May 2023 arXiv papers — page 148

Xiao Shang, Zhiying Liu, Jiahui Zhang, Tianyi Lyu

Materials-by-design has been historically challenging due to complex process-microstructure-property relations. Conventional analytical or simulation-based approaches suffer from low accuracy or long computational time and poor transferability, further limiting their applications in solving the inverse material design problem. Here, we establish a deep learn

Thomas Suchanek, Klaus Kroy, Sarah A. M. Loos

We study time-reversal symmetry breaking in non-Hermitian fluctuating field theories with conserved dynamics, comprising the mesoscopic descriptions of a wide range of nonequilibrium phenomena. They exhibit continuous parity-time ($\mathcal{PT}$) symmetry breaking phase transitions to dynamical phases. For two concrete transition scenarios, exclusive to non-

Benedek Kovács, Zoltán Lóránt Nagy

We study the set of intersection sizes of a k-dimensional affine subspace and a point set of size m \in [0, 2^n] of the n-dimensional binary affine space AG(n,2). Following the theme of Erd\H{o}s, F\"uredi, Rothschild and T. S\'os, we partially determine which local densities in k-dimensional affine subspaces are unavoidable in all $m$-element point sets in

Somayajulu L. N. Dhulipala

Due to the paucity of strong recorded accelerograms, earthquake engineering analysis relies on accelerogram amplitude scaling for structural damage/collapse assessment and target spectrum matching. This paper investigates seismological characteristics of scaled accelerograms so as to inform future ground motion selection and seismic risk assessment methods.

Anton Kovalyov, Kashyap Patel, Issa Panahi

This paper presents a method for real-time estimation of 2-dimensional direction of arrival (2D-DOA) of one or more sound sources using a nonlinear array of three microphones. 2D-DOA is estimated employing frame-level time difference of arrival (TDOA) measurements. Unlike conventional methods, which infer location parameters from TDOAs using a theoretical mo

Sk. Safique Ahmad, Pinki Khatun

This paper addresses structured normwise, mixed, and componentwise condition numbers (CNs) for a linear function of the solution to the generalized saddle point problem (GSPP). We present a general framework that enables us to measure the structured CNs of the individual components of the solution. Then, we derive their explicit formulae when the input matri

Liam Heidt, Tim Colonius

Many turbulent flows exhibit time-periodic statistics. These include turbomachinery flows, flows with external harmonic forcing, and the wakes of bluff bodies. Many existing techniques for identifying turbulent coherent structures, however, assume the statistics are statistically stationary. In this paper, we leverage cyclostationary analysis, an extension o

Yova Kementchedjhieva, Ilias Chalkidis

Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we compare four methods for multi-label classification, two based on an encoder only, and two based on an encoder-decoder. We carry out experimen

Ognjen Tošić

For an irreducible representation $\rho:\pi_1(\Sigma_g)\to\mathrm{GL}(n,\mathbb{C})$ there is an energy functional $\mathrm{E}_\rho:\mathcal{T}_g\to\mathbb{R}$, defined on Teichm\"uller space by taking the energy of the associated equivariant harmonic map into the symmetric space $\mathrm{GL}(n,\mathbb{C})/\mathrm{U}(n)$. It follows from a result of Toledo t

Becca Ewing, Rachael Huxford, Divya Singh, Leo Tsukada

GstLAL is a stream-based matched-filtering search pipeline aiming at the prompt discovery of gravitational waves from compact binary coalescences such as the mergers of black holes and neutron stars. Over the past three observation runs by the LIGO, Virgo, and KAGRA (LVK) collaboration, the GstLAL search pipeline has participated in several tens of gravitati

Tommi Muller, Vidit Nanda, Anna Seigal

We count singular vector tuples of a system of tensors assigned to the edges of a directed hypergraph. To do so, we study the generalisation of quivers to directed hypergraphs. Assigning vector spaces to the nodes of a hypergraph and multilinear maps to its hyperedges gives a hyperquiver representation. Hyperquiver representations generalise quiver represent

Delphine Moussard

A multisection is a decomposition of a manifold into 1-handlebodies, where each subcollection of the pieces intersects along a 1-handlebody except the global intersection which is a closed surface. These generalizations of Heegaard splittings and Gay-Kirby trisections were introduced by Ben Aribi, Courte, Golla and the author, who proved in particular that a

Jyunji Inoue, Sin-Ei Takahasi

Let $G$ be a non-discrete LCA group with the dual group $\Gamma$. We define generalized group algebra, ${\mathcal L}^1(G)$, and generalized measure algebra, ${\mathcal M}(G),$ on $G$ as generalizations of the group algebra $L^1(G)$ and the measure algebra $M(G)$, respectively. Generalized Fourier transforms of elements of ${\mathcal L}^1(G)$ and generalized

Zhang Ze Yu, Lau Jia Jaw, Zhang Hui, Bryan Kian Hsiang Low

Reinforcement Learning with Human Feedback (RLHF) has been demonstrated to significantly enhance the performance of large language models (LLMs) by aligning their outputs with desired human values through instruction tuning. However, RLHF is constrained by the expertise and productivity limitations of human evaluators. A response to this downside is to fall

Ravid Shaniv, Chris Reetz, Cindy A. Regal

Micro-mechanical resonator performance is fundamentally limited by the coupling to a thermal environment. The magnitude of this thermodynamical effect is typically considered in accordance with a physical temperature, assumed to be uniform across the resonator's physical span. However, in some circumstances, e.g. quantum optomechanics or interferometric grav

Joshua T. Majekodunmi, Raj Kumar, M. Bhuyan

This study presents a holistic picture of the preformation of nuclear clusters with credence to the kinematics of their emissions. Besides the fitting of the preformation formula to reproduce the experimental half-lives, we have investigated the interrelationship between the parameters involved in the cluster decay process for medium, heavy and superheavy nu

Enver Kapan, Sertan Alkan, C. Can Aydıner, Jeremy K. Mason

Modeling deformation twin nucleation in magnesium has proven to be a challenging task. In particular, the absence of a heterogeneous twin nucleation model which provides accurate energetic descriptions for twin-related structures belies a need to more deeply understand twin energetics. To address this problem, molecular dynamics simulations are performed to

Rayna Andreeva, Katharina Limbeck, Bastian Rieck, Rik Sarkar

Deep learning models have seen significant successes in numerous applications, but their inner workings remain elusive. The purpose of this work is to quantify the learning process of deep neural networks through the lens of a novel topological invariant called magnitude. Magnitude is an isometry invariant; its properties are an active area of research as it

Jadie Adams, Shireen Elhabian

Statistical Shape Modeling (SSM) is a valuable tool for investigating and quantifying anatomical variations within populations of anatomies. However, traditional correspondence-based SSM generation methods have a prohibitive inference process and require complete geometric proxies (e.g., high-resolution binary volumes or surface meshes) as input shapes to co

Timothy Poterucha, Pierre Elias, Christopher M. Haggerty

Background: Artificial intelligence language models have shown promise in various applications, including assisting with clinical decision-making as demonstrated by strong performance of large language models on medical licensure exams. However, their ability to solve complex, open-ended cases, which may be representative of clinical practice, remains unexpl

Aparna Balagopalan, Abigail Z. Jacobs, Asia Biega

Online platforms mediate access to opportunity: relevance-based rankings create and constrain options by allocating exposure to job openings and job candidates in hiring platforms, or sellers in a marketplace. In order to do so responsibly, these socially consequential systems employ various fairness measures and interventions, many of which seek to allocate

Adam J. Thorpe

Data-driven algorithms for human-centered autonomy use observed data to compute models of human behavior in order to ensure safety, correctness, and to avoid potential errors that arise at runtime. However, such algorithms often neglect useful a priori knowledge, known as side information, that can improve the quality of data-driven models. We identify sever

Xiaoxu Li, Qing Guo, Yatong Zhao, Ping Chen

This study examines the anisotropic dissolution of the basal plane gibbsite ({\gamma}-Al(OH)3) nanoplates in sodium hydroxide solution using in situ atomic force microscopy (AFM) and density functional theory (DFT) calculations. In the surface-reaction controlled regime, in situ AFM measurements reveal anisotropic dissolution of hillocks and etch pits on the

Arnasli Yahya, Jenő Szirmai

After investigating the $3$-dimensional case [35], we continue to address and close the problems of optimal ball and horoball packings in truncated Coxeter orthoschemes with parallel faces that exist in $n$-dimensional hyperbolic space $\overline{\mathbb{H}}^n$ up to $n=6$. In this paper, we determine the optimal ball and horoball packing configurations and

Tobias Barthel, Natalia Castellana, Drew Heard, Beren Sanders

We prove that a jointly conservative family of geometric functors between rigidly-compactly generated tensor triangulated categories induces a surjective map on Balmer spectra. From this we deduce a fiberwise criterion for Balmer's comparison map to be a continuous bijection. This gives short alternative proofs of the Hopkins--Neeman theorem and its generali

Maureen Monnet, Hanady Gebran, Andrea Matic-Flierl, Florian Kiwit

Quantum machine learning has received significant interest in recent years, with theoretical studies showing that quantum variants of classical machine learning algorithms can provide good generalization from small training data sizes. However, there are notably no strong theoretical insights about what makes a quantum circuit design better than another, and

Shangchao Su, Haiyang Yu, Bin Li, Xiangyang Xue

In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organization to collect a large amount of data from similar organizations. However, due to the natural presence of private information in text data, such as addresses and phone numbers,

R. Fioresi, F. Zanchetta

In this expository paper we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successfull algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks. We go over the key ingredients for these algorithms: the score and loss function and we explain the main

Ari Le, Adam Stanier, Lin Yin, Blake Wetherton

Hybrid-VPIC is an extension of the open-source high-performance particle-in-cell (PIC) code VPIC incorporating hybrid kinetic ion/fluid electron solvers. This paper describes the models that are available in the code and gives an overview of applications of the code to space and laboratory plasma physics problems. Particular choices in how the hybrid solvers

Jun Chen, Wei Rao, Zilin Wang, Jiuxin Lin

Subband-based approaches process subbands in parallel through the model with shared parameters to learn the commonality of local spectrums for noise reduction. In this way, they have achieved remarkable results with fewer parameters. However, in some complex environments, the lack of global spectral information has a negative impact on the performance of the

Ho Hin Lee, Alberto Santamaria-Pang, Jameson Merkow, Ozan Oktay

We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) that demonstrates the feasibility of medical image retrieval with similar anatomical regions. RegionMIR addresses two major challenges for medical image retrieval i) standardization of clinically relevant searching criteria (e.g., anatomical, pathology-based), a

Valentina Andries, Judy Robertson

As voice-based Conversational Assistants (CAs), including Alexa, Siri, Google Home, have become commonly embedded in households, many children now routinely interact with Artificial Intelligence (AI) systems. It is important to research children's experiences with consumer devices which use AI techniques because these shape their understanding of AI and its

Harshithanjani Athi, Rasagna Chigullapally, Prasad Krishnan, Lalitha Vadlamani

A code of length $n$ is said to be (combinatorially) $(\rho,L)$-list decodable if the Hamming ball of radius $\rho n$ around any vector in the ambient space does not contain more than $L$ codewords. We study a recently introduced class of higher order MDS codes, which are closely related (via duality) to codes that achieve a generalized Singleton bound for l

Mohammad Reza Alipour, Mohammad Ali S. Afshar, Saeed Noori Gashti, Jafar Sadeghi

One of the new methods that can be used to study the thermodynamics critical points of a system based on a topological approach is the study of topological charges using Duan's $\phi$-mapping method. In this article, we will attempt to use this method to study three different black holes, each with different coefficients in their metric function, in order to

Yiqun Wang, Ivan Skorokhodov, Peter Wonka

A signed distance function (SDF) parametrized by an MLP is a common ingredient of neural surface reconstruction. We build on the successful recent method NeuS to extend it by three new components. The first component is to borrow the tri-plane representation from EG3D and represent signed distance fields as a mixture of tri-planes and MLPs instead of represe

E. F. Talantsev

Nickelate superconductors, ${R_{1-x}}{A_x}Ni{O_2}$ (where R is a rare earth metal and A = Sr, Ca), experimentally discovered in 2019 exhibit many unexplained mysteries as the existence of a superconducting state with $T_c$ up to 18 K in thin films and its absence in bulk materials. Another unexplained mystery of nickelates is their temperature-dependent uppe

Ela Liberman-Pincu, Tal Oron-Gilad

Medical clowns help hospitalized children in reducing pain and anxiety symptoms and increase the level of satisfaction in children's wards. Unfortunately, there is a shortage of medical clowns around the world. Furthermore, isolated children can not enjoy this service. This study explored the concept of a Robotic Medical Clown (RMC) and its role. We used mix

Pradyumna Reddy, Scott Wisdom, Klaus Greff, John R. Hershey

In a range of recent works, object-centric architectures have been shown to be suitable for unsupervised scene decomposition in the vision domain. Inspired by these methods we present AudioSlots, a slot-centric generative model for blind source separation in the audio domain. AudioSlots is built using permutation-equivariant encoder and decoder networks. The

Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng, Pratik Worah

Myerson's regularity condition of a distribution is a standard assumption in economics. In this paper, we study the complexity of describing a regular distribution within a small statistical distance. Our main result is that $\tilde{\Theta}{(\epsilon^{-0.5})}$ bits are necessary and sufficient to describe a regular distribution with support $[0,1]$ within $\

Mattia Opper, Victor Prokhorov, N. Siddharth

This work presents StrAE: a Structured Autoencoder framework that through strict adherence to explicit structure, and use of a novel contrastive objective over tree-structured representations, enables effective learning of multi-level representations. Through comparison over different forms of structure, we verify that our results are directly attributable t

SooJean Han, Soon-Jo Chung, John C. Doyle

Incorporating pattern-learning for prediction (PLP) in many discrete-time or discrete-event systems allows for computation-efficient controller design by memorizing patterns to schedule control policies based on their future occurrences. In this paper, we demonstrate the effect of PLP by designing a controller architecture for a class of linear Markovian jum

Partha Chakraborty, Mahmoud Alfadel, Meiyappan Nagappan

Software developers spend a significant portion of time fixing bugs in their projects. To streamline this process, bug localization approaches have been proposed to identify the source code files that are likely responsible for a particular bug. Prior work proposed several similarity-based machine-learning techniques for bug localization. Despite significant

Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren

Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user feedback for such target behavior prediction purposes is still an open question. Existing studies that attempted

A. Tononi, L. Salasnich

Atomic gases confined in curved geometries are characterized by distinctive features that are absent in their flat counterparts, such as periodic boundaries, local curvature, and nontrivial topologies. The recent experiments with shell-shaped quantum gases and the study of ring-shaped superfluids point out that the manifold of a quantum gas could soon become

Youliang Zhang, Zhuo Zhou, Wenxuan Liu, Danni Xu

Previous group activity recognition approaches were limited to reasoning using human relations or finding important subgroups and tended to ignore indispensable group composition and human-object interactions. This absence makes a partial interpretation of the scene and increases the interference of irrelevant actions on the results. Therefore, we propose ou

Sheng Wan, Dashan Gao, Hanlin Gu, Daning Hu

Cross-platform recommendation aims to improve recommendation accuracy by gathering heterogeneous features from different platforms. However, such cross-silo collaborations between platforms are restricted by increasingly stringent privacy protection regulations, thus data cannot be aggregated for training. Federated learning (FL) is a practical solution to d

Kallol Mondal, Sudin Ganguly, Santanu K. Maiti

The charge and spin-dependent thermoelectric responses are investigated on a single-helical molecule possessing a collinear antiferromagnetic spin arrangement with zero net magnetization in the presence of a transverse electric field. Both the short and long-range hopping scenarios are considered, which mimic biological systems like single-stranded DNA and $

Andor Menczer, Örs Legeza

The interplay of quantum and classical simulation and the delicate divide between them is in the focus of massively parallelized tensor network state (TNS) algorithms designed for high performance computing (HPC). In this contribution, we present novel algorithmic solutions together with implementation details to extend current limits of TNS algorithms on HP

Enric Moreu, Alex Martinelli, Martina Naughton, Philip Kelly

Accurate product information is critical for e-commerce stores to allow customers to browse, filter, and search for products. Product data quality is affected by missing or incorrect information resulting in poor customer experience. While machine learning can be used to correct inaccurate or missing information, achieving high performance on fashion image c

Jarret Rock, Ying Wang

The radar altimeter is installed on most 14 CFR Pt 25 category aircraft, which are applicable to passenger travel and represent most airline traffic. The radar altimeter system is highly accurate and reports the height above the terrain. It plays a significant role in the take-off, approach, and landing phases of the applicable aircraft. In critical conditio

Shuning Sun, Qiankun Diao, Dongpo Xu, Pauline Bourigault

Convex optimization methods have been extensively used in the fields of communications and signal processing. However, the theory of quaternion optimization is currently not as fully developed and systematic as that of complex and real optimization. To this end, we establish an essential theory of convex quaternion optimization for signal processing based on

Pratyush Kumar

Large Language Models (LLMs) have made rapid progress in recent months and weeks, garnering significant public attention. This has sparked concerns about aligning these models with human values, their impact on labor markets, and the potential need for regulation in further research and development. However, the discourse often lacks a focus on the imperativ

Nicolò Rubattu, Gabriele Maroni, Giorgio Corani

We describe our experience in developing a predictive model that placed high position in the BigDeal Challenge 2022, an energy competition of load and peak forecasting. We present a novel procedure for feature engineering and feature selection, based on cluster permutation of temperatures and calendar variables. We adopted gradient boosting of trees and we e

Soumajyoti Sarkar

Decentralized multi-agent reinforcement learning (MARL) algorithms have become popular in the literature since it allows heterogeneous agents to have their own reward functions as opposed to canonical multi-agent Markov Decision Process (MDP) settings which assume common reward functions over all agents. In this work, we follow the existing work on collabora

Artem O. Denisov, Gordian Fuchs, Seong W. Oh, Jason R. Petta

We demonstrate dispersive charge sensing of Si/SiGe single and double quantum dots (DQD) by coupling sub-micron floating gates to a radio frequency reflectometry (rf-reflectometry) circuit using the tip of an atomic force microscope (AFM). Charge stability diagrams are obtained in the phase response of the reflected rf signal. We demonstrate single-electron

Arthur Correnson, Dominic Steinhoefel

Symbolic execution is a program analysis technique executing programs with symbolic instead of concrete inputs. This principle allows for exploring many program paths at once. Despite its wide adoption -- in particular for program testing -- little effort was dedicated to studying the semantic foundations of symbolic execution. Without these foundations, cri

Stefano Radrizzani, Giulio Panzani, Sergio M. Savaresi

Combining the advantages of standard bicycles and electrified vehicles, electric bikes (e-Bikes) are promising vehicles to reduce emission and traffic. The current literature on e-Bikes ranges from works on the energy management to the vehicle control to properly govern the human-vehicle interaction. This last point is fundamental in chain-less series bikes,

Jaume Anguera Peris, Viktoria Fodor

Edge intelligence is an emerging technology where the base stations located at the edge of the network are equipped with computing units that provide machine learning services to the end users. To provide high-quality services in a cost-efficient way, the wireless and computing resources need to be dimensioned carefully. In this paper, we address the problem

Leonard Peter Binamungu, Salome Maro

Context: Behaviour Driven Development (BDD) uses scenarios written in semi-structured natural language to express software requirements in a way that can be understood by all stakeholders. The resulting natural language specifications can also be executed to reveal correct and problematic parts of a software. Although BDD was introduced about two decades ago

Adam Michalski, Filippos Christianos, Stefano V. Albrecht

There is a lack of standard benchmarks for Multi-Agent Reinforcement Learning (MARL) algorithms. The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, but is built on top of a heavy, closed-source computer game, StarCraft II. Thus, SMAC is computationally expensive and requires knowledge and the use of proprietary tools specific t

Gabriel Arpino, Daniil Dmitriev, Nicolo Grometto

Consider the Hitting Set problem where, for a given universe $\mathcal{X} = \left\{ 1, ... , n \right\}$ and a collection of subsets $\mathcal{S}_1, ... , \mathcal{S}_m$, one seeks to identify the smallest subset of $\mathcal{X}$ which has nonempty intersection with every element in the collection. We study a probabilistic formulation of this problem, where

Sophie Lebowitz, Bjorn Emonts, Donald M. Terndrup, Joseph N. Burchett

The Dragonfly Galaxy (MRC 0152-209), the most infrared-luminous radio galaxy at redshift z~2, is a merger system containing a powerful radio source and large displacements of gas. We present kpc-resolution data from ALMA and the VLA of carbon monoxide (6-5), dust, and synchrotron continuum, combined with Keck integral-field spectroscopy. We find that the Dra

Cheng-Rong Deng

We reveal the quark exchange effects related to both the kinetic energy and various interactions in the single flavored dibaryon bound states with $^1S_0$ in the quark models. The hadron covalent bond can be established by the shared identical quarks due to the quark exchange effect between two colorless baryons. Such hadron covalent bond plays a decisive ro

Pawel Pukowski, Haiping Lu

Despite their limited interpretability, weights and biases are still the most popular encoding of the functions learned by ReLU Neural Networks (ReLU NNs). That is why we introduce SkelEx, an algorithm to extract a skeleton of the membership functions learned by ReLU NNs, making those functions easier to interpret and analyze. To the best of our knowledge, t

Dominik Gutwein

This article constructs coassociative submanifolds in $G_2$-manifolds arising from Joyce's generalised Kummer construction. The novelty compared to previous constructions is that these submanifolds all lie within the critical region of the $G_2$-manifold in which the metric degenerates. This forces the volume of the coassociatives to shrink to zero when the

Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley

For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker. We explore both what policies these sets should contain and how such sets can be computed efficiently. With this in mind, we take a distributional approach and introduce a novel dominance criterion relating re

Paul Scheffler, Florian Zaruba, Fabian Schuiki, Torsten Hoefler

Sparse linear algebra is crucial in many application domains, but challenging to handle efficiently in both software and hardware, with one- and two-sided operand sparsity handled with distinct approaches. In this work, we enhance an existing memory-streaming RISC-V ISA extension to accelerate both one- and two-sided operand sparsity on widespread sparse ten

David P. Blecher, Mehrdad Kalantar

Given a finite group G, a central subgroup H of G, and an operator space X equipped with an action of H by complete isometries, we construct an operator space $X_G$ equipped with an action of G which is unique under a `reasonable' condition. This generalizes the operator space complexification $X_c$ of $X$. As a linear space $X_G$ is the space obtained from

Fernando Sancho de Salas, Alejandro Torres Sancho

We develop Grothendieck's theory of dualizing complexes on finite posets, and its subsequent theory of Cohen-Macaulayness.

Pontus Vikstål, Laura García-Álvarez, Shruti Puri, Giulia Ferrini

The Quantum Approximate Optimization Algorithm (QAOA) -- one of the leading algorithms for applications on intermediate-scale quantum processors -- is designed to provide approximate solutions to combinatorial optimization problems with shallow quantum circuits. Here, we study QAOA implementations with cat qubits, using coherent states with opposite amplitud

Cristian D. Panda, Matt Tao, Miguel Ceja, Andrew Reynoso

Atomic accelerometers and gravimeters are usually based on freely-falling atoms in atomic fountains, which not only limits their size, but also their robustness to environmental factors such as tilts, magnetic fields or vibrations. Such limitations have precluded their broad adoption in the field, for geophysics, geology, and inertial navigation. More recent

A. Bershadskii

The transition from hard/soft turbulence to deterministic chaos in freely decaying fluid dynamics (incompressible and compressible) has been studied using the results of laboratory measurements and numerical simulations. The notion of distributed chaos has been applied in order to quantify differences in the intermediate regimes appearing during free decay w

Masashi K. Kajita, Yoshiyuki Konishi, Tetsuhiro S. Hatakeyama

Mitochondria are critical organelles in eukaryotes that produce the energy currency ATP. In nerve axons, mitochondria are known to align at almost regular intervals to maintain a constant ATP concentration, but little is known about the mechanism. In this letter, we show theoretically that ATP production and ATP-dependent non-directional movement of mitochon

Samuel Lensgraf, Devin Balkcom, Alberto Quattrini Li

We present the first free-floating autonomous underwater construction system capable of using active ballasting to transport cement building blocks efficiently. It is the first free-floating autonomous construction robot to use a paired set of resources: compressed air for buoyancy and a battery for thrusters. In construction trials, our system built structu

Paolo Ciancarini, Raffaele Giancarlo, Gennaro Grimaudo

Digital Transformation (DT) is the process of integrating digital technologies and solutions into the activities of an organization, whether public or private. This paper focuses on the DT of public sector organizations, where the targets of innovative digital solutions are either the citizens or the administrative bodies or both. This paper is a guided tour

Giada Pistilli, Carlos Munoz Ferrandis, Yacine Jernite, Margaret Mitchell

The growing need for accountability of the people behind AI systems can be addressed by leveraging processes in three fields of study: ethics, law, and computer science. While these fields are often considered in isolation, they rely on complementary notions in their interpretation and implementation. In this work, we detail this interdependence and motivate

Chih-Whi Chen, Shun-Jen Cheng

We study the Whittaker category $\mathcal N(\zeta)$ of the Lie superalgebra $\mathfrak g$ for an arbitrary character $\zeta$ of the even subalgebra of the nilpotent radical associated with a triangular decomposition of $\mathfrak g$. We prove that the Backelin functor from either the integral subcategory or any strongly typical block of the BGG category to t

Daniel Collins, Conor Houghton, Nirav Ajmeri

Human social behavior is influenced by individual differences in social preferences. Social value orientation (SVO) is a measurable personality trait which indicates the relative importance an individual places on their own and on others' welfare when making decisions. SVO and other individual difference variables are strong predictors of human behavior and

Javier Morlana, Juan D. Tardós, J. M. M. Montiel

We propose a topological mapping and localization system able to operate on real human colonoscopies, despite significant shape and illumination changes. The map is a graph where each node codes a colon location by a set of real images, while edges represent traversability between nodes. For close-in-time images, where scene changes are minor, place recognit

Graeme Wilkin

Convolution in Borel-Moore homology plays an important role in Nakajima's construction of representations of the Heisenberg algebra and of modified enveloping algebras of Kac-Moody algebras. In its most basic form, convolution between two quiver varieties is given by pullback and then pushforward via the Hecke correspondence for quivers. In previous work we

Mattia Sandri, Matteo Pagin, Marco Giordani, Michele Zorzi

While the 5th generation (5G) of mobile networks has landed in the commercial area, the research community is exploring new functionalities for 6th generation (6G) networks, for example non-terrestrial networks (NTNs) via space/air nodes such as Unmanned Aerial Vehicles (UAVs), High Altitute Platforms (HAPs) or satellites. Specifically, satellite-based commu

Zoé Faure Beaulieu, Thomas C. Nicholas, John L. A. Gardner, Andrew L. Goodwin

Zeolitic imidazolate frameworks are widely thought of as being analogous to inorganic AB$_{2}$ phases. We test the validity of this assumption by comparing simplified and fully atomistic machine-learning models for local environments in ZIFs. Our work addresses the central question to what extent chemical information can be "coarse-grained" in hybrid framewo

Seongmin Jeon, Stefano Vita

Aim of this paper is to provide higher order boundary Harnack principles [De Silva-Savin 15] for elliptic equations in divergence form under Dini type regularity assumptions on boundaries, coefficients and forcing terms. As it was proven in [Terracini-Tortone-Vita 22], the ratio $v/u$ of two solutions vanishing on a common portion $\Gamma$ of a regular bound

Yini Fang, Liang Wu, Frederic Jumelle, Bertram Shi

Video-based Emotional Reaction Intensity (ERI) estimation measures the intensity of subjects' reactions to stimuli along several emotional dimensions from videos of the subject as they view the stimuli. We propose a multi-modal architecture for video-based ERI combining video and audio information. Video input is encoded spatially first, frame-by-frame, comb

Radosław Zaleski, Olga Kotowicz, Agnieszka Górska, Kamil Zaleski

Various concentrations (8{\div}300 mmol/L) of NaCl, KCl and NaCl + KCl aqueous solutions were investigated using the positron annihilation lifetime spectroscopy (PALS). A strong dependence of the o-Ps intensity as a function of solution concentration was demonstrated. On this basis, the mean positron lifetime or the sum of counts in a selected time interval

Haldun Balim, Seonwook Park, Xi Wang, Xucong Zhang

Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is believed to benefit the final model performa

Rami Ezzine, Moritz Wiese, Christian Deppe, Holger Boche

We consider a standard two-source model for uniform common randomness (UCR) generation, in which Alice and Bob observe independent and identically distributed (i.i.d.) samples of a correlated finite source and where Alice is allowed to send information to Bob over an arbitrary single-user channel. We study the \(\boldsymbol{\epsilon}\)-UCR capacity for the p

Yini Fang, Didan Deng, Liang Wu, Frederic Jumelle

Micro-expressions (MEs) are involuntary and subtle facial expressions that are thought to reveal feelings people are trying to hide. ME spotting detects the temporal intervals containing MEs in videos. Detecting such quick and subtle motions from long videos is difficult. Recent works leverage detailed facial motion representations, such as the optical flow,

Sigurd Holmsen, Sølve Eidnes, Signe Riemer-Sørensen

Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data. In this work, we consider systems that can be modelled as first-order ordinary differential equations. By assuming a certain pseudo-Hamiltonian formulation, we are able to learn the analytic terms of internal dynamics even if the model is tr

Samuel Goodman

In this paper, we investigate the stabilizers of certain multisets $\mod p^k$ with respect to their natural multiplicative action, completely describing them for a certain family of polynomials whenever $p$ is an odd prime. This elucidates an underlying structure on the level of elements that yields a new proof of the classical Kummer Congruences. Hence the

Rakesh Das, Takahiro Sakaue, G. V. Shivashankar, Jacques Prost

Spatiotemporal coordination of chromatin and subnuclear compartments is crucial for cells. Numerous enzymes act inside nucleus\textemdash some of those transiently link and pass two chromatin segments. Here we study how such an active perturbation affects fluctuating dynamics of an inclusion in the chromatic medium. Using numerical simulations and a versatil

Bikramjit Das

We consider a model for multivariate data with heavy-tailed marginal distributions and a Gaussian dependence structure. The different marginals in the model are allowed to have non-identical tail behavior in contrast to most popular modeling paradigms for multivariate heavy-tail analysis. Despite being a practical choice, results on parameter estimation and

Vishnu Dutt Sharma, Jingxi Chen, Pratap Tokekar

In a typical path planning pipeline for a ground robot, we build a map (e.g., an occupancy grid) of the environment as the robot moves around. While navigating indoors, a ground robot's knowledge about the environment may be limited due to occlusions. Therefore, the map will have many as-yet-unknown regions that may need to be avoided by a conservative plann

Joonas Hämäläinen, Antoine Hubermont, Amauri Souza, César L. C. Mattos

Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate how their core component, the distance mapping, can be adapted to multi-label learning. The proposed approach is based on combining the dis

Jingfu Li, Gaojie Chen, Tong Zhang, Wenjiang Feng

In space-air-ground integrated networks (SAGIN), receivers experience diverse interference from both the satellite and terrestrial transmitters. The heterogeneous structure of SAGIN poses challenges for traditional interference management (IM) schemes to effectively mitigate interference. To address this, a novel UAV-RIS-aided IM scheme is proposed for SAGIN

Fulin Guo

This paper explores the use of Generative Pre-trained Transformers (GPT) in strategic game experiments, specifically the ultimatum game and the prisoner's dilemma. I designed prompts and architectures to enable GPT to understand the game rules and to generate both its choices and the reasoning behind decisions. The key findings show that GPT exhibits behavio

Gilles Dowek

The rewriting system sigma is the set of rules propagating explicit substitutions in the lambda-calculus with explicit substitutions. In this note, we prove the undecidability of unification modulo sigma.

S. D. Odintsov, V. K. Oikonomou

In this work we study static neutron stars in the context of several inflationary models which are popular in cosmology. These inflationary models are non-minimally coupled scalar theories which yield a viable inflationary phenomenology in both Jordan and Einstein frames. By considering the constraints from inflationary theories, which basically determine th

Srinivas Reddy Kota, Nikhil Karamchandani

We consider the multi-access coded caching problem, which contains a central server with $N$ files, $K$ caches with $M$ units of memory each and $K$ users where each one is connected to $L (\geq 1)$ consecutive caches, with a cyclic wrap-around. Caches are populated with content related to the files and each user then requests a file that has to be served vi

K. Yokoyama, J. S. Lord, P. W. Mengyan, M. R. Goeks

Muonium (Mu), a pseudo-isotope atom of hydrogen with a positively charged muon at the place of the proton, can form in a wide range of semiconductor materials. They can appear in different states, depending on their charge state and microscopic site within a crystal lattice. After the Mu formation, they undergo interactions with free charge carriers, electro