learning neglects the resource heterogeneity of edge devices, which may lead to the straggler issue. In this paper, we propose EdgeSplit, a novel …

Spatio-Temporal Graph Convolutional Network Combined Large Language Model: A Deep Learning Framework for Bike Demand Forecasting
Peisen Li, Yizhe Pang, Junyu Ren
https://arxiv.org/abs/2403.15733

Spatio-Temporal Graph Convolutional Network Combined Large Language Model: A Deep Learning Framework for Bike Demand Forecasting
This study presents a new deep learning framework, combining Spatio-Temporal Graph Convolutional Network (STGCN) with a Large Language Model (LLM), for bike demand forecasting. Addressing challenges in transforming discrete datasets and integrating unstructured language data, the framework leverages LLMs to extract insights from Points of Interest (POI) text data. The proposed STGCN-L model demonstrates competitive performance compared to existing models, showcasing its potential in predicting …

Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning
Matteo Caldana, Paola F. Antonietti, Luca Dede'
https://arxiv.org/abs/2402.16517

Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning
Finite element-based high-order solvers of conservation laws offer large accuracy but face challenges near discontinuities due to the Gibbs phenomenon. Artificial viscosity is a popular and effective solution to this problem based on physical insight. In this work, we present a physics-informed machine learning algorithm to automate the discovery of artificial viscosity models in a non-supervised paradigm. The algorithm is inspired by reinforcement learning and trains a neural network acting ce…

This https://arxiv.org/abs/2305.07164 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning-Augmented Online Packet Scheduling with Deadlines
The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to…

This https://arxiv.org/abs/2308.04666 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Speaker Recognition Using Isomorphic Graph Attention Network Based Pooling on Self-Supervised Representation
The emergence of self-supervised representation (i.e., wav2vec 2.0) allows speaker-recognition approaches to process spoken signals through foundation models built on speech data. Nevertheless, effective fusion on the representation requires further investigating, due to the inclusion of fixed or sub-optimal temporal pooling strategies. Despite of improved strategies considering graph learning and graph attention factors, non-injective aggregation still exists in the approaches, which may influ…

Order from chaos: Interplay of development and learning in recurrent networks of structured neurons
Laura Kriener, Kristin V\"olk, Ben von H\"unerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici
https://arxiv.org/abs/2402.16763

Order from chaos: Interplay of development and learning in recurrent networks of structured neurons
Behavior can be described as a temporal sequence of actions driven by neural activity. To learn complex sequential patterns in neural networks, memories of past activities need to persist on significantly longer timescales than relaxation times of single-neuron activity. While recurrent networks can produce such long transients, training these networks in a biologically plausible way is challenging. One approach has been reservoir computing, where only weights from a recurrent network to a read…

This https://arxiv.org/abs/2404.12633 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Gene…

This https://arxiv.org/abs/2404.15419 has been replaced.
link: https://scholar.google.com/scholar?q=a

Using Deep Learning to Identify Initial Error Sensitivity of ENSO Forecasts
We introduce a hybrid method that integrates deep learning with model-analog forecasting, a straightforward yet effective approach that generates forecasts from similar initial climate states in a repository of model simulations. This hybrid framework employs a convolutional neural network to estimate state-dependent weights to identify analog states. The advantage of our method lies in its physical interpretability, offering insights into initial-error-sensitive regions through estimated weigh…

This https://arxiv.org/abs/2307.02603 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison
Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is known as structure learning. Bayesian methods can measure the uncertainty of conditional relationships and include prior information. However, frequentist methods are often preferred due to the computational burden of the Bayesian approach. Over the last decade, Bayesian methods have seen substantial…

Q-adaptive: A Multi-Agent Reinforcement Learning Based Routing on Dragonfly Network
Yao Kang, Xin Wang, Zhiling Lan
https://arxiv.org/abs/2403.16301 https:…

Q-adaptive: A Multi-Agent Reinforcement Learning Based Routing on Dragonfly Network
on adaptive routing to balance network traffic for optimum performance. Ideally, adaptive routing attempts to forward packets between minimal and non-minimal paths with the least congestion. In practice, current adaptive routing algorithms estimate routing path congestion based on local information such as output queue occupancy. Using local information to estimate global path congestion is inevitably inaccurate because a router has no precise knowledge of link states a few hops away. This inac…

This https://arxiv.org/abs/2307.06575 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Deep learning based enhancement of ordered statistics decoding of short LDPC codes
In the search for highly efficient decoders for short LDPC codes approaching maximum likelihood performance, a relayed decoding strategy, specifically activating the ordered statistics decoding process upon failure of a neural min-sum decoder, is enhanced by instilling three innovations. Firstly, soft information gathered at each step of the neural min-sum decoder is leveraged to forge a new reliability measure using a convolutional neural network. This measure aids in constructing the most rel…

Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
Mingjin Zhang, Jiannong Cao, Yuvraj Sahni, Xiangchun Chen, Shan Jiang
https://arxiv.org/abs/2403.15815 <…

Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
Edge AI has been recently proposed to facilitate the training and deployment of Deep Neural Network (DNN) models in proximity to the sources of data. To enable the training of large models on resource-constraint edge devices and protect data privacy, parallel split learning is becoming a practical and popular approach. However, current parallel split learning neglects the resource heterogeneity of edge devices, which may lead to the straggler issue. In this paper, we propose EdgeSplit, a novel …

Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems
Jianglin Lan, Siyuan Zhan, Ron Patton, Xianxian Zhao
https://arxiv.org/abs/2403.16132

Runtime Monitoring and Fault Detection for Neural Network-Controlled Systems
There is an emerging trend in applying deep learning methods to control complex nonlinear systems. This paper considers enhancing the runtime safety of nonlinear systems controlled by neural networks in the presence of disturbance and measurement noise. A robustly stable interval observer is designed to generate sound and precise lower and upper bounds for the neural network, nonlinear function, and system state. The obtained interval is utilised to monitor the real-time system safety and detec…

This https://arxiv.org/abs/2404.04496 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Towards Better Graph Neural Neural Network-based Fault Localization Through Enhanced Code Representation
Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization, a widely used technique, employs statistics on coverage spectra to rank code based on suspiciousness scores. However, the rigidity of statistical approaches calls for learning-based techniques. Amongst all, Grace, a graph-neural network (GNN) based technique has achieved state-of-the-art due to its capacity to preser…

Improving Line Search Methods for Large Scale Neural Network Training
Philip Kenneweg, Tristan Kenneweg, Barbara Hammer
https://arxiv.org/abs/2403.18519 ht…

Improving Line Search Methods for Large Scale Neural Network Training
In recent studies, line search methods have shown significant improvements in the performance of traditional stochastic gradient descent techniques, eliminating the need for a specific learning rate schedule. In this paper, we identify existing issues in state-of-the-art line search methods, propose enhancements, and rigorously evaluate their effectiveness. We test these methods on larger datasets and more complex data domains than before. Specifically, we improve the Armijo line search by inte…

Discretize first, filter next: learning divergence-consistent closure models for large-eddy simulation
Syver D{\o}ving Agdestein, Benjamin Sanderse
https://arxiv.org/abs/2403.18088

Discretize first, filter next: learning divergence-consistent closure models for large-eddy simulation
We propose a new neural network based large eddy simulation framework for the incompressible Navier-Stokes equations based on the paradigm "discretize first, filter and close next". This leads to full model-data consistency and allows for employing neural closure models in the same environment as where they have been trained. Since the LES discretization error is included in the learning process, the closure models can learn to account for the discretization. Furthermore, we introduce a new d…

livemocha: Livemocha friendship network (2010)
A network of friendships among users on Livemocha, a large online language learning community. Nodes represent users and edges represent a mutual declaration of friendship.
This network has 104103 nodes and 2193083 edges.
Tags: Social, Online, Unweighted
https://

This https://arxiv.org/abs/2204.05382 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning
This paper is concerned with the modeling and analysis of two of the most commonly used recurrent neural network models (i.e., Hopfield neural network and firing-rate neural network) with dynamic recurrent connections undergoing Hebbian learning rules. To capture the synaptic sparsity of neural circuits we propose a low dimensional formulation. We then characterize certain key dynamical properties. First, we give biologically-inspired forward invariance results. Then, we give sufficient conditi…

This https://arxiv.org/abs/2309.08849 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
Autonomous Dynamic System (DS)-based algorithms hold a pivotal and foundational role in the field of Learning from Demonstration (LfD). Nevertheless, they confront the formidable challenge of striking a delicate balance between achieving precision in learning and ensuring the overall stability of the system. In response to this substantial challenge, this paper introduces a novel DS algorithm rooted in neural network technology. This algorithm not only possesses the capability to extract critic…

On the role of network structure in learning to coordinate with bounded rationality
Yifei Zhang, Marcos M. Vasconcelos
https://arxiv.org/abs/2403.15683 htt…

On the role of network structure in learning to coordinate with bounded rationality
Many socioeconomic phenomena, such as technology adoption, collaborative problem-solving, and content engagement, involve a collection of agents coordinating to take a common action, aligning their decisions to maximize their individual goals. We consider a model for networked interactions where agents learn to coordinate their binary actions under a strict bound on their rationality. We first prove that our model is a potential game and that the optimal action profile is always to achieve perf…

This https://arxiv.org/abs/1807.03162 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Learning Based Sphere Decoding
In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. This improvement is attributed to DNN's ability of inte…

Privacy-Preserving Distributed Nonnegative Matrix Factorization
Ehsan Lari, Reza Arablouei, Stefan Werner
https://arxiv.org/abs/2403.18326 https://<…

Privacy-Preserving Distributed Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is an effective data representation tool with numerous applications in signal processing and machine learning. However, deploying NMF in a decentralized manner over ad-hoc networks introduces privacy concerns due to the conventional approach of sharing raw data among network agents. To address this, we propose a privacy-preserving algorithm for fully-distributed NMF that decomposes a distributed large data matrix into left and right matrix factors while sa…

Study of Workload Interference with Intelligent Routing on Dragonfly
Yao Kang, Xin Wang, Zhiling Lan
https://arxiv.org/abs/2403.16288 https://

Study of Workload Interference with Intelligent Routing on Dragonfly
Dragonfly interconnect is a crucial network technology for supercomputers. To support exascale systems, network resources are shared such that links and routers are not dedicated to any node pair. While link utilization is increased, workload performance is often offset by network contention. Recently, intelligent routing built on reinforcement learning demonstrates higher network throughput with lower packet latency. However, its effectiveness in reducing workload interference is unknown. In t…

This https://arxiv.org/abs/2312.11834 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics
In recent years, simulations of pedestrians using the multi-agent reinforcement learning (MARL) have been studied. This study considered the roads on a grid-world environment, and implemented pedestrians as MARL agents using an echo-state network and the least squares policy iteration method. Under this environment, the ability of these agents to learn to move forward by avoiding other agents was investigated. Specifically, we considered two types of tasks: the choice between a narrow direct ro…

Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics
Laura J. Johnston, Takoua Jendoubi
https://arxiv.org/abs/2403.14686

Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics
In the context of higher education's evolving dynamics post-COVID-19, this paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London. We employ a mixed methods approach, combining learning analytics with qualitative data, to evaluate the effectiveness of these incentives on increasing student engagement. A longitudinal overview of resource interactions is mapped through Bayesian network analysis of Moodle ac…

This https://arxiv.org/abs/2305.07164 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

Learning-Augmented Online Packet Scheduling with Deadlines
The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to…

This https://arxiv.org/abs/2204.01707 has been replaced.
link: https://scholar.google.com/scholar?q=a

Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic function. Employing such a novel type of neurons offers a new perspective on developing deep learning. When analyzing quadratic neurons, we find that there exists a function such that a heterogeneous network can approximate it well with a polynomial number of neurons but a purely conventional or quadratic network needs an e…

This https://arxiv.org/abs/2404.04496 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Towards Better Graph Neural Neural Network-based Fault Localization Through Enhanced Code Representation
Automatic software fault localization plays an important role in software quality assurance by pinpointing faulty locations for easier debugging. Coverage-based fault localization, a widely used technique, employs statistics on coverage spectra to rank code based on suspiciousness scores. However, the rigidity of statistical approaches calls for learning-based techniques. Amongst all, Grace, a graph-neural network (GNN) based technique has achieved state-of-the-art due to its capacity to preser…

This https://arxiv.org/abs/2201.06180 has been replaced.
link: https://scholar.google.com/scholar?q=a

Nonlinear Control Allocation: A Learning Based Approach
Modern aircraft are designed with redundant control effectors to cater for fault tolerance and maneuverability requirements. This leads to aircraft being over-actuated and requires control allocation schemes to distribute the control commands among control effectors. Traditionally, optimization-based control allocation schemes are used; however, for nonlinear allocation problems, these methods require large computational resources. In this work, an artificial neural network (ANN) based nonlinea…

Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention
Diego Valsesia, Tiziano Bianchi, Enrico Magli
https://arxiv.org/abs/2403.17677

Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention
Deep learning methods have traditionally been difficult to apply to compression of hyperspectral images onboard of spacecrafts, due to the large computational complexity needed to achieve adequate representational power, as well as the lack of suitable datasets for training and testing. In this paper, we depart from the traditional autoencoder approach and we design a predictive neural network, called LineRWKV, that works recursively line-by-line to limit memory consumption. In order to achieve…

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations
Jae Yong Lee, Steffen Schotth\"ofer, Tianbai Xiao, Sebastian Krumscheid, Martin Frank
https://arxiv.org/abs/2402.16613

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations
This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e.g., in radiation transport. We investigate the capabilities of the Deep Operator network (DeepONet) approach to modelling the high dimensi…

This https://arxiv.org/abs/2404.15419 has been replaced.
link: https://scholar.google.com/scholar?q=a

Using Deep Learning to Identify Initial Error Sensitivity of ENSO Forecasts
We introduce a hybrid method that integrates deep learning with model-analog forecasting, a straightforward yet effective approach that generates forecasts from similar initial climate states in a repository of model simulations. This hybrid framework employs a convolutional neural network to estimate state-dependent weights to identify analog states. The advantage of our method lies in its physical interpretability, offering insights into initial-error-sensitive regions through estimated weigh…

This https://arxiv.org/abs/2307.11792 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Quantum Convolutional Neural Networks with Interaction Layers for Classification of Classical Data
Quantum Machine Learning (QML) has come into the limelight due to the exceptional computational abilities of quantum computers. With the promises of near error-free quantum computers in the not-so-distant future, it is important that the effect of multi-qubit interactions on quantum neural networks is studied extensively. This paper introduces a Quantum Convolutional Network with novel Interaction layers exploiting three-qubit interactions, while studying the network's expressibility and entang…

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach
Diego A. de Aguiar, Hugo L. Fran\c{c}a, Cassio M. Oishi
https://arxiv.org/abs/2403.16144

Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach
Neural networks in fluid mechanics offer an efficient approach for exploring complex flows, including multiphase and free surface flows. The recurrent neural network, particularly the Long Short-Term Memory (LSTM) model, proves attractive for learning mappings from transient inputs to dynamic outputs. This study applies LSTM to predict transient and static outputs for fluid flows under surface tension effects. Specifically, we explore two distinct droplet dynamic scenarios: droplets with divers…

A Transformer-Based Framework for Payload Malware Detection and Classification
Kyle Stein, Arash Mahyari, Guillermo Francia III, Eman El-Sheikh
https://arxiv.org/abs/2403.18223

A Transformer-Based Framework for Payload Malware Detection and Classification
As malicious cyber threats become more sophisticated in breaching computer networks, the need for effective intrusion detection systems (IDSs) becomes crucial. Techniques such as Deep Packet Inspection (DPI) have been introduced to allow IDSs analyze the content of network packets, providing more context for identifying potential threats. IDSs traditionally rely on using anomaly-based and signature-based detection techniques to detect unrecognized and suspicious activity. Deep learning techniqu…

This https://arxiv.org/abs/2310.05022 has been replaced.
link: https://scholar.google.com/scholar?q=a

Fully Spiking Neural Network for Legged Robots
In recent years, legged robots based on deep reinforcement learning have made remarkable progress. Quadruped robots have demonstrated the ability to complete challenging tasks in complex environments and have been deployed in real-world scenarios to assist humans. Simultaneously, bipedal and humanoid robots have achieved breakthroughs in various demanding tasks. Current reinforcement learning methods can utilize diverse robot bodies and historical information to perform actions. However, prior …

Network Learning with Directional Sign Patterns
Anqi Dong, Can Chen, Tryphon T. Georgiou
https://arxiv.org/abs/2403.14915 https://arx…

Network Learning with Directional Sign Patterns
Complex systems can be effectively modeled via graphs that encode networked interactions, where relations between entities or nodes are often quantified by signed edge weights, e.g., promotion/inhibition in gene regulatory networks, or encoding political of friendship differences in social networks. However, it is often the case that only an aggregate consequence of such edge weights that characterize relations may be directly observable, as in protein expression of in gene regulatory networks.…

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations
Jae Yong Lee, Steffen Schotth\"ofer, Tianbai Xiao, Sebastian Krumscheid, Martin Frank
https://arxiv.org/abs/2402.16613

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations
This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a differential advection operator and an integral collision operator, which is a powerful yet expensive mathematical model for interacting particle systems with ample applications, e.g., in radiation transport. We investigate the capabilities of the Deep Operator network (DeepONet) approach to modelling the high dimensi…

Demystify Adult Learning: A Social Network and Large Language Model Assisted Approach
Fang Liu, Bosheng Ding, Chong Guan, Zhang Wei, Dusit Niyato, Justina Tan
https://arxiv.org/abs/2404.13267

Demystify Adult Learning: A Social Network and Large Language Model Assisted Approach
Adult learning is increasingly recognized as a crucial way for personal development and societal progress. It however is challenging, and adult learners face unique challenges such as balancing education with other life responsibilities. Collecting feedback from adult learners is effective in understanding their concerns and improving learning experiences, and social networks provide a rich source of real-time sentiment data from adult learners. Machine learning technologies especially large la…

Revisiting Neural Networks for Continual Learning: An Architectural Perspective
Aojun Lu, Tao Feng, Hangjie Yuan, Xiaotian Song, Yanan Sun
https://arxiv.org/abs/2404.14829

Revisiting Neural Networks for Continual Learning: An Architectural Perspective
Efforts to overcome catastrophic forgetting have primarily centered around developing more effective Continual Learning (CL) methods. In contrast, less attention was devoted to analyzing the role of network architecture design (e.g., network depth, width, and components) in contributing to CL. This paper seeks to bridge this gap between network architecture design and CL, and to present a holistic study on the impact of network architectures on CL. This work considers architecture design at the…

This https://arxiv.org/abs/2308.05254 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Data-driven Intra-Autonomous Systems Graph Generator
Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication netwo…

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Tianfu Wang, Qilin Fan, Chao Wang, Long Yang, Leilei Ding, Nicholas Jing Yuan, Hui Xiong
https://arxiv.org/abs/2404.12633

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Gene…

This https://arxiv.org/abs/2401.12686 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
Learning the behavior of large agent populations is an important task for numerous research areas. Although the field of multi-agent reinforcement learning (MARL) has made significant progress towards solving these systems, solutions for many agents often remain computationally infeasible and lack theoretical guarantees. Mean Field Games (MFGs) address both of these issues and can be extended to Graphon MFGs (GMFGs) to include network structures between agents. Despite their merits, the real wo…

Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Dominik M\"uller, Philip Meyer, Lukas Rentschler, Robin Manz, Daniel Hieber, Jonas B\"acker, Samantha Cramer, Christoph Wengenmayr, Bruno M\"arkl, Ralf Huss, Frank Kramer, I\~naki Soto-Rey, Johannes Raffler
https://arxiv.or…

Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue …

Structurally Flexible Neural Networks: Evolving the Building Blocks for General Agents
Joachim Winther Pedersen, Erwan Plantec, Eleni Nisioti, Milton Montero, Sebastian Risi
https://arxiv.org/abs/2404.15193

Structurally Flexible Neural Networks: Evolving the Building Blocks for General Agents
Artificial neural networks used for reinforcement learning are structurally rigid, meaning that each optimized parameter of the network is tied to its specific placement in the network structure. It also means that a network only works with pre-defined and fixed input- and output sizes. This is a consequence of having the number of optimized parameters being directly dependent on the structure of the network. Structural rigidity limits the ability to optimize parameters of policies across multi…

This https://arxiv.org/abs/2308.01562 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity
While a practical wireless network has many tiers where end users do not directly communicate with the central server, the users' devices have limited computation and battery powers, and the serving base station (BS) has a fixed bandwidth. Owing to these practical constraints and system models, this paper leverages model pruning and proposes a pruning-enabled hierarchical federated learning (PHFL) in heterogeneous networks (HetNets). We first derive an upper bound of the convergence rate that c…

Resource and Mobility Management in Hybrid LiFi and WiFi Networks: A User-Centric Learning Approach
Han Ji, Xiping Wu
https://arxiv.org/abs/2403.16823 http…

Resource and Mobility Management in Hybrid LiFi and WiFi Networks: A User-Centric Learning Approach
Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) are an emerging indoor wireless communication paradigm, which combines the advantages of the capacious optical spectra of LiFi and ubiquitous coverage of WiFi. Meanwhile, load balancing (LB) becomes a key challenge in resource management for such hybrid networks. The existing LB methods are mostly network-centric, relying on a central unit to make a solution for the users all at once. Consequently, the solution needs t…

Improved impedance inversion by deep learning and iterated graph Laplacian
Davide Bianchi, Florian Bossmann, Wenlong Wang, Mingming Liu
https://arxiv.org/abs/2404.16324

Improved impedance inversion by deep learning and iterated graph Laplacian
Deep learning techniques have shown significant potential in many applications through recent years. The achieved results often outperform traditional techniques. However, the quality of a neural network highly depends on the used training data. Noisy, insufficient, or biased training data leads to suboptimal results. We present a hybrid method that combines deep learning with iterated graph Laplacian and show its application in acoustic impedance inversion which is a routine procedure in sei…

Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning
Huilin Yin, Shengkai Su, Yinjia Lin, Pengju Zhen, Karin Festl, Daniel Watzenig
https://arxiv.org/abs/2404.12594

Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning
With the flourishing development of intelligent warehousing systems, the technology of Automated Guided Vehicle (AGV) has experienced rapid growth. Within intelligent warehousing environments, AGV is required to safely and rapidly plan an optimal path in complex and dynamic environments. Most research has studied deep reinforcement learning to address this challenge. However, in the environments with sparse extrinsic rewards, these algorithms often converge slowly, learn inefficiently or fail t…

This https://arxiv.org/abs/2404.13327 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques
The rapid advancement of machine learning techniques has led to their widespread application in various domains including water resources. However, snowmelt modeling remains an area that has not been extensively explored. In this study, we propose a state-of-the-art (SOTA) deep learning sequential model, leveraging the Temporal Convolutional Network (TCN), for snowmelt-driven discharge modeling in the Himalayan basin of the Hindu Kush Himalayan Region. To evaluate the performance of our propose…

Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning
Huilin Yin, Shengkai Su, Yinjia Lin, Pengju Zhen, Karin Festl, Daniel Watzenig
https://arxiv.org/abs/2404.12594

Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning
With the flourishing development of intelligent warehousing systems, the technology of Automated Guided Vehicle (AGV) has experienced rapid growth. Within intelligent warehousing environments, AGV is required to safely and rapidly plan an optimal path in complex and dynamic environments. Most research has studied deep reinforcement learning to address this challenge. However, in the environments with sparse extrinsic rewards, these algorithms often converge slowly, learn inefficiently or fail t…