February 2024 arXiv papers — page 38

Shanjian Tang, Jianjun Zhou

Optimal control and the associated second-order path-dependent Hamilton-Jacobi-Bellman (PHJB) equation are studied for unbounded functional stochastic evolution systems in Hilbert spaces. The notion of viscosity solution without B-continuity is introduced in the sense of Crandall and Lions, and is shown to coincide with the classical solutions and to satisfy

Matt-Heun Hong, Zachary N. Sunberg, Danielle Albers Szafir

Quality colormaps can help communicate important data patterns. However, finding an aesthetically pleasing colormap that looks "just right" for a given scenario requires significant design and technical expertise. We introduce Cieran, a tool that allows any data analyst to rapidly find quality colormaps while designing charts within Jupyter Notebooks. Our sy

Ruijing Wang, Desheng Li

This work aims to study the initial-boundary value problem of the reaction-diffusion equation $\pa_{t}u-\Delta u=f(u)+g(u(t-\tau(t,u_t)))+h(t,x)$ in a bounded domain with state-dependent delay and supercritical nonlinearities. We establish the global existence and discuss the regularity and dissipativity of the problem under weaker assumptions. In particular

Stefan Tiegel

We show strong (and surprisingly simple) lower bounds for weakly learning intersections of halfspaces in the improper setting. Strikingly little is known about this problem. For instance, it is not even known if there is a polynomial-time algorithm for learning the intersection of only two halfspaces. On the other hand, lower bounds based on well-established

Qishuo Cheng, Le Yang, Jiajian Zheng, Miao Tian

Portfolio management issues have been extensively studied in the field of artificial intelligence in recent years, but existing deep learning-based quantitative trading methods have some areas where they could be improved. First of all, the prediction mode of stocks is singular; often, only one trading expert is trained by a model, and the trading decision i

Haruka Ezoe, Kazuhiro Sato

To implement deep learning models on edge devices, model compression methods have been widely recognized as useful. However, it remains unclear which model compression methods are effective for Structured State Space Sequence (S4) models incorporating Diagonal State Space (DSS) layers, tailored for processing long-sequence data. In this paper, we propose to

Michael Milgram

Glasser's Master Theorem arXiv:1308.6361v2 is essentially a restatement of Cauchy's integral Theorem reduced to a specialized form. Here we extend that theorem by introducing two new parameters, but still retain a simple form. Because of wide interest in entities involving Riemann's zeta function, the focus is on the evaluation of improper integrals with alm

Md Mahmudul Hasan, Shaikh Anowarul Fattah

Since internet technologies have advanced, one of the primary factors in company development is customer happiness. Online platforms have become prominent places for sharing reviews. Twitter is one of these platforms where customers frequently post their thoughts. Reviews of flights on these platforms have become a concern for the airline business. A positiv

Taixi Lu, Haoyu Wang, Huajie Shao, Jing Gao

Cross-lingual natural language understanding (NLU) is a critical task in natural language processing (NLP). Recent advancements have seen multilingual pre-trained language models (mPLMs) significantly enhance the performance of these tasks. However, mPLMs necessitate substantial resources and incur high computational costs during inference, posing challenges

Zhimin Zhao, Yihao Chen, Abdul Ali Bangash, Bram Adams

In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a streamlined development lifecycle. This enables quicker iterations, adaptability, reduced development-to-deployment time, and reliable outputs. Despite existing research, a significant k

Tam Nguyen, César A. Uribe, Tan M. Nguyen, Richard G. Baraniuk

In this work, we address two main shortcomings of transformer architectures: input corruption and rank collapse in their output representation. We unveil self-attention as an autonomous state-space model that inherently promotes smoothness in its solutions, leading to lower-rank outputs and diminished representation capacity. Moreover, the steady-state solut

Neng Kai Nigel Neo, Yeon-Chang Lee, Yiqiao Jin, Sang-Wook Kim

The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive subgroups. However, the current literature does not comprehensively discuss this problem, nor does it provide realistic datasets that encompass actual graph structures, anomaly lab

Masanari Oi, Masahiro Kaneko, Ryuto Koike, Mengsay Loem

Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. However, the likelihood, a measure of LLM's plausibility for a sentence, can vary due to superficial differences in sentences, such as word order and sentence structure. It is therefore possible that there might be a likelihood bias if LLMs are us

Li Li, Yi-Jia Liu, Xiao-Long Zhou, Ze-Min Shen

The generation of cold molecules is a core topic in the field of cold atoms and molecules, which has advanced relevant research like ultracold chemistry, quantum computation, and quantum metrology. With high atomic phase space density, optical dipole trap has been widely performed to prepare and trap cold molecules, and can also be further developed for mult

Md. Motaleb Hossen Manik, Md. Ahsan Habib, Md. Zabirul Islam, Tanim Ahmed

The COVID-19 pandemic caused by the coronavirus had a significant effect on social, economic, and health systems globally. The virus emerged in Wuhan, China, and spread worldwide resulting in severe disease, death, and social interference. Countries implemented lockdowns in various regions to limit the spread of the virus. Some of them were successful and so

Xingyuan Li, Sinong Wang, Zeyu Xie, Mengyue Wu

This paper delves into the pioneering exploration of potential communication patterns within dog vocalizations and transcends traditional linguistic analysis barriers, which heavily relies on human priori knowledge on limited datasets to find sound units in dog vocalization. We present a self-supervised approach with HuBERT, enabling the accurate classificat

Sho Sonoda, Isao Ishikawa, Masahiro Ikeda

To investigate neural network parameters, it is easier to study the distribution of parameters than to study the parameters in each neuron. The ridgelet transform is a pseudo-inverse operator that maps a given function $f$ to the parameter distribution $\gamma$ so that a network $\mathtt{NN}[\gamma]$ reproduces $f$, i.e. $\mathtt{NN}[\gamma]=f$. For depth-2

Hongjie Wu, Linchao He, Mingqin Zhang, Dongdong Chen

Diffusion models have demonstrated remarkable efficacy in generating high-quality samples. Existing diffusion-based image restoration algorithms exploit pre-trained diffusion models to leverage data priors, yet they still preserve elements inherited from the unconditional generation paradigm. These strategies initiate the denoising process with pure white no

Zuxin Fu, Kuanrong Hao, Min Guo, Jingjing He

Two-dimensional (2D) multiferroic materials with coexisting ferroelectricity and ferromagnetism have garnered substantial attention for their intriguing physical properties and diverse promising applications in spintronics. For example, multiferroic materials with electronically controlled broken central symmetry provide a versatile platform for designing an

Xinhua Mao

Higher spatial resolution and larger imaging scene are always the goals pursued by advanced space-borne SAR system.High resolution and wide swath SAR imaging can provide more information about the illuminated scene of interest on one hand,but also come with some new challenges on the other hand.The induced new challenging problems include curved orbit,Earth

Francisco M. Gonzalez, Cary Rock, Leah J. Broussard, Lisa DeBeer-Schmitt

Conversions between neutrons $n$ and Dark Matter candidate sterile neutrons $n'$ have been proposed as a mechanism for Baryon Number $\mathcal{B}$ violation. In the case that there is a small mass difference $\Delta{m}$ between the $n$ and the $n'$ states, oscillations can be induced by compensating for $\Delta{m}$ with a magnetic field. A search for such ne

Zeyu Zhang, Peiyao Zhao, Xin Li, Jiamou Liu

With the prevalence of social media, the connectedness between people has been greatly enhanced. Real-world relations between users on social media are often not limited to expressing positive ties such as friendship, trust, and agreement, but they also reflect negative ties such as enmity, mistrust, and disagreement, which can be well modelled by signed gra

Angus Alexander

We realise the number of bound states of a Schr\"{o}dinger operator on $\mathbb{R}^n$ as an index pairing in all dimensions. Expanding on ideas of Guillop\'{e} and others, we use high-energy corrections to find representatives of the $K$-theory class of the scattering operator. These representatives allow us to compute the number of bound states using an int

Rayen Dhahri, Alexander Immer, Betrand Charpentier, Stephan Günnemann

Neural network sparsification is a promising avenue to save computational time and memory costs, especially in an age where many successful AI models are becoming too large to na\"ively deploy on consumer hardware. While much work has focused on different weight pruning criteria, the overall sparsifiability of the network, i.e., its capacity to be pruned wit

Shaojie Zhang, Yinghui Wang, Peixuan Liu, Yukai Wang

This paper presents, for the first time, an image enhancement methodology designed to enhance the clarity of small intestinal villi in Wireless Capsule Endoscopy (WCE) images. This method first separates the low-frequency and high-frequency components of small intestinal villi images using guided filtering. Subsequently, an adaptive light gain factor is gene

Suliang Si

In this paper, we show for the first time the increasing stability of the inverse source problem for the n-dimensional Helmholtz equation at multiple wave numbers, which is different from the two-or three-dimensional Helmholtz equation. In addition, we develop a new, unified approach to study increasing stability in any dimension. The method is based on the

Yu Zhang, Xiaoqing Chen, Mingwen Zhang, Xianghu Wu

We demonstrate an efficient nonlinear photodetector (NLPD) with quadratic response based on a few-layer InSe p-n homojunction, which is beneficial from the strong second harmonic generation (SHG) process in InSe and effective harvest of photocarriers actuated by the high-quality homojunction. The NLPD can sense light with photon energy smaller than InSe elec

Amir Reza Asadi, Reza Hemadi

This research presents a proof-of-concept prototype of an all-in-one mixed reality application platform, developed to investigate the needs and expectations of users from mixed reality systems. The study involved an extensive user study with 1,052 participants, including the collection of diaries from 6 users and conducting interviews with 15 participants to

Suliang Si

We are concerned with increasing stability in the inverse source problems for the time-dependent Maxwell equations in R^3 , where the source term is compactly supported in both time and spatial variables. By using the Fourier transform, sharp bounds of the analytic continuation and the Huygens principle, increasing stability estimates of the L^2 -norm of the

Ruijin Sun, Yao Wen, Nan Cheng, Wei Wan

Task offloading is a potential solution to satisfy the strict requirements of computation-intensive and latency-sensitive vehicular applications due to the limited onboard computing resources. However, the overwhelming upload traffic may lead to unacceptable uploading time. To tackle this issue, for tasks taking environmental data as input, the data perceive

Yuki Wakata, Xing Chao, Chao Sun, Christian Diddens

Evaporation of multicomponent droplets is important in a wide range of applications, albeit complex, and requires a careful investigation. We experimentally and numerically investigate the evaporation characteristics of spherical, ethanol-water droplets with different initial concentration ratios in the acoustic levitation field. Imaging techniques and infra

Hongjie Fan, Kai Wang, Yanling Zhu

In this paper, a stochastic SEQIR epidemic model with Markovian regime-switching is proposed and investigated. The governmental policy and implement efficiency are concerned by a generalized incidence function of the susceptible class. We have the existence and uniqueness of the globally positive solution to the stochastic model by using the Lyapunov method.

Yujia Yin, Xinyi Chen, Chenxiang Ma, Jibin Wu

The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signals. Recently, a novel multi-compartment spiking neuron model, namely the Two-Compartment LIF (TC-LIF) model, has been proposed and exhibited a remarkable capacity for sequential mod

Abhishek Singh, Gauri Gupta, Ritvik Kapila, Yichuan Shi

Federated Learning (FL) enables collaborative optimization of machine learning models across decentralized data by aggregating model parameters. Our approach extends this concept by aggregating "knowledge" derived from models, instead of model parameters. We present a novel framework called CoDream, where clients collaboratively optimize randomly initialized

Prabhjot Kaur, L. Andrew M. Bush, Weisong Shi

Speech-to-speech translation is yet to reach the same level of coverage as text-to-text translation systems. The current speech technology is highly limited in its coverage of over 7000 languages spoken worldwide, leaving more than half of the population deprived of such technology and shared experiences. With voice-assisted technology (such as social robots

Jacinto Ulloa, Laurent Stainier, Michael Ortiz, José E. Andrade

This paper explores the role of generalized continuum mechanics, and the feasibility of model-free data-driven computing approaches thereof, in solids undergoing failure by strain localization. Specifically, we set forth a methodology for capturing material instabilities using data-driven mechanics without prior information regarding the failure mode. We sho

Lynn Huang

Amidst the era of networking, the e-commerce sector has undergone notable expansion, notably with the advent of Cross-border E-commerce (CBEC) in recent times. This growth trend persists, necessitating robust logistical frameworks to sustainably support operations. However, the current e-commerce logistics paradigm faces challenges in meeting evolving user d

Liang Mao, Hui Zhai, Fan Yang

Topological band theory has been studied for free fermions for decades, and one of the most profound physical results is the bulk-boundary correspondence. Recently a focus in topological physics is extending topological classification to mixed states. Here, we focus on Gaussian mixed states where the modular Hamiltonians of the density matrix are quadratic f

Lei-Ming Hua, Jun Xu

We have studied the shear and bulk viscosities of asymmetric nuclear matter in the mechanical and chemical instability region based on IBUU transport simulations in a box system. The Green-Kubo method is used to calculate these viscosities with a prepared dynamically equilibrated nuclear system with hot clusters. While the behavior of the shear viscosity is

Ankur Verma, Seog-Chan Oh, Jorge Arinez, Soundar Kumara

Manufacturing energy consumption data contains important process signatures required for operational visibility and diagnostics. These signatures may be of different temporal scales, ranging from monthly to sub-second resolutions. We introduce a hierarchical machine learning approach to identify automotive process signatures from paint shop electricity consu

Xudong Cai, Yongcai Wang, Zhe Huang, Yu Shao

The availability of city-scale Lidar maps enables the potential of city-scale place recognition using mobile cameras. However, the city-scale Lidar maps generally need to be compressed for storage efficiency, which increases the difficulty of direct visual place recognition in compressed Lidar maps. This paper proposes VOLoc, an accurate and efficient visual

Yuanhang Zheng, Peng Li, Ming Yan, Ji Zhang

Despite intensive efforts devoted to tool learning, the problem of budget-constrained tool learning, which focuses on resolving user queries within a specific budget constraint, has been widely overlooked. This paper proposes a novel method for budget-constrained tool learning. Our approach involves creating a preferable plan under the budget constraint befo

Zhiying Jiang, Xingyuan Li, Jinyuan Liu, Xin Fan

Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Such integration not only broadens the captured scene but also augments holistic perception in computer vision applications. Given a pair of captured images, subtle perturbations and distortions which go unnoticed by the human visual system

Zheng-An Chen, Tao Luo

Empirical and theoretical works show that the input weights of two-layer neural networks, when initialized with small values, converge towards isolated orientations. This phenomenon, referred to as condensation, indicates that the gradient descent methods tend to spontaneously reduce the complexity of neural networks during the training process. In this work

Anthony Liang, Guy Tennenholtz, Chih-wei Hsu, Yinlam Chow

We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates. We model episode sessions - parts of the episode where the latent state is fixed - and propose three key modifications to existing meta-RL methods: consistency of latent information within sessio

Jessica Echterhoff, Yao Liu, Abeer Alessa, Julian McAuley

Large language models (LLMs) offer significant potential as tools to support an expanding range of decision-making tasks. Given their training on human (created) data, LLMs have been shown to inherit societal biases against protected groups, as well as be subject to bias functionally resembling cognitive bias. Human-like bias can impede fair and explainable

Jisuke Kubo, Taichiro Kugo

We argue that Lee-Wick's complex ghost appearing in any higher derivative theory is stable and its asymptotic field exists. It may be more appropriate to call it ``anti-unstable" in the sense that, the more the ghost `decays' into lighter ordinary particles, the larger the probability the ghost remains as itself becomes. This is explicitly shown by analyzing

Yan Chang, Yukun Guo, Yue Zhao

In this paper, we study the direct and inverse scattering of the Schr\"odinger equation in a three-dimensional planar waveguide. For the direct problem, we derive a resonance-free region and resolvent estimates for the resolvent of the Schr\"odinger operator in such a geometry. Based on the analysis of the resolvent, several inverse problems are investigated

Dan Zhao, Siddharth Samsi, Joseph McDonald, Baolin Li

As research and deployment of AI grows, the computational burden to support and sustain its progress inevitably does too. To train or fine-tune state-of-the-art models in NLP, computer vision, etc., some form of AI hardware acceleration is virtually a requirement. Recent large language models require considerable resources to train and deploy, resulting in s

Joseph P. Molnar, Elijah J. LaLonde, Christopher S. Combs, Olivier Léon

We report a novel "cone-ray" model of background-oriented schlieren (BOS) imaging that accounts for depth-of-field effects. Reconstructions of the density field performed with this model are far more robust to the blur associated with a finite aperture than conventional reconstructions, which presume a "thin-ray" pinhole camera. Our model is characterized an

Mike Heddes, Igor Nunes, Tony Givargis, Alex Nicolau

With the increasing rate of data generated by critical systems, estimating functions on streaming data has become essential. This demand has driven numerous advancements in algorithms designed to efficiently query and analyze one or more data streams while operating under memory constraints. The primary challenge arises from the rapid influx of new items, re

Yuchen He, Zeqing Yuan, Yihong Wu, Liqi Cheng

The immense popularity of racket sports has fueled substantial demand in tactical analysis with broadcast videos. However, existing manual methods require laborious annotation, and recent attempts leveraging video perception models are limited to low-level annotations like ball trajectories, overlooking tactics that necessitate an understanding of stroke tec

Md Tawkat Islam Khondaker, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan

Prior works on detoxification are scattered in the sense that they do not cover all aspects of detoxification needed in a real-world scenario. Notably, prior works restrict the task of developing detoxification models to only a seen subset of platforms, leaving the question of how the models would perform on unseen platforms unexplored. Additionally, these w

Chad Berner, John E. Herr, Palle E. T. Jorgensen, Eric S. Weber

For multi-variable finite measure spaces, we present in this paper a new framework for non-orthogonal $L^2$ Fourier expansions. Our results hold for probability measures $\mu$ with finite support in $\mathbb{R}^d$ that satisfy a certain disintegration condition that we refer to as ``slice-singular''. In this general framework, we present explicit $L^{2}(\mu)

Zhaojun Li, Lorenzo Varrassi, Yali Yang, Cesare Franchini

Tuning the properties of materials using external stimuli is crucial for developing versatile smart materials. A strong coupling among order parameters within a single-phase material constitutes a potent foundation for achieving precise property control. However, cross-coupling is pretty weak in most single materials. Leveraging first principles calculations

Feiyu Zhu, Reid Simmons

Large language models contain noisy general knowledge of the world, yet are hard to train or fine-tune. On the other hand cognitive architectures have excellent interpretability and are flexible to update but require a lot of manual work to instantiate. In this work, we combine the best of both worlds: bootstrapping a cognitive-based model with the noisy kno

Danlin Li, Johannes Milz

Motivated by optimization with differential equations, we consider optimization problems with Hilbert spaces as decision spaces. As a consequence of their infinite dimensionality, the numerical solution necessitates finite dimensional approximations and discretizations. We develop an approximation framework and demonstrate criticality measure-based error est

Hugo Aimar, Carlos Exequiel Arias, Ivana Gómez

In this paper we use the neighborhood topology generated by affinities between pairs of points in a set, in orden to explore the underlying dynamics of connectivity by thresholding of the affinity. We apply the method to the connectivity provided by the public transport system in Buenos Aires.

Mohammed Abo Sen

This paper proposes an innovative Attention-GAN framework for enhancing cybersecurity, focusing on anomaly detection. In response to the challenges posed by the constantly evolving nature of cyber threats, the proposed approach aims to generate diverse and realistic synthetic attack scenarios, thereby enriching the dataset and improving threat identification

Gang Li, Qiuwei Li, Shuang Li, Wu Angela Li

Sparse signal recovery deals with finding the sparsest solution of an under-determined linear system $\vx = \mQ\vs$. In this paper, we propose a novel greedy approach to addressing the challenges from such a problem. Such an approach is based on a characterization of solutions to the system, which allows us to work on the sparse recovery in the $\vs$-space d

Li Zhong, Zilong Wang, Jingbo Shang

Large language models (LLMs) are leading significant progress in code generation. Beyond one-pass code generation, recent works further integrate unit tests and program verifiers into LLMs to iteratively refine the generated programs. However, these works consider the generated programs as an indivisible entity, which falls short for LLMs in debugging the pr

Ahmed E. Hassan, Dayi Lin, Gopi Krishnan Rajbahadur, Keheliya Gallaba

Foundation models (FMs), such as Large Language Models (LLMs), have revolutionized software development by enabling new use cases and business models. We refer to software built using FMs as FMware. The unique properties of FMware (e.g., prompts, agents, and the need for orchestration), coupled with the intrinsic limitations of FMs (e.g., hallucination) lead

Kohei Morimoto, Kenji Kashima

In this paper, we newly formulate and solve the optimal density control problem with Gromov-Wasserstein (GW) terminal cost in discrete-time linear Gaussian systems. Differently from the Wasserstein or Kullback-Leibler distances employed in the existing works, the GW distance quantifies the difference in shapes of the distribution, which is invariant under tr

Ainara Garcia, Sihong Xie, Arielle Carr

Sequences of linear systems arise in the predictor-corrector method when computing the Pareto front for multi-objective optimization. Rather than discarding information generated when solving one system, it may be advantageous to recycle information for subsequent systems. To accomplish this, we seek to reduce the overall cost of computation when solving lin

Julian Andrej, Nabil Atallah, Jan-Phillip Bäcker, John Camier

The MFEM (Modular Finite Element Methods) library is a high-performance C++ library for finite element discretizations. MFEM supports numerous types of finite element methods and is the discretization engine powering many computational physics and engineering applications across a number of domains. This paper describes some of the recent research and develo

Zi Wang, Min Xiao, Yirong Zhou, Chengyan Wang

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a nov

Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin

Recent statements about the impressive capabilities of large language models (LLMs) are usually supported by evaluating on open-access benchmarks. Considering the vast size and wide-ranging sources of LLMs' training data, it could explicitly or implicitly include test data, leading to LLMs being more susceptible to data contamination. However, due to the opa

Jennifer E. Fromm, Nils Wunsch, Kurt Maute, John A. Evans

Immersed boundary methods are high-order accurate computational tools used to model geometrically complex problems in computational mechanics. While traditional finite element methods require the construction of high-quality boundary-fitted meshes, immersed boundary methods instead embed the computational domain in a background grid. Interpolation-based imme

Vikranth Lokeshwar Dhandapani, Shashi Jain

This paper presents a Monte-Carlo-based artificial neural network framework for pricing Bermudan options, offering several notable advantages. These advantages encompass the efficient static hedging of the target Bermudan option and the effective generation of exposure profiles for risk management. We also introduce a novel optimisation algorithm designed to

R. L. Gomes, B. L. Canto Martins, D. O. Fontinele, L. A. Almeida

We report the discovery of three Transiting Exoplanet Survey Satellite Objects of Interest (TOI) with signatures of pulsation, observed in more than one sector. Our main goal is to explore how large is the variety of classical pulsators such as $\delta$ Sct, $\gamma$ Dor, RR Lyrae and Cepheid among TOI pulsators. The analysis reveals two stars with signature

Alexander Cerjan, Vasile Lauric, Terry A. Loring

We look at various forms of spectrum and associated pseudospectrum that can be defined for noncommuting $d$-tuples of Hermitian elements of a $C^*$-algebra. The emphasis is on theoretical calculations of examples, in particular for noncommuting pairs and triple of operators on infinite dimensional Hilbert space. In particular, we look at the universal pair o

Wentao Mo, Yang Liu

In 3D Visual Question Answering (3D VQA), the scarcity of fully annotated data and limited visual content diversity hampers the generalization to novel scenes and 3D concepts (e.g., only around 800 scenes are utilized in ScanQA and SQA dataset). Current approaches resort supplement 3D reasoning with 2D information. However, these methods face challenges: eit

Alaa Selim, Yanzhu Ye, Junbo Zhao, Bo Yang

In the rapidly evolving domain of electrical power systems, the Volt-VAR optimization (VVO) is increasingly critical, especially with the burgeoning integration of renewable energy sources. Traditional approaches to learning-based VVO in expansive and dynamically changing power systems are often hindered by computational complexities. To address this challen

Jungyeul Park, Mengyang Qiu

This paper introduces a novel perspective on the automated essay scoring (AES) task, challenging the conventional view of the ASAP dataset as a static entity. Employing simple text denoising techniques using prompting, we explore the dynamic potential within the dataset. While acknowledging the previous emphasis on building regression systems, our paper unde

Min Zeng, Jiexin Kuang, Mengyang Qiu, Jayoung Song

The writing examples of English language learners may be different from those of native speakers. Given that there is a significant differences in second language (L2) learners' error types by their proficiency levels, this paper attempts to reduce overcorrection by examining the interaction between LLM's performance and L2 language proficiency. Our method f

Isha Chaudhary, Vedaant V. Jain, Gagandeep Singh

Large Language Models (LLMs) are increasingly deployed in safety-critical systems where they provide answers based on in-context information derived from knowledge bases. As LLMs are increasingly envisioned as superhuman agents, their proficiency in knowledge comprehension-extracting relevant information and reasoning over it to answer questions, a key facet

A. H. Mughal

This paper presents and implements the re-usability of scenarios within scenarios for behavior-driven development (BDD) Gherkin test scripts in the Cucumber Java framework. Though the focus of the presented work is on scenario re-usability through an implementation within the Cucumber BDD Java framework, the paper also dives a little into the limitations of

Luhang Yang, Adrian E. Feiguin

We present a density matrix renormalization group (DMRG) study of an extended $t-J$ model with hopping to the first and second neighbors -- the one dimensional $t_1-t_2-J$ model. The full phase diagram as a function of the density $n$ and exchange strength $J$, for both positive and negative values of $t_2$, is obtained. For $t_2=-0.5$ we observe that, in th

Jingfeng Wu, Peter L. Bartlett, Matus Telgarsky, Bin Yu

We consider gradient descent (GD) with a constant stepsize applied to logistic regression with linearly separable data, where the constant stepsize $\eta$ is so large that the loss initially oscillates. We show that GD exits this initial oscillatory phase rapidly -- in $\mathcal{O}(\eta)$ steps -- and subsequently achieves an $\tilde{\mathcal{O}}(1 / (\eta t

Zhe Wang

Bluetooth Low Energy (BLE) technology, operating within the widely used 2.4 GHz ISM band, stands as a cornerstone in modern wireless communication frameworks alongside its classic Bluetooth counterpart. This paper delves into the foundational aspects of BLE, excluding niche components, to explore its core functionalities and pivotal role in diverse connectiv

Seraphina Goldfarb-Tarrant, Pedro Rodriguez, Jane Dwivedi-Yu, Patrick Lewis

Dense retrievers compress source documents into (possibly lossy) vector representations, yet there is little analysis of what information is lost versus preserved, and how it affects downstream tasks. We conduct the first analysis of the information captured by dense retrievers compared to the language models they are based on (e.g., BERT versus Contriever).

Michele Pacenti, Mark F. Flanagan, Dimitris Chytas, Bane Vasic

Topological quantum codes, such as toric and surface codes, are excellent candidates for hardware implementation due to their robustness against errors and their local interactions between qubits. However, decoding these codes efficiently remains a challenge: existing decoders often fall short of meeting requirements such as having low computational complexi

Kittimate Chulajata, Sean Wu, Fabien Scalzo, Eun Sang Cha

Forecasting winners in E-sports with real-time analytics has the potential to further engage audiences watching major tournament events. However, making such real-time predictions is challenging due to unpredictable variables within the game involving diverse player strategies and decision-making. Our work attempts to enhance audience engagement within video

Everton M. C. Abreu

One of the greatest challenges of theoretical physics today is to unveil the quantum information theory concerning what happens when one bit of information enters the black hole (BH) horizon. The Landauer principle showed that a certain amount of energy is generated when one-bit of information is erased as it enters the event horizon system. In this paper we

Zehua Zhang, Zijie Li, Amir Barati Farimani

We propose a mask pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems. GNNs are pretrained by recovering spatial information related to masked-out atoms from molecules, then transferred and finetuned on atomic forcefields. Through such pretraining, GNNs learn mea

Eric A. Carlen, Michael P. Loss

We give an elementary proof of an inequality of Lin, Kim and Hsieh that implies strong subadditivity of the non Neumann entropy.

Xiaopeng Peng, Erin F. Fleet, Abbie T. Watnik, Grover A. Swartzlander

Machine vision is susceptible to laser dazzle, where intense laser light can blind and distort its perception of the environment through oversaturation or permanent damage to sensor pixels. Here we employ a wavefront-coded phase mask to diffuse the energy of laser light and introduce a sandwich generative adversarial network (SGAN) to restore images from com

N. Ahmadkhah, M. Zarrin

For any group G, let $cent(G)$ denote the set of all centralizers of $G$. The authors in \cite{KZ}, Groups with the same number of centralizers, J. Algebra Appl. (2021) 2150012 (6 pages), posed the following conjecture: Let $G$ and $S$ be finite groups. Is it true that if $|Cent(G)|=|Cent(S)|$ and $|G'|=|S'|$, then $G$ is isoclonic to $S$? In this paper, amo

S. M. Mallikarjunaiah, Dambaru Bhatta

In this article, a finite element model is implemented to analyze hydro-thermal convective flow in a porous medium. The mathematical model encompasses Darcy's law for incompressible fluid behavior, which is coupled with a convection-diffusion-type energy equation to characterize the temperature in the porous medium. The current investigation presents an effi

N. Ahmadkhah, M. Zarrin

For a finite group $G$ and an element $x\in G$, the subset $$ nil_G(x)=\{y\in G \mid <x,y>~~ is ~~ nilpotent\}$$ is called nilpotentizer of $x$ in $G$. In this paper, we give two solvabilty criteria for a finite group by the structure and the size of nilpotentizer of an element on finite group. In fact, we show that if there exists an element $x$ of $G$ such

Francis Bernardeau, Christophe Pichon

Rayleigh-Levy flights have played a significant role in cosmology as simplified models for understanding how matter distributes itself under gravitational influence. These models also exhibit numerous remarkable properties that enable the prediction of a wide range of characteristics. Here, we derive the one and two point statistics of extreme points within

Steven Boyer, Cameron McA. Gordon, Ying Hu

In this article we show that all cyclic branched covers of a Seifert link have left-orderable fundamental groups, and therefore admit co-oriented taut foliations and are not $L$-spaces, if and only if it is not an $ADE$ link up to orientation. This leads to a proof of the $ADE$ link conjecture for Seifert links. When $L$ is an $ADE$ link up to orientation, w

Yahya Ladghami, Taoufik Ouali

In this paper, we study the thermodynamic behavior of charged AdS black holes in a conformal holographic extended thermodynamic. Our setup is constructed using a new dictionary that relates AdS black hole quantities to the corresponding dual conformal field theory (CFT) one, with the conformal factor being treated as a variable thermodynamic. In this thermod

Gianluca Francica, Luca Dell'Anna

Fluctuations can challenge the possibility of improving work extraction from quantum correlations. This uncertainty in the work extraction process can be addressed resorting to the expected utility hypothesis which can provide an optimal method for work extraction. We study a bipartite quantum system and examine the role of quantum correlations in a daemonic

Raven Rothkopf, Hannah Tongxin Zeng, Mark Santolucito

The surge in popularity of large language models (LLMs) has opened doors for new approaches to the creation of interactive agents. However, managing and interpreting the temporal behavior of such agents over the course of a potentially infinite interaction remain challenging. The stateful, long-term horizon reasoning required for coherent agent behavior does

Neal Mangaokar, Ashish Hooda, Jihye Choi, Shreyas Chandrashekaran

Large language models (LLMs) are typically aligned to be harmless to humans. Unfortunately, recent work has shown that such models are susceptible to automated jailbreak attacks that induce them to generate harmful content. More recent LLMs often incorporate an additional layer of defense, a Guard Model, which is a second LLM that is designed to check and mo

George Lertzman-Lepofsky, Afan Terko, Sabri Koraltan, Dieter Suess

We conduct an exploration of the energy landscape of two coupled ferromagnetic layers with perpendicular-to-plane uniaxial anisotropy using finite-element micromagnetic simulations. These multilayers can be used to produce noncollinearity in spin-transfer torque magnetic random-access memory cells, which has been shown to increase the performance of this cla

Tan-Hanh Pham, Kim-Doang Nguyen

Precision devices play an important role in enhancing production quality and productivity in agricultural systems. Therefore, the optimization of these devices is essential in precision agriculture. Recently, with the advancements of deep learning, there have been several studies aiming to harness its capabilities for improving spray system performance. Howe

Melvin Adekanye, Christopher Bury, Ben Cameron, Thaler Knodel

A graph is $k$-vertex-critical if $\chi(G)=k$ but $\chi(G-v)<k$ for all $v\in V(G)$ and $(G,H)$-free if it contains no induced subgraph isomorphic to $G$ or $H$. We show that there are only finitely many $k$-vertex-critical $(2P_2,H)$-free graphs for all $k$ when $H$ is isomorphic to any of the following graphs of order $5$: $bull$, $chair$, $claw+P_1$, or $

Nikolaos Masios, Felix Hummel, Andreas Grüneis, Andreas Irmler

We investigate the convergence of coupled-cluster correlation energies and related quantities with respect to the employed basis set size for the uniform electron gas to gain a better understanding of the basis set incompleteness error. To this end, coupled-cluster doubles (CCD) theory is applied to the three dimensional uniform electron gas for a range of d