October 2023 arXiv papers — page 152

Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu

Recently, learning with soft labels has been shown to achieve better performance than learning with hard labels in terms of model generalization, calibration, and robustness. However, collecting pointwise labeling confidence for all training examples can be challenging and time-consuming in real-world scenarios. This paper delves into a novel weakly supervis

Emin Martirosyan, Ming Cao

$ $This paper addresses the inverse problem for Linear-Quadratic (LQ) nonzero-sum $N$-player differential games, where the goal is to learn parameters of an unknown cost function for the game, called observed, given the demonstrated trajectories that are known to be generated by stationary linear feedback Nash equilibrium laws. Towards this end, using the de

Clara Cufí-Cabré, Ernest Fontich

We consider analytic maps and vector fields defined in $\mathbb{R}^2 \times \mathbb{T}^d$, having a $d$-dimensional invariant torus $\mathcal{T}$. The map (resp. vector field) restricted to $\mathcal{T}$ defines a rotation of frequency $\omega$, and its derivative restricted to transversal directions to $\mathcal{T}$ does not diagonalize. In this context, we

Junkang Yang, Hongqing Liu, Lu Gan, Yi Zhou

Speech super-resolution (SSR) aims to predict a high resolution (HR) speech signal from its low resolution (LR) corresponding part. Most neural SSR models focus on producing the final result in a noise-free environment by recovering the spectrogram of high-frequency part of the signal and concatenating it with the original low-frequency part. Although these

Marco Bronzini, Carlo Nicolini, Bruno Lepri, Andrea Passerini

Over the last decade, several regulatory bodies have started requiring the disclosure of non-financial information from publicly listed companies, in light of the investors' increasing attention to Environmental, Social, and Governance (ESG) issues. Publicly released information on sustainability practices is often disclosed in diverse, unstructured, and mul

Yujie Ding, Shuai Jia, Tianyi Ma, Bingcheng Mao

The remarkable achievements and rapid advancements of Large Language Models (LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in quantitative investment. Traders can effectively leverage these LLMs to analyze financial news and predict stock returns accurately. However, integrating LLMs into existing quantitative models presents two pri

Luca Cassia, Vera Posch, Maxim Zabzine

We propose formulas for the large $N$ expansion of the generating function of connected correlators of the $\beta$-deformed Gaussian and Wishart-Laguerre matrix models. We show that our proposal satisfies the known transformation properties under the exchange of $\beta$ with $1/\beta$ and, using Virasoro constraints, we derive a recursion formula for the coe

Taejun Park, Yuji Nakatsukasa

The computation of a matrix function $f(A)$ is an important task in scientific computing appearing in machine learning, network analysis and the solution of partial differential equations. In this work, we use only matrix-vector products $x\mapsto Ax$ to approximate functions of sparse matrices and matrices with similar structures such as sparse matrices $A$

Doyup Lee, Chiheon Kim, Minsu Cho, Wook-Shin Han

Generalizable implicit neural representation (INR) enables a single continuous function, i.e., a coordinate-based neural network, to represent multiple data instances by modulating its weights or intermediate features using latent codes. However, the expressive power of the state-of-the-art modulation is limited due to its inability to localize and capture f

Kevin Reuzé, Wassim Hamidouche, Pierrick Philippe, Olivier Déforges

The high efficiency video coding (HEVC) standard and the joint exploration model (JEM) codec incorporate 35 and 67 intra prediction modes (IPMs) respectively, which are essential for efficient compression of Intra coded blocks. These IPMs are transmitted to the decoder through a coding scheme. In our paper, we present an innovative approach to construct a de

Junfeng Guo, Yiming Li, Lixu Wang, Shu-Tao Xia

The prosperity of deep neural networks (DNNs) is largely benefited from open-source datasets, based on which users can evaluate and improve their methods. In this paper, we revisit backdoor-based dataset ownership verification (DOV), which is currently the only feasible approach to protect the copyright of open-source datasets. We reveal that these methods a

Florian Klinglmüller, Tobias Fellinger, Franz König, Tim Friede

While well-established methods for time-to-event data are available when the proportional hazards assumption holds, there is no consensus on the best inferential approach under non-proportional hazards (NPH). However, a wide range of parametric and non-parametric methods for testing and estimation in this scenario have been proposed. To provide recommendatio

Mario Gaspar Quarenta, Mithuss Tharmalingam, Tim Ludwig, H. Y. Yuan

Spin dynamics is usually described as massless or, more precisely, as free of inertia. Recent experiments, however, found direct evidence for inertial spin dynamics. In turn, it is necessary to rethink the basics of spin dynamics. Focusing on a macrospin in an environment (bath), we show that the spin-to-bath coupling gives rise to spin inertia. This bath-in

Yongfu Dai, Duanyu Feng, Jimin Huang, Haochen Jia

General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI. However, the current evaluations of these LLMs in LegalAI are defined by the experts of computer science, lacking consistency with the logic of legal practice, making it difficult to judge their practical capabilities. To address this challenge, we are the first t

Jonathan Kamp, Lisa Beinborn, Antske Fokkens

Feature attribution scores are used for explaining the prediction of a text classifier to users by highlighting a k number of tokens. In this work, we propose a way to determine the number of optimal k tokens that should be displayed from sequential properties of the attribution scores. Our approach is dynamic across sentences, method-agnostic, and deals wit

Ziyang Zhang, Xiao Sun, Liuwei An, Meng Wang

Given the similarity between facial expression categories, the presence of compound facial expressions, and the subjectivity of annotators, facial expression recognition (FER) datasets often suffer from ambiguity and noisy labels. Ambiguous expressions are challenging to differentiate from expressions with noisy labels, which hurt the robustness of FER model

Xiaochun Fang, N. C. Phllips, Junqi Yang

In this paper, we construct large subalgebras of crossed product C*-algebras of noncommutative C*-dynamics from ideals. We apply our results to study locally trivial unital $C(X)$-algebras such as mapping tori.

Horst Lenske

In--medium interactions of $\omega$--mesons are investigated microscopically by coupling to $NN^{-1}$ and $N^*N^{-1}$ particle--hole excitations of the medium, including elativistic mean--field dynamics by self--consistent scalar and vector fields. The resulting self--energies are transmitted to finite nuclei in local density approximation. Real and imaginar

Lei Wang, Piotr Koniusz, Tom Gedeon, Liang Zheng

In contrastive learning, two views of an original image, generated by different augmentations, are considered a positive pair, and their similarity is required to be high. Similarly, two views of distinct images form a negative pair, with encouraged low similarity. Typically, a single similarity measure, provided by a lone projection head, evaluates positive

Donghwa Kang

The IceCube Neutrino Observatory is a cubic-kilometer Cherenkov detector that is deployed deep in the Antarctic ice at the South Pole. A square kilometer companion surface detector, IceTop, located directly above in the in-ice array, measures cosmic-ray initiated extensive air showers with primary energies between 100 TeV and 1 EeV. By combining the events m

Alexandre Mascarenhas

The aim of this paper is to show the relationship that lies in the fact of a person being right or left handed, in their skateboarding stance. Starting from the null hypothesis that there is no relationship, the Pearson's X^2 with Yates correction tests, as well as its respective p-value will be used to test the hypothesis. It will also be calculated and ana

Marvin Dippell

To study coisotropic reduction in the context of deformation quantization we introduce constraint manifolds and constraint algebras as the basic objects encoding the additional information needed to define a reduction. General properties of various categories of constraint objects and their compatiblity with reduction are examined. A constraint Serre-Swan th

J. Dienstbier, F. Liers, J. Rolfes

Single-level reformulations of (non-convex) distributionally robust optimization (DRO) problems are often intractable, as they contain semiinfinite dual constraints. Based on such a semiinfinite reformulation, we present a safe approximation, that allows for the computation of feasible solutions for DROs that depend on nonconvex multivariate simple functions

Sandra Benítez-Peña, Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo

Support vector machines (SVMs) are widely used and constitute one of the best examined and used machine learning models for two-class classification. Classification in SVM is based on a score procedure, yielding a deterministic classification rule, which can be transformed into a probabilistic rule (as implemented in off-the-shelf SVM libraries), but is not

Stéphane Charpentier, Myrto Manolaki, Konstantinos Maronikolakis

A holomorphic function $f$ on the unit disc $\mathbb{D}$ belongs to the class $\mathcal{U}_A(\mathbb{D})$ of Abel universal functions if the family $\{f_r: 0\leq r<1\}$ of its dilates $f_r(z):=f(rz)$ is dense in the space of continuous functions on $K$, for any proper compact subset $K$ of the unit circle. It has been recently shown that $\mathcal{U}_A(\math

Nikolai Meshcheriakov, Victoria Shatalova, Konstantin Stepanyantz

We consider a version of dimensional regularization (reduction) in which the dimensionful regularization parameter $\Lambda$ is in general different from the renormalization scale $\mu$. Then in the scheme analogous to the minimal subtraction the renormalization constants contain $\varepsilon$-poles, powers of $\ln\Lambda/\mu$, and mixed terms of the structu

M. Korivand, D. A. Mojdeh, Edy Tri Baskoro, A. Erfanian

An edge-locating coloring of a simple connected graph $G$ is a partition of its edge set into matchings such that the vertices of $G$ are distinguished by the distance to the matchings. The minimum number of the matchings of $G$ that admits an edge-locating coloring is the edge-locating chromatic number of $G$, and denoted by $\chi'_L(G)$. In this paper we i

Stefan Heimersheim, Leiv Rønneberg, Henry Linton, Filippo Pagani

The cosmological 21 cm signal is one of the most promising avenues to study the Epoch of Reionization. One class of experiments aiming to detect this signal is global signal experiments measuring the sky-averaged 21 cm brightness temperature as a function of frequency. A crucial step in the interpretation and analysis of such measurements is separating foreg

G. Cassella, W. M. C. Foulkes, D. Pfau, J. S. Spencer

Quantum chemical calculations of the ground-state properties of positron-molecule complexes are challenging. The main difficulty lies in employing an appropriate basis set for representing the coalescence between electrons and a positron. Here, we tackle this problem with the recently developed Fermionic neural network (FermiNet) wavefunction, which does not

Jin Hu

In this paper, we present a detailed analysis of normal modes based on the Boltzmann equation within the mutilated relaxation time approximation (RTA). Using this linearized effective kinetic description, our analysis encompasses a complete order calculation in wavenumber k, extending the conventional hydrodynamic mode analysis to intermediate and short-wave

Aadharsh Roshan Nandhakumar, Ayush Baranwal, Priyanshukumar Choudhary, Muhammed Golec

To meet next-generation IoT application demands, edge computing moves processing power and storage closer to the network edge to minimise latency and bandwidth utilisation. Edge computing is becoming popular as a result of these benefits, but resource management is still challenging. Researchers are utilising AI models to solve the challenge of resource mana

E. V. Stolyarov, O. V. Kliushnichenko, V. S. Kovtoniuk, A. A. Semenov

We study counting photons confined in a mode of a microwave resonator via repeated measurements by a Josephson photomultiplier (JPM). The considered JPM is essentially a flux-biased phase qubit operating as a single-photon detector. We identify optimal operational regimes that maximize photon-number resolution within a predetermined range. Two counting techn

Priyo Adhikary, Mayank Gupta, Amit Chauhan, Sashi Satpathy

Recent discovery of superconductivity at a transition temperature of $73$K in the doped layered compound Ba$_{2}$CuO$_{3+x}$ for $x\sim 0.2$ has generated a lot of interest. Experiments in this alternately stacked oxygen octahedral and chain layered structure reveal that a compression of the octahedra causes the Cu- {$d_{z^{2}}$} orbital to lie above the Cu-

Daoyi Wang

In earlier work by den Hollander, K\"onig, and dos Santos, the asymptotics of the total mass of the solution to the parabolic Anderson model was studied on an almost surely infinite Galton-Watson tree with an i.i.d. potential having a double-exponential distribution. The second-order contribution to this asymptotics was identified in terms of a variational f

Dániel Gerbner, Michael E. Picollelli

We say that a hypergraph $\mathcal{H}$ contains a graph $H$ as a trace if there exists some set $S\subset V(\mathcal{H})$ such that $\mathcal{H}|_S=\{h\cap S: h\in E(\mathcal{H})\}$ contains a subhypergraph isomorphic to $H$. We study the largest number of hyperedges in 3-uniform hypergraphs avoiding some graph $F$ as trace. In particular, we improve a bound

Michael G. Adam, Sebastian Eger, Martin Piccolrovazzi, Maged Iskandar

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured e

P. Gorrini, J. Kemmer, S. Dreizler, R. Burn

We report the discovery of two exoplanets around the M dwarfs GJ 724 and GJ 3988 using the radial velocity (RV) method. We obtained a total of 153 3.5 m Calar Alto/CARMENES spectra for both targets and measured their RVs and activity indicators. We also added archival ESO/HARPS data for GJ 724 and infrared RV measurements from Subaru/IRD for GJ 3988. We sear

Teresa Scantamburlo, Joachim Baumann, Christoph Heitz

An implicit ambiguity in the field of prediction-based decision-making regards the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply speaks of 'fair prediction.' In this paper, we point out that a differentiation of these concepts is helpful whe

Molly R. Petersen, Lonneke van der Plas

While analogies are a common way to evaluate word embeddings in NLP, it is also of interest to investigate whether or not analogical reasoning is a task in itself that can be learned. In this paper, we test several ways to learn basic analogical reasoning, specifically focusing on analogies that are more typical of what is used to evaluate analogical reasoni

Michael Gößwein, Matteo Novaga, Paola Pozzi

In this article we study the anisotropic curve shortening flow for a planar network of three curves with fixed endpoints and which meet in a triple junction. We show that the anisotropic curvature energy fulfills a Lojasiewicz-Simon gradient inequality and use this knowledge to derive stability results for the flow. Precisely, in our main theorem we show tha

Polra Victor Falade

In the ever-evolving realm of cybersecurity, the rise of generative AI models like ChatGPT, FraudGPT, and WormGPT has introduced both innovative solutions and unprecedented challenges. This research delves into the multifaceted applications of generative AI in social engineering attacks, offering insights into the evolving threat landscape using the blog min

Sarath Nelleri, Navaneeth Poonthottathil

Recent cosmological observations show a statistically significant tension in the estimated values of the cosmological parameters within the standard $\Lambda$CDM framework. In a recent study, Li and Shafieloo introduced a simple Phenomenological Emergent Dark Energy (PEDE) model, which possesses the same number of parameters as that of the $\Lambda$CDM model

Gernot Schaller

External piecewise-constant feedback control can modify energetic and entropic balances, allowing in extreme scenarios for Maxwell demon operational modes. Without specifying the actual implementation of external feedback loops, one can only partially quantify the additional contributions to entropy production. This is different in autonomously operating sys

Nils Feldhus, Qianli Wang, Tatiana Anikina, Sahil Chopra

While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface. Such a dialogue system can help users explore datasets and models with explanations in a contextualized manner, e.g. via clarification or follow-

Lei Ming, Shi-Dong Liang, Hong-Hao Zhang, Tiberiu Harko

One of the important extensions of Riemann geometry is Weyl geometry, which is essentially based on the ideas of conformal invariance and nonmetricity. A similar non-Riemannian geometry was proposed by Erwin Schr\"{o}dinger in the late 1940s, in a geometry which is simpler, and (probably) more elegant than the Weyl geometry. Even it contains nonmetricity, th

Lingzhi Zhang, Zhengjie Xu, Connelly Barnes, Yuqian Zhou

Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks. However, these generated images often exhibit perceptual artifacts in specific regions, necessitating manual correction. In this study, we present a comprehensive empirical examination of Perceptual Artifacts Localization (PAL) spanning

Shangyu Xing, Fei Zhao, Zhen Wu, Chunhui Li

Multimodal Entity Linking (MEL) is a task that aims to link ambiguous mentions within multimodal contexts to referential entities in a multimodal knowledge base. Recent methods for MEL adopt a common framework: they first interact and fuse the text and image to obtain representations of the mention and entity respectively, and then compute the similarity bet

Farnoud Ghasemi, Peyman Ashkrof, Rafal Kucharski

Ride-sourcing platforms such as Uber and Lyft offer drivers (i.e., platform suppliers) considerable freedom of choice in multiple aspects. At the operational level, drivers can freely accept or decline trip requests that can significantly impact system performance in terms of travellers' waiting time, drivers' idle time and income. Despite the extensive rese

Andreas Morr, Niklas Boers

Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicators will only produce reliable results if the noise driving the system is white and stationary. In the more re

Sebastiano Nicolussi Golo, Ben Warhurst

We solve the contact equivalence problem for generalised sub-Laplacians on $\He^2$ and show that the family of sub-Laplacians on $\He^2$ modulo contact equivalence, is parameterised by $\R^+$

T. Hurth, F. Mahmoudi, S. Neshatpour

We discuss the status of $b \to s \ell^+\ell^-$ decays in the post-$R_{K^{(*)}}$ era. The recent LHCb update of $R_K$ and $R_{K^*}$ measurements which are now compatible with the Standard Model, constrain new physics contributions to be lepton flavor universal, allowing only small deviations from this limit. Besides the latest LHCb measurements of $R_K$ and

V. V. Nesterenko

A simple and clear method is proposed to calculate the averaged motion of the apsis line in the Moon orbit. The obtained result is $3^{\circ}1'12''$ for the starry period of the Moon revolution around the Earth or $40^{\circ}22'48''$ per year. The modern observed value of the latter quantity is $40^{\circ}41'$ per year. In "Principia" Newton derived $1^{\cir

Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng

The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently

M Gómez Ruiz, Aron Castro, Jesús Herranz, Alessandra da Silva

(In,Ga) alloy droplets are used to catalyse the growth of (In,Ga)As nanowires by molecular beam epitaxy on Si(111) substrates. The composition, morphology and optical properties of these nanowires can be tuned by the employed elemental fluxes. To incorporate more than 10% of In, a high In/(In+Ga) flux ratio above 0.7 is required. We report a maximum In conte

Luca Rondi

In this paper we develop in detail the geometric constructions that lead to many uniqueness results for the determination of polyhedral sets, typically scatterers, by a finite minimal number of measurements. We highlight how unique continuation and a suitable reflection principle are enough to proceed with the constructions, without any other assumption on t

Alexander Moretó

Let $G$ be a finite group. We study the generalized character defined by $\Xi(g)=|G|o(g)$, for $g\in G$, which is closely related to a function that has been very studied recently from a group theoretical point of view.

Ana Flack, Sergei Nechaev

We discuss a two-parameter renormalization group (RG) consideration of a phyllotaxis model in the framework of the ``energetic approach'' proposed by L. Levitov in 1991. Following L. Levitov, we consider an equilibrium distribution of strongly repulsive particles on the surface of a finite cylinder and study the redistribution of these particles when the cyl

Alexander Moretó

Let $G$ be a finite solvable group. We prove that if $\chi\in{\rm Irr}(G)$ has odd degree and $\chi(1)$ is the minimal degree of the non-linear irreducible characters of $G$, then $G/{\rm Ker} \chi$ is nilpotent-by-abelian.

Anirban Chatterjee, Rahul Roy, Sayantan Dey, Abhijit Bandyopadhyay

In this study, we analyze the dynamics of the interaction between dark matter and curvature-driven dark energy in viable $f(R)$ gravity models using the framework of dynamical system analysis. We incorporate this interaction by introducing a source term in their respective continuity equations, given by $Q = \frac{\kappa^2 \alpha}{3H}\tilde{\rho}_{\rm m}\rho

Ahmet A. Husainov

The article is devoted to a comparison of the \v{C}ech cohomology with the coefficients in a presheaf of Abelian groups and the topos cohomology of the sheaf generated by this presheaf for a poset with the Aleksandrov topology. The article consists of three parts. The first part provides information from the theory of cohomology of small categories and cohom

Arthur C. van Wijk, Christopher R. Doerr, B. Imran Akca

Arrayed waveguide gratings (AWGs) are key optical components of various new applications in telecommunication, astrology, medical imaging, and spectroscopy. It is a very powerful integrated light dispersion technology with significant flexibility for tailoring its performance to the individual system needs of each application. There are several examples of c

Iver Brevik, Masud Chaichian, Amedeo M. Favitta

We analyze the Axion Electrodynamics in a two-dimensional slab of finite width $L$ containing a homogeneous and isotropic dielectric medium with constant permittivity and permeability. We start from the known decomposition of modes in the nonaxion case and then solve perturbatively the governing equations for the electromagnetic fields to which the axions ar

Kishore C. Patra, Wenbin Lu, Yilun Ma, Eliot Quataert

The origins of quasi-periodic eruptions (QPEs) are poorly understood, although most theoretical explanations invoke an accretion disk around a supermassive black hole. The gas and stellar environments in the galactic nuclei of these sources are also poorly constrained. In this paper, we present an analysis of archival Hubble Space Telescope (HST) images to s

Stéphane d'Ascoli, Sören Becker, Alexander Mathis, Philippe Schwaller

We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory. We perform extensive evaluations on two datasets: (i) the existing "Strogatz" dataset featuring two-dimensional systems; (ii) ODEBench, a collection of one- to four-di

Matteo Bastico, David Ryckelynck, Laurent Corté, Yannick Tillier

When it comes to clinical images, automatic segmentation has a wide variety of applications and a considerable diversity of input domains, such as different types of Magnetic Resonance Images (MRIs) and Computerized Tomography (CT) scans. This heterogeneity is a challenge for cross-modality algorithms that should equally perform independently of the input im

Pengwen Chen, Albert Fannjiang

This study presents a noise-robust framework for 1-bit diffraction tomography, a novel imaging approach that relies on intensity-only binary measurements obtained through coded apertures. The proposed reconstruction scheme leverages random matrix theory and iterative algorithms to effectively recover 3D object structures under high-noise conditions. A key co

Pablo Montealegre

Let (M, g) be a compact manifold endowed with a possibly singular Riemannian metric. The metric induces a norm on the homology of M , called the stable norm. We provide explicit computations of the stable norm of flat slit tori using the Farey sequence. We then glue several slit tori together to produce half-translation surfaces whose unit ball of the stable

Nicholas Nordlund, Leandros Tassiulas, Jan-Hendrik Lange

The energy transition in transportation benefits from demand-based models to determine the optimal placement of refueling stations for alternative fuel vehicles such as battery electric trucks. A formulation known as the refueling station location problem with routing (RSLP-R) is concerned with minimizing the number of stations necessary to cover a set of or

Dominique Bourn

We investigate the split epimorphisms in the categories of digroups and left skew braces. We show that, unlike the category DiGp of digroups, the category SkB of left skew braces is strongly protomodular. From that, we describe the expected Baer sums of exact sequences of left skew braces with abelian kernel.

Aditya Kailas Jadhav, Anantha Raj Pandi, Abhilash Somayajula

As the demand for transportation through waterways continues to rise, the number of vessels plying the waters has correspondingly increased. This has resulted in a greater number of accidents and collisions between ships, some of which lead to significant loss of life and financial losses. Research has shown that human error is a major factor responsible for

Mathieu Vu, Emilie Chouzenoux, Ismail Ben Ayed, Jean-Christophe Pesquet

Ensemble learning leverages multiple models (i.e., weak learners) on a common machine learning task to enhance prediction performance. Basic ensembling approaches average the weak learners outputs, while more sophisticated ones stack a machine learning model in between the weak learners outputs and the final prediction. This work fuses both aforementioned fr

Erik Fransson, Petter Rosander, Paul Erhart, Göran Wahnström

Barium zirconate BaZrO3 is one of few perovskites that is claimed to retain an average cubic structure down to 0K at ambient pressure, while being energetically very close to a tetragonal phase obtained by condensation of a soft phonon mode at the R-point. Previous studies suggest, however, that the local structure of BaZrO3 may change at low temperature for

Yejin Yang, Miao Ye, Qiuxiang Jiang, Peng Wen

The distributed edge storage system can store data collected at the edge of the network in a decentralised manner, with low latency, high security, and flexibility. Traditional edge-distributed storage systems only consider one single factor, such as node capacity, when storing data, ignoring network and storage node load conditions that affecting the system

Yuwei Wang, Enmeng Lu, Zizhe Ruan, Yao Liang

This paper presents Social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models "follow good advice as naturally as a stream follows its course". By creating a comprehensiv

G. Di Bello, G. De Filippis, A. Hamma, C. A. Perroni

We investigate a system of two coupled qubits interacting with an Ohmic bath as a physical model for the implementation of one logical qubit. In this model, the interaction with the other qubit represents unitary noise while the Ohmic bath is responsible for finite temperature. In the presence of a one-dimensional decoherence-free subspace (DFS), we show tha

Tanja Vojkovic, Damir Vukicevic

We observe a network scenario where parts of a secret are distributed among its nodes. Within the network, a group of attackers is actively trying to obtain the complete secret, while there is also the issue of some nodes malfunctioning or being absent. In this paper, we address this problem by employing graph multicoloring techniques, focusing on the case o

Gideon Klaila, Anastasios Stefanou, Lena Ranke

In this paper, we introduce the persistence transformation, a novel methodology in Topological Data Analysis (TDA) for applications in time series data which can be obtained in various areas such as science, politics, economy, healthcare, engineering, and beyond. This approach captures the enduring presence or `persistence' of signal peaks in time series dat

Prayas Sanyal, Srinjay Mukherjee, Arkapravo Das, Anindya Sen

Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder affecting millions of individuals today. The prognosis of the disease solely depends on treating symptoms as they arise and proper caregiving, as there are no current medical preventative treatments apart from newly developing drugs which can, at most, slow the progression. Thus, early de

Michel Takken, Robert Wille

The design of microfluidic devices is a cumbersome and tedious process that can be significantly improved by simulation. Methods based on Computational Fluid Dynamics (CFD) are considered state-of-the-art, but require extensive compute time - oftentimes limiting the size of microfluidic devices that can be simulated. Simulation methods that abstract the unde

Jiyuan Wang, Chunyu Lin, Lang Nie, Shujun Huang

Depth estimation models have shown promising performance on clear scenes but fail to generalize to adverse weather conditions due to illumination variations, weather particles, etc. In this paper, we propose WeatherDepth, a self-supervised robust depth estimation model with curriculum contrastive learning, to tackle performance degradation in complex weather

Joel Hutchinson, Dmitry Miserev, Jelena Klinovaja, Daniel Loss

This work is an analytic theoretical study of a 2D semiconductor with a Fermi surface that is split by the Zeeman coupling of electron spins to an external magnetic field in the presence of electron-electron interactions. For the first time, we calculate the spin susceptibility for long-range and finite-range interactions diagrammatically, and find a resonan

Dóra Bárdfalvy, Viktor Škultéty, Cesare Nardini, Alexander Morozov

Self-propelled micron-size particles suspended in a fluid, like bacteria or synthetic microswimmers, are strongly non-equilibrium systems where particle motility breaks the microscopic detailed balance, often resulting in large-scale collective motion. Previous theoretical work has identified long-range hydrodynamic interactions as the main driver of collect

Catalina Goanta, Nikolaos Aletras, Ilias Chalkidis, Sofia Ranchordas

The scientific innovation in Natural Language Processing (NLP) and more broadly in artificial intelligence (AI) is at its fastest pace to date. As large language models (LLMs) unleash a new era of automation, important debates emerge regarding the benefits and risks of their development, deployment and use. Currently, these debates have been dominated by oft

Si Suo, Seyed Morteza Habibi Khorasani, Shervin Bagheri

In this study, we investigate the stability of a liquid film that partially wets a corner between a cylinder and a substrate, using a combination of theoretical and numerical approaches. The film stability, which depends on the film size and the wall wettability, is firstly predicted by a standard linear stability analysis (LSA) within the long-wave theoreti

Zhiming Li, Junzhe Jiang, Yushi Cao, Aixin Cui

Deep reinforcement learning (DRL) has revolutionized quantitative trading (Q-trading) by achieving decent performance without significant human expert knowledge. Despite its achievements, we observe that the current state-of-the-art DRL models are still ineffective in identifying the market trends, causing them to miss good trading opportunities or suffer fr

Leonid Positselski

This paper is a sequel to arXiv:2307.13358 and arXiv:2308.16090. A construction associating a semialgebra with an algebra, subalgebra, and a coalgebra dual to the subalgebra played a central role in the author's book arXiv:0708.3398. In this paper, we extend this construction to certain nonunital algebras. The resulting semialgebra is still semiunital over a

Kun Li, Liangshu Zhu

Uplift modeling has been used effectively in fields such as marketing and customer retention, to target those customers who are more likely to respond due to the campaign or treatment. Essentially, it is a machine learning technique that predicts the gain from performing some action with respect to not taking it. A popular class of uplift models is the trans

Z. Y. Tho, F. K. C. Hui, A. H. Welsh, T. Zou

Spatially misaligned data, where the response and covariates are observed at different spatial locations, commonly arise in many environmental studies. Much of the statistical literature on handling spatially misaligned data has been devoted to the case of a single covariate and a linear relationship between the response and this covariate. Motivated by spat

Yulin Li, Xindong Tang, Kai Chen, Chunxin Zheng

This work proposes a safety-critical local reactive controller that enables the robot to navigate in unknown and cluttered environments. In particular, the trajectory tracking task is formulated as a constrained polynomial optimization problem. Then, safety constraints are imposed on the control variables invoking the notion of polynomial positivity certific

Michael Hediger

Let $(X,\mathcal{B},P)$ be a probability space and $\mathit{a}$ be a sub $\sigma$-field that is generated by an increasing sequence of sub $\sigma$-fields $(\mathit{a}_{n})_{n \in \mathbb{N}}$. Given $\theta \in \Theta$, where $\Theta$ is some set, let $(X_{n}^{\theta})_{n \in \mathbb{N}}$ be a martingale adapted to $(\mathit{a}_{n})_{n \in \mathbb{N}}$. Mar

Laura Dal Compare, Flavio Romano, Jared A. Wood, Asaph Widmer-Cooper

The phase diagram of hard helices differs from its hard rods counterpart by the presence of chiral "screw" phases stemming from the characteristic helical shape, in addition to the conventional liquid crystal phases also found for rod-like particles. Using extensive Monte Carlo and Molecular Dynamics simulations, we study the effect of the addition of a shor

Gerrit Grutzeck, Ingo Krämer, Miroslaw Ciechanowicz, Nicolas Reyes

Within this proceeding, we introduce the U-Board platform, a versatile platform for signal generation, acquisition and processing, based on a heterogenous processing architecture. Based on this platform we present a readout for Microwave Kinetic Inductance Detectors (MKIDs) for the A-MKID camera for APEX. In addition to the implementation of the readout on t

Farnoud Ghasemi, Arkadiusz Drabicki, Rafał Kucharski

There is a fierce competition between two-sided mobility platforms (e.g., Uber and Lyft) fueled by massive subsidies, yet the underlying dynamics and interactions between the competing plat-forms are largely unknown. These platforms rely on the cross-side network effects to grow, they need to attract agents from both sides to kick-off: travellers are needed

Kaspara Skovli Gåsvær, Pedro G. Lind, Johannes Langguth, Morten Hjorth-Jensen

Shortly after the first COVID-19 cases became apparent in December 2020, rumors spread on social media suggesting a connection between the virus and the 5G radiation emanating from the recently deployed telecommunications network. In the course of the following weeks, this idea gained increasing popularity, and various alleged explanations for how such a con

Yiming Li, Zonglin Lyu, Mingxuan Lu, Chao Chen

Visual place recognition (VPR) capabilities enable autonomous robots to navigate complex environments by discovering the environment's topology based on visual input. Most research efforts focus on enhancing the accuracy and robustness of single-robot VPR but often encounter issues such as occlusion due to individual viewpoints. Despite a number of research

Luis H. Gallardo, Olivier Rahavandrainy

We give all splitting bi-unitary perfect polynomials over the field $\mathbb{F}_4$ and some splitting ones over $\mathbb{F}_{p^2}$, if $p$ is an odd prime.

Yinan Lin, Zijian Guo, Baoluo Sun, Zhenhua Lin

In response to the unique challenge created by high-dimensional mediators in mediation analysis, this paper presents a novel procedure for testing the nullity of the mediation effect in the presence of high-dimensional mediators. The procedure incorporates two distinct features. Firstly, the test remains valid under all cases of the composite null hypothesis

Ju-Hyeon Nam, Seo-Hyeong Park, Nur Suriza Syazwany, Yerim Jung

Polyp segmentation is crucial for preventing colorectal cancer a common type of cancer. Deep learning has been used to segment polyps automatically, which reduces the risk of misdiagnosis. Localizing small polyps in colonoscopy images is challenging because of its complex characteristics, such as color, occlusion, and various shapes of polyps. To address thi

Philipp Scholl, Katharina Bieker, Hillary Hauger, Gitta Kutyniok

The problem of symbolic regression (SR) arises in many different applications, such as identifying physical laws or deriving mathematical equations describing the behavior of financial markets from given data. Various methods exist to address the problem of SR, often based on genetic programming. However, these methods are usually complicated and involve var

LHC Collaboration

The Scattering and Neutrino Detector at the LHC (\SND) started taking data at the beginning of Run 3 of the LHC. The experiment is designed to perform measurements with neutrinos produced in proton-proton collisions at the LHC in an energy range between 100GeV and 1 TeV. It covers a previously unexplored pseudo-rapidity range of $7.2<\eta<8.4$. The detector