December 2023 arXiv papers — page 106

Haowen Wang

Existing work has revealed that large-scale offline evaluation of recommender systems for user-item interactions is prone to bias caused by the deployed system itself, as a form of closed loop feedback. Many adopt the \textit{propensity} concept to analyze or mitigate this empirical issue. In this work, we extend the analysis to session-based setup and adapt

Tianxun Zhou, Muhammad Nur Shahril Iskandar, Keng-Hwee Chiam

The application of 2D markerless gait analysis has garnered increasing interest and application within clinical settings. However, its effectiveness in the realm of lower-limb amputees has remained less than optimal. In response, this study introduces an innovative zero-shot method employing image generation diffusion models to achieve markerless pose estima

Liuxiang Qiu, Si Chen, Yan Yan, Jing-Hao Xue

Visible-infrared person re-identification (VI-ReID) aims to retrieve images of the same persons captured by visible (VIS) and infrared (IR) cameras. Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space due to the large modality discrepancy between VIS and IR i

Simone Leo, Michael R. Crusoe, Laura Rodríguez-Navas, Raül Sirvent

Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and shari

Karthik Elamvazhuthi, Samet Oymak, Fabio Pasqualetti

In Score based Generative Modeling (SGMs), the state-of-the-art in generative modeling, stochastic reverse processes are known to perform better than their deterministic counterparts. This paper delves into the heart of this phenomenon, comparing neural ordinary differential equations (ODEs) and neural stochastic differential equations (SDEs) as reverse proc

Feibo Jiang, Li Dong, Yubo Peng, Kezhi Wang

The rapid development of the Large Language Model (LLM) presents huge opportunities for 6G communications, e.g., network optimization and management by allowing users to input task requirements to LLMs by nature language. However, directly applying native LLMs in 6G encounters various challenges, such as a lack of private communication data and knowledge, li

Yuanbo Wen, Tao Gao, Ziqi Li, Jing Zhang

Haze obscures remote sensing images, hindering valuable information extraction. To this end, we propose RSHazeNet, an encoder-minimal and decoder-minimal framework for efficient remote sensing image dehazing. Specifically, regarding the process of merging features within the same level, we develop an innovative module called intra-level transposed fusion mod

Shane Storm Strachan

The open-source publishing of large language models (LLMs) has created many possibilities for how anyone who understands language and has access to a computer can interact with significant tools of artificial intelligence, particularly in the context of learning and knowledge dissemination. However, the utility of these models in specialized fields like Clas

Kanta Koeda, Ryuma Orita, Kanon Yashiro

We study the bipersistence modules obtained from the action-window homology of Floer-type complexes over $Λ^{\mathbb{F},\{0\}}=\mathbb{F}$. We prove that these modules are rectangle-decomposable and establish an explicit dictionary between their graded rectangle barcodes and the classical one-parameter barcodes. This dictionary is an isometry for the bottlen

Chenglong Ma, Zilong Li, Junjun He, Junping Zhang

Incomplete-view computed tomography (CT) can shorten the data acquisition time and allow scanning of large objects, including sparse-view and limited-angle scenarios, each with various settings, such as different view numbers or angular ranges. However, the reconstructed images present severe, varying artifacts due to different missing projection data patter

Pak-Yeung Chan, Man-Chun Lee

In this work, we show that complete non-compact manifolds with non-negative Ricci curvature, Euclidean volume growth and sufficiently small curvature concentration are necessarily flat Euclidean space.

Taekho You, Jinseo Park, June Young Lee, Jinhyuk Yun

Countries and authors in the academic periphery occasionally have been criticized for contributing to the expansion of questionable publishing because they share a major fraction of papers in questionable journals. On the other side, topics preferred by mainstream journals sometimes necessitate large-scale investigation, which is impossible for developing co

Roya Firoozi, Johnathan Tucker, Stephen Tian, Anirudha Majumdar

We survey applications of pretrained foundation models in robotics. Traditional deep learning models in robotics are trained on small datasets tailored for specific tasks, which limits their adaptability across diverse applications. In contrast, foundation models pretrained on internet-scale data appear to have superior generalization capabilities, and in so

Jane Shaw MacDonald, Yves Bourgault, Frithjof Lutscher

Moving-habitat models track the density of a population whose suitable habitat shifts as a consequence of climate change. Whereas most previous studies in this area consider 1-dimensional space, we derive and study a spatially 2-dimensional moving-habitat model via reaction-diffusion equations. The population inhabits the whole space. The suitable habitat is

Xiong Zhou, Xianming Liu, Hanzhang Wang, Deming Zhai

Recent works have studied implicit biases in deep learning, especially the behavior of last-layer features and classifier weights. However, they usually need to simplify the intermediate dynamics under gradient flow or gradient descent due to the intractability of loss functions and model architectures. In this paper, we introduce the unhinged loss, a concis

Maria Violaris

Thought experiments are where logical reasoning meets storytelling, catalysing progress in quantum science and technology. Schr\"odinger's famous cat brought quantum science to the public consciousness, while Deutsch's thought experiment to test the many-worlds and Copenhagen interpretations involved the first conception of a quantum computer. I will show ho

Subhro Ghosh, Soumendu Sundar Mukherjee, Jing Bin Pan

The Multi-Reference Alignment (MRA) problem aims at the recovery of an unknown signal from repeated observations under the latent action of a group of cyclic isometries, in the presence of additive noise of high intensity $\sigma$. It is a more tractable version of the celebrated cryo EM model. In the crucial high noise regime, it is known that its sample co

Berkay H. Tosunlu, Joseph H. A. Guillaume, Alexis Tsoukiàs

Conflict transformation and management are complex decision processes with extremely high stakes at hand and could greatly benefit from formal approaches to decision support. For this purpose we develop a general framework about how to use problem structuring methods for such purposes. More precisely we show how to transform cognitive maps to value trees in

Alexander Decruyenaere, Heidelinde Dehaene, Paloma Rabaey, Christiaan Polet

Recent advances in generative models facilitate the creation of synthetic data to be made available for research in privacy-sensitive contexts. However, the analysis of synthetic data raises a unique set of methodological challenges. In this work, we highlight the importance of inferential utility and provide empirical evidence against naive inference from s

Richard C. Brower, Christopher Culver, Kimmy K. Cushman, George T. Fleming

We present non-perturbative lattice calculations of the low-lying meson and baryon spectrum of the SU(4) gauge theory with fundamental fermion constituents. This theory is one instance of stealth dark matter, a class of strongly coupled theories, where the lowest mass stable baryon is the dark matter candidate. This work constitutes the first milestone in th

Gaurav Shrivastava, Ser-Nam Lim, Abhinav Shrivastava

In this paper, we present a novel robust framework for low-level vision tasks, including denoising, object removal, frame interpolation, and super-resolution, that does not require any external training data corpus. Our proposed approach directly learns the weights of neural modules by optimizing over the corrupted test sequence, leveraging the spatio-tempor

Mengfei Sun, Jin Li

With the anticipated launch of space-based gravitational wave detectors, including LISA, TaiJi, TianQin, and DECIGO, expected around 2030, the detection of gravitational waves generated by intermediate-mass black hole binaries (IMBBHs) becomes a tangible prospect. However, due to the detector's reception of a substantial amount of non-Gaussian, non-stationar

C Kupferschmidt, A. D. Binns, K. L. Kupferschmidt, G. W Taylor

Text-to-image (TTI) generative models can be used to generate photorealistic images from a given text-string input. These models offer great potential to mitigate challenges to the uptake of machine learning in the earth sciences. However, the rapid increase in their use has raised questions about fairness and biases, with most research to-date focusing on s

Kang-Hyun Lee, Gun Jin Yun

Integrated computational materials engineering (ICME) has significantly enhanced the systemic analysis of the relationship between microstructure and material properties, paving the way for the development of high-performance materials. However, analyzing microstructure-sensitive material behavior remains challenging due to the scarcity of three-dimensional

Nhu-Thanh Nguyen, Khoa Thi-Kim Phan, Duc-Vu Nguyen, Ngan Luu-Thuy Nguyen

Abuse in its various forms, including physical, psychological, verbal, sexual, financial, and cultural, has a negative impact on mental health. However, there are limited studies on applying natural language processing (NLP) in this field in Vietnam. Therefore, we aim to contribute by building a human-annotated Vietnamese dataset for detecting abusive conten

Bowen Zhao, Changkai Ji, Yuejie Zhang, Wen He

With the Generative Pre-trained Transformer 3.5 (GPT-3.5) exhibiting remarkable reasoning and comprehension abilities in Natural Language Processing (NLP), most Question Answering (QA) research has primarily centered around general QA tasks based on GPT, neglecting the specific challenges posed by Complex Table QA. In this paper, we propose to incorporate GP

Jiawei Fan, Zhiqiang Liu, Dong Yang, Jiazhou Wang

In this work, we propose a new computing process, named DeepBEVdose, which is essentially distinct to the previous deep learning-based dose calculation methods.We present a novel image-domain dose calculation algorithm to automatically compute dose distributions from the computer tomography images and radiation field fluence maps.

Wenze Tang, Donna Spiegelman, Yujie Wu, Molin Wang

In this paper, we identify the criteria for the selection of the minimal and most efficient covariate adjustment sets for the regression calibration method developed by Carroll, Rupert and Stefanski (CRS, 1992), used to correct bias due to continuous exposure measurement error. We utilize directed acyclic graphs to illustrate how subject matter knowledge can

Pedram Rabiee, Amirsaeid Safari

This paper introduces the reinforcement learning backup shield (RLBUS), an algorithm that guarantees safe exploration in reinforcement learning (RL) by incorporating backup control barrier functions (BCBFs). RLBUS constructs an implicit control forward invariant subset of the safe set using multiple backup policies, ensuring safety in the presence of input c

Richard Li, Kent Quanrud

We give a fully dynamic algorithm maintaining a $(1-\varepsilon)$-approximate directed densest subgraph in $\tilde{O}(\log^3(n)/\varepsilon^6)$ amortized time or $\tilde{O}(\log^4(n)/\varepsilon^7)$ worst-case time per edge update (where $\tilde{O}$ hides $\log\log$ factors), based on earlier work by Chekuri and Quanrud [arXiv:2210.02611, arXiv:2310.18146].

Sungjin Lim, Bilal Sadiq, Yongsik Jin, Sangho Lee

Active collision avoidance system plays a crucial role in ensuring the lateral safety of autonomous vehicles, and it is primarily related to path planning and tracking control algorithms. In particular, the direct yaw-moment control (DYC) system can significantly improve the lateral stability of a vehicle in environments with sudden changes in road condition

Dashuang Ye, Cuihua Du, Jianrong Shi, Jun Ma

Based on 4,\,098 very metal-poor (VMP) stars with 6D phase-space and chemical information from \textit{Gaia} DR3 and LAMOST DR9 as tracers, we apply an unsupervised machine learning algorithm, Shared Nearest Neighbor (SNN), to identify stellar groups in the action-energy (\textbf{\textit{J}}-$E$) space. We detect seven previously known mergers in local sampl

Minh Duong, Long Nguyen, Yen Vuong, Trong Le

This paper presents a deep learning-based system for efficient automatic case summarization. Leveraging state-of-the-art natural language processing techniques, the system offers both supervised and unsupervised methods to generate concise and relevant summaries of lengthy legal case documents. The user-friendly interface allows users to browse the system's

Qi Tang, Yao Zhao, Meiqin Liu, Jian Jin

As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to this issue, we introduce a novel paradigm for VSR named Semantic Lens, predicated on semantic priors drawn from

Srishti Gautam, Ahcene Boubekki, Marina M. C. Höhne, Michael C. Kampffmeyer

Explainable AI (XAI) has unfolded in two distinct research directions with, on the one hand, post-hoc methods that explain the predictions of a pre-trained black-box model and, on the other hand, self-explainable models (SEMs) which are trained directly to provide explanations alongside their predictions. While the latter is preferred in safety-critical scen

Yanqiu Wu, Eromanga Adermann, Chandra Thapa, Seyit Camtepe

Radio signal classification plays a pivotal role in identifying the modulation scheme used in received radio signals, which is essential for demodulation and proper interpretation of the transmitted information. Researchers have underscored the high susceptibility of ML algorithms for radio signal classification to adversarial attacks. Such vulnerability cou

Walker Melton, Atul Sharma, Andrew Strominger

Celestial amplitudes may be decomposed as weighted integrals of AdS$_3$-Witten diagrams associated to each leaf of a hyperbolic foliation of spacetime. We show, for the Kleinian three-point MHV amplitude, that each leaf subamplitude is smooth except for the expected light-cone singularities. Moreover, we find that the full translationally-invariant celestial

E. En-naoui

The Additive Transform of an arithmetic function represents a novel approach to examining the interplay between multiplicative arithmetic function and additive functions. This transform concept introduces a method to systematically generate new arithmetic functions by combining the values of an existing function under an additive operation. The resulting fra

Wei Zhang, Alexandre Salle

We present the first experiments on Native Language Identification (NLI) using LLMs such as GPT-4. NLI is the task of predicting a writer's first language by analyzing their writings in a second language, and is used in second language acquisition and forensic linguistics. Our results show that GPT models are proficient at NLI classification, with GPT-4 sett

Guodong Xiong, Xinyan Ma, Wei Li, Jiaqi Cao

With the development of artificial intelligence and unmanned equipment, human-machine hybrid formations will be the main focus in future combat formations. With the development of big data and various situational awareness technologies, while enhancing the breadth and depth of information, decision-making has also become more complex. The operation mode of e

Herbert Batte

Let $ \{L_n\}_{n\geq 0} $ be the sequence of Lucas numbers. In this paper, we determine all Lucas numbers that are palindromic concatenations of two distinct repdigits.

Huao Li, Yao Fan, Keyang Zheng, Michael Lewis

In this paper, we propose a novel personalized decision support system that combines Theory of Mind (ToM) modeling and explainable Reinforcement Learning (XRL) to provide effective and interpretable interventions. Our method leverages DRL to provide expert action recommendations while incorporating ToM modeling to understand users' mental states and predict

Keunwoo Lim, Molei Tao

We consider the convergence of kinetic Langevin dynamics to its ergodic invariant measure, which is Gibbs distribution. Instead of the standard setup where the friction coefficient is a constant scalar, we investigate position-dependent friction coefficient and the possible accelerated convergence it enables. We show that by choosing this coefficient matrix

Shamik Ghosh, Mathieu Remazeilles, Jacques Delabrouille

The Planck space mission has observed the first three rotational lines of emission of Galactic CO. Those maps, however, are either noisy, or contaminated by astrophysical emissions from different origin. We revisit those data products to deliver new full-sky CO maps with low astrophysical contamination and significantly enhanced noise properties. To that eff

S. A. Pustilnik, A. Y. Kniazev, A. L. Tepliakova, Y. A. Perepelitsyna

In the framework of the ongoing project, aimed at the systematical studying galaxies in nearby voids, we conducted spectroscopy with the Southern African Large Telescope (SALT) of 62 objects from the Nearby Void Galaxy (NVG) sample. They include 8 remaining objects of the 60 preselected candidates to eXtremely Metal-Poor (XMP) dwarfs, two known void XMP dwar

Ming Y. Lu, Bowen Chen, Drew F. K. Williamson, Richard J. Chen

The field of computational pathology has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants tailored to pa

Divyanshu Saxena, Nihal Sharma, Donghyun Kim, Rohit Dwivedula

This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that several OS components such as CPU, memory, and network subsystems are interrelated and that OS traces offer the ideal dataset for a foundation model to grasp the intricacies of div

Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu

Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence, including deep learning, have boosted the field of computational pathology. This field holds tremendous potential to automate clinical diagnosis, predict patient prognosis and response to therapy, and discover new morphological biomarkers from tissue images. Some of

Pierfrancesco Dionigi, Diego Garlaschelli, Rajat Subhra Hazra, Frank den Hollander

We analyse the largest eigenvalue of the adjacency matrix of the configuration model with large degrees, where the latter are treated as hard constraints. In particular, we compute the expectation of the largest eigenvalue for degrees that diverge as the number of vertices $n$ tends to infinity, uniformly on a scale between $1$ and $\sqrt{n}$, and show that

Cristian F. Coletti, Lucas R. de Lima

This study delves into the exploration of the limiting shape theorem for subadditive processes on finitely generated groups with polynomial growth, commonly referred to as virtually nilpotent groups. Investigating the algebraic structures underlying these processes, we present a generalized form of the asymptotic shape theorem within this framework. Extendin

Abdullah S. Abbas, Beiye C. Li, Richard D. Schaller, Vitali B. Prakapenka

Lead halide perovskite nanocrystals demonstrate efficient up-conversion, although the precise mechanism remains a subject of active research. This study utilizes steady-state and time-resolved spectroscopy methods to unravel the mechanism driving the up-conversion process in CsPbBr3 nanocrystals. Employing above- and below-gap photoluminescence measurements,

Nan Li, Arnd Scheel

We study existence, asymptotics, and stability of spiral waves in a driven curvature approximation, supplemented with an anchoring condition on a circle of finite radius. We analyze the motion of curves written as graphs in polar coordinates, finding spiral waves as rigidly rotating shapes. The existence analysis reduces to a planar ODE and asymptotics are g

Adam E. Rubinstein, Neal J. Evans, Himanshu Tyagi, Mayank Narang

We investigate the bright CO fundamental emission in the central regions of five protostars in their primary mass assembly phase using new observations from JWST's Near-Infrared Spectrograph (NIRSpec) and Mid-Infrared Instrument (MIRI). CO line emission images and fluxes are extracted for a forest of $\sim$150 ro-vibrational transitions from two vibrational

Chunlin Yu, Ye Shi, Jingya Wang

Previous endeavors in self-supervised learning have enlightened the research of deep clustering from an instance discrimination perspective. Built upon this foundation, recent studies further highlight the importance of grouping semantically similar instances. One effective method to achieve this is by promoting the semantic structure preserved by neighborho

Heikki Mäntysaari

We discuss recent progress towards developing accurate initial state descriptions for heavy ion collisions focusing on weak coupling based approaches, that enable one to constrain the high-energy structure of nuclei from deep inelastic scattering or proton-nucleus collisions. We review recent developments to determine the event-by-event fluctuating nuclear g

Omer Yair, Elias Nehme, Tomer Michaeli

In ill-posed inverse problems, it is commonly desirable to obtain insight into the full spectrum of plausible solutions, rather than extracting only a single reconstruction. Information about the plausible solutions and their likelihoods is encoded in the posterior distribution. However, for high-dimensional data, this distribution is challenging to visualiz

Hardik Parwana, Mitchell Black, Bardh Hoxha, Hideki Okamoto

Quadratic programs (QP) subject to multiple time-dependent control barrier function (CBF) based constraints have been used to design safety-critical controllers. However, ensuring the existence of a solution at all times to the QP subject to multiple CBF constraints (hereby called compatibility) is non-trivial. We quantify the feasible control input space de

Golara Ahmadi Azar, Melika Emami, Alyson Fletcher, Sundeep Rangan

Embeddings are a basic initial feature extraction step in many machine learning models, particularly in natural language processing. An embedding attempts to map data tokens to a low-dimensional space where similar tokens are mapped to vectors that are close to one another by some metric in the embedding space. A basic question is how well can such embedding

Valeri Frolov

In the present paper, we consider a rotating black hole moving in a homogeneous massless scalar field. We assume that the field is weak and neglect its backreaction, so that the metric at far distance from the black hole is practically flat. In this domain one can introduce two reference frames, $K$ and $\tilde{K}$. The frame $\tilde{K}$ is associated with t

Purbita Purkayastha, Shaun Gallagher, Yuxi Jiang, Chang-Min Lee

Halide perovskite semiconductors have emerged as promising materials for the development of solution-processed, scalable, high performance optoelectronic devices such as light-emitting diodes (LEDs) as well as coherent single photon emitters. Their integration to nanophotonic cavities for radiative enhancement and strong nonlinearity is underexplored. In thi

Kelcey Davis, Jonathan R. Trump, Raymond C. Simons, Elizabeth J. Mcgrath

We present a sample of 1165 extreme emission-line galaxies (EELGs) at 4<z<9 selected using James Webb Space Telescope (JWST) NIRCam photometry in the Cosmic Evolution Early Release Science (CEERS) program. We use a simple method to photometrically identify EELGs with Hb + [OIII] (combined) or Ha emission of observed-frame equivalent width EW >5000 AA. JWST/N

Ryan Krueger, Ella King, Michael Brenner

The precise control of complex reactions is critical for biological processes yet our inability to design for specific outcomes limits the development of synthetic analogues. Here, we leverage differentiable simulators to design nontrivial reaction pathways in colloidal assemblies. By optimizing over external structures, we achieve controlled disassembly and

Ibrahim Bouabdallaoui, Fatima Guerouate, Samya Bouhaddour, Chaimae Saadi

Undoubtedly that the Bidirectional Encoder representations from Transformers is the most powerful technique in making Natural Language Processing tasks such as Named Entity Recognition, Question & Answers or Sentiment Analysis, however, the use of traditional techniques remains a major potential for the improvement of recent models, in particular word tokeni

Joan Figuerola Hurtado

The paper presents a methodology for uncovering knowledge gaps on the internet using the Retrieval Augmented Generation (RAG) model. By simulating user search behaviour, the RAG system identifies and addresses gaps in information retrieval systems. The study demonstrates the effectiveness of the RAG system in generating relevant suggestions with a consistent

Xingshuai Huang, Di Wu, Benoit Boulet

Efficient traffic signal control is critical for reducing traffic congestion and improving overall transportation efficiency. The dynamic nature of traffic flow has prompted researchers to explore Reinforcement Learning (RL) for traffic signal control (TSC). Compared with traditional methods, RL-based solutions have shown preferable performance. However, the

Shunrui Li, Yang Liu

Although General Relativity (GR) is a very successful theory of gravity, it cannot explain every observational phenomenon. People have tried many kinds of modified gravity theory to explain these phenomena which GR cannot explain very well, such as string theory. In recent years Double Field Theory (DFT) has been an exciting research area in string theory. T

Masaki Tsukamoto

Let $X$ be a full-shift on the alphabet $[0, 1]^a$ and let $(Y, S)$ be an arbitrary dynamical system. We prove that any equivariant continuous map from $X$ to $Y$ has conditional metric mean dimension not less than $a-\mathrm{mdim}(Y, S)$. This solves a problem posed in a paper of Shi--Tsukamoto (2023).

Kelly Ramsay, Dylan Spicker

We develop $(\epsilon,\delta)$-differentially private projection-depth-based medians using the propose-test-release (PTR) and exponential mechanisms. Under general conditions on the input parameters and the population measure, (e.g. we do not assume any moment bounds), we quantify the probability the test in PTR fails, as well as the cost of privacy via fini

Andrés Gómez, R. Martínez von Dossow, A. Martín-Ruiz, Luis F. Urrutia

We derive the electromagnetic response of a particular fermionic sector in the minimal QED contribution to the Standard Model Extension (SME), which can be physically realized in terms of a model describing a tilted and anisotropic Weyl semimetal (WSM). The contact is made through the identification of the Dirac-like Hamiltonian resulting from the SME with t

Zhongjie Yu, Martin Trapp, Kristian Kersting

In many real-world scenarios, it is crucial to be able to reliably and efficiently reason under uncertainty while capturing complex relationships in data. Probabilistic circuits (PCs), a prominent family of tractable probabilistic models, offer a remedy to this challenge by composing simple, tractable distributions into a high-dimensional probability distrib

Eduardo A. Soto Rodríguez, Ana Fernández Vilas, Rebeca P. Díaz Redondo

This study reports the impact of examining either with digital or paper-based tests in science subjects taught across the second-ary level. With our method, we compare the percentile ranking scores of two cohorts earned in computer- and paper-based teacher-made assessments to find signals of a testing mode effect. It was found that overall, at cohort and gen

Ralph Sabbagh, Olga Movilla Miangolarra, Tryphon T. Georgiou

Physical systems transition between states with finite speed that is limited by energetic costs. In this work, we derive bounds on transition times for general Langevin systems that admit a decomposition into reversible and irreversible dynamics, in terms of the Wasserstein distance between states and the energetic costs associated with respective reversible

Mónica Aguilar Igartua, Florina Almenares, Rebeca P. Díaz Redondo, Manuela I. Martín

Major advances in information and communication technologies (ICTs) make citizens to be considered as sensors in motion. Carrying their mobile devices, moving in their connected vehicles or actively participating in social networks, citizens provide a wealth of information that, after properly processing, can support numerous applications for the benefit of

Jaemin Lee, Jeeseop Kim, Aaron D. Ames

This paper addresses the challenge of integrating explicit hard constraints into the control barrier function (CBF) framework for ensuring safety in autonomous systems, including robots. We propose a novel data-driven method to derive CBFs from these hard constraints in practical scenarios. Our approach assumes that the forward invariant safe set is either a

Sheldon Rego, Ralitsa Sharankova, Alexander Shemyakin

The Fermilab Linac accepts the 0.75 MeV H- ions from the front end and accelerates them to 400 MeV for injection into the Booster. Day-to-day drifts of the longitudinal trajectory in the Linac, reconstructed from phase readings of Beam Position Monitors, are at the level of several degrees. They are believed to cause additional losses both in the Linac and B

Shijun Liang, Van Hoang Minh Nguyen, Jinghan Jia, Ismail Alkhouri

As the popularity of deep learning (DL) in the field of magnetic resonance imaging (MRI) continues to rise, recent research has indicated that DL-based MRI reconstruction models might be excessively sensitive to minor input disturbances, including worst-case additive perturbations. This sensitivity often leads to unstable, aliased images. This raises the que

Peter Horvath, Lukasz Chmielewski, Leo Weissbart, Lejla Batina

Over the last decade, applications of neural networks (NNs) have spread to various aspects of our lives. A large number of companies base their businesses on building products that use neural networks for tasks such as face recognition, machine translation, and self-driving cars. Much of the intellectual property underpinning these products is encoded in the

M. G. Kozlov, Yu. A. Demidov, M. Y. Kaygorodov, E. V. Triapitsyna

Most modern calculations of many-electron atoms use basis sets of atomic orbitals. An accurate account for the electronic correlations in heavy atoms is very difficult computational problem and optimization of the basis sets can reduce computational costs and increase final accuracy. Here we suggest a simple differential ansatz to form virtual orbitals from

Kiana Farhadyar, Federico Bonofiglio, Maren Hackenberg, Daniela Zoeller

In settings requiring synthetic data generation based on a clinical cohort, e.g., due to data protection regulations, heterogeneity across individuals might be a nuisance that we need to control or faithfully preserve. The sources of such heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and thus reflected only in pro

Krishna Srikar Durbha, Hassene Tmar, Cosmin Stejerean, Ioannis Katsavounidis

Recently proposed perceptually optimized per-title video encoding methods provide better BD-rate savings than fixed bitrate-ladder approaches that have been employed in the past. However, a disadvantage of per-title encoding is that it requires significant time and energy to compute bitrate ladders. Over the past few years, a variety of methods have been pro

Alexander Meinke, Owain Evans

We examine how large language models (LLMs) generalize from abstract declarative statements in their training data. As an illustration, consider an LLM that is prompted to generate weather reports for London in 2050. One possibility is that the temperatures in the reports match the mean and variance of reports from 2023 (i.e. matching the statistics of pretr

Jaemin Lee, Jeeseop Kim, Wyatt Ubellacker, Tamas G. Molnar

This paper presents a safety-critical control framework tailored for quadruped robots equipped with a roller arm, particularly when performing locomotive tasks such as autonomous robotic inspection in complex, multi-tiered environments. In this study, we consider the problem of operating a quadrupedal robot in distillation columns, locomoting on column trays

Pratyusha Das, Sarath Shekkizhar, Antonio Ortega

Spatiotemporal graph convolutional networks (STGCNs) have emerged as a desirable model for skeleton-based human action recognition. Despite achieving state-of-the-art performance, there is a limited understanding of the representations learned by these models, which hinders their application in critical and real-world settings. While layerwise analysis of CN

Jeonghwa Lee

We propose a new method to construct a stationary process and random field with a given decreasing covariance function and any one-dimensional marginal distribution. The result is a new class of stationary processes and random fields. The construction method utilizes a correlated binary sequence, and it allows a simple and practical way to model dependence s

A. Acharyya, C. B. Adams, A. Archer, P. Bangale

Compilation of papers presented by the VERITAS Collaboration at the 38th International Cosmic Ray Conference (ICRC), held July 26 through August 3, 2023 in Nagoya, Japan.

Christopher J. Lustri, Samuel C. Crew, S. Jonathan Chapman

Singularly-perturbed ordinary differential equations often exhibit Stokes' phenomenon, which describes the appearance and disappearance of oscillating exponentially small terms across curves in the complex plane known as Stokes curves. These curves originate at singular points in the leading-order solution to the differential equation. In many important prob

Menghe Zhang, Chen Chen, Matin Yarmand, Nadir Weibel

Acupuncture is a form of medicine that involves inserting needles into targeted areas of the body and requires knowledge of both Traditional Chinese Medicine (TCM) and Evidence-Based Medicine (EBM). The process of acquiring such knowledge and using it for practical treatment is challenging due to the need for a deep understanding of human anatomy and the abi

Shu Kanazawa, Khanh Duy Trinh, D. Yogeshwaran

We prove normal approximation bounds for statistics of randomly weighted (simplicial) complexes. In particular, we consider the complete $d$-dimensional complex on $n$ vertices with $d$-simplices equipped with i.i.d. weights. Our normal approximation bounds are quantified in terms of stabilization of difference operators, i.e., the effect on the statistic un

Erhan Bayraktar, Zhenhua Wang

We investigate an infinite-horizon time-inconsistent mean-field game (MFG) in a discrete time setting. We first present a classic equilibrium for the MFG and its associated existence result. This classic equilibrium aligns with the conventional equilibrium concept studied in MFG literature when the context is time-consistent. Then we demonstrate that while t

Fernando Simeone, Maik Olher Chaves, Ahmed Esmin

The growth in Internet usage has contributed to a large volume of continuously available data, and has created the need for automatic and efficient organization of the data. In this context, text clustering techniques are significant because they aim to organize documents according to their characteristics. More specifically, hierarchical and incremental clu

P. E. Nissen, A. M. Amarsi, Á. Skúladóttir, W. J. Schuster

Previous work on the abundances of C, O, Na, Mg, Si, Ca, Ti, Cr, Mn, Fe, Ni, Cu, and Zn in low-alpha (accreted) and high-alpha (in situ born) halo stars is extended to include the abundances of Sc, V, and Co, enabling us to study the nucleosynthesis of all iron-peak elements along with the lighter elements. The Sc, V, and Co abundances were determined from a

Zelin Xu, Tingsong Xiao, Wenchong He, Yu Wang

Deep learning for Earth imagery plays an increasingly important role in geoscience applications such as agriculture, ecology, and natural disaster management. Still, progress is often hindered by the limited training labels. Given Earth imagery with limited training labels, a base deep neural network model, and a spatial knowledge base with label constraints

Tirth Surti, Roger W. Romani, Julia Scharwächter, Alison Peck

We report on IFU measurements of the host of the radio source 4C+37.11. This massive elliptical contains the only resolved double compact nucleus at pc-scale separation, likely a bound supermassive black hole binary (SMBHB). $i$-band photometry and GMOS-N IFU spectroscopy show that the galaxy has a large $r_b=1.5^{\prime\prime}$ core and that the stellar vel

Alberto Fritis, Daniel Villalobos-Silva, Yerko Vásquez, Carlos H. López-Caraballo

We study the dynamics of homogeneous and isotropic Friedmann-Lema\^itre-Robertson-Walker cosmological models with positive spatial curvature within the context of mimetic gravity theory by employing dynamical system techniques. Our analysis yields phase-space trajectories that describe physically relevant solutions, capturing various stages of cosmic evoluti

Mi Kyoung Kim

This study delves into the interplay between architectural spaces and human emotions, leveraging the emergent field of neuroarchitecture. It examines the functional and aesthetic influence of architectural design on individual users, with a focus on biosensing data such as brainwave and eye tracking information to understand user preferences.

Jing Wang, Bo Fan, Tivadar Pongó, Tamás Börzsönyi

We investigate the force of flowing granular material on an obstacle. A sphere suspended in a discharging silo experiences both the weight of the overlaying layers and drag of the surrounding moving grains. In experiments with frictional hard glass beads, the force on the obstacle was practically flow-rate independent. In contrast, flow of nearly frictionles

Min Zhang, Jianfeng He, Shuo Lei, Murong Yue

The meaning of complex phrases in natural language is composed of their individual components. The task of compositional generalization evaluates a model's ability to understand new combinations of components. Previous studies trained smaller, task-specific models, which exhibited poor generalization. While large language models (LLMs) exhibit impressive gen

Mitra Koley, Arvind Kumar

In this article, we extend the notion of the $F$-thresholds of ideals to the $F$-thresholds for filtrations of ideals. The existence of $F$-thresholds of filtrations are established for various types of filtrations. Moreover, various necessary and sufficient conditions for finiteness of $F$-thresholds are given. We also give some effective upper bounds of th

Trevor Londt, Xiaoying Gao, Peter Andreae, Yi Mei

Convolutional Neural Networks (CNNs) continue to achieve great success in classification tasks as innovative techniques and complex multi-path architecture topologies are introduced. Neural Architecture Search (NAS) aims to automate the design of these complex architectures, reducing the need for costly manual design work by human experts. Cellular Encoding

Sean Jaffe, Ambuj K. Singh, Francesco Bullo

Compressing large neural networks with minimal performance loss is crucial to enabling their deployment on edge devices. (Cho et al., 2022) proposed a weight quantization method that uses an attention-based clustering algorithm called differentiable $k$-means (DKM). Despite achieving state-of-the-art results, DKM's performance is constrained by its heavy mem