July 2023 arXiv papers — page 38

A. Rahmatinejad, T. M. Shneidman, G. G. Adamian, N. V. Antonenko

The probabilities of $xn$-, $pxn$-, and $\alpha xn$-evaporation channels in excited superheavy nuclei were evaluated using the Monte Carlo method. The calculations utilized microscopically determined nuclear level densities and were compared with results obtained from the phenomenological Jackson formula. Effective temperatures derived from the microscopic a

Jing Wang, Jie-Qiong Li, Wen-Hao Bian, Qiao-Chu Zhang

The interplay among Coulomb interaction, electron-phonon coupling, and phonon-phonon coupling has a significant impact on the low-energy behavior of three-dimensional type-I tilted Dirac semimetals. To investigate this phenomenon, we construct an effective theory, calculate one-loop corrections arising from all these interactions, and establish the coupled e

N. V. Nunes, N. Bartel, A. Belonenko, G. D. Manucharyan

The Einstein Equivalence Principle (EEP) is a cornerstone of general relativity and predicts the existence of gravitational redshift. We report on new results of measuring this shift with RadioAstron (RA), a space VLBI spacecraft launched into an evolving high eccentricity orbit around Earth with geocentric distances reaching 353,000 km. The spacecraft and g

M. Bousder

This letter provides evidence of complexity-volume and complexity-action conjectures by examining the structure of a black hole, which comprises a horizon linked to the singularity through a wormhole. In this situation, the shape of the black hole's geometry resembles that of Gabriel's horn. In essence, our results indicate that the information paradox is me

Josef Koumar, Karel Hynek, Tomáš Čejka

Network traffic monitoring using IP flows is used to handle the current challenge of analyzing encrypted network communication. Nevertheless, the packet aggregation into flow records naturally causes information loss; therefore, this paper proposes a novel flow extension for traffic features based on the time series analysis of the Single Flow Time series, i

P. Weber, S. Pérez, A. Zurlo, J. Miley

The formation of giant planets has traditionally been divided into two pathways: core accretion and gravitational instability. However, in recent years, gravitational instability has become less favored, primarily due to the scarcity of observations of fragmented protoplanetary disks around young stars and low occurrence rate of massive planets on very wide

Tian Zhang, Xin Guo, Pan Wang, Linjun Li

Artificial visual systems (AVS) have gained tremendous momentum because of its huge potential in areas such as autonomous vehicles and robotics as part of artificial intelligence (AI) in recent years. However, current machine visual systems composed of complex circuits based on complementary metal oxide semiconductor (CMOS) platform usually contains photosen

A. Söll, E. Lopriore, A. K. Ottesen, J. Luxa

Van der Waals heterostructures of two-dimensional materials have opened up new frontiers in condensed matter physics, unlocking unexplored possibilities in electronic and photonic device applications. However, the investigation of wide-gap high-$\kappa$ layered dielectrics for devices based on van der Waals structures has been relatively limited. In this wor

Hongchang Gao

The stochastic compositional minimax problem has attracted a surge of attention in recent years since it covers many emerging machine learning models. Meanwhile, due to the emergence of distributed data, optimizing this kind of problem under the decentralized setting becomes badly needed. However, the compositional structure in the loss function brings uniqu

Xinyu Gao, Wenqiang Yi, Yuanwei Liu, Lajos Hanzo

Metaverse aims for building a fully immersive virtual shared space, where the users are able to engage in various activities. To successfully deploy the service for each user, the Metaverse service provider and network service provider generally localise the user first and then support the communication between the base station (BS) and the user. A reconfigu

Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Jose M. Buades, Prayag Tiwari

This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classificat

Sumit Sharma, Sarika Jain

Situation awareness is a crucial cognitive skill that enables individuals to perceive, comprehend, and project the current state of their environment accurately. It involves being conscious of relevant information, understanding its meaning, and using that understanding to make well-informed decisions. Awareness systems often need to integrate new knowledge

Martin Heßler, Tobias Wand, Oliver Kamps

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understand the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis which solves significant memory and computing time limitations to extract crisis inf

Cynthia Kop, Deivid Vale

In this short paper, we consider a form of higher-order rewriting with a call-by-value evaluation strategy so as to model call-by-value programs. We briefly present a cost-size semantics to call-by-value rewriting: a class of algebraic interpretations that map terms to tuples that bound both the reductions' cost and the size of normal forms.

Luis A. Zavala-Mondragón, Peter H. N. de With, Fons van der Sommen

Encoding-decoding CNNs play a central role in data-driven noise reduction and can be found within numerous deep-learning algorithms. However, the development of these CNN architectures is often done in ad-hoc fashion and theoretical underpinnings for important design choices is generally lacking. Up to this moment there are different existing relevant works

Hexuan Deng, Xin Zhang, Meishan Zhang, Xuebo Liu

In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for UDS parsing that decomposes the complex parsing task into semantically appropriate subtasks. Our approach outperforms the prior models, while significantly reducing inference time. We also incorporate syntactic info

George Close, Thomas Hain, Stefan Goetze

Self-supervised speech representations (SSSRs) have been successfully applied to a number of speech-processing tasks, e.g. as feature extractor for speech quality (SQ) prediction, which is, in turn, relevant for assessment and training speech enhancement systems for users with normal or impaired hearing. However, exact knowledge of why and how quality-relate

Ivan Letteri

Volatility-based trading strategies have attracted a lot of attention in financial markets due to their ability to capture opportunities for profit from market dynamics. In this article, we propose a new volatility-based trading strategy that combines statistical analysis with machine learning techniques to forecast stock markets trend. The method consists o

Rahul Vashisht, Harish G. Ramaswamy

Attention models are typically learned by optimizing one of three standard loss functions that are variously called -- soft attention, hard attention, and latent variable marginal likelihood (LVML) attention. All three paradigms are motivated by the same goal of finding two models -- a `focus' model that `selects' the right \textit{segment} of the input and

Jeremy B. Hume

We compute the K-theory of the three C*-algebras associated to a rational function R acting on the Riemann sphere, its Fatou set, and its Julia set. The latter C*-algebra is a unital UCT Kirchberg algebra and is thus classified by its K-theory. The K-theory in all three cases is shown to depend only on the degree of R, the critical points of R, and the Fatou

Michael Yuhas, Arvind Easwaran

Learning enabled components (LECs), while critical for decision making in autonomous vehicles (AVs), are likely to make incorrect decisions when presented with samples outside of their training distributions. Out-of-distribution (OOD) detectors have been proposed to detect such samples, thereby acting as a safety monitor, however, both OOD detectors and LECs

Yang Li, Diederik Roest, Tonnis ter Veldhuis

We perform a systematic classification of scalar field theories whose amplitudes admit a double copy formulation and identify two building blocks at 4-point and 13 at 5-point. Using the 4-point blocks as bootstrap seeds, this naturally leads to a single copy theory that is a gauged NLSM. Moreover, its double copy includes a novel theory that can be written i

Matthias Thurnbauer, Johannes Reisinger, Christoph Goller, Andreas Fischer

Ambiguity is ubiquitous in natural language. Resolving ambiguous meanings is especially important in information retrieval tasks. While word embeddings carry semantic information, they fail to handle ambiguity well. Transformer models have been shown to handle word ambiguity for complex queries, but they cannot be used to identify ambiguous words, e.g. for a

Julio Arrechea, Carlos Barceló, Valentin Boyanov

Standard General Relativity assumes that, in the absence of classical matter sources, spacetime is empty. This chapter considers and analyses the new behaviours of the gravitational field that appear when one substitutes this emptiness by a reactive vacuum, stemming in particular from the idea of vacuum provided by quantum field theory. We restrict our study

Wei Shi, Milad Ganjalizadeh, Hossein Shokri Ghadikolaei, Marina Petrova

Ultra-reliable low latency communications (URLLC) service is envisioned to enable use cases with strict reliability and latency requirements in 5G. One approach for enabling URLLC services is to leverage Reinforcement Learning (RL) to efficiently allocate wireless resources. However, with conventional RL methods, the decision variables (though being deployed

Javier Segovia-Aguas, Jonathan Ferrer-Mestres, Sergio Jiménez

This paper introduces a general approach for synthesizing procedural models of the state-transitions of a given discrete system. The approach is general in that it accepts different target languages for modeling the state-transitions of a discrete system; different model acquisition tasks with different target languages, such as the synthesis of STRIPS actio

Wojciech Rudziński, Józef Barnaś, Anna Dyrdał

Van der Waals magnetic materials are currently of great interest as materials for applications in future ultrathin nanoelectronics and nanospintronics. Due to weak coupling between individual monolayers, these materials can be easily obtained in the monolayer and bilayer forms. The latter are of specific interest as they may be considered as natural two-dime

Sören Christensen, Kristoffer Lindensjö, Berenice Anne Neumann

We study a general formulation of the classical two-player Dynkin game in a discrete time Markovian setting. We identify an appropriate class of mixed strategies -- \textit{Markovian randomized stopping times} -- in which players stop at any given state with a state-dependent probability. One main result is an explicit characterization of Wald-Bellman-type f

Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane

The unprecedented accuracy of convolutional neural networks (CNNs) across a broad range of AI tasks has led to their widespread deployment in mobile and embedded settings. In a pursuit for high-performance and energy-efficient inference, significant research effort has been invested in the design of FPGA-based CNN accelerators. In this context, single comput

Carlos Ferreira

Artificial Intelligence is not a new subject, and business, industry and public sectors have used it in different ways and contexts and considering multiple concerns. This work reviewed research papers published in ACM Digital Library and IEEE Xplore conference proceedings in the last two years supported by fundamental concepts of Natural Language Processing

Andrea Carati, Fabrizio Gangemi, Roberto Gangemi, Luigi Galgani

General features of the $\alpha-\beta$ transition of quartz are investigated. Molecular dynamics methods are mainly used, an analytic treatment being deferred to a work in preparation. A basic preliminary observation is that the transition involves only a subsystem of four normal modes on which the remaining ones just act as a reservoir. The dynamics of the

Leon Ofman, Scott A Boardsen, Lan K Jian, Parisa Mostafavi

Past observations show that solar wind (SW) acceleration occurs inside the sub-Alfvenic region, reaching the local Alfven speed at typical distances ~ 10 - 20 Rs (solar radii). Recently, Parker Solar Probe (PSP) traversed regions of sub-Alfvenic SW near perihelia in encounters E8-E12 for the first time providing data in these regions. It became evident that

Zhen-Zhen He, Guang-Xing Li, Andreas Burkert

Gravity is the driving force of star formation. Although gravity is caused by the presence of matter, its role in complex regions is still unsettled. One effective way to study the pattern of gravity is to compute the accretion it exerts on the gas by providing gravitational acceleration maps. A practical way to study acceleration is by computing it using 2D

Savina Dine Kim, Galina Andreeva, Michael Rovatsos

This research article analyses and demonstrates the hidden implications for fairness of seemingly neutral data coupled with powerful technology, such as machine learning (ML), using Open Banking as an example. Open Banking has ignited a revolution in financial services, opening new opportunities for customer acquisition, management, retention, and risk asses

Julia Boeyens, Björn Annby-Andersson, Pharnam Bakhshinezhad, Géraldine Haack

Temperature estimation plays a vital role across natural sciences. A standard approach is provided by probe thermometry, where a probe is brought into contact with the sample and examined after a certain amount of time has passed. In many situations however, continuously monitoring the probe may be preferred. Here, we consider a minimal model, where the prob

Alix Malfondet, Moise Deroh, Sidi-Ely Ahmedou, Alexandre Parriaux

In this paper, we introduce an all-fibered dual-comb spectrometer based on a new design of highly nonlinear fiber to efficiently convert frequency combs from 1.55 micron to 2 micron We show that our spectrometer can be used to measure absorption profiles of rovibrational transitions of CO2 and N2O molecules, and especially their collisional self-broadening c

Gábor Bella, Paula Helm, Gertraud Koch, Fausto Giunchiglia

It is a well-known fact that current AI-based language technology -- language models, machine translation systems, multilingual dictionaries and corpora -- focuses on the world's 2-3% most widely spoken languages. Recent research efforts have attempted to expand the coverage of AI technology to `under-resourced languages.' The goal of our paper is to bring a

Xiao Wang, Zhi-Yu Lin, Yu-Min Zhang, Guo-Qiang Ren

The effects of different substrates on the off state leakage current in gallium nitride planar diodes are experimentally demonstrated and studied by analyzing temperature-dependent current voltage characteristics.

José Cáceres, Ignacio M. Pelayo

The aim of this paper is to revise the literature on different metric locations in the families of paths, cycles, trees and unicyclic graphs, as well as, provide several new results on that matter.

Sigrid Leyendecker, Sofya Maslovskaya, Sina Ober-Blobaum, Rodrigo T. Sato Martin de Almagro

In this work, we consider optimal control problems for mechanical systems on vector spaces with fixed initial and free final state and a quadratic Lagrange term. Specifically, the dynamics is described by a second order ODE containing an affine control term and we allow linear coordinate changes in the configuration space. Classically, Pontryagin's maximum p

Qingqiang He, Nan Guan, Mingsong Lv

DAG (directed acyclic graph) tasks are widely used to model parallel real-time workload. The real-time performance of a DAG task not only depends on its total workload, but also its graph structure. Intuitively, with the same total workload, a DAG task with looser precedence constraints tends to have better real-time performance in terms of worst-case respon

Suvodeep Paul, Devesh Negi, Saswata Talukdar, Saheb Karak

Tailoring the quantum many-body interactions in layered materials through appropriate heterostructure engineering can result in emergent properties that are absent in the constituent materials thus promising potential future applications. In this article, we have demonstrated controlling the otherwise robust magnetic properties of transition metal phosphorus

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The first observation and study of two new baryonic structures in the final state $\Xi_b^0\pi^+\pi^-$ and the confirmation of the $\Xi_b(6100)^-$ state in the $\Xi_b^-\pi^+\pi^-$ decay mode are reported using proton-proton collision data collected by the LHCb experiment, corresponding to an integrated luminosity of 9$\mathrm{fb}^{-1}$. In addition, the prope

Aneesh P. Naik, Axel Widmark

In an earlier work, we demonstrated the effectiveness of Bayesian neural networks in estimating the missing line-of-sight velocities of Gaia stars, and published an accompanying catalogue of blind predictions for the line-of-sight velocities of stars in Gaia DR3. These were not merely point predictions, but probability distributions reflecting our state of k

Miguel Costa, Manuel Marques, Felix Wilhelm Siebert, Carlos Lima Azevedo

Today, many cities seek to transition to more sustainable transportation systems. Cycling is critical in this transition for shorter trips, including first-and-last-mile links to transit. Yet, if individuals perceive cycling as unsafe, they will not cycle and choose other transportation modes. This study presents a novel approach to identifying how the perce

Irmak Sağlam, Anne-Kathrin Schmuck

This paper discusses the problem of efficiently solving parity games where player Odd has to obey an additional 'strong transition fairness constraint' on its vertices -- given that a player Odd vertex $v$ is visited infinitely often, a particular subset of the outgoing edges (called live edges) of $v$ has to be taken infinitely often. Such games, which we c

Lei Chen, Weiyao Zhaoa, Meng Li, Guangsai Yang

As one of the most important n-type thermoelectric (TE) materials, Bi2Te3 has been studied for decades, with efforts to enhance the thermoelectric performance based on element doping, band engineering, etc. In this study, we report a novel bulk-insulating topological material system as a replacement for n-type Bi2Te3 materials: V doped Bi1.08Sn0.02Sb0.9Te2S

Andrew Aquilina, Sean Diacono, Panagiotis Papapetrou, Maria Movin

The consumption of podcast media has been increasing rapidly. Due to the lengthy nature of podcast episodes, users often carefully select which ones to listen to. Although episode descriptions aid users by providing a summary of the entire podcast, they do not provide a topic-by-topic breakdown. This study explores the combined application of topic segmentat

Sougata Biswas, Arunava Chakrabarti

An infinitely large Koch fractal is shown to be capable of sustaining only extended, Bloch-like eigenstates, if certain parameters of the Hamiltonian describing the lattice are numerically correlated in a special way, and a magnetic flux of a special strength is trapped in every loop of the geometry. We describe the system within a tight binding formalism an

Ming-Yuan Yan, Shuang-Shuang Li, Jian-Min Yan, Li Xie

Spin-orbit interaction (SOI) offers a nonferromagnetic scheme to realize spin polarization through utilizing an electric field. Electrically tunable SOI through electrostatic gates have been investigated, however, the relatively weak and volatile tunability limit its practical applications in spintronics. Here, we demonstrate the nonvolatile electric-field c

Andrey Piatnitski, Elena Zhizhina

We study homogenization problem for non-autonomous parabolic equations of the form $\partial_t u=L(t)u$ with an integral convolution type operator $L(t)$ that has a non-symmetric jump kernel which is periodic in spatial variables and stationary random in time. We show that the homogenized equation is a SPDE with a finite dimensional multiplicative noise.

Xuan Zhao, Klaus Broelemann, Gjergji Kasneci

Counterfactual Explanations (CEs) are an important tool in Algorithmic Recourse for addressing two questions: 1. What are the crucial factors that led to an automated prediction/decision? 2. How can these factors be changed to achieve a more favorable outcome from a user's perspective? Thus, guiding the user's interaction with AI systems by proposing easy-to

Mateo Anarella, Joeri Van der Veken

In this paper, we study Lagrangian submanifolds of the pseudo-nearly K\"ahler $\mathrm{SL}(2,\mathbb{R})\times\mathrm{SL}(2,\mathbb{R})$. First, we show that they split into four different classes depending on their behaviour with respect to a certain almost product structure on the ambient space. Then, we give a complete classification of totally geodesic L

K. Hori, S. Souma, C. -W. Chuang, Y. Nakata

Understanding the mechanism of ferromagnetism in ferromagnetic topological insulators (TIs) is a key to realize exotic time-reversal-symmetry-broken quantum phases. However, electronic states relevant to the ferromagnetism are highly controversial. Here we report angle-resolved photoemission spectroscopy on (CrxSb1-x)2Te3 thin films, high-Curie-temperature (

Michael T. Jury, Georgios Tsikalas

We introduce and study a scale of operator classes on the annulus that is motivated by the $\mathcal{C}_{\rho}$ classes of $\rho$-contractions of Nagy and Foia\c{s}. In particular, our classes are defined in terms of the contractivity of the double-layer potential integral operator over the annulus. We prove that if, in addition, complete contractivity is as

Fenglin Bi, Zhiwei Zhu, Wei Wang, Xiaoya Xia

Social coding platforms have revolutionized collaboration in software development, leading to using software bots for streamlining operations. However, The presence of open-source software (OSS) bots gives rise to problems including impersonation, spamming, bias, and security risks. Identifying bot accounts and behavior is a challenging task in the OSS proje

Lei Guo, Meng Xu, Lei Chen, Ting Wei Chen

Weak antilocalization (WAL) effect is commonly observed in 2D systems, or 3D topological insulators, topological semimetal systems. Here we report the clear sign of WAL effect in high quality Ta$_{0.7}$Nb$_{0.3}$Sb$_2$ single crystals, in below 50$^\circ$ K region. The chemical vapor transport method was employed to grow the single crystal samples, the high

Teng Man, Herbert E. Huppert, Sergio Andres Galindo-Torres

Granular column collapse is a simple but important problem to the granular material community, due to its links to dynamics of natural hazards, such as landslides and pyroclastic flows, and many industrial situations, as well as its potential of analyzing transient and non-local rheology of granular flows. This article proposes a new dimensionless number to

Michele Tufano, Shubham Chandel, Anisha Agarwal, Neel Sundaresan

Code coverage is a widely used metric for quantifying the extent to which program elements, such as statements or branches, are executed during testing. Calculating code coverage is resource-intensive, requiring code building and execution with additional overhead for the instrumentation. Furthermore, computing coverage of any snippet of code requires the wh

Alex J. Matthies, Jonathan M. Mortlock, Lewis A. McArd, Adarsh P. Raghuram

We report on the transport of ultracold cesium and rubidium atoms over $37.2\,$cm in under $25\,$ms using an optical conveyor belt formed by two counter-propagating beams with a controllable frequency difference that generate a movable optical lattice. By carefully selecting the waists and focus positions, we are able to use two static Gaussian beams for the

Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, Michael I. Jordan

We present Scaff-PD, a fast and communication-efficient algorithm for distributionally robust federated learning. Our approach improves fairness by optimizing a family of distributionally robust objectives tailored to heterogeneous clients. We leverage the special structure of these objectives, and design an accelerated primal dual (APD) algorithm which uses

Soichiro Morisaki, Rory Smith, Leo Tsukada, Surabhi Sachdev

We present a rapid parameter estimation framework for compact binary coalescence (CBC) signals observed by the LIGO-Virgo-KAGRA (LVK) detector network. The goal of our framework is to enable optimal source localization of binary neutron star (BNS) signals in low latency, as well as improve the overall scalability of full CBC parameter estimation analyses. Ou

Edmund Heng

Recent developments in the theory of stability conditions and its relation to Teichmuller theory have revealed a deep connection between triangulated categories and surfaces. Motivated by this, we prove a categorical analogue of the Nielsen-Thurston classification theorem for the rank two generalised braid groups by viewing them as (sub)groups of autoequival

Tiantian Zhang, Zhiheng Huang, Zitian Pan, Luojun Du

Chirality is an indispensable concept that pervades fundamental science and nature, manifesting itself in diverse forms such as chiral quasiparticles and chiral structures. Of particular interest are Weyl phonons carrying specific Chern numbers and chiral phonons doing circular motions in crystals. Up to now, Weyl and chiral phonons have been studied indepen

Nina Smirnova, Philipp Mayr

Acknowledgments in scientific papers may give an insight into aspects of the scientific community, such as reward systems, collaboration patterns, and hidden research trends. The aim of the paper is to evaluate the performance of different embedding models for the task of automatic extraction and classification of acknowledged entities from the acknowledgmen

Arthur Perodou, Christophe Combastel, Ali Zolghadri

This paper investigates the critical-time criteria as a security metric for controlled systems subject to sharp input anomalies (attack, fault), characterized by having high impact in a reduced amount of time (e.g. denial-of-service, attack by upper saturation). The critical-time is the maximal time-horizon for which a system can be considered to be safe aft

Alexander Jaus, Constantin Seibold, Kelsey Hermann, Alexandra Walter

In this study, we present a method for generating automated anatomy segmentation datasets using a sequential process that involves nnU-Net-based pseudo-labeling and anatomy-guided pseudo-label refinement. By combining various fragmented knowledge bases, we generate a dataset of whole-body CT scans with $142$ voxel-level labels for 533 volumes providing compr

Quentin G. Bailey, Alexander S. Gard, Nils A. Nilsson, Rui Xu

An effective field theory framework is used to investigate some Lorentz-violating effects on the generation of electromagnetic and gravitational waves, complementing previous work on propagation. Specifically we find solutions to a modified, anisotropic wave equation, sourced by charge or fluid matter. We derive the radiation fields for scalars, classical el

Kiwan Park

We demonstrated that a weak magnetic field can increase the permittivity, leading to a reduction in the potential barrier within the Debye sphere consisting of electrons and a nucleus. By solving the Boltzmann equation with the inclusion of the magnetic field, we obtained the magnetized permittivity. The resulting enhanced permittivity field inversely decrea

Manish Prajapat, Mojmír Mutný, Melanie N. Zeilinger, Andreas Krause

In reinforcement learning (RL), rewards of states are typically considered additive, and following the Markov assumption, they are $\textit{independent}$ of states visited previously. In many important applications, such as coverage control, experiment design and informative path planning, rewards naturally have diminishing returns, i.e., their value decreas

Fengxue Zhang, Jialin Song, James Bowden, Alexander Ladd

We study Bayesian optimization (BO) in high-dimensional and non-stationary scenarios. Existing algorithms for such scenarios typically require extensive hyperparameter tuning, which limits their practical effectiveness. We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest (ROI) as a superlevel-set of a nonp

Lénaïc Chizat, Tomas Vaškevičius

We study the computation of doubly regularized Wasserstein barycenters, a recently introduced family of entropic barycenters governed by inner and outer regularization strengths. Previous research has demonstrated that various regularization parameter choices unify several notions of entropy-penalized barycenters while also revealing new ones, including a sp

Matthew Pressland

By work of a number of authors, beginning with Scott and culminating with Galashin and Lam, the coordinate rings of positroid varieties in the Grassmannian carry cluster algebra structures. In fact, they typically carry many such structures, the two best understood being the source-labelled and target-labelled structures, referring to how the initial cluster

Haitian Zeng, Xiaohan Wang, Wenguan Wang, Yi Yang

We introduce a novel speaker model \textsc{Kefa} for navigation instruction generation. The existing speaker models in Vision-and-Language Navigation suffer from the large domain gap of vision features between different environments and insufficient temporal grounding capability. To address the challenges, we propose a Knowledge Refinement Module to enhance

Mudit Garg, Shubhanshu Tiwari, Andrea Derdzinski, John G. Baker

We explore the eccentricity measurement threshold of LISA for gravitational waves radiated by massive black hole binaries (MBHBs) with redshifted BH masses $M_z$ in the range $10^{4.5}$-$10^{7.5}~{\rm M}_\odot$ at redshift $z=1$. The eccentricity can be an important tracer of the environment where MBHBs evolve to reach the merger phase. To consider LISA's mo

Piotr Stefański

A tunneling junction between normal electrode and a topological superconducting wire, mediated by a quantum dot, is considered theoretically. We show that the presence of the dot in the junction can be advantageous to Majorana zero modes identification. Namely, we demonstrate that for the dot strongly coupled to the wire, the Majorana mode from the upper chi

Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos, Michalis Vazirgiannis

In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches. However, most existing methods formulate the task as a multi-classification problem, i.e. assigning predefined labels to proteins. In this work, we propose a novel approach, Prot2Text, which predicts a p

Yifei Gao, Shaohong Chen, Lei Wang, Ruiting Dai

Although large language models (LLMs) have achieved significant success in natural language processing, they still struggle with long-context comprehension. Traditional approaches to mitigating this issue typically rely on fine-tuning or retraining, which is both resource-intensive and challenging to deploy in lightweight industrial settings. In this paper,

Jad Beyhum, Jonas Striaukas

We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative augmenting model with sparse idiosyncratic components. The asymptotic properties of the test are established under time series dependence and polynomial tails. We outline a data-driven rule to select the tuning parameter and prove its theoret

Zehan Wang, Haifeng Huang, Yang Zhao, Linjun Li

3D visual grounding aims to localize the target object in a 3D point cloud by a free-form language description. Typically, the sentences describing the target object tend to provide information about its relative relation between other objects and its position within the whole scene. In this work, we propose a relation-aware one-stage framework, named 3D Rel

Maxime Herda, Pierre Monmarché, Benoît Perthame

Neuron models have attracted a lot of attention recently, both in mathematics and neuroscience. We are interested in studying long-time and large-population emerging properties in a simplified toy model. From a mathematical perspective, this amounts to study the long-time behaviour of a degenerate reflected diffusion process. Using coupling arguments, the fl

Jose Sosa, Sharn Perry, Jane Alty, David Hogg

Numerous fields, such as ecology, biology, and neuroscience, use animal recordings to track and measure animal behaviour. Over time, a significant volume of such data has been produced, but some computer vision techniques cannot explore it due to the lack of annotations. To address this, we propose an approach for estimating 2D mouse body pose from unlabelle

Ivan Lanese, Iain Phillips, Irek Ulidowski

Undoing computations of a concurrent system is beneficial in many situations, e.g., in reversible debugging of multi-threaded programs and in recovery from errors due to optimistic execution in parallel discrete event simulation. A number of approaches have been proposed for how to reverse formal models of concurrent computation including process calculi suc

Yoon-Hyun Ryu, Andrzej Udalski, Jennifer C. Yee, Weicheng Zang

We complete the analysis of planetary candidates found by the KMT AnomalyFinder for the 2017 prime fields that cover $\sim 13\,{\rm deg}^2$. We report 3 unambiguous planets: OGLE-2017-BLG-0640, OGLE-2017-BLG-1275, and OGLE-2017-BLG-1237. The first two of these were not previously identified, while the last was not previously published due to technical compli

Leonid Positselski

We explain how to attach a coalgebra $\mathcal C$ over a field $k$ to a small $k$-linear category $\mathsf E$ satisfying suitable finiteness conditions. In this context, we study full-and-faithfulness of the contramodule forgetful functor, and describe explicitly the categories of locally finite left $\mathcal C$-comodules and left $\mathcal C$-contramodules

Si-wen Li, Yi-peng Zhang, Hao-qian Li

By using the gauge-gravity duality, we investigate the correlation function of flavored fermion in the \mathrm{D}_{p}/\mathrm{D}_{p+4} model as top-down approaches of holographic QCD for p=4,3. The bulk spinor, as the source of the flavored fermion in QCD, is identified to the worldvolume fermion on the flavor \mathrm{D}_{p+4}-branes and the standard form of

Florian Honz, Nemanja Vokić, Philip Walther, Hannes Hübel

We investigate the behavior of a Ge-on-Si light source and demonstrate its feasibility for polarization-encoded discrete-variable quantum key distribution following the BB84 protocol, enabling a potential "all-silicon" QKD scheme which can operate well below the necessary QBER limit and successfully generate secret keys.

Shaoshuai Chu, Alexander Kurganov

We introduce new second-order adaptive low-dissipation central-upwind (LDCU) schemes for the one- and two-dimensional hyperbolic systems of conservation laws. The new adaptive LDCU schemes employ the LDCU numerical fluxes (recently proposed in [{\sc A. Kurganov and R. Xin}, J. Sci. Comput., 96 (2023), Paper No. 56]) computed using the point values reconstruc

W. Mercier, B. Epinat, T. Contini, D. Krajnović

Aims: At intermediate redshift, galaxy groups/clusters are thought to impact galaxies (e.g. their angular momentum). We investigate whether the environment has an impact on the galaxies' angular momentum and identify underlying driving physical mechanisms. Methods: We derive robust estimates of the stellar angular momentum using Hubble Space Telescope (HST)

Michael H. Palmer, Søren Vrønning Hoffmann, Nykola C. Jones, Marcello Coreno

A new synchrotron study for CH$_2$F$_2$ from has been combined with earlier data. The onset of absorption, band I and also band IV, is resolved into broad vibrational peaks, which contrast with the continuous absorption previously claimed. A new theoretical analysis, using a combination of time dependent density functional theory (TDDFT) calculations and com

Xinke Li, Peng Ge, Yuting Shen, Feng Gao

Photoacoustic imaging (PAI) has been applied to many biomedical applications over the past decades. However, the received PA signal usually suffers from poor signal-to-noise ratio (SNR). Conventional solution of employing higher-power laser, or doing long-time signal averaging, may raise the system cost, time consumption, and tissue damage. Another strategy

Wenyu Liu, Tianqiang Huang, Pengfei Zhang, Zong Ke

Adversarial attacks pose a major challenge to distributed learning systems, prompting the development of numerous robust learning methods. However, most existing approaches suffer from the curse of dimensionality, i.e. the error increases with the number of model parameters. In this paper, we make a progress towards high dimensional problems, under arbitrary

Abraham Gale, Amélie Marian

Ranking functions that are used in decision systems often produce disparate results for different populations because of bias in the underlying data. Addressing, and compensating for, these disparate outcomes is a critical problem for fair decision-making. Recent compensatory measures have mostly focused on opaque transformations of the ranking functions to

Guangzhi Ren, Lai Wei, Zheng-Xiong Wang, Jiquan Li

Turbulence spreading from edge to core region with resonance magnetic perturbation (RMP) is investigated using an electromagnetic Landau-fluid model in toroidal geometry. When RMP field with appropriate amplitude are employed in the simulation, long wavelength fluctuations around the resonance surface are excited due to the forced magnetic reconnection. Stro

T. J. Millar

Fifty years on from the first detailed chemical kinetic modelling of astronomical sources, I provide some introductory comments on the history of astrochemistry, summarise some personal views on the topics covered in this discussion meeting, and conclude with some thoughts on its future development. I have left out the jokes.

Zhuoyang Qin, Zhecheng Wang, Fei Kong, Jia Su

An ultimate goal of electron paramagnetic resonance (EPR) spectroscopy is to analyze molecular dynamics in place where it occurs, such as in a living cell. The nanodiamond (ND) hosting nitrogen-vacancy (NV) centers will be a promising EPR sensor to achieve this goal. However, ND-based EPR spectroscopy remains elusive, due to the challenge of controlling NV c

Dara Cassidy, Yann-Aël Le Borgne, Francisco Bellas, Riina Vuorikari

This report presents a set of use scenarios based on existing resources that teachers can use as inspiration to create their own, with the aim of introducing artificial intelligence (AI) at different pre-university levels, and with different goals. The Artificial Intelligence Education field (AIEd) is very active, with new resources and tools arising continu

Nicolò Bonaldi, Martina Rossi, Daniele Mattioli, Michele Grapulin

Gaussian Boson Sampling (GBS) is a recently developed paradigm of quantum computing consisting of sending a Gaussian state through a linear interferometer and then counting the number of photons in each output mode. When the system encodes a symmetric matrix, GBS can be viewed as a tool to sample subgraphs: the most sampled are those with a large number of p

Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh

Motivated by real-life deployments of multi-round federated analytics with secure aggregation, we investigate the fundamental communication-accuracy tradeoffs of the heavy hitter discovery and approximate (open-domain) histogram problems under a linear sketching constraint. We propose efficient algorithms based on local subsampling and invertible bloom look-

Li Xiao, Xiuping Yang, Xinhong Li, Weiping Tu

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a chronic breathing disorder caused by a blockage in the upper airways. Snoring is a prominent symptom of OSAHS, and previous studies have attempted to identify the obstruction site of the upper airways by snoring sounds. Despite some progress, the classification of the obstruction site remains challenging