December 2023 arXiv papers — page 47

Siddharth Barman, Debajyoti Kar, Shraddha Pathak

We study fair allocation of indivisible goods among agents with additive valuations. We obtain novel approximation guarantees for three of the strongest fairness notions in discrete fair division, namely envy-free up to the removal of any positively-valued good (EFx), pairwise maximin shares (PMMS), and envy-free up to the transfer of any positively-valued g

Andrew Harris, Andrea Cremaschi, Tse Siang Lim, Maria De Iorio

Over the past two decades, Digital Humanities has transformed the landscape of humanities and social sciences, enabling advanced computational analysis and interpretation of extensive datasets. Notably, recent initiatives in Southeast Asia, particularly in Singapore, focus on categorising and archiving historical data such as artwork, literature and, most no

Han Shu, Wenshuo Li, Yehui Tang, Yiman Zhang

Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed various applications based on the pre-trained SAM and achieved impressive performance on downstream vision tasks. However, SAM consists of heavy architectures and requires massive com

Zichun Xu, Yuntao Li, Xiaohang Yang, Zhiyuan Zhao

This paper presents three open-source reinforcement learning environments developed on the MuJoCo physics engine with the Franka Emika Panda arm in MuJoCo Menagerie. Three representative tasks, push, slide, and pick-and-place, are implemented through the Gymnasium Robotics API, which inherits from the core of Gymnasium. Both the sparse binary and dense rewar

Ryo Yanagimoto, Yunosuke Kubo, Miki Oshio, Mikio Nakano

We introduce our system developed for Dialogue Robot Competition 2023 (DRC2023). First, rule-based utterance selection and utterance generation using a large language model (LLM) are combined. We ensure the quality of system utterances while also being able to respond to unexpected user utterances. Second, dialogue flow is controlled by considering the resul

Wenshuai Liu

We investigate the accretion flow around a giant planet using two-dimensional hydrodynamical simulations by studying the local region of accretion disk around the planet. The results show that, when the initial orbit of the planet embedded in protoplanetary disk is eccentric, the accretion disk formed around the planet is retrograde during the evolution and

Chengyin Hu, Weiwen Shi

Deep neural network security is a persistent concern, with considerable research on visible light physical attacks but limited exploration in the infrared domain. Existing approaches, like white-box infrared attacks using bulb boards and QR suits, lack realism and stealthiness. Meanwhile, black-box methods with cold and hot patches often struggle to ensure r

Tobias Kuhn, Björn Sothmann, Jorge Cayao

Higgs modes emerge in superconductors as collective excitations of the order parameter amplitude when periodically driven by electromagnetic radiation. In this work, we develop a Floquet approach to study Higgs modes in superconductors under time-periodic driving, where the dynamics of the order parameter is captured by anomalous Floquet Green's functions. W

Giordano Paoletti, Luca Gioacchini, Marco Mellia, Luca Vassio

In dynamic complex networks, entities interact and form network communities that evolve over time. Among the many static Community Detection (CD) solutions, the modularity-based Louvain, or Greedy Modularity Algorithm (GMA), is widely employed in real-world applications due to its intuitiveness and scalability. Nevertheless, addressing CD in dynamic graphs r

Soopil Kim, Sion An, Philip Chikontwe, Myeongkyun Kang

Logical anomalies (LA) refer to data violating underlying logical constraints e.g., the quantity, arrangement, or composition of components within an image. Detecting accurately such anomalies requires models to reason about various component types through segmentation. However, curation of pixel-level annotations for semantic segmentation is both time-consu

Alexander Kuznetsov, Yuri Prokhorov

We classify all 1-nodal degenerations of smooth Fano threefolds with Picard number 1 (both nonfactorial and factorial) and describe their geometry. In particular, we describe a relation between such degenerations and smooth Fano threefolds of higher Picard rank and with unprojections of complete intersection varieties.

J. Alme T. Alt H. Appelshäuser M. Arslandok R. Averbeck E. Bartsch P. Becht L. Bratrud P. Braun-Munzinger H. Buesching H. Caines P. Christiansen F. Costa U. Frankenfeld J. J. Gaardhøje C. Garabatos P. Glässel T. Gunji H. Hamagaki J. W. Harris E. Hellbär H. Helstrup M. Ivanov J. Jung M. Jung A. Junique A. Kalweit R. Keidel S. Kirsch M. Kleiner M. Kowalski M. Krüger C. Lippmann M. Mager S. Masciocchi A. Matyja D. Miśkowiec R. H. Munzer L. Musa B. S. Nielsen J. Otwinowski M. Pikna A. Rehman R. Renfordt D. Röhrich H. S. Scheid C. Schmidt H. R. Schmidt K. Schweda Y. Sekiguchi D. Silvermyr B. Sitar J. Stachel K. Ullaland R. Veenhof V. Vislavicius J. Wiechula B. Windelband

A large-volume Time Projection Chamber (TPC) is the main tracking and particle identification (PID) detector of the ALICE experiment at the CERN LHC. PID in the TPC is performed via specific energy-loss measurements (dE/dx), which are derived from the average pulse-height distribution of ionization generated by charged-particle tracks traversing the TPC volu

Jingtian Liu, Élie Awwad, Hartmut Hafermann, Yves Jaouën

We introduce a novel sign-dependent metric: the energy dispersion index (EDI) of sequences that endured chromatic dispersion, denoted as D-EDI, which exhibits a more accurate opposite variations with the transmission performance compared to the standard EDI metric. Then, by applying D-EDI and EDI to the sequence selection (SS) process, %with enumerative sphe

Brian Nlong Zhao, Yuhang Xiao, Jiashu Xu, Xinyang Jiang

The popularization of Text-to-Image (T2I) diffusion models enables the generation of high-quality images from text descriptions. However, generating diverse customized images with reference visual attributes remains challenging. This work focuses on personalizing T2I diffusion models at a more abstract concept or category level, adapting commonalities from a

XiuJuan Li, ZhiBin Zhang, YongFeng Huang, Fan Xu

Multi-wavelength properties of the nearby Supernova(SN)-associated low-luminosity GRB 171205A are investigated in depth to constrain its physicalan origin synthetically. The pulse width is found to be correlated with energy with a power-law index of $-0.24\pm0.07 $, which is consistent with the indices of other SN/GRBs but larger than those of long GRBs. By

Linger Deng, Mingxin Huang, Xudong Xie, Yuliang Liu

The advancement of text shape representations towards compactness has enhanced text detection and spotting performance, but at a high annotation cost. Current models use single-point annotations to reduce costs, yet they lack sufficient localization information for downstream applications. To overcome this limitation, we introduce Point2Polygon, which can ef

Yacov Manevich

Arma is a Byzantine Fault Tolerant (BFT) consensus system designed to achieve linear scalability across all hardware resources: network bandwidth, CPU, and disk I/O. As opposed to preceding BFT protocols, Arma separates the dissemination and validation of client transactions from the consensus process, restricting the latter to totally ordering only metadata

Sean Memery, Mirella Lapata, Kartic Subr

Several machine learning methods aim to learn or reason about complex physical systems. A common first-step towards reasoning is to infer system parameters from observations of its behavior. In this paper, we investigate the performance of Large Language Models (LLMs) at performing parameter inference in the context of physical systems. Our experiments sugge

Haozheng Zhang, Edmond S. L. Ho, Xiatian Zhang, Silvia Del Din

Purpose:Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73% and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting system for PD symptom identification would support clinicians in making more robust PD diagnostic decisions. M

Nobuhiro Maekawa, Taiju Tanii

The natural grand unified theories solve various problems of the supersymmetric grand unified theory and give realistic quark and lepton mass matrices under the natural assumption that all terms allowed by the symmetry are introduced with O(1) coefficients. However, because of the natural assumption, it is difficult to achieve the gauge coupling unification

Ross Glyn MacDonald, Alex Yakovlev, Victor Pacheco-Peña

Photonic computing has recently become an interesting paradigm for high-speed calculation of computing processes using light-matter interactions. Here, we propose and study an electromagnetic wave-based structure with the ability to calculate the solution of partial differential equations in the form of the Helmholtz wave equation. To do this, we make use of

M. V. Kondrin, Y. B. Lebed

We have investigated by molecular dynamics method the influence of a finite number of particles used in computer simulations on fluctuations of thermodynamic properties. As a case study, we used the two-dimensional Lennard-Jones system. 2D Lennard-Jones system, besides being an archetypal one, is a subject of long debate, as to whether it has continuous (inf

Giulio Caldarelli

Given the rising interest in the circular economy and blockchain hype, numerous integrations were proposed. However, studies on the practical feasibility were scarce, and the assumptions of blockchain potential in the circular economy were rarely questioned. With the help of eleven of the most prominent blockchain experts, the present study critically analyz

Zikai Xiong, Robert M. Freund

There has been a recent surge in development of first-order methods (FOMs) for solving huge-scale linear programming (LP) problems. The attractiveness of FOMs for LP stems in part from the fact that they avoid costly matrix factorization computation. However, the efficiency of FOMs is significantly influenced - both in theory and in practice - by certain ins

Chengzu Li, Han Zhou, Goran Glavaš, Anna Korhonen

When adapting ICL with or without fine-tuning, we are curious about whether the instruction-tuned language model is able to achieve well-calibrated results without suffering from the problem of overconfidence (i.e., miscalibration) considering its strong instruction following ability, especially in such limited data setups. In this work, we deliver an in-dep

Zheheng Jiang, Hossein Rahmani, Sue Black, Bryan M. Williams

We present 3D Points Splatting Hand Reconstruction (3D-PSHR), a real-time and photo-realistic hand reconstruction approach. We propose a self-adaptive canonical points upsampling strategy to achieve high-resolution hand geometry representation. This is followed by a self-adaptive deformation that deforms the hand from the canonical space to the target pose,

Volker Turau

Cellular automata are synchronous discrete dynamical systems used to describe complex dynamic behaviors. The dynamic is based on local interactions between the components, these are defined by a finite graph with an initial node coloring with two colors. In each step, all nodes change their current color synchronously to the least/most frequent color in thei

Sergi Aliaga, Vitaly Petrov, Josep M. Jornet

High-rate satellite communications among hundreds and even thousands of satellites deployed at low-Earth orbits (LEO) will be an important element of the forthcoming sixth-generation (6G) of wireless systems beyond 2030. With millimeter wave communications (mmWave, ~30GHz-100GHz) completely integrated into 5G terrestrial networks, exploration of its potentia

Emilie Charlier, Célia Cisternino, Zuzana Masáková, Edita Pelantová

We consider Cantor real numeration system as a frame in which every non-negative real number has a positional representation. The system is defined using a bi-infinite sequence $\Beta=(\beta_n)_{n\in\Z}$ of real numbers greater than one. We introduce the set of $\Beta$-integers and code the sequence of gaps between consecutive $\Beta$-integers by a symbolic

Bowen Xing, Ivor W. Tsang

Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. In this paper, we present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both langua

Reon Ohashi, Shinjitsu Agatsuma, Kazuya Tsubokura, Yurie Iribe

This paper describes the dialog robot system designed by Team Irisapu for the preliminary round of the Dialogue Robot Competition 2023 (DRC2023). In order to generate dialogue responses flexibly while adhering to predetermined scenarios, we attempted to generate dialogue response sentences using OpenAI's GPT-3. We aimed to create a system that can appropriat

Junfei Xiao, Ziqi Zhou, Wenxuan Li, Shiyi Lan

This paper introduces ProLab, a novel approach using property-level label space for creating strong interpretable segmentation models. Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common sense knowledge for supervising segmentation models. It is based on two core designs. First, we employ Large La

Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fidler

Text-guided diffusion models have revolutionized image and video generation and have also been successfully used for optimization-based 3D object synthesis. Here, we instead focus on the underexplored text-to-4D setting and synthesize dynamic, animated 3D objects using score distillation methods with an additional temporal dimension. Compared to previous wor

Ramandeep S. Johal

Legendre transform between thermodynamic quantities such as the Helmholtz free energy and entropy plays a key role in the formulation of the canonical ensemble. In the standard treatment, the transform exchanges the independent variable from the system's internal energy to its conjugate variable -- the inverse temperature of the heat reservoir. In this artic

Roman Zwicky

Following previous work we further explore the possibility that the chirally broken phase of gauge theories admits an infrared fixed point interpretation. The slope of the $\beta$ function, $\beta'_*$, is found to vanish at the infrared fixed point which has several attractive features such as logarithmic running. We provide a more in-depth analysis of our p

Pasquale Ambrosio, Fabian Bäuerlein

We consider weak solutions $u:\Omega_{T}\rightarrow\mathbb{R}^{N}$ to parabolic systems of the type \[ u_{t}-\mathrm{div}\,A(x,t,Du)=f \qquad \mathrm{in}\ \Omega_{T}=\Omega\times(0,T), \] where $\Omega$ is a bounded open subset of $\mathbb{R}^{n}$ for $n\geq2$, $T>0$ and the datum $f$ belongs to a suitable Orlicz space. The main novelty here is that the part

Niklas Wolff, Georg Schoenweger, Isabel Streicher, Md Redwanul Islam

Wurtzite-type Al$_{1-x}$Sc$_x$N solid solutions grown by metal organic chemical vapour deposition are for the first time confirmed to be ferroelectric. The film with 230 nm thickness and x = 0.15 exhibits a coercive field of 5.5 MV/cm at a measurement frequency of 1.5 kHz. Single crystal quality and homogeneous chemical composition of the film was confirmed

Yu-Cheng Qiu, Jie Sheng, Liang Tan, Chuan-Yang Xing

Atomic dark matter is usually considered to be produced asymmetrically in the early Universe. In this work, we first propose that the symmetric atomic dark matter can be thermally produced through the freeze-out mechanism. The dominant atom anti-atom annihilation channel is the atomic rearrangement. It has a geometrical cross section much larger than that of

Alexander Zapryagaev

B\"uchi arithmetics $\mathop{\mathbf{BA}}\nolimits_n$, $n\ge 2$, are extensions of Presburger arithmetic with an unary functional symbol $V_n(x)$ denoting the largest power of $n$ that divides $x$. We explore the structure of non-standard models of B\"uchi arithmetics and construct an example of a countable non-standard model of $\mathop{\mathbf{BA}}\nolimit

Riccardo Ciccone, Lorenzo Di Pietro, Marco Serone

We study the $2d$ chiral Gross-Neveu model at finite temperature $T$ and chemical potential $\mu$. The analysis is performed by relating the theory to a $SU(N)\times U(1)$ Wess-Zumino-Witten model with appropriate levels and global identifications necessary to keep track of the fermion spin structures. At $\mu=0$ we show that a certain $\mathbb{Z}_2$-valued

Henning Krause

For an abelian category and a distinguished object with a graded endomorphism ring a necessary and sufficient criterion is given so that the category is equivalent to the abelian quotient of the category of finitely presented graded modules modulo the Serre subcategory of finite length modules. A particular example is the category of coherent sheaves on a pr

Qing Zhang, Cheng Liu, Bo Liu, Haitong Huang

Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics. Since deep learning is known to be computing- and memory-intensive, traditional fault-tolerant approaches based on redundant computing will incur substantial ove

Haofeng Yuan, Lichang Fang, Shiji Song

Column generation (CG) is one of the most successful approaches for solving large-scale linear programming (LP) problems. Given an LP with a prohibitively large number of variables (i.e., columns), the idea of CG is to explicitly consider only a subset of columns and iteratively add potential columns to improve the objective value. While adding the column wi

Simon Kuberski

The experimental uncertainty on the anomalous magnetic moment of the muon has been significantly reduced with the recent results of the Fermilab $g-2$ experiment, and a further reduction is expected in the near future. The precision of the Standard Model prediction needs to improve correspondingly to increase the sensitivity of tests for physics beyond the S

Yang Nan, Xiaodan Xing, Shiyi Wang, Zeyu Tang

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moder

Gatti Barbara, Ghiandoni Francesco, Gábor Korchmáros

Let $F=F|\mathbb{K}$ a be function field over an algebraically closed constant field $\mathbb{K}$ of positive characteristic $p$. For a $\mathbb{K}$-automorphism group $G$ of $F$, the invariant of $G$ is the fixed field $F^G$ of $G$. If $F$ has transendency degree $1$ (i.e. $F$ is the function field of an irreducible curve) and $F^G$ is rational, then each g

Walter Hernandez Cruz, Firas Dahi, Yebo Feng, Jiahua Xu

Automated Market Maker (AMM)-based Decentralized Exchanges (DEXs) are crucial in Decentralized Finance (DeFi), but Ethereum implementations suffer from high transaction costs and price synchronization challenges. To address these limitations, we compare the XRP Ledger (XRPL)-AMM-Decentralized Exchange (DEX), a protocol-level implementation, against a Generic

C. A. Downing, M. E. Portnoi

In Westminster Abbey, in a nave near to Newton's monument, lies a memorial stone to Paul Dirac. The inscription on the stone includes the relativistic wave equation for an electron: the Dirac equation. At the turn of the 21st century, it was discovered that this eponymous equation was not simply the preserve of particle physics. The isolation of graphene by

Lorenzo Cavallina, Kei Funano, Antoine Henrot, Antoine Lemenant

Neumann eigenvalues being non-decreasing with respect to domain inclusion, it makes sense to study the two shape optimization problems $\min\{\mu_k(\Omega):\Omega \mbox{ convex},\Omega \subset D, \}$ (for a given box $D$) and $\max\{\mu_k(\Omega):\Omega \mbox{ convex},\omega \subset \Omega, \}$ (for a given obstacle $\omega$). In this paper, we study existen

Mingfei Han, Linjie Yang, Xiaojie Jin, Jiashi Feng

The creation of new datasets often presents new challenges for video recognition and can inspire novel ideas while addressing these challenges. While existing datasets mainly comprise landscape mode videos, our paper seeks to introduce portrait mode videos to the research community and highlight the unique challenges associated with this video format. With t

Francisco Meseguer, Fernando Ramiro-Manzano

The human brain is one of the most complex and intriguing scientific topics. The most established theory on neuronal communication is a pure electrical model based on the propagation of intracell cationic charges along the neurons. Here we propose a complementary model based on two properties of brain communication: A) The Coulomb interaction associated to t

Sankalp Gilda

Traditional spectral energy distribution (SED) fitting techniques face uncertainties due to assumptions in star formation histories and dust attenuation curves. We propose an advanced machine learning-based approach that enhances flexibility and uncertainty quantification in SED fitting. Unlike the fixed NGBoost model used in mirkwood, our approach allows fo

Klaus-Dieter Sommer, Peter Harris, Sascha Eichstädt, Roland Füssl

Mathematical modelling is at the core of metrology as it transforms raw measured data into useful measurement results. A model captures the relationship between the measurand and all relevant quantities on which the measurand depends, and is used to design measuring systems, analyse measured data, make inferences and predictions, and is the basis for evaluat

Xu-Jie Wang, Guoqi Huang, Ming-Yang Li, Yuan-Zhuo Wang

Resonance fluorescence of a two-level emitter displays persistently anti-bunching irrespective of the excitation intensity, but inherits the driving laser's linewidth under weak monochromatic excitation. These properties are commonly explained in terms of two disjoined pictures, i.e., the emitter's single photon saturation or passively scattering light. Here

M. Sguazzin, B. Jurado, J. Pibernat, J. A. Swartz

Neutron-induced reaction cross sections of short-lived nuclei are imperative to understand the origin of heavy elements in stellar nucleosynthesis and for societal applications, but their measurement is extremely complicated due to the radioactivity of the targets involved. One way of overcoming this issue is to combine surrogate reactions with the unique po

Elizaveta Rastorgueva-Foi, Ossi Kaltiokallio, Yu Ge, Matias Turunen

In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks, with minimal prior knowledge. Firstly, we p

Kåre Fridell, Chandan Hati, Volodymyr Takhistov

Nucleon decays are generic predictions of motivated theories, including those based on the unification of forces and supersymmetry. We demonstrate that non-canonical nucleon decays offer a unique opportunity to broadly probe light new particles beyond the Standard Model with masses below $\sim$few GeV over decades in mass range, including axion-like particle

Marco Drewes, Sebastian Zell

Axion-like particles with a coupling to non-Abelian gauge fields at finite temperature can experience dissipation due to sphaleron heating. This could play an important role for warm inflation or dynamical dark energy. We investigate to what degree the efficiency of this non-perturbative mechanism depends on the details of the underlying particle physics mod

Lucas Moulin

We complete the classification of the real forms of almost homogeneous SL$_2$-threefolds. More precisely, we use the Luna-Vust theory to determine the real forms of minimal smooth complete SL$_2$-varieties containing an orbit isomorphic to SL$_2/H$, where $H$ is a finite cyclic subgroup of SL$_2$. Moreover, we study the rationality and the set of real points

Ömer Sen, Philipp Malskorn, Simon Glomb, Immanuel Hacker

Power grids are becoming more digitized, resulting in new opportunities for the grid operation but also new challenges, such as new threats from the cyber-domain. To address these challenges, cybersecurity solutions are being considered in the form of preventive, detective, and reactive measures. Machine learning-based intrusion detection systems are used as

Jorge Baeza-Ballesteros, Andrea Donini, Gabriel Molina-Terriza, Francesc Monrabal

A novel experimental setup to measure deviations from the $1/r^2$ distance dependence of Newtonian gravity was proposed in arXiv:1609.05654. The underlying theoretical idea was to study the orbits of a microscopically-sized planetary system composed of a ``Satellite'', with mass $m_{\rm S} \sim {\cal O}(10^{-9})$ g, and a ``Planet'', with mass $M_{\rm P} \si

Xinghao Chen, Siwei Li, Yijing Yang, Yunhe Wang

Transformer and its variants have shown great potential for various vision tasks in recent years, including image classification, object detection and segmentation. Meanwhile, recent studies also reveal that with proper architecture design, convolutional networks (ConvNets) also achieve competitive performance with transformers. However, no prior methods hav

Yuki Kubo, Tomoya Yamashita, Masanori Yamada

We developed a dialogue system as a team NTT-EASE in the Dialogue Robot Competition 2023 (DRC2023). We introduce a dialogue system (EASE-DRCBot) constructed for DRC2023. EASE-DRCBot incorporates a manually defined dialogue flow. The conditions for system utterances are based on keyword extraction, example-based method, and sentiment analysis. For answering a

Bao Chen, Kaiyun Pang, Ru Zheng, Feng Liu

The integration of topological concepts into electronic energy band theory has been a transformative development in condensed matter physics. Since then, this paradigm has broadened its reach, extending to a variety of physical systems, including open ones. In this study, we employ analogues of the generalized $n$-dimensional Su-Schrieffer-Heeger model, a co

Natsumi Ikeno, Wei-Hong Liang, Eulogio Oset

We make a study of the $\Omega_c(3120)$, one of the five $\Omega_c$ states observed by the LHCb collaboration, which is well reproduced as a molecular state from the $\Xi^*_c \bar K$ and $\Omega^*_c \eta$ channels mostly. The state with $J^P = 3/2^-$ decays to $\Xi_c \bar K$ in $D$-wave and we include this decay channel in our approach, as well as the effect

Vadim Shcherbakov

This survey concerns probabilistic models motivated by cooperative sequential adsorption (CSA) models. CSA models are widely used in physics and chemistry for modelling adsorption processes in which adsorption rates depend on the spatial configuration of already adsorbed particles. Corresponding probabilistic models describe random sequential allocation of p

Zanqiu Shen, Kun Wang

We propose an efficient protocol to estimate the fidelity of an $n$-qubit entangled measurement device, requiring only qubit state preparations and classical data post-processing. It works by measuring the eigenstates of Pauli operators, which are strategically selected according to their importance weights and collectively contributed by all measurement ope

Dawid Malarz, Weronika Smolak, Jacek Tabor, Sławomir Tadeja

Neural Radiance Fields (NeRFs) have demonstrated the remarkable potential of neural networks to capture the intricacies of 3D objects. By encoding the shape and color information within neural network weights, NeRFs excel at producing strikingly sharp novel views of 3D objects. Recently, numerous generalizations of NeRFs utilizing generative models have emer

Qianhang Ding

We propose that the merger rate of primordial black hole (PBH) binaries can be a probe of Hubble parameter by constraining PBH mass function in the redshifted mass distribution of PBH binaries. In next-generation gravitational wave (GW) detectors, the GWs from PBH binaries would be detected at high redshifts, which gives their redshifted mass and luminosity

Lingyu Yang, Yang Yang, Gia-Wei Chern

We study post-quench dynamics of charge-density-wave (CDW) order in the square-lattice $t$-$V$ model. The ground state of this system at half-filling is characterized by a checkerboard modulation of particle density. A generalized self-consistent mean-field method, based on the time-dependent variational principle, is employed to describe the dynamical evolu

Thibault Delarue, Goulven Quéméner

We show that the precise preparation of a quantum superposition between three rotational states of an ultracold dipolar molecule generates controllable interferences in their two-body scattering dynamics and collisional rate coefficients, at an electric field that produces a F\"orster resonance. This proposal represents a feasible protocol to achieve coheren

Henry Elsom, Matthew Pawley

We present the methods employed by team `Uniofbathtopia' as part of the Data Challenge organised for the 13th International Conference on Extreme Value Analysis (EVA2023), including our winning entry for the third sub-challenge. Our approaches unite ideas from extreme value theory, which provides a statistical framework for the estimation of probabilities/re

Chen Chen, Christian S. Fischer, Craig D. Roberts

A symmetry-preserving continuum approach to the calculation of baryon properties in relativistic quantum field theory is used to predict all form factors associated with nucleon-to-$\Delta$ axial and pseudoscalar transition currents, thereby unifying them with many additional properties of these and other baryons. The new parameter-free predictions can serve

Melina Filzinger, Ashlee R. Caddell, Dhruv Jani, Martin Steinel

We devise and demonstrate a method to search for non-gravitational couplings of ultralight dark matter to standard model particles using space-time separated atomic clocks and cavity-stabilized lasers. By making use of space-time separated sensors, which probe different values of an oscillating dark matter field, we can search for couplings that cancel in ty

Guochen Yu, Xiguang Zheng, Nan Li, Runqiang Han

Speech bandwidth extension (BWE) has demonstrated promising performance in enhancing the perceptual speech quality in real communication systems. Most existing BWE researches primarily focus on fixed upsampling ratios, disregarding the fact that the effective bandwidth of captured audio may fluctuate frequently due to various capturing devices and transmissi

Peng Liu, Ke Ye

Positive semidefinite (PSD) matrices are indispensable in many fields of science. A similarity measurement for such matrices is usually an essential ingredient in the mathematical modelling of a scientific problem. This paper proposes a unified framework to construct similarity measurements for PSD matrices. The framework is obtained by exploring the fiber b

Malte C. Tichy

Assume that a grocery item is sold 1'234 times on a given day. What should an ideal forecast have predicted for such a well-selling item, on average? More generally, when considering a given outcome value, should the empirical average of forecasted expectation values for that outcome ideally match it? Many people will intuitively answer the first question wi

Ju-Hong Lee, Bayartsetseg Kalina, KwangTek Na

Traditional risk-adjusted returns, such as the Treynor, Sharpe, Sortino, and Information ratios, have been pivotal in portfolio asset allocation, focusing on minimizing risk while maximizing profit. Nevertheless, these metrics often fail to account for the distinct characteristics of bull and bear markets, leading to sub-optimal investment decisions. This pa

Jia Tian, Tengzhou Lai

We address the problem of describing the thermodynamics and holography of three-dimensional accelerating black holes. By embedding the solutions in the Chern-Simons formalism, we identify two distinct masses, each with its associated first law of thermodynamics. We also show that a boundary entropy should be included (or excluded) in the black hole entropy.

Michael P. Tuite, Michael Welby

We describe Zhu recursion for a vertex operator algebra (VOA) and its modules on a genus $g$ Riemann surface in the Schottky uniformisation. We show that $n$-point (intertwiner) correlation functions are written as linear combinations of $(n-1)$-point functions with universal coefficients given by derivatives of the differential of the third kind, the Bers q

Yuanfu Wang, Chao Yang, Ying Wen, Yu Liu

Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm that learns the action distribution based on target returns for each state in a supervised manner. However, prevailing RCSL methods largely focus on deterministic trajectory modeling, disregarding stochasti

Kotaro Shukuri, Ryoma Ishigaki, Jundai Suzuki, Tsubasa Naganuma

Utilizing Large Language Models (LLMs) facilitates the creation of flexible and natural dialogues, a task that has been challenging with traditional rule-based dialogue systems. However, LLMs also have the potential to produce unexpected responses, which may not align with the intentions of dialogue system designers. To address this issue, this paper introdu

Haochen Wang, Junsong Fan, Yuxi Wang, Kaiyou Song

Masked visual modeling has attracted much attention due to its promising potential in learning generalizable representations. Typical approaches urge models to predict specific contents of masked tokens, which can be intuitively considered as teaching a student (the model) to solve given problems (predicting masked contents). Under such settings, the perform

Josh Dees, Antoine Jacquier, Sylvain Laizet

Classical Physics-informed neural networks (PINNs) approximate solutions to PDEs with the help of deep neural networks trained to satisfy the differential operator and the relevant boundary conditions. We revisit this idea in the quantum computing realm, using parameterised random quantum circuits as trial solutions. We further adapt recent PINN-based techni

Kjeld Beeks, Tomas Sikorsky, Fabian Schaden, Martin Pressler

The 8 eV first nuclear excited state in $^{229}$Th is a candidate for implementing an nuclear clock. Doping $^{229}$Th into ionic crystals such as CaF$_2$ is expected to suppress non-radiative decay, enabling nuclear spectroscopy and the realization of a solid-state optical clock. Yet, the inherent radioactivity of $^{229}$Th prohibits the growth of high-qua

Jordi Soria-Comas, David Sánchez, Josep Domingo-Ferrer, Sergio Martínez

$\epsilon$-Differential privacy (DP) is a well-known privacy model that offers strong privacy guarantees. However, when applied to data releases, DP significantly deteriorates the analytical utility of the protected outcomes. To keep data utility at reasonable levels, practical applications of DP to data releases have used weak privacy parameters (large $\ep

Kishu Gupta, Ashwani Kush

Data is the key asset for organizations and data sharing is lifeline for organization growth; which may lead to data loss. Data leakage is the most critical issue being faced by organizations. In order to mitigate the data leakage issues data leakage prevention systems (DLPSs) are deployed at various levels by the organizations. DLPSs are capable to protect

Len Bos, Alvise Sommariva, Marco Vianello

In this note we prove almost sure unisolvence of RBF interpolation on randomly distributed sequences by a wide class of polyharmonic splines (including Thin-Plate Splines), without polynomial addition.

Miłosz Kasak, Kamil Deja, Maja Karwowska, Monika Jakubowska

In this work, we introduce a novel method for Particle Identification (PID) within the scope of the ALICE experiment at the Large Hadron Collider at CERN. Identifying products of ultrarelativisitc collisions delivered by the LHC is one of the crucial objectives of ALICE. Typically employed PID methods rely on hand-crafted selections, which compare experiment

Sergi Blanco-Cuaresma, Ioana Ciucă, Alberto Accomazzi, Michael J. Kurtz

Open-source Large Language Models enable projects such as NASA SciX (i.e., NASA ADS) to think out of the box and try alternative approaches for information retrieval and data augmentation, while respecting data copyright and users' privacy. However, when large language models are directly prompted with questions without any context, they are prone to halluci

Matteo Novaga, Emanuele Paolini, Vincenzo Maria Tortorelli

Locally isoperimetric $N$-partitions are partitions of the space $\mathbb R^d$ into $N$ regions with prescribed, finite or infinite measure, which have minimal perimeter (which is the $(d-1)$-dimensional measure of the interfaces between the regions) among all variations with compact support preserving the total measure of each region. In the case when only

Pino D'Amico, Alessandra Catellani, Alice Ruini, Stefano Curtarolo

Transparent Conductors (TCs) exhibit optical transparency and electron conductivity, and are essential for many opto-electronic and photo-voltaic devices. The most common TCs are electron-doped oxides, which have few limitations when transition metals are used as dopants. Non-oxides TCs have the potential of extending the class of materials to the magnetic r

Yogev Hadadi, Vladimir Tourbabin, Paul Calamia, Boaz Rafaely

Blind estimation of early room reflections, without knowledge of the room impulse response, holds substantial value. The FF-PHALCOR (Frequency Focusing PHase ALigned CORrelation), method was recently developed for this objective, extending the original PHALCOR method from spherical to arbitrary arrays. However, previous studies only compared the two methods

NA61/SHINE, :, H. Adhikary, P. Adrich

This paper presents the energy dependence of multiplicity and net-electric charge fluctuations in p+p interactions at beam momenta 20, 31, 40, 80, and 158 GeV/c. Results are corrected for the experimental biases and quantified with the use of cumulants and factorial cumulants. Cumulant ratios are an essential tool in the search for the critical point of stro

Ömer Sen, Simon Glomb, Martin Henze, Andreas Ulbig

The increasing digitization of smart grids has made addressing cybersecurity issues crucial in order to secure the power supply. Anomaly detection has emerged as a key technology for cybersecurity in smart grids, enabling the detection of unknown threats. Many research efforts have proposed various machine-learning-based approaches for anomaly detection in g

Kishu Gupta, Ashwani Kush

Sensitive data leakage is the major growing problem being faced by enterprises in this technical era. Data leakage causes severe threats for organization of data safety which badly affects the reputation of organizations. Data leakage is the flow of sensitive data/information from any data holder to an unauthorized destination. Data leak prevention (DLP) is

Arne Bahr, Matteo Boselli, Benjamin Huard, Audrey Bienfait

High-quality factor microwave resonators operating in a magnetic field are a necessity for some quantum sensing applications and hybrid platforms. Losses in microwave superconducting resonators can have several origins, including microscopic defects, usually known as two-level-systems (TLS). Here, we characterize the magnetic field response of NbTiN resonato

Nicolas Bousquet, Laurent Feuilloley, Sébastien Zeitoun

Local certification is a distributed mechanism enabling the nodes of a network to check the correctness of the current configuration, thanks to small pieces of information called certificates. For many classic global properties, like checking the acyclicity of the network, the optimal size of the certificates depends on the size of the network, $n$. In this

Canze Zhu, Qunying Liao, Haibo Liu

Combinatorial designs are closely related to linear codes. In recent year, there are a lot of $t$-designs constructed from certain linear codes. In this paper, we aim to construct $2$-designs from binary three-weight codes. For any binary three-weight code $\mathcal{C}$ with length $n$, let $A_{n}(\mathcal{C})$ be the number of codewords in $\mathcal{C}$ wit

Giuseppe Habib, Ádám Horváth

This study employs scientific machine learning to identify transient time series of dynamical systems near a fold bifurcation of periodic solutions. The unique aspect of this work is that a convolutional neural network (CNN) is trained with a relatively small amount of data and on a single, very simple system, yet it is tested on much more complicated system