February 2024 arXiv papers — page 124

Minoo Shayaninasab, Bagher Babaali

Due to the complex nature of human emotions and the diversity of emotion representation methods in humans, emotion recognition is a challenging field. In this research, three input modalities, namely text, audio (speech), and video, are employed to generate multimodal feature vectors. For generating features for each of these modalities, pre-trained Transfor

Minoo Shayaninasab, Bagher Babaali

Given the significance of speech emotion recognition, numerous methods have been developed in recent years to create effective and efficient systems in this domain. One of these methods involves the use of pretrained transformers, fine-tuned to address this specific problem, resulting in high accuracy. Despite extensive discussions and global-scale efforts t

Maria Emelianenko, Guy B. Oldaker

In data analysis, there continues to be a need for interpretable dimensionality reduction methods whereby instrinic meaning associated with the data is retained in the reduced space. Standard approaches such as Principal Component Analysis (PCA) and the Singular Value Decomposition (SVD) fail at this task. A popular alternative is the CUR decomposition. In a

Jacob Jeffries, Fadi Abdeljawad, Suveen Mathaudhu, Emmanuelle Marquis

The alleged existence of sluggish diffusion in high entropy alloys has drawn controversy. In high entropy alloys, and in general in all solids, transport properties are controlled by point defect concentration, which must be known before performing atomistic simulations to compute transport coefficients. In this work, we present a general Langmuir-like model

David Garoz, R. González-Arrabal, R. Juárez, J. Álvarez

Nowadays, the projects LIFE (Laser Inertial Fusion Energy) in USA and HiPER (High Power Laser Energy Research) in Europe are the most advanced ones to demonstrate laser fusion energy viability. One of the main points of concern to properly achieve ignition is the performance of the final optics (lenses) under the severe irradiation conditions that take place

D Garoz, A. R. Páramo, A Rivera, J. M. Perlado

The behaviour of a tungsten first wall is studied under the irradiation conditions predicted for the different operation scenarios of the European Laser fusion project HiPER, which is based on direct drive targets and an evacuated dry wall chamber. The scenarios correspond to different stages in the development of a nuclear fusion reactor, from proof of prin

Joel Castaño, Silverio Martínez-Fernández, Xavier Franch

The rapidly evolving fields of Machine Learning (ML) and Artificial Intelligence have witnessed the emergence of platforms like Hugging Face (HF) as central hubs for model development and sharing. This experience report synthesizes insights from two comprehensive studies conducted on HF, focusing on carbon emissions and the evolutionary and maintenance aspec

Luofeng Liao, Christian Kroer, Sergei Leonenkov, Okke Schrijvers

Online A/B testing is widely used in the internet industry to inform decisions on new feature roll-outs. For online marketplaces (such as advertising markets), standard approaches to A/B testing may lead to biased results when buyers operate under a budget constraint, as budget consumption in one arm of the experiment impacts performance of the other arm. To

Bilal Chughtai, Alan Cooney, Neel Nanda

How do transformer-based large language models (LLMs) store and retrieve knowledge? We focus on the most basic form of this task -- factual recall, where the model is tasked with explicitly surfacing stored facts in prompts of form `Fact: The Colosseum is in the country of'. We find that the mechanistic story behind factual recall is more complex than previo

Ross Greer, Mohan Trivedi

This research explores the integration of language embeddings for active learning in autonomous driving datasets, with a focus on novelty detection. Novelty arises from unexpected scenarios that autonomous vehicles struggle to navigate, necessitating higher-level reasoning abilities. Our proposed method employs language-based representations to identify nove

Lichang Chen, Chen Zhu, Davit Soselia, Jiuhai Chen

In this work, we study the issue of reward hacking on the response length, a challenge emerging in Reinforcement Learning from Human Feedback (RLHF) on LLMs. A well-formatted, verbose but less helpful response from the LLMs can often deceive LLMs or even human evaluators to achieve high scores. The same issue also holds for some reward models in RL. To addre

David Garoz, Juan Fernández de la Mora

The charge/mass distribution f(q/m) of nanodrops and ions electrosprayed in vacuum from mixtures of formamide (FM) and methylammonium formate (MAF) is studied by time of flight mass spectrometry at MAF/FM volumetric concentrations of 5%, 10%, 25% and 50%. Positive and negative polarities yield comparable f(q/m) curves, though the negative mode yields ~30% la

Johannes Müller, Marius Zeinhofer

Scientific machine learning (SciML) is a relatively new field that aims to solve problems from different fields of natural sciences using machine learning tools. It is well-documented that the optimizers commonly used in other areas of machine learning perform poorly on many SciML problems. We provide an infinite-dimensional view on optimization problems enc

Matteo Tamiozzo

We prove, under suitable assumptions, that $p$-torsion Tate-Shafarevich classes for elliptic curves over the rationals are visible in quotients of Jacobians of modular curves, as predicted by a conjecture of Jetchev-Stein. The key ingredient is the non-triviality of the Bertolini-Darmon bipartite Kolyvagin system, which implies that suitable cohomology class

Samuel Zbarsky

We consider 2D point vortex systems and, under certain conditions on the masses of the point vortices, prove that collapse is impossible and provide bounds on the growth of the system. The bounds are typically of the form $O(t^a)$ for some $a<1/2$, but we obtain various results for various assumptions on the masses.

Jami Rönkkö, Olli Ahonen, Ville Bergholm, Alessio Calzona

With a growing interest in quantum technology globally, there is an increasing need for accessing relevant physical systems for education and research. In this paper we introduce a commercially available on-site quantum computer utilizing superconducting technology, offering insights into its fundamental hardware and software components. We show how this sys

Benjamin Paulson, Joshua Goldshteyn, Sydney Balboni, John Cisler

Computed tomography (CT) is a beneficial imaging tool for diagnostic purposes. CT scans provide detailed information concerning the internal anatomic structures of a patient, but present higher radiation dose and costs compared to X-ray imaging. In this paper, we build on previous research to convert orthogonal X-ray images into simulated CT volumes by explo

Chenlu Ye, Wei Xiong, Yuheng Zhang, Hanze Dong

We investigate Reinforcement Learning from Human Feedback (RLHF) in the context of a general preference oracle. In particular, we do not assume the existence of a reward function and an oracle preference signal drawn from the Bradley-Terry model as most of the prior works do. We consider a standard mathematical formulation, the reverse-KL regularized minimax

Miguel J. Carballido, Simon Svab, Rafael S. Eggli, Taras Patlatiuk

Across leading qubit platforms, a common trade-off persists: increasing coherence comes at the cost of operational speed, reflecting the notion that protecting a qubit from its noisy surroundings also limits control over it. This speed-coherence dilemma limits qubit performance across various technologies. Here, we demonstrate a hole spin qubit in a Ge/Si co

Eduardo Ochoa Rivera, Ambuj Tewari

We study a novel pure exploration problem: the $\epsilon$-Thresholding Bandit Problem (TBP) with fixed confidence in stochastic linear bandits. We prove a lower bound for the sample complexity and extend an algorithm designed for Best Arm Identification in the linear case to TBP that is asymptotically optimal.

Ionel Lazanu, Mihaela Parvu

Recent work from the last years has raised the possibility that a portion of Dark Matter could consist of exotic particles, such as axion (anti)quark nuggets (AQN, A\bar{Q}N). After a brief review outlining the main features of axion antiquark nuggets, we explore potential experimental signatures that can be leveraged to search for these stable supermassive

Taushif Ahmed, Ekta Chaubey, Mandeep Kaur, Sara Maggio

We study a set of two-loop non-planar master integrals needed for the NNLO QCD corrections to diphoton and dijet production at hadron colliders. The top-sector topology contains an internal massive fermion loop and is known to contain elliptic curves. Leveraging the method of differential equations, we provide a comprehensive discussion for deriving an $\eps

Leandro A. Passos, Douglas Rodrigues, Danilo Jodas, Kelton A. P. Costa

This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields. Since NeRF relies on the network weights to store the scene's 3-dimensional representation, BioNeRF implements a cognitive-inspired mechanism that fuses inputs from multiple sources into a memory-like

Adrián Bazaga, Pietro Liò, Gos Micklem

Hypergraphs are characterized by complex topological structure, representing higher-order interactions among multiple entities through hyperedges. Lately, hypergraph-based deep learning methods to learn informative data representations for the problem of node classification on text-attributed hypergraphs have garnered increasing research attention. However,

Harald Hanche-Olsen, Helge Holden

A fundamental issue in the theory of time-dependent differential equations is to characterize precompact sets in Bochner spaces. We here survey the theory, starting with the classical Aubin-Lions inequality and its important extension by Dubinskii. In particular, we give a simple and self-contained proof of the compactness result due to Chen, Jungel, and Liu

Lars van der Laan, Ahmed M. Alaa

In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision-making. Recognizing the complementary roles of point and interval predictions, we introduce Self-Calibrating Conformal Prediction, a method that combines Venn-Abers calibration and conformal predicti

M. Murgia, F. Govoni, V. Vacca, F. Loi

We present deep total intensity and polarization observations of the Coma cluster at 1.4 and 6.6 GHz performed with the Sardinia Radio Telescope. By combining the single-dish 1.4 GHz data with archival Very Large Array observations we obtain new images of the central radio halo and of the peripheral radio relic where we properly recover the brightness from t

Calum Gibb, Jordan Hobbs, Diana Nikolova, Tom Raistrick

Spontaneous symmetry breaking and emergent polar order are each of fundamental importance to a range of scientific disciplines, as well as generating rich phase behaviour in liquid crystals (LCs). Here, we show the union of these phenomena to lead to two previously undiscovered polar liquid states of matter. Both phases have a lamellar structure with an inhe

Zheng Ning, Yuan Tian, Zheng Zhang, Tianyi Zhang

Querying structured databases with natural language (NL2SQL) has remained a difficult problem for years. Recently, the advancement of machine learning (ML), natural language processing (NLP), and large language models (LLM) have led to significant improvements in performance, with the best model achieving ~85% percent accuracy on the benchmark Spider dataset

Rüdiger Valk

Cycloids are particular Petri nets for modelling processes of actions or events. They belong to the fundaments of Petri's general systems theory and have very different interpretations, ranging from Einstein's relativity theory and elementary information processing gates to the modelling of interacting sequential processes. This article contains previously u

Arthur K. Barnes, Adam Mate, Russell Bent

Space weather poses a tremendous threat to power systems: geomagnetic disturbances could result in widespread disruptions and long-duration blackouts, including severe damage to system components. To mitigate their impacts, a handful of strategies exist, with the most promising being the deployment of transformer neutral blocking devices. The high cost of th

Atharva Pandey, Vishal Yadav, Rajendra Nagar, Santanu Chaudhury

Implicit 3D surface reconstruction of an object from its partial and noisy 3D point cloud scan is the classical geometry processing and 3D computer vision problem. In the literature, various 3D shape representations have been developed, differing in memory efficiency and shape retrieval effectiveness, such as volumetric, parametric, and implicit surfaces. Ra

Zheng Ning, Brianna L. Wimer, Kaiwen Jiang, Keyi Chen

Blind or Low-Vision (BLV) users often rely on audio descriptions (AD) to access video content. However, conventional static ADs can leave out detailed information in videos, impose a high mental load, neglect the diverse needs and preferences of BLV users, and lack immersion. To tackle these challenges, we introduce SPICA, an AI-powered system that enables B

Jonathan R. Mannouch, Aaron Kelly

Nonadiabatic dynamics methods are an essential tool for investigating photochemical processes. In the context of employing first principles electronic structure techniques, such simulations can be carried out in a practical manner using semiclassical trajectory-based methods or wave packet approaches. While all approaches applicable to first principles simul

Evan Bell, Michael T. McCann, Marc Klasky

In this paper, we introduce silhouette tomography, a novel formulation of X-ray computed tomography that relies only on the geometry of the imaging system. We formulate silhouette tomography mathematically and provide a simple method for obtaining a particular solution to the problem, assuming that any solution exists. We then propose a supervised reconstruc

Vincenzo Nardelli, Giuseppe Arbia

As a rule statistical measures are often vulnerable to the presence of outliers and spatial correlation coefficients, critical in the assessment of spatial data, remain susceptible to this inherent flaw. In contexts where data originates from a variety of domains (such as, e. g., socio-economic, environmental or epidemiological disciplines) it is quite commo

Steffen Grünewälder, Azadeh Khaleghi

We propose methods to estimate the individual $\beta$-mixing coefficients of a real-valued geometrically ergodic Markov process from a single sample-path $X_0,X_1, \dots,X_n$. Under standard smoothness conditions on the densities, namely, that the joint density of the pair $(X_0,X_m)$ for each $m$ lies in a Besov space $B^s_{1,\infty}(\mathbb R^2)$ for some

Mohak Chadha, Pulkit Khera, Jianfeng Gu, Osama Abboud

Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can

Amir M. Mir, Mehdi Keshani, Sebastian Proksch

Static call graph (CG) construction often over-approximates call relations, leading to sound, but imprecise results. Recent research has explored machine learning (ML)-based CG pruning as a means to enhance precision by eliminating false edges. However, current methods suffer from a limited evaluation dataset, imbalanced training data, and reduced recall, wh

Zalán Gyenis, Zalán Molnár

In a recent paper, Krawczyk proved that there are continuum many axiomatic extensions of global consequence associated with the modal system $E$ that do not admit the local deduction detachment theorem. In algebraic parlance, he showed that there are continuum many varieties of modal algebras lacking the congruence extension property. In this paper, we exten

Lev Ivan Bulyk, Ajeesh Kumar Somakumar, Hanka Przybylińska, P. Ciepielewski

Results of spectroscopic studies at ambient and high pressures of a LuAlO3:Ce3+ (LuAP:Ce) single crystalline film (SCF) as well as LuAP:Ce and YAlO3:Ce (YAP:Ce) single crystals are reported. Room temperature absorption measurements of the single crystals in the vacuum UV region allowed establishing the bandgap energies of 7.63 eV for YAP and 7.86 eV for LuAP

D. I. Karasev, A. N. Semena, I. A. Mereminskiy, A. A. Lutovinov

We present a catalog of sources detected by the Mikhail Pavlinsky ART-XC telescope onboard the SRG space observatory during the observations of the Galactic plane region near a longitude $l\simeq20$ deg (L20 field) in October 2019. The L20 field was observed four times in the scanning mode, which provided a uniform coverage of the sky region with a total are

Thomas Wilhelm, Trang Thu Võ, Orkun Furat, Urs A. Peuker

This paper introduces a novel method for characterizing fracture mechanisms in composite materials using 3D image data gained by computed tomography (CT) measurements. In mineral liberation, the understanding of these mechanisms is crucial, particularly whether fractures occur along the boundaries of mineral phases (intergranular fracture) and/or within mine

G. Röpke, C. Xu, B. Zhou, Z. Z. Ren

$^{20}$Ne can be considered as a double-magic $^{16}$O core nucleus surrounded by four nucleons, the constituents of an $\alpha$-like quartet. Similar to other nuclei ($^{212}$Po, $^{104}$Ti, etc.) with a quartet on top of a double-magic core nucleus, significant $\alpha$-like correlations are expected. Correlations in the ground state of $^{20}$Ne are inves

V. Manuilov

We consider several natural ways of expressing the idea that a one-sided ideal in a C*-algebra (or a submodule in a Hilbert C*-module) is large, and show that they differ, unlike the case of two-sided ideals in C*-algebras. We then show how these different notions, for ideals and for submodules, are related. We also study some permanence properties for these

Ali Eghbali, Simin Ghasemi-Sorkhabi, Adel Rezaei-Aghdam

We proceed to investigate the solutions of generalized supergravity equations (GSE) in three dimensions. Our candidate is the metric of BTZ black hole. It is shown that only the cases with $J=M=0$ and $J=0,~ M\neq 0$ of the BTZ metric satisfy the GSE. In the former, we find a family of solutions including the field strength $H_{_{r \varphi t}}=2r/l$, the cos

Tamar Datuashvili, Osman Mucuk, Nazmiye Alemdar, Tunçar Şahan

For any cssc-crossed module a category is constructed, equipped with a structure and proved that this is a coherent categorical group. Together with a result of the previous paper, where to any categorical group the cssc-crossed module is associated, this construction will enable us to prove an equivalence between the categories of categorical groups and of

Ram Sudhir Sharma, Wladimir Sarlin, Langqi Xing, Cyprien Morize

The presence of interparticle cohesion can drastically change the behavior of granular materials. For instance, powders are challenging to handle, and one can make a sandcastle using wet grains. In this study, we report experimental results for columns of model cohesive grains collapsing under their own weight in air and spreading on a rough horizontal surfa

Parker C. Lusk, Jonathan P. How

Identifying correspondences in noisy data is a critically important step in estimation processes. When an informative initial estimation guess is available, the data association challenge is less acute; however, the existence of a high-quality initial guess is rare in most contexts. We explore graph-theoretic formulations for data association, which do not r

Chao Wang, Zhuo Chen, Ziyan Zhang, Chiyi Li

In this paper, we address the challenge of learning with limited fault data for power transformers. Traditional operation and maintenance tools lack effective predictive capabilities for potential faults. The scarcity of extensive fault data makes it difficult to apply machine learning techniques effectively. To solve this problem, we propose a novel approac

Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths

In day-to-day communication, people often approximate the truth - for example, rounding the time or omitting details - in order to be maximally helpful to the listener. How do large language models (LLMs) handle such nuanced trade-offs? To address this question, we use psychological models and experiments designed to characterize human behavior to analyze LL

Shanay Mehta, Shlok Mehendale, Nicole Fernandes, Jyotirmoy Sarkar

Detection of anomalous situations for complex mission-critical systems hold paramount importance when their service continuity needs to be ensured. A major challenge in detecting anomalies from the operational data arises due to the imbalanced class distribution problem since the anomalies are supposed to be rare events. This paper evaluates a diverse array

Camille Bishop, Thomas J. Ferron, Marie E. Fiori, Connor G. Bischak

Multi-phase vapor-deposited glasses are an important class of materials for organic electronics, particularly organic photovoltaics and thermoelectrics. These blends are frequently regarded as molecular alloys and there have been few studies of their structure at nanometer scales. Here we show that a co-deposited system of TPD and Disperse Orange 37, two sma

Rahul Chandra, Analabha Roy

This paper investigates the possibility of generating Floquet-time crystals in higher dimensions ($d\geq 2$) through the time-periodic driving of integrable free-fermionic models. The realization leads to rigid time-crystal phases that are ideally resistant to thermalization and decoherence. By utilizing spin-orbit coupling, we are able to realize a robust t

Gregory Quiroz, Bibek Pokharel, Joseph Boen, Lina Tewala

Decoherence-free subspaces and subsystems (DFS) preserve quantum information by encoding it into symmetry-protected states unaffected by decoherence. An inherent DFS of a given experimental system may not exist; however, through the use of dynamical decoupling (DD), one can induce symmetries that support DFSs. Here, we provide the first experimental demonstr

Gaurab Aryal, Charles Murry, Pallavi Pal, Arnab Palit

We study a new market design for K-12 school broadband procurement that switched from school-specific bidding to a system that bundled schools into groups. Using an event study approach, we estimate that the program reduced internet prices by \$9.17 (55\%) per Mbps per month while increasing bandwidth by 380.06 Mbps (136\%). These benefits resulted primarily

Luca Griguolo, Jacopo Papalini, Lorenzo Russo, Domenico Seminara

We propose a refined expression for the large genus asymptotics of the Weil-Petersson volumes of the moduli space of super-Riemann surfaces with an arbitrary number of boundaries. Our formula leverages the connection between JT supergravity and its matrix model definition, utilizing some basic tools of resurgence theory. The final result holds for arbitrary

Meicun Hou, Zhensong Hu, Zhiyuan Li

We present a uniform and sensitive X-ray census of active galactic nuclei (AGNs) in the two nearest galaxy clusters, Virgo and Fornax, utilizing the newly released X-ray source catalogs from the first all-sky scan of SRG/eROSITA. A total of 50 and 10 X-ray sources are found positionally coincident with the nuclei of member galaxies in Virgo and Fornax, respe

Andrea Del Prete

In the homogeneous manifold $\mathbb{E}(-1,\tau),$ for $-\tfrac{1}{2}<H<\tfrac{1}{2},$ {we define a new product compactification in which the slices $\left\{t=c\right\}_{c\in\R}$ are rotational $H$-surfaces. This product compatification is the natural setting where it makes sense to study the asymptotic Dirichlet Problem for the constant mean curvature equat

Andrea Bonito, Claudio Canuto, Ricardo H. Nochetto, Andreas Veeser

This is a survey on the theory of adaptive finite element methods (AFEMs), which are fundamental in modern computational science and engineering but whose mathematical assessment is a formidable challenge. We present a self-contained and up-to-date discussion of AFEMs for linear second order elliptic PDEs and dimension d>1, with emphasis on foundational issu

V. Avramov, H. Dimov, M. Radomirov, R. C. Rashkov

This study is aimed at providing a thorough analysis of the classical thermodynamic stability of black holes within the Anti-de Sitter (AdS) family. We utilize the Nambu bracket formalism to calculate local heat capacities and employ Sylvester's criterion in the mass-energy ensemble to determine both local and global thermodynamic stability regions. Emphasiz

Fanfei Meng, Chen-Ao Wang, Lele Zhang

The paper provides a comprehensive overview of Neural Architecture Search (NAS), emphasizing its evolution from manual design to automated, computationally-driven approaches. It covers the inception and growth of NAS, highlighting its application across various domains, including medical imaging and natural language processing. The document details the shift

Jiangnan Li, Qiujing Wang, Liyan Xu, Wenjie Pang

Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand the ongoing plot. Despite its usefulness, this application has not been well studied in the NLP community. We propose the first benchmark on this useful task called Recap Snippet

Simon Ging, María A. Bravo, Thomas Brox

The evaluation of text-generative vision-language models is a challenging yet crucial endeavor. By addressing the limitations of existing Visual Question Answering (VQA) benchmarks and proposing innovative evaluation methodologies, our research seeks to advance our understanding of these models' capabilities. We propose a novel VQA benchmark based on well-kn

Qian Tang, Xiaomeng Xu

In this article, we study a special class of Jimbo-Miwa-Mori-Sato isomonodromy equations, which can be seen as a higher-dimensional generalization of Painlev\'e VI. We first construct its convergent $n\times n$ matrix series solutions satisfying certain boundary condition. We then use the Riemann-Hilbert approach to prove that the resulting solutions are alm

Zehao Dong, Qihang Zhao, Philip R. O. Payne, Michael A Province

Biomarker identification is critical for precise disease diagnosis and understanding disease pathogenesis in omics data analysis, like using fold change and regression analysis. Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. However, we found two major limitations of existing GNNs in omics data an

Bo Huang, Josep M. Girart, Ian W. Stephens, Manuel Fernandez-Lopez

We present 870 um polarimetric observations toward 61 protostars in the Orion molecular clouds, with ~400 au (1") resolution using the Atacama Large Millimeter/submillimeter Array. We successfully detect dust polarization and outflow emission in 56 protostars, in 16 of them the polarization is likely produced by self-scattering. Self-scattering signatures ar

Povilas Lastauskas, Anh Dinh Minh Nguyen

This paper examines the impact of US monetary policy tightening on emerging markets, distinguishing between direct and indirect spillover effects using the global vector autoregression with stochastic volatility covering 32 countries. The paper demonstrates that an increase in the US interest rate significantly reduces output for emerging markets, leading to

Stephan Mescher

Manifolds occur naturally as configuration spaces of robotic systems. They provide global descriptions of local coordinate systems that are common tools in expressing positions of robots. The purpose of this survey is threefold. Firstly, we present an overview over various results on topological complexities of manifolds and related topics. Several construct

Jose A. Pereira Frugone

In a previous work we constructed a new kind of moduli background space by identifying regions of space-time where an observation of space-time is implied. We called it Observation Modular space (OM-space). Quantum Mechanics (QM) on this moduli space gets mapped into a very rich and highly non trivial dual Number Theory which we call Observation Modular Quan

Muhammad Zeshan Alam, Sousso kelowani, Mohamed Elsaeidy

Ensuring robustness in face recognition systems across various challenging conditions is crucial for their versatility. State-of-the-art methods often incorporate additional information, such as depth, thermal, or angular data, to enhance performance. However, light field-based face recognition approaches that leverage angular information face computational

Mithun Das, Saurabh Kumar Pandey, Shivansh Sethi, Punyajoy Saha

With the rise of online abuse, the NLP community has begun investigating the use of neural architectures to generate counterspeech that can "counter" the vicious tone of such abusive speech and dilute/ameliorate their rippling effect over the social network. However, most of the efforts so far have been primarily focused on English. To bridge the gap for low

Kushal Chakraborty, Aritra Kumar Dutta, Mohammad Avesh Hussain, Syed Raafay Mohiuddin

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the Time Slotted Channel Hopping mode of IEEE 802.15.4e) as an enabling technology facilitates low-power and low-latency comm

A. Malfondet, G. Millot, P. Grelu, P. Tchofo-Dinda

We address the challenge of configuring a fiber laser cavity to enable efficient access to multi-pulse structures such as dissipative soliton molecules. We theoretically compare multi-pulsing routes in the parameter space of the laser. By using a two-dimensional parameter space, we experimentally demonstrate an important reduction in the laser pumping power

Jiguang He, Aymen Fakhreddine, George C. Alexandropoulos

In the past decade, the number of amateur drones is increasing, and this trend is expected to continue in the future. The security issues brought by abuse and misconduct of drones become more and more severe and may incur a negative impact to the society. In this paper, we leverage existing cellular multiple-input multiple-output (MIMO) base station (BS) inf

Harshita Gandhi, Ritesh Patel, Vaibhav Pant, Satabdwa Majumdar

This study addresses the limitations of single-viewpoint observations of Coronal Mass Ejections (CMEs) by presenting results from a 3D catalog of 360 CMEs during solar cycle 24, fitted using the GCS model. The dataset combines 326 previously analyzed CMEs and 34 newly examined events, categorized by their source regions into active region (AR) eruptions, act

Samiha Mirza, Vuong D. Nguyen, Pranav Mantini, Shishir K. Shah

In the midst of the rapid integration of artificial intelligence (AI) into real world applications, one pressing challenge we confront is the phenomenon of model drift, wherein the performance of AI models gradually degrades over time, compromising their effectiveness in real-world, dynamic environments. Once identified, we need techniques for handling this

Martin Zukerstein, Vladimir P. Zhukov, Yuri P. Meshcheryakov, Nadezhda M. Bulgakova

Volumetric modification of glass materials by ultrashort laser pulses is a powerful technique enabling direct writing of three-dimensional structures for fabrication of optical, photonic, and microfluidic devices. The level of modification is determined by the locally absorbed energy density, which depends on numerous factors. In this work, the effect of the

Irene Mediavilla-Martinez, Christian Kramberger, Shabnam Dadgostar, Juan Jimenez

Bismuth selenide, a benchmark topological insulator, grows in a trigonal structure at ambient conditions and exhibits a number of enticing properties related to the formation of Dirac surface states. Besides this polytype, a metastable orthorhombic modification with Pnma space group has been produced by electrodeposition and high-pressure high-temperature sy

Parsheeta Roy, Ji-Eun Han, Srishti Chouhan, Bhaavanaa Thumu

Sign language to text is a crucial technology that can break down communication barriers for individuals with hearing difficulties. We replicate and try to improve on a recently published study. We evaluate models using BLEU and rBLEU metrics to ensure translation quality. During our ablation study, we found that the model's performance is significantly infl

Giorgio Ottaviani

Horrocks proved in 1964 that vector bundles on $P^n$ without intermediate cohomology split as direct sum of line bundles. This result has been the starting point of a great research activity on other varieties, showing interesting connections with derived categories and other areas. We follow some paths into this fascinating story, which has classical roots.

Debarshi Basu, Vinayak Raj

We develop a covariant formalism to investigate the mixed state entanglement structure of time-dependent boosted subsystems in $\textrm{T}\bar{\textrm{T}}$ deformed CFT$_2$s through the reflected entropy. To this end we utilize the conformal perturbation theory to obtain the R\'enyi reflected entropy through the partition function on replica manifold. The co

Pierre Barrat-Charlaix, Richard A. Neher

As pathogens spread in a population of hosts, immunity is built up and the pool of susceptible individuals is depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host's immune systems adapt an

Hao Chen, Gonzalo E. Constante Flores, Can Li

Surrogate modeling is used to replace computationally expensive simulations. Neural networks have been widely applied as surrogate models that enable efficient evaluations over complex physical systems. Despite this, neural networks are data-driven models and devoid of any physics. The incorporation of physics into neural networks can improve generalization

Minglang Yin, Nicolas Charon, Ryan Brody, Lu Lu

The solution of a PDE over varying initial/boundary conditions on multiple domains is needed in a wide variety of applications, but it is computationally expensive if the solution is computed de novo whenever the initial/boundary conditions of the domain change. We introduce a general operator learning framework, called DIffeomorphic Mapping Operator learNin

Taojie Kuang, Pengfei Liu, Zhixiang Ren

The precise prediction of molecular properties is essential for advancements in drug development, particularly in virtual screening and compound optimization. The recent introduction of numerous deep learning-based methods has shown remarkable potential in enhancing molecular property prediction (MPP), especially improving accuracy and insights into molecula

Itay Safran, Daniel Reichman, Paul Valiant

We prove an exponential size separation between depth 2 and depth 3 neural networks (with real inputs), when approximating a $\mathcal{O}(1)$-Lipschitz target function to constant accuracy, with respect to a distribution with support in the unit ball, under the mild assumption that the weights of the depth 2 network are exponentially bounded. This resolves a

David Azriel, Abba M. Krieger, Adam Kapelner

We consider the general performance of the difference-in-means estimator in an equally-allocated two-arm randomized experiment under common experimental endpoints such as continuous (regression), incidence, proportion, count and uncensored survival. We consider two sources of randomness: the subject-specific assignments and the contribution of unobserved sub

Chaosheng Dong, Yijia Wang

This paper studies generalized inverse reinforcement learning (GIRL) in Markov decision processes (MDPs), that is, the problem of learning the basic components of an MDP given observed behavior (policy) that might not be optimal. These components include not only the reward function and transition probability matrices, but also the action space and state spa

Chao Ma, Ziyang Wang

Medical image segmentation is essential in diagnostics, treatment planning, and healthcare, with deep learning offering promising advancements. Notably, the convolutional neural network (CNN) excels in capturing local image features, whereas the Vision Transformer (ViT) adeptly models long-range dependencies through multi-head self-attention mechanisms. Desp

Junhao Song, Yingfang Yuan, Wei Pang

We propose a novel type of Artificial Immune System (AIS): Symbiotic Artificial Immune Systems (SAIS), drawing inspiration from symbiotic relationships in biology. SAIS parallels the three key stages (i.e., mutualism, commensalism and parasitism) of population updating from the Symbiotic Organisms Search (SOS) algorithm. This parallel approach effectively ad

Jiahao Pang, Kevin Bui, Dong Tian

The universality of the point cloud format enables many 3D applications, making the compression of point clouds a critical phase in practice. Sampled as discrete 3D points, a point cloud approximates 2D surface(s) embedded in 3D with a finite bit-depth. However, the point distribution of a practical point cloud changes drastically as its bit-depth increases,

Tommaso Boccato, Matteo Ferrante, Nicola Toschi

There is growing consensus among neuroscientists that neural circuits critical for survival are the result of genomic decompression processes. We introduce SynaptoGen, a novel computational framework--member of the Connectome Models family--bringing synthetic biological intelligence closer, facilitating neural biological agent development through precise gen

Peiyao Sheng, Ranvir Rana, Senthil Bala, Himanshu Tyagi

Layer 1 (L1) blockchains such as Ethereum are secured under an "honest supermajority of stake" assumption for a large pool of validators who verify each and every transaction on it. This high security comes at a scalability cost which not only effects the throughput of the blockchain but also results in high gas fees for executing transactions on chain. The

Syamantak Kumar, Purnamrita Sarkar

Oja's algorithm for Streaming Principal Component Analysis (PCA) for $n$ data-points in a $d$ dimensional space achieves the same sin-squared error $O(r_{\mathsf{eff}}/n)$ as the offline algorithm in $O(d)$ space and $O(nd)$ time and a single pass through the datapoints. Here $r_{\mathsf{eff}}$ is the effective rank (ratio of the trace and the principal eige

Polina Matveeva, Dmitri Gutman, Sam T. Carr

We investigate the topological properties of one-dimensional weakly interacting topological insulators using bosonization. To do that we study the topological edge states that emerge at the edges of a model realized by a strong impurity or at the boundary between topologically distinct phases. In the bosonic model, the edge states are manifested as degenerat

A. A. Coley, A. Landry, R. J. van den Hoogen, D. D. McNutt

We are interested in the development of spherically symmetric geometries in $F(T)$ teleparallel gravity which are of physical importance. We first express the general forms for the spherically symmetric frame and the zero curvature, metric compatible, spin connection. We then analyse the antisymmetric field equations (the solutions of which split into two ca

Antonio Bueno, Irene Ortiz

Given $\lambda\in\mathbb{R}$ and $\textbf{v}\in\mathbb{L}^3$, a $\lambda$-translator with velocity $\textbf{v}$ is an immersed surface in $\mathbb{L}^3$ whose mean curvature satisfies $H=\langle N,\textbf{v}\rangle+\lambda$, where $N$ is a unit normal vector field. When $\lambda=0$, we fall into the class of translating solitons of the mean curvature flow. I

E. Papapetros

We say that a $C^*$-algebra $\mathcal{A}$ satisfies the similarity property ((SP)) if every bounded homomorphism $u\colon \mathcal{A} \to \mathcal{B}(\mathit{H})$, where $\mathit{H}$ is a Hilbert space, is similar to a $*$-homomorphism. We introduce the following hypothesis (EP). (EP): Every separably acting von Neumann algebra with a cyclic vector is hyperr

Guanbo Wang, Ting-Wei Ernie Liao, David Furfaro, Leo Anthony Celi

Objective: Randomized controlled trial (RCT) results often inform clinical decision-making, but the highly curated populations of trials and the care provided during the trial are often not reflective of real-world practice. The objective of this scoping review is to identify the ability of methods to transport findings from RCTs to target populations. Study