February 2024 arXiv papers — page 43

Hernando Quevedo, Maria N. Quevedo, Alberto Sanchez

We perform a statistical and geometrothermodynamic analysis of three different models of magnetic materials, namely, the translational free model, the spin model, and the mean-field model. First, we derive the fundamental equation for each model, which is then used as input to compute the metrics of the corresponding equilibrium spaces. Analyzing the corresp

Emanuel Herrendorf, Christian Komusiewicz, Nils Morawietz, Frank Sommer

Network sparsification is the task of reducing the number of edges of a given graph while preserving some crucial graph property. In community-aware network sparsification, the preserved property concerns the subgraphs that are induced by the communities of the graph which are given as vertex subsets. This is formalized in the $\Pi$-Network Sparsification pr

Samir F. Matar

Original carbon allotropes, orthorhombic C8, C10, C12 and C14 presenting mixed sp2 and sp3 carbon hybridizations exhibiting ethene like and propadiene like embedded units are proposed from crystal chemistry and calculations within the quantum density functional theory DFT. The carbon allotropes with topologies related with jeb, mog, as well as new topologies

Xuyang Li, Hamed Bolandi, Mahdi Masmoudi, Talal Salem

Structural health monitoring (SHM) ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively without human intervention, and are customized for each mechanical s

Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan

There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there is tremendous interest in methods that can acquire sufficient quantities of train and test data that involve calls to tools / APIs. Two lines of research have emerged as the predom

Abolfazl Younesi, Mohsen Ansari, MohammadAmin Fazli, Alireza Ejlali

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning (DL), are widely used for various computer vision tasks such as image classification, object detection, and image segmentation. There are numerous types of CNNs designed to meet specific needs and requirements, including 1D, 2D, and 3D CNNs, as well as dilated, grouped, a

Anirban Mitra, Konasale Prasad, Joshua Cape

This paper revisits the classical concept of network modularity and its spectral relaxations used throughout graph data analysis. We formulate and study several modularity statistic variants for which we establish asymptotic distributional results in the large-network limit for networks exhibiting nodal community structure. Our work facilitates testing for n

Lorenzo Bertini, Alberto De Sole, Gustavo Posta, Carlo Presilla

We introduce a class of quantum Markov semigroups describing the evolution of interacting quantum lattice systems, specified either as generic qudits or as fermions. The corresponding generators, which include both conservative and dissipative evolutions, are given by the superposition of local generators in the Lindblad form. Under general conditions, we sh

Hanxiao Jiang, Binghao Huang, Ruihai Wu, Zhuoran Li

We introduce the novel task of interactive scene exploration, wherein robots autonomously explore environments and produce an action-conditioned scene graph (ACSG) that captures the structure of the underlying environment. The ACSG accounts for both low-level information (geometry and semantics) and high-level information (action-conditioned relationships be

Maria Bazotte, Margarida Carvalho, Thibaut Vidal

Real-world decision-making problems often involve decision-dependent uncertainty, where the probability distribution of the random vector depends on the model decisions. Few studies focus on two-stage stochastic programs with this type of endogenous uncertainty, and those that do lack general methodologies. We propose a general method for solving a class of

Majid Behbahani, Mina Dalirrooyfard, Elaheh Fata, Yuriy Nevmyvaka

In many real world networks, there already exists a (not necessarily optimal) $k$-partitioning of the network. Oftentimes, one aims to find a $k$-partitioning with a smaller cut value for such networks by moving only a few nodes across partitions. The number of nodes that can be moved across partitions is often a constraint forced by budgetary limitations. M

Sergei V. Konyagin, Jonathan Passant, Misha Rudnev

We prove that if $N$ points lie in convex position in the plane then they determine $\Omega(N^{5/4})$ distinct angles, provided that the points do not lie on a common circle. This is derived from a more general claim that if $N$ points in the convex position in the real plane determine $KN$ distinct angles, then $K=\Omega(N^{1/4})$ or $\Omega(N/K)$ points ar

D. Herb, M. Rossini, J. Ankerhold

After photo-excitation of DNA, the excited electron (in the LUMO) and the remaining hole (in the HOMO) localized on the same DNA base form a bound pair, called the Frenkel exciton, due to their mutual Coulomb interaction. In this study, we demonstrate that a tight-binding (TB) approach, parametrized by ab initio data, allows to correlate relaxation propertie

Taysa M. Mendonça, Lucas C. Céleri, Mauro Paternostro, Diogo O. Soares-Pinto

The return of the information from the environment to the system is a phenomenon can be related to existence of non-Markovian mechanisms in the environment and such transformation of resources can be useful for quantum information applications. Thus, understanding the details of the system-environment information dynamics, i.e., the transference of quantum r

Neeru Bala, Sudip Ranjan Bhuia

In this note, we study the composition operators on Segal-Bargmann spaces, which attains its norm and we show that every composition operators on the classical Fock space over $\mathbb{ C}^n$ is norm attaining. Also, we establish a necessary and sufficient condition for a sum of two kernel functions to be an extremal function for the norm of composition oper

Yiran Liu, Ke Yang, Zehan Qi, Xiao Liu

We present a novel statistical framework for analyzing stereotypes in large language models (LLMs) by systematically estimating the bias and variation in their generation. Current alignment evaluation metrics often overlook stereotypes' randomness caused by LLMs' inconsistent generative behavior. For instance, LLMs may display contradictory stereotypes, such

Jean-Nicolas Jérémie, Emmanuel Daucé, Laurent U Perrinet

From falcons spotting preys to humans recognizing faces, rapid visual abilities depend on a foveated retinal organization which delivers high-acuity central vision while preserving low-resolution periphery. This organization is conserved along early visual pathways but remains underexplored in machine learning. Here we examine how embedding a foveated retino

U. R. Olimov, U. A. Rozikov

We investigate discrete-time dynamical systems generated by an infinite-dimensional non-linear operator that maps the Banach space $l_1$ to itself. It is demonstrated that this operator possesses up to seven fixed points. By leveraging the specific form of our operator, we illustrate that analyzing the operator can be simplified to a two-dimensional approach

Swaroop Nath, Harshad Khadilkar, Pushpak Bhattacharyya

Transformers have become pivotal in Natural Language Processing, demonstrating remarkable success in applications like Machine Translation and Summarization. Given their widespread adoption, several works have attempted to analyze the expressivity of Transformers. Expressivity of a neural network is the class of functions it can approximate. A neural network

Renan D. B. Brotto, Jean-Michel Loubes, Laurent Risser, Jean-Pierre Florens

We tackle the problem of bias mitigation of algorithmic decisions in a setting where both the output of the algorithm and the sensitive variable are continuous. Most of prior work deals with discrete sensitive variables, meaning that the biases are measured for subgroups of persons defined by a label, leaving out important algorithmic bias cases, where the s

Mingfeng Chen, Shaoming Guo

We show that Nikodym sets and local smoothing estimates for linear wave equations form a dichotomy: If Nikodym sets for a family of curves exist, then the related maximal operator is not bounded on $L^p(\mathbb{R}^2)$ for any $p<\infty$; if Nikodym sets do not exist, then local smoothing estimates hold, and the related maximal operator is bounded on $L^p(\ma

Debanjan Sengupta, Jeffrey N. Cuzzi, Orkan M. Umurhan, Wladimir Lyra

In the theory of protoplanetary disk turbulence, a widely adopted \emph{ansatz}, or assumption, is that the turnover frequency of the largest turbulent eddy, $\Omega_L$, is the local Keplerian frequency $\Omega_K$. In terms of the standard dimensionless Shakura-Sunyaev $\alpha$ parameter that quantifies turbulent viscosity or diffusivity, this assumption lea

Mohammed Osman, Defu Hou, Wentao Wang, Hui Zhang

We employ the functional renormalization group flow equations to investigate the phase structure of the two-flavor quark-meson model in the presence of a finite isospin chemical potential, incorporating interactions with omega and rho vector mesons. For comparison, we also compute the phase diagram in the chiral limit using the mean-field approximation. Our

Swaroop Nath, Tejpalsingh Siledar, Sankara Sri Raghava Ravindra Muddu, Rupasai Rangaraju

Reinforcement Learning from Human Feedback (RLHF) has become a dominating strategy in aligning Language Models (LMs) with human values/goals. The key to the strategy is learning a reward model ($\varphi$), which can reflect the latent reward model of humans. While this strategy has proven effective, the training methodology requires a lot of human preference

Divya Jyoti Bajpai, Ayush Maheshwari, Manjesh Kumar Hanawal, Ganesh Ramakrishnan

The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervision offers a promising alternative by accelerating the creation of labeled training data using domain-specific rules. However, it requires users to write a diverse set of high-qua

Eric Yelton, Clayton P. Larson, Vito Iaia, Kenneth Dodge

Correlated errors caused by ionizing radiation impacting superconducting qubit chips are problematic for quantum error correction. Such impacts generate quasiparticle (QP) excitations in the qubit electrodes, which temporarily reduce qubit coherence significantly. The many energetic phonons produced by a particle impact travel efficiently throughout the devi

Joao Domingos Gomes da Silva Junior, Carla Silva Oliveira, Liliana Manuela Gaspar C. da Costa

Let $G$ be a simple graph with adjacency matrix $A(G)$, signless Laplacian matrix $Q(G)$, degree diagonal matrix $D(G)$ and let $l(G)$ be the line graph of $G$. In 2017, Nikiforov defined the $A_\alpha$-matrix of $G$, $A_\alpha(G)$, as a linear convex combination of $A(G)$ and $D(G)$, the following way, $A_\alpha(G):=\alpha A(G)+(1-\alpha)D(G),$ where $\alph

Yining Zha, Benjamin H. Kann, Zezhong Ye, Anna Zapaishchykova

Introduction: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor char

Yiting Wang, Haonan Zhao, Daniel Gummadi, Mehrdad Dianati

Precise situational awareness is required for the safe decision-making of assisted and automated driving (AAD) functions. Panoptic segmentation is a promising perception technique to identify and categorise objects, impending hazards, and driveable space at a pixel level. While segmentation quality is generally associated with the quality of the camera data,

Marc Casals, Stefan Hollands, Adam Pound, Vahid Toomani

We construct retarded and advanced Green's functions for gravitational perturbations in Kerr in an ingoing radiation gauge. Our Green's functions have a frequency domain piece that has previously been obtained by Ori [Phys. Rev. D 67 (2003)] based on the Chrzanowski-Cohen-Kegeles metric reconstruction method. As is well known, this piece by itself is not suf

Sharva V. Hiremath, Etika Goyal, Gregory T. Reeves, Cranos M. Williams

Understanding transcription factor dynamics is crucial for unraveling the regulatory mechanisms of gene expression that underpin cellular function and development. Measurements of transcription factor subcellular movements are essential for developing predictive models of gene expression. However, obtaining these quantitative measurements poses significant c

Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang

The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative approaches to produce diverse, high-quality content across media, thereby reshaping online ecosystems but also raising concerns about market over-saturation and the potential marginalization of human creativity. Our work intro

Gregory Szalkowski, Xuanang Xu, Shiva Das, Pew-Thian Yap

This study investigates the applicability of 3D dose predictions from a model trained on one modality to a cross-modality automated planning workflow. Additionally, we explore the impact of integrating a multi-criteria optimizer on adapting predictions to different clinical preferences. Using a previously created three-stage UNet in-house model trained on th

Kaveh Fathian, Tyler Summers

We present CLIPPER+, an algorithm for finding maximal cliques in unweighted graphs for outlier-robust global registration. The registration problem can be formulated as a graph and solved by finding its maximum clique. This formulation leads to extreme robustness to outliers; however, finding the maximum clique is an NP-hard problem, and therefore approximat

Kassie Archer, Robert P. Laudone

We enumerate 132-avoiding permutations of order 3 in terms of the Catalan and Motzkin generating functions, answering a question of B\'{o}na and Smith from 2019. We also enumerate 231-avoiding permutations that are composed only of 3-cycles, 2-cycles, and fixed points.

Xinqi Hu, Gaogao Dong, Kim Christensen, Hanlin Sun

Quantum networks (QNs) exhibit stronger connectivity than predicted by classical percolation, yet the origin of this phenomenon remains unexplored. We apply a statistical physics model -- concurrence percolation -- to uncover the origin of stronger connectivity on hierarchical scale-free networks, the ($U,V$) flowers. These networks allow full analytical con

Yezeng Chen, Zui Chen, Yi Zhou

Although large language models demonstrate emergent abilities in solving math word problems, there is a challenging task in complex multi-step mathematical reasoning tasks. To improve model performance on mathematical reasoning tasks, previous work has conducted supervised fine-tuning on open-source models by improving the quality and quantity of data. In th

Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas

In this study we introduce Logarithmic Frequency Shift Keying (Log-FSK), a novel frequency modulation for over-the-air computation (AirComp). Log-FSK leverages non-linear signal processing to produce AirComp in the frequency domain, this is, the maximum frequency of the received signal corresponds to the sum of the individual transmitted frequencies. The dem

Zui Chen, Yezeng Chen, Jiaqi Han, Zhijie Huang

Large language models (LLMs) are displaying emergent abilities for math reasoning tasks,and there is a growing attention on enhancing the ability of open-source LLMs through supervised fine-tuning (SFT).In this paper, we aim to explore a general data strategy for supervised data to help optimize and expand math reasoning ability.Firstly, we determine the abi

Michael Sweeting, Daniel Slade, Dan Jackson, Kristian Brock

Dose-finding trials are a key component of the drug development process and rely on a statistical design to help inform dosing decisions. Triallists wishing to choose a design require knowledge of operating characteristics of competing methods. This is often assessed using a large-scale simulation study with multiple designs and configurations investigated,

Alexander A. Zyuzin

We report on a study of the interplay between the supercurrent and spin-polarization in a two-dimensional superconducting system in the presence of a d-wave symmetric antiferromagnetic exchange interaction (altermagnetism). It is demonstrated that the supercurrent exhibits a transverse contribution in the presence of both constant and momentum-dependent exch

Junpeng Wang, Rüdiger Westermann, Xifeng Gao, Jun Wu

Aligning lattices based on local stress distribution is crucial for achieving exceptional structural stiffness. However, this aspect has primarily been investigated under a single load condition, where stress in 2D can be described by two orthogonal principal stress directions. In this paper, we introduce a novel approach for designing and optimizing triangu

Gerardo Barrera, Liliana Esquivel

The present manuscript is devoted to the study of the convergence to equilibrium as the noise intensity $\varepsilon>0$ tends to zero for ergodic random systems out of equilibrium of the type \begin{align*} \mathrm{d} X^{\varepsilon}_t(x) = (\mathfrak{b}-\mathfrak{a} X^{\varepsilon}_t(x))\mathrm{d} t+\varepsilon \sqrt{X^{\varepsilon}_t(x)}\mathrm{d} B_t, \qu

Daniel D. Sega, Glen. Stewart, Josh E. Colwell, Girish M. Duvvuri

The satellite Mimas launches a bending wave -- a warping of the rings that propagates radially through self-gravity -- at the 5:3 inner vertical resonance with Saturn's rings. We present a modification of the linear bending wave theory which includes the effects of satellite self-gravity wakes on the particles in the wave. We show that, when treated as rigid

Peter Danchev, Arash Javan, Omid Hasanzadeh, Ahmad Moussavi

We define and explore in-depth the notion of {\it UQ rings} by showing their important properties and by comparing their behavior with that of the well-known classes of UU rings and JU rings, respectively. Specifically, among the other established results, we prove that UQ rings are always Dedekind finite (often named directly finite) as well as that, for se

Roosmarijn de Wit, Jonathan Keeling, Brendon W. Lovett, Alex W. Chin

Problems in the field of open quantum systems often involve an environment that strongly influences the dynamics of excited states. Here we present a numerical method to model optical spectra of non-Markovian open quantum systems. The method employs a process tensor framework to efficiently compute multi-time correlations in a numerically exact way. To demon

Victor Reiner, Dorian Smith

Sandpile groups are a subtle graph isomorphism invariant, in the form of a finite abelian group, whose cardinality is the number of spanning trees in the graph. We study their group structure for graphs obtained by attaching a cone vertex to a tree. For example, it is shown that the number of generators of the sandpile group is at most one less than the numb

René Walendy, Markus Weber, Jingjie Li, Steffen Becker

Trust in digital systems depends on secure hardware, often assured through Hardware Reverse Engineering (HRE). This work develops methods for investigating human problem-solving processes in HRE, an underexplored yet critical aspect. Since reverse engineers rely heavily on visual information, eye tracking holds promise for studying their cognitive processes.

Pierre Schapira

This paper, to appear in the ``Notices of the AMS'' 2024, is a modified version of a text already appeared in this journal, Feb. 2007 after a first publication in French, in ``La Gazette des Math{\'e}maticiens'' 97 (2003) on the occasion of Sato's reception of the 2002/2003 Wolf prize.

Matilde Fiori, Satyajit Pramanik, Christopher W. MacMinn

In soft porous media, deformation drives solute transport via the intrinsic coupling between flow of the fluid and rearrangement of the pore structure. Solute transport driven by periodic loading, in particular, can be of great relevance in applications including the geomechanics of contaminants in the subsurface and the biomechanics of nutrient transport in

Dominik Engl, Carolin Kreisbeck, Marco Morandotti

Lower semicontinuity of surface energies in integral form is known to be equivalent to BV-ellipticity of the surface density. In this paper, we prove that BV-ellipticity coincides with the simpler notion of biconvexity for a class of densities that depend only on the jump height and jump normal, and are positively 1-homogeneous in the first argument. The sec

Jacob Mitchell Springer, Suhas Kotha, Daniel Fried, Graham Neubig

Bidirectional models are considered essential for strong text embeddings. Recent approaches to adapt autoregressive language models (LMs) into strong text embedding models have largely had the requirement to modify the LM architecture to be bidirectional. We challenge this premise by introducing "echo embeddings" which converts autoregressive LMs into high q

Opeyemi Bamigbade, John Sheppard, Mark Scanlon

The task of multimedia geolocation is becoming an increasingly essential component of the digital forensics toolkit to effectively combat human trafficking, child sexual exploitation, and other illegal acts. Typically, metadata-based geolocation information is stripped when multimedia content is shared via instant messaging and social media. The intricacy of

Daniel Ordoñez-Apraez, Giulio Turrisi, Vladimir Kostic, Mario Martin

We present a comprehensive framework for studying and leveraging morphological symmetries in robotic systems. These are intrinsic properties of the robot's morphology, frequently observed in animal biology and robotics, which stem from the replication of kinematic structures and the symmetrical distribution of mass. We illustrate how these symmetries extend

Šeila Bećirović Ramić, Irfan Prazina, Damir Pozderac, Razija Turčinhodžić Mulahasanović

Digital credentials represent crucial elements of digital identity on the Internet. Credentials should have specific properties that allow them to achieve privacy-preserving capabilities. One of these properties is selective disclosure, which allows users to disclose only the claims or attributes they must. This paper presents a novel approach to selective d

Jiangfu Wang, Huazhong Tang, Kailiang Wu

This paper proposes novel high-order accurate discontinuous Galerkin (DG) schemes for the one- and two-dimensional ten-moment Gaussian closure equations with source terms defined by a known potential function. Our DG schemes exhibit the desirable capability of being well-balanced (WB) for a known hydrostatic equilibrium state while simultaneously preserving

Paolo Liberatore

Forgetting a belief acquisition episode may not cause information loss because of the others. Checking whether it does is not obvious, as the contribution of each belief revision is not isolated from the others, and the same information may be given not directly but by deduction. An algorithm for checking whether forgetting reduces information is given for a

Andrea Rossetti, Huatian Hu, Tommaso Venanzi, Adel Bousseksou

The development of nanoscale nonlinear elements in photonic integrated circuits is hindered by the physical limits to the nonlinear optical response of dielectrics, which requires that the interacting waves propagate in transparent volumes for distances much longer than their wavelength. Here we present experimental evidence that optical nonlinearities in do

Irene Moskowitz, Eric Gawiser, John Franklin Crenshaw, Brett H. Andrews

Large imaging surveys will rely on photometric redshifts (photo-z's), which are typically estimated through machine learning methods. Currently planned spectroscopic surveys will not be deep enough to produce a representative training sample for LSST, so we seek methods to improve the photo-z estimates that arise from non-representative training samples. Spe

Alejandro Cholaquidis, Emilien Joly, Leonardo Moreno

A new, very general, robust procedure for combining estimators in metric spaces is introduced GROS. The method is reminiscent of the well-known median of means, as described in \cite{devroye2016sub}. Initially, the sample is divided into $K$ groups. Subsequently, an estimator is computed for each group. Finally, these $K$ estimators are combined using a robu

Cédric Arhancet

We investigate a new class of unital quantum channels on $\mathrm{M}_{2^k}$, acting as radial multipliers when we identify the matrix algebra $\mathrm{M}_{2^k}$ with a finite-dimensional fermion algebra. Our primary contribution lies in the precise computation of the (optimal) rate at which classical information can be transmitted through these channels from

Steve Benford, Clara Mancini, Alan Chamberlain, Eike Schneiders

While ethical challenges are widely discussed in HCI, far less is reported about the ethical processes that researchers routinely navigate. We reflect on a multispecies project that negotiated an especially complex ethical approval process. Cat Royale was an artist-led exploration of creating an artwork to engage audiences in exploring trust in autonomous sy

A. Moór, P. Ábrahám, K. Y. L. Su, T. Henning

Extreme debris discs (EDDs) are bright and warm circumstellar dusty structures around main sequence stars. They may represent the outcome of giant collisions occuring in the terrestrial region between large planetesimals or planetary bodies, and thus provide a rare opportunity to peer into the aftermaths of these events. Here, we report on results of a mini-

Shengrong Ding, Kailiang Wu

This paper develops novel and robust central discontinuous Galerkin (CDG) schemes of arbitrarily high-order accuracy for special relativistic magnetohydrodynamics (RMHD) with a general equation of state (EOS). These schemes are provably bound-preserving (BP), i.e., consistently preserve the upper bound for subluminal fluid velocity and the positivity of dens

Y. Nishimura, S. Aalto, M. D. Gorski, S. König

Nuclear regions of ultraluminous and luminous infrared galaxies (U/LIRGs) are powered by starbursts and/or active galactic nuclei (AGNs). These regions are often obscured by extremely high columns of gas and dust. Molecular lines in the submillimeter windows have the potential to determine the physical conditions of these compact obscured nuclei (CONs). We a

Yamil Essus, Benjamin Rachunok

Electric vehicles (EVs) link mobility and electric power availability, posing a risk of making transportation unavailable during blackouts. We develop a computational framework to quantify the impact of EVs on mobility and access to services and find that existing access issues are exacerbated by EVs. Our results demonstrate that larger batteries reduce mobi

J A Sellwood

When starting an N-body simulation of an isolated galaxy, it is desirable to select particles from a distribution function to ensure that the model is in equilibrium. Random sampling from a DF is widely used, but results in a set of particles that differs by shot noise from that intended. This paper presents a method to reduce sampling noise that has been de

Junwei Ma, Ali Mostafavi

Examining the impact of disasters on life activities of populations is critical for understanding community resilience dynamics, yet it remains insufficiently studied in the existing literature. In this study, we leveraged data from more than 1.2 million anonymized human mobility communications across 30 parishes in Louisiana to construct a temporal network

Alexandra Djorno, Forrest W. Crawford

Crowdfunding is a powerful tool for individuals or organizations seeking financial support from a vast audience. Despite widespread adoption, managers often lack information about dynamics of their platforms. Hawkes processes have been used to represent self-exciting behavior in a wide variety of empirical fields, but have not been applied to crowdfunding pl

Maximilien Dreveton, Alperen Gözeten, Matthias Grossglauser, Patrick Thiran

Clustering is a pivotal challenge in unsupervised machine learning and is often investigated through the lens of mixture models. The optimal error rate for recovering cluster labels in Gaussian and sub-Gaussian mixture models involves ad hoc signal-to-noise ratios. Simple iterative algorithms, such as Lloyd's algorithm, attain this optimal error rate. In thi

Eike Schneiders, Steve Benford, Alan Chamberlain, Clara Mancini

We reflect on the design of a multispecies world centred around a bespoke enclosure in which three cats and a robot arm coexist for six hours a day during a twelve-day installation as part of an artist-led project. In this paper, we present the project's design process, encompassing various interconnected components, including the cats, the robot and its aut

Shuren Qi, Yushu Zhang, Chao Wang, Zhihua Xia

Developing robust and interpretable vision systems is a crucial step towards trustworthy artificial intelligence. In this regard, a promising paradigm considers embedding task-required invariant structures, e.g., geometric invariance, in the fundamental image representation. However, such invariant representations typically exhibit limited discriminability,

Yi Zhang, Yun Tang, Wenjie Ruan, Xiaowei Huang

Text-to-Image (T2I) Diffusion Models (DMs) have shown impressive abilities in generating high-quality images based on simple text descriptions. However, as is common with many Deep Learning (DL) models, DMs are subject to a lack of robustness. While there are attempts to evaluate the robustness of T2I DMs as a binary or worst-case problem, they cannot answer

Nishant Rathee

Rota--Baxter operators over groups have been recently defined in \cite{LHY2021}, and they share a close connection with skew braces, as demonstrated in \cite{VV2022}. In this paper, we classify all Rota--Baxter operators of weight 1 over the Heisenberg Lie algebra of dimension 3 by directly solving the operators defining equations. Using the fact that the ex

Eike Schneiders, Christopher Fourie, Stanley Celestin, Julie Shah

Successful entrainment during collaboration positively affects trust, willingness to collaborate, and likeability towards collaborators. In this paper, we present a mixed-method study to investigate characteristics of successful entrainment leading to pair and group-based synchronisation. Drawing inspiration from industrial settings, we designed a fast-paced

Bálint Koczor

Successful implementations of quantum technologies require protocols and algorithms that use as few quantum resources as possible. However, many important quantum operations, such as continuous rotation gates in quantum computing or broadband pulses in NMR or MRI applications, can only be implemented approximately using finite quantum resources. This work de

Mohammadreza Molaei, Christian Corda

In this paper, in the classical framework we evaluate the lower bounds for the sensitivities of the generalized Lemaitre Tolman Bondi metric. The calculated lower bounds via the linear dynamical systems L_{\frac{\partial}{\partial\theta}}, L_{\frac{\partial}{\partial r}}, and L_{\frac{\partial}{\partial\phi}} are -\ln2+\ln|(\dot{R}B)^{2}-(R')^{2}|-2\ln|B|, 2

Eddy Soria Leyva, Aida Valls Mateu, Ana Beatriz Hernandez Lara

This book chapter conducts a comparative bibliometric analysis of literacies in the tourism labor market, drawing from the Web of Science (WoS) and Scopus databases. The objective is to assess scientific outputs and identify key patterns of scientific collaboration. Findings suggest a statistically significant difference between the two databases with an ove

Jaehyuk Lee, Fan Sang, Taesoo Kim

Caches on the modern commodity CPUs have become one of the major sources of side-channel leakages and been abused as a new attack vector. To thwart the cache-based side-channel attacks, two types of countermeasures have been proposed: detection-based ones that limit the amount of microarchitectural traces an attacker can leave, and cache prefetching-and-lock

Michael Y. Pei, Stephen R. Clark

Projected variational wavefunctions such as the Gutzwiller, many-body correlator and Jastrow ansatzes have provided crucial insight into the nature of superfluid-Mott insulator transition in the Bose Hubbard model (BHM) in two or more spatial dimensions. However, these ansatzes have no obvious tractable and systematic way of being improved. A promising alter

Dominik Semmler, Josef A. Nossek, Michael Joham, Wolfgang Utschick

We analyze and compare different methods for handling the mutual coupling in RIS-aided communication systems. A new mutual coupling aware algorithm is derived where the reactance of each element is updated successively with a closed-form solution. In comparison to existing element-wise methods, this approach leads to a considerably reduced computational comp

Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse, Monica Agrawal

Patients often face difficulties in understanding their hospitalizations, while healthcare workers have limited resources to provide explanations. In this work, we investigate the potential of large language models to generate patient summaries based on doctors' notes and study the effect of training data on the faithfulness and quality of the generated summ

J. Song, D. Rotsch, J. A. Nolen, R. Gampa

A method was developed for measuring photonuclear reactions concurrently at several discrete photon beam energies on a stack of different target materials via a single irradiation. Concentric ring targets of the materials (in order from front to back targets: Au, TiO$_2$, Zn, Os, and Au) were irradiated at the High Intensity Gamma-ray Source (HI$\gamma$S). A

Simon Holk, Daniel Marta, Iolanda Leite

Preference-based reinforcement learning (RL) has emerged as a new field in robot learning, where humans play a pivotal role in shaping robot behavior by expressing preferences on different sequences of state-action pairs. However, formulating realistic policies for robots demands responses from humans to an extensive array of queries. In this work, we approa

I. R. Best, T. J. Sullivan, J. R. Kermode

Atomistic simulations often rely on interatomic potentials to access greater time- and length- scales than those accessible to first principles methods such as density functional theory (DFT). However, since a parameterised potential typically cannot reproduce the true potential energy surface of a given system, we should expect a decrease in accuracy and in

Gregory Weitzner

People are often reluctant to incorporate information produced by algorithms into their decisions, a phenomenon called ``algorithm aversion''. This paper shows how algorithm aversion arises when the choice to follow an algorithm conveys information about a human's ability. I develop a model in which workers make forecasts of an uncertain outcome based on the

Katsumi Ishikawa

We give a complete description of the associated group of any quandle as a central extension of the inner-automorphism group. As an application, we compute the second quandle homology groups of quandles of some families, including those of Alexander quandles.

Firdous Ee Jannat, Rajat Kanti Nath, Kinkar Chandra Das

In this paper we establish connections between common neighborhood Laplacian and common neighborhood signless Laplacian energies and the first Zagreb index of a graph $\mathcal{G}$. We introduce the concepts of CNL-hyperenergetic and CNSL-hyperenergetic graphs and showed that $\mathcal{G}$ is neither CNL-hyperenergetic nor CNSL-hyperenergetic if $\mathcal{G}

Nader Asadi, Mahdi Beitollahi, Yasser Khalil, Yinchuan Li

Parameter-efficient fine-tuning stands as the standard for efficiently fine-tuning large language and vision models on downstream tasks. Specifically, the efficiency of low-rank adaptation has facilitated the creation and sharing of hundreds of custom LoRA modules, each trained on distinct data from various downstream tasks. In this paper, we explore the com

Sourya Basu, Suhas Lohit, Matthew Brand

Group equivariance is a strong inductive bias useful in a wide range of deep learning tasks. However, constructing efficient equivariant networks for general groups and domains is difficult. Recent work by Finzi et al. (2021) directly solves the equivariance constraint for arbitrary matrix groups to obtain equivariant MLPs (EMLPs). But this method does not s

Claudia Alfes, Joshua Maglione, Christopher Voll

Given a lattice polytope $P$ and a prime $p$, we define a function from the set of primitive symplectic $p$-adic lattices to the rationals that extracts the $\ell$th coefficient of the Ehrhart polynomial of $P$ relative to the given lattice. Inspired by work of Gunnells and Rodriguez-Villegas in type $\mathsf{A}$, we show that these functions are eigenfuncti

Gergely Neu, Matteo Papini, Ludovic Schwartz

We study the problem of online learning in contextual bandit problems where the loss function is assumed to belong to a known parametric function class. We propose a new analytic framework for this setting that bridges the Bayesian theory of information-directed sampling due to Russo and Van Roy (2018) and the worst-case theory of Foster, Kakade, Qian, and R

D. P. Aguillard, T. Albahri, D. Allspach, A. Anisenkov

We present details on a new measurement of the muon magnetic anomaly, $a_\mu = (g_\mu -2)/2$. The result is based on positive muon data taken at Fermilab's Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1$ GeV$/c$ polarized muons stored in a $7.1$-m-radius storage ring with a $1.45$ T uniform magnetic field. The value of $ a_{

Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi

It is well-known that the statistical performance of Lasso can suffer significantly when the covariates of interest have strong correlations. In particular, the prediction error of Lasso becomes much worse than computationally inefficient alternatives like Best Subset Selection. Due to a large conjectured computational-statistical tradeoff in the problem of

Tomasz Krajewski, Marek Lewicki, Mateusz Zych

We perform real-time hydrodynamical simulations of the growth of bubbles formed during cosmological first-order phase transitions under the assumption of local thermal equilibrium. We confirm that pure hydrodynamic backreaction can lead to steady-state expansion and that bubble-wall velocity in such case agrees very well with the analytical estimates. Howeve

Andrea Diercke, Robert Jarolim, Christoph Kuckein, Sergio J. González Manrique

Filaments are omnipresent features in the solar atmosphere. Their location, properties and time evolution can provide information about changes in solar activity and assist the operational space weather forecast. Therefore, filaments have to be identified in full disk images and their properties extracted from these images. Manual extraction is tedious and t

Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu

In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression. Initially, we enhance the uncertainty quantification frameworks (B-DeepONet and Prob-DeepONet) previously proposed by the authors by us

Miguel Sanz-Novo, Víctor M. Rivilla, Izaskun Jiménez-Serra, Jesús Martín-Pintado

We present the first detection in space of O-protonated carbonyl sulfide (\ch{HOCS+}), in the midst of an ultradeep molecular line survey toward the G+0.693-0.027 molecular cloud. From the observation of all $K$$_a$ = 0 transitions ranging from $J$$_{lo}$ = 2 to $J$$_{lo}$ = 13 of \ch{HOCS+} covered by our survey, we derive a column density of $N$ = (9 $\pm$

Maurice Kraus, Felix Divo, David Steinmann, Devendra Singh Dhami

In natural language processing and vision, pretraining is utilized to learn effective representations. Unfortunately, the success of pretraining does not easily carry over to time series due to potential mismatch between sources and target. Actually, common belief is that multi-dataset pretraining does not work for time series! Au contraire, we introduce a n

Kechun Xu, Zhongxiang Zhou, Jun Wu, Haojian Lu

We focus on the task of unknown object rearrangement, where a robot is supposed to re-configure the objects into a desired goal configuration specified by an RGB-D image. Recent works explore unknown object rearrangement systems by incorporating learning-based perception modules. However, they are sensitive to perception error, and pay less attention to task