May 2023 arXiv papers — page 14

Shams El-Adawy, Alexandra C. Lau, Eleanor C. Sayre, Claudia Fracchiolla

Physicists engage with the public to varying degrees at different stages of their careers. However, their public engagement covers many activities, events, and audiences, making their motivations and professional development needs not well understood. As part of ongoing efforts to build and support community in the informal physics space, we conducted interv

Maria M. Davis, Dávid Papp

In (Davis and Papp, 2022), the authors introduced the concept of dual certificates of (weighted) sum-of-squares polynomials, which are vectors from the dual cone of weighted sums of squares (WSOS) polynomials that can be interpreted as nonnegativity certificates. This initial theoretical work showed that for every polynomial in the interior of a WSOS cone, t

Qiaoyi Wen, Fanrong Xu

The measurement of lepton universality parameters $R_{K^{(*)}}$ was updated by LHCb in December 2022, which indicated that the well-known anomalies in flavor-changing neutral current (FCNC) processes of B meson decays have faded away. However, does this mean that all new physics possibilities related to $b\to s\ell^+\ell^-$ have been excluded? We aim to answ

Lioba Heimbach, Lucianna Kiffer, Christof Ferreira Torres, Roger Wattenhofer

With Ethereum's transition from Proof-of-Work to Proof-of-Stake in September 2022 came another paradigm shift, the Proposer-Builder Separation (PBS) scheme. PBS was introduced to decouple the roles of selecting and ordering transactions in a block (i.e., the builder), from those validating its contents and proposing the block to the network as the new head o

Emmanuel Esposito, Saeed Masoudian, Hao Qiu, Dirk van der Hoeven

We study a $K$-armed bandit with delayed feedback and intermediate observations. We consider a model where intermediate observations have a form of a finite state, which is observed immediately after taking an action, whereas the loss is observed after an adversarially chosen delay. We show that the regime of the mapping of states to losses determines the co

Etash Kumar Guha

Reinforcement Learning is a powerful framework for training agents to navigate different situations, but it is susceptible to changes in environmental dynamics. However, solving Markov Decision Processes that are robust to changes is difficult due to nonconvexity and size of action or state spaces. While most works have analyzed this problem by taking differ

J. Zhang, Y. L. Zhou, Y. L. Zuo, P. X. Chen

In this paper we study exceptional-point (EP) effects and quantum sensing in a parity-time (PT)-symmetric two-qubit system with the Ising-type interaction. We explore EP properties of the system by analyzing degeneracy of energy eigenvalues or entanglement of eigenstates. We investigate entanglement dynamics of the two qubits in detail. In particular, we dem

Z. Wu, T. I. Weinberger, J. Chen, A. Cabala

The unconventional superconductor UTe$_2$ exhibits numerous signatures of spin-triplet superconductivity -- a rare state of matter which could enable quantum computation protected against decoherence. UTe$_2$ possesses a complex phase landscape comprising two magnetic field-induced superconducting phases, a metamagnetic transition to a field-polarised state,

P. Vermot, J. Palouš, B. Barna, S. Ehlerová

Aims. We aim to characterize the properties of the stellar populations in the central few hundred parsecs of nearby galactic nuclei; specifically their age, mass, and 3D geometry. Methods. We use spatially resolved spectroscopic observations of NGC 1433, NGC 1566, and NGC 1808 obtained with SINFONI to constrain a 3D model composed of a spherically symmetric

Cajetan Ihemebiri, Elochukwu Ukwandu, Lizzy Ofusori, Comfort Olebara

As several countries were experiencing unprecedented economic slowdowns due to the outbreak of COVID-19 pandemic in early 2020, small business enterprises started adapting to digital technologies for business transactions. However, in Africa, particularly Nigeria, COVID-19 pandemic resulted to some financial crisis that impacted negatively on the sustainabil

Bruno Ebner, Adrian Fischer, Robert E. Gaunt, Babette Picker

Stein operators allow to characterise probability distributions via differential operators. Based on these characterisations, we develop a new method of point estimation for marginal parameters of strictly stationary and ergodic processes, which we call \emph{Stein's Method of Moments} (SMOM). These SMOM estimators satisfy the desirable classical properties

Irene Spelta, Carolina Tamborini

We consider families of abelian Galois coverings of the line. When the Jacobian of the general element is totally decomposable, i.e., is isogenous to a product of elliptic curves, we prove that they yield special subvarieties of $\A_g$ if and only if a numerical condition holds, which in the general case is only known to be sufficient.

B. B. Brandt, F. Cuteri, G. Endrődi, G. Markó

Off-central heavy-ion collisions are known to feature magnetic fields with magnitudes and characteristic gradients corresponding to the scale of the strong interactions. In this work, we employ equilibrium lattice simulations of the underlying theory, QCD, involving similar inhomogeneous magnetic field profiles to achieve a better understanding of this syste

Eda Oktay, Erin Carson

With the recent emergence of mixed precision hardware, there has been a renewed interest in its use for solving numerical linear algebra problems fast and accurately. The solution of total least squares problems, i.e., solving $\min_{E,r} \| [E, r]\|_F$ subject to $(A+E)x=b+r$, arises in numerous applications. Solving this problem requires finding the smalle

Nicola Durante, Giovanni Giuseppe Grimaldi, Giovanni Longobardi

By using the notion of $d$-embedding $\Gamma$ of a (canonical) subgeometry $\Sigma$ and of exterior set with respect to the $h$-secant variety $\Omega_{h}(\mathcal{A})$ of a subset $\mathcal{A}$, $ 0 \leq h \leq n-1$, in the finite projective space $\mathrm{PG}(n-1,q^n)$, $n \geq 3$, in this article we construct a class of non-linear $(n,n,q;d)$-MRD codes fo

Boyu Zhang, Yali Dong, Cheng-Zhong Qin, Sergey Gavrilets

Kin selection and direct reciprocity are two most basic mechanisms for promoting cooperation in human society. Generalizing the standard models of the multi-player Prisoner's Dilemma and the Public Goods games for heterogeneous populations, we study the effects of genetic relatedness on cooperation in the context of repeated interactions. Two sets of interre

Badr Missaoui, Nicholas H. Bingham

In this paper, we extend some classical results of the Szego theory of orthogonal polynomials on the unit circle to the infinite-dimensional case, and we establish the corresponding Szego limit theorem.

A. N. Kvinikhidze, B. Blankleider

We derive covariant equations describing the three-quark bound state in terms of quark and diquark degrees of freedom. The equations are exact in the approximation where three-body forces are neglected. A feature of these equations is that they unify two often-used but seemingly unrelated approaches that model baryons as quark-diquark systems; namely, (i) th

Ugochukwu Orji, Modesta Ezema, Elochukwu Ukwandu, Chikaodili Ugwuishiwu

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily mak

Sei Zhen Khong, Lanlan Su

Compartmental epidemic models with dynamics that evolve over a graph network have gained considerable importance in recent years but analysis of these models is in general difficult due to their complexity. In this paper, we develop two positive feedback frameworks that are applicable to the study of steady-state values in a wide range of compartmental epide

Roberto Fantin, Ambroise van Roekeghem, Anass Benayad

Understanding the role of metal and oxygen in the redox process of layered 3d transition metal oxides is crucial to build high density and stable next generation Li-ion batteries. We combine hard X-ray photoelectron spectroscopy and ab-initio-based cluster model simulations to study the electronic structure of prototypical end-members LiCoO2 and CoO2. The ro

Lucia Salvi, Giulia Rossi, Giovanni Bartolini, Ali Behcet Alpat

The personal (or active) electronic dosimeters (PEDs) are devices used to determine the individual exposure to ionizing radiations and they are employed in hospitals, research laboratories and nuclear power plants. The PDOZ project is a personal electronic dosimeter able to detect, discriminate and measure the delivered dose by beta particles and gamma rays.

Ugochukwu Orji, Ezugwu Obianuju, Modesta Ezema, Chikodili Ugwuishiwu

The data revolution experienced in recent times has thrown up new challenges and opportunities for businesses of all sizes in diverse industries. Big data analytics is already at the forefront of innovations to help make meaningful business decisions from the abundance of raw data available today. Business intelligence and analytics has become a huge trend i

Qing Wang, Jixun Yao, Ziqian Wang, Pengcheng Guo

In this study, we propose a timbre-reserved adversarial attack approach for speaker identification (SID) to not only exploit the weakness of the SID model but also preserve the timbre of the target speaker in a black-box attack setting. Particularly, we generate timbre-reserved fake audio by adding an adversarial constraint during the training of the voice c

Yanan Zhang, Weijie Cui, Yangfan Zhang, Xiaoling Bai

In search engines, query expansion (QE) is a crucial technique to improve search experience. Previous studies often rely on long-term search log mining, which leads to slow updates and is sub-optimal for time-sensitive news searches. In this work, we present Event-Centric Query Expansion (EQE), a novel QE system that addresses these issues by mining the best

Emese Kővári, Emese Forgács-Dajka, Tamás Kovács, Csaba Kiss

On long enough timescales, chaotic diffusion has the potential to significantly alter the appearance of a dynamical system. The solar system is no exception: diffusive processes take part in the transportation of small bodies and provide dynamical pathways even for the distant trans-Neptunian objects to reach the inner solar system. In this Letter, we carry

Fabio Taddei, Rosario Fazio

We derive bounds to the thermodynamic uncertainty relations in the linear-response regime for steady-state transport in two-terminal systems when time reversal symmetry is broken. We find that such bounds are different for charge and heat currents and depend on the details of the system, through the Onsager coefficients, and on the ratio between applied volt

Ogechukwu Ukwandu, Hanan Hindy, Elochukwu Ukwandu

Timely and rapid diagnoses are core to informing on optimum interventions that curb the spread of COVID-19. The use of medical images such as chest X-rays and CTs has been advocated to supplement the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test, which in turn has stimulated the application of deep learning techniques in the development of au

Dani Dorfman, Haim Kaplan, Robert E. Tarjan, Uri Zwick

A weighted directed graph $G=(V,A,c)$, where $A\subseteq V\times V$ and $c:A\to R$, describes a road network in which an electric car can roam. An arc $uv$ models a road segment connecting the two vertices $u$ and $v$. The cost $c(uv)$ of an arc $uv$ is the amount of energy the car needs to traverse the arc. This amount may be positive, zero or negative. To

John P. T. Stenger, C. Stephen Hellberg, Daniel Gunlycke

A Jastrow--Gutzwiller operator adds many-body correlations to a quantum state. However, the operator is non-unitary, making it difficult to implement directly on a quantum computer. We present a novel implementation of the Jastrow--Gutzwiller operator using the cascaded variational quantum eigensolver algorithm. We demonstrate the method on IBM Q Lagos for a

Sophie M. Moufawad

Enlarged Krylov subspace methods and their s-step versions were introduced [7] in the aim of reducing communication when solving systems of linear equations Ax = b. These enlarged CG methods consist of enlarging the Krylov subspace by a maximum of t vectors per iteration based on the domain decomposition of the graph of A. As for the s-step versions, s itera

Chi Zhang, Yiwen Chen, Yijun Fu, Zhenglin Zhou

The recent advancements in image-text diffusion models have stimulated research interest in large-scale 3D generative models. Nevertheless, the limited availability of diverse 3D resources presents significant challenges to learning. In this paper, we present a novel method for generating high-quality, stylized 3D avatars that utilizes pre-trained image-text

Yu-Hsiang Wang, Huang-Yu Chen, Kai-Wei Chang, Winston Hsu

SUPERB was proposed to evaluate the generalizability of self-supervised learning (SSL) speech models across various tasks. However, it incurs high computational costs due to the large datasets and diverse tasks. In this paper, we introduce MiniSUPERB, a lightweight benchmark that efficiently evaluates SSL speech models with comparable results to SUPERB but l

Aristides Moustakas, Panagiotis Georgiakakis, Elzbieta Kret, Eleftherios Kapsalis

Wind turbines (WT) cause bird and bat mortalities which depend on the WT and landscape features. The effects of WT features and environmental variables at different spatial scales associated to bat deaths in a mountainous and forested area in Thrace, NE Greece were investigated. Initially, we sought to quantify the most lethal WT characteristic between tower

Ning Ma, Xiao-Bin Qiang, Zhijian Xie, Yu Zhang

The unique band structure in topological materials frequently results in unusual magneto-transport phenomena, one of which is in-plane longitudinal negative magnetoresistance (NMR) with the magnetic field aligned parallel to the electrical current direction. This NMR is widely considered as a hallmark of chiral anomaly in topological materials. Here we repor

Arthur Jacot

Previous work has shown that DNNs with large depth $L$ and $L_{2}$-regularization are biased towards learning low-dimensional representations of the inputs, which can be interpreted as minimizing a notion of rank $R^{(0)}(f)$ of the learned function $f$, conjectured to be the Bottleneck rank. We compute finite depth corrections to this result, revealing a me

Laura Smets, Werner Van Leekwijck, Ing Jyh Tsang, Steven Latre

Hyperdimensional computing (HDC) has become popular for light-weight and energy-efficient machine learning, suitable for wearable Internet-of-Things (IoT) devices and near-sensor or on-device processing. HDC is computationally less complex than traditional deep learning algorithms and achieves moderate to good classification performance. This article propose

Christian H. Weiß

If monitoring Poisson count data for a possible mean shift (while the Poisson distribution is preserved), then the ordinary Poisson exponentially weighted moving-average (EWMA) control chart proved to be a good solution. In practice, however, mean shifts might occur in combination with further changes in the distribution family. Or due to a misspecification

Shuntian Zheng, Sheng Wu, Chunxiao Jiang, Wei Zhang

Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information. In this paper, we address the problem of uplink wideband channel estimation for IRS aide

Mengmeng Li, Daniel Kuhn, Tobias Sutter

We propose policy gradient algorithms for robust infinite-horizon Markov decision processes (MDPs) with non-rectangular uncertainty sets, thereby addressing an open challenge in the robust MDP literature. Indeed, uncertainty sets that display statistical optimality properties and make optimal use of limited data often fail to be rectangular. Unfortunately, t

Ahmed M. Nor, S. S. Sefati, Octavian Fratu, Simona Halunga

Recently, reconfigurable intelligent surface (RIS) has immensely been deployed to overcome blockage issue and widen coverage for enabling superior performance 6G networks. Mainly, systems use RIS as an assistant to redirect the transmitter (TX) incident signal towards the receiver (RX) by configuring RIS elements amplitudes and phase shifts in a passive beam

Yu-Xin Wang, Shang-Jie Jin, Tian-Yang Sun, Jing-Fei Zhang

Recent developments in deep learning techniques have offered an alternative and complementary approach to traditional matched filtering methods for the identification of gravitational wave (GW) signals. The rapid and accurate identification of GW signals is crucial for the progress of GW physics and multi-messenger astronomy, particularly in light of the upc

Samuel Castro-Pardo, Anand B. Puthirath, Shaoxun Fan, Sreehari Saju

Use of electrode materials that show phase change behavior and hence drastic changes in electrochemical activity during operation, have not been explored for Li-ion batteries. Here we demonstrate the vanadium oxide (VO2) cathode that undergoes metal-insulator transition due to first-order structural phase transition at accessible temperature of 68{\deg}C for

Gen Li, Weichen Wu, Yuejie Chi, Cong Ma

This paper is concerned with the problem of policy evaluation with linear function approximation in discounted infinite horizon Markov decision processes. We investigate the sample complexities required to guarantee a predefined estimation error of the best linear coefficients for two widely-used policy evaluation algorithms: the temporal difference (TD) lea

Dmitry Senushkin, Nikolay Patakin, Arseny Kuznetsov, Anton Konushin

In a multi-task learning (MTL) setting, a single model is trained to tackle a diverse set of tasks jointly. Despite rapid progress in the field, MTL remains challenging due to optimization issues such as conflicting and dominating gradients. In this work, we propose using a condition number of a linear system of gradients as a stability criterion of an MTL o

Alexander Streltsov

A key problem in quantum information science is to determine optimal protocols for the interconversion of entangled states shared between remote parties. While for two parties a large number of results in this direction is available, the multipartite setting still remains a major challenge. In this article, this problem is addressed by extending the resource

Shadi Haj-Yahia, Omar Mansour, Tomer Toledo

Discrete choice models (DCM) are widely employed in travel demand analysis as a powerful theoretical econometric framework for understanding and predicting choice behaviors. DCMs are formed as random utility models (RUM), with their key advantage of interpretability. However, a core requirement for the estimation of these models is a priori specification of

Wenhai Lai, Wenyu Wang, Fan Xu, Xin Li

Configuring intelligent surface (IS) or passive antenna array without any channel knowledge, namely blind beamforming, is a frontier research topic in the wireless communication field. Existing methods in the previous literature for blind beamforming include the RFocus and the CSM, the effectiveness of which has been demonstrated on hardware prototypes. Howe

Abdullahi Saka, Ridwan Taiwo, Nurudeen Saka, Babatunde Salami

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the

Marianne Clausel, Joscha Diehl, Raphael Mignot, Leonard Schmitz

We establish the well-definedness of the barycenter (in the sense of Buser and Karcher) for every integrable measure on the free nilpotent Lie group of step $L$ (over $\mathbb{R}^d$). We provide two algorithms for computing it, using methods from Lie theory (namely, the Baker-Campbell-Hausdorff formula) and from the theory of Gr\"obner bases of modules. Our

Monica Bapna, Subhro Bhattacharjee

We study the elastic signatures -- renormalisation of sound velocity and magnetostriction -- of the spin-nematic phase of a spin-$1$ magnet on a triangular lattice described by the bilinear-biquadratic spin Hamiltonian. We show that at low temperatures, the scattering of the acoustic phonons from the Goldstone modes of the nematic phase lead to a powerlaw re

Zeyu Xiao, Ruisheng Gao, Yutong Liu, Yueyi Zhang

Deep learning has opened up new possibilities for light field super-resolution (SR), but existing methods trained on synthetic datasets with simple degradations (e.g., bicubic downsampling) suffer from poor performance when applied to complex real-world scenarios. To address this problem, we introduce LytroZoom, the first real-world light field SR dataset ca

Huahui Yi, Ziyuan Qin, Wei Xu, Miaotian Guo

Large pre-trained vision-language models have shown great prominence in transferring pre-acquired knowledge to various domains and downstream tasks with appropriate prompting or tuning. Existing prevalent tuning methods can be generally categorized into three genres: 1) prompt engineering by creating suitable prompt texts, which is time-consuming and require

Laurenţiu Bubuianu, Sergiu I. Vacaru, Elşen Veli Veliev

We extend to a theory of nonassociative geometric flows a string-inspired model of nonassociative gravity determined by star product and R-flux deformations. The nonassociative Ricci tensor and curvature scalar defined by (non) symmetric metric structures and generalized (non) linear connections are used for defining nonassociative versions of Grigori Perelm

Ahmed M. Nor, Octavian Fratu, Simona Halunga

Aerial reconfigurable intelligent surface (ARIS), is an intelligent reflecting surface (IRS) mounted by unmanned aerial vehicle (UAV), represent a promising candidate for assisting device to device (D2D) millimeter wave (mmWave) communication in temporal and urgent situations, e.g., open-air events. IRS can efficiently mitigate the high blockage impact on mm

Anshu Kumari, Diana E. Morosan, E. K. J. Kilpua, F. Daei

Metre wavelength type II solar radio bursts are believed to be the signatures of shock-accelerated electrons in the corona. Studying these bursts can give information about the initial kinematics, dynamics and energetics of CMEs in the absence of white-light observations. In this study, we investigate the occurrence of type II bursts in solar cycles 23 and 2

Andrew J. Patton, Yasin Simsek

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal et. al, 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests. These methods avoid the curse of dimensionality faced by kernel-based approaches, and allow one to draw on information from multiple state variables simultane

James Cruickshank, Fatemeh Mohammadi, Anthony Nixon, Shin-ichi Tanigawa

The identifiability problem arises naturally in a number of contexts in mathematics and computer science. Specific instances include local or global rigidity of graphs and unique completability of partially-filled tensors subject to rank conditions. The identifiability of points on secant varieties has also been a topic of much research in algebraic geometry

Gerard Derosiere

In this Habilitation Thesis, I synthesize 10 years of work on the role of the motor system in sensorimotor decision-making. First, a large part of the work we initially performed (2014-2020) questioned the functional role of the motor system in the integration of so-called decision variables such as the reward associated with different actions, the sensory e

Omar Seddati, Nathan Hubens, Stéphane Dupont, Thierry Dutoit

Sketch-Based Image Retrieval (SBIR) is a crucial task in multimedia retrieval, where the goal is to retrieve a set of images that match a given sketch query. Researchers have already proposed several well-performing solutions for this task, but most focus on enhancing embedding through different approaches such as triplet loss, quadruplet loss, adding data a

Mengchu Li, Yudong Chen, Tengyao Wang, Yi Yu

We study mean change point testing problems for high-dimensional data, with exponentially- or polynomially-decaying tails. In each case, depending on the $\ell_0$-norm of the mean change vector, we separately consider dense and sparse regimes. We characterise the boundary between the dense and sparse regimes under the above two tail conditions for the first

Sébastien Ferenczi, Luca Q. Zamboni

We characterize the clustering of a word under the Burrows-Wheeler transform in terms of the resolution of a bounded number of bispecial factors belonging to the language generated by all its powers. We use this criterion to compute, in every given Arnoux-Rauzy language on three letters, an explicit bound $K$ such that each word of length at least $K$ is not

Chenyu Zhao, Minghua Ma, Zhenyu Zhong, Shenglin Zhang

Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based nomaly detection methods have been proposed. However, they tend to miss a large number of failur

T. J. Kools, Y. L. W. van Hees, K. Poissonnier, P. Li

Synthetic ferrimagnets based on Co and Gd bear promise for directly bridging the gap between volatile information in the photonic domain and non-volatile information in the magnetic domain, without the need for any intermediary electronic conversion. Specifically, these systems exhibit strong spin-orbit torque effects, fast domain wall motion and single-puls

H. Smith, A. Shankar, J. Gielis, J. Blumenkamp

Multirotors flying in close proximity induce aerodynamic wake effects on each other through propeller downwash. Conventional methods have fallen short of providing adequate 3D force-based models that can be incorporated into robust control paradigms for deploying dense formations. Thus, learning a model for these downwash patterns presents an attractive solu

Peter Semrl

Let $n$ be a positive integer and $H$ a Hilbert space. The description of the general form of bijective maps on the set of $n$-dimensional subspaces of $H$ preserving the maximal principal angle has been obtained recently. This is a generalization of Wigner's unitary-antiunitary theorem. In this paper we will obtain another extension of Wigner's theorem in w

Wenbin He, Jianxu Mao, Yaonan Wang, Zhe Li

To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis model. Specifically, the original mechanical signal is first decomposed by wavelet packet decomposition (WPD) to obtain mu

Yifan Xu, Mengdan Zhang, Chaoyou Fu, Peixian Chen

We introduce MQ-Det, an efficient architecture and pre-training strategy design to utilize both textual description with open-set generalization and visual exemplars with rich description granularity as category queries, namely, Multi-modal Queried object Detection, for real-world detection with both open-vocabulary categories and various granularity. MQ-Det

Hunduma Legesse Geleta

The corona problem was motivated by the question of the density of the open unit disc in the maximal ideal space of the algebra of bounded holomorphic functions on the unit disc. The corona problem connects operator theory, function theory, and geometry. It has been studied by different scholars in different contexts and found to be an important part of clas

Jia-Qi Yang, Yucheng Xu, Jia-Lei Shen, Kebin Fan

Aiding humans with scientific designs is one of the most exciting of artificial intelligence (AI) and machine learning (ML), due to their potential for the discovery of new drugs, design of new materials and chemical compounds, etc. However, scientific design typically requires complex domain knowledge that is not familiar to AI researchers. Further, scienti

Hyun Seung Lee, Seungtaek Choi, Yunsung Lee, Hyeongdon Moon

Text classification in education, usually called auto-tagging, is the automated process of assigning relevant tags to educational content, such as questions and textbooks. However, auto-tagging suffers from a data scarcity problem, which stems from two major challenges: 1) it possesses a large tag space and 2) it is multi-label. Though a retrieval approach i

Ginés López-Pérez, Rubén Medina

We obtain a result in the spirit of the well-known W. Schachermeyer and H. P. Rosenthal research about the equivalence between Radon-Nikodym and Krein-Milman properties, by showing that, for closed, bounded and convex subsets C of a separable Banach space, under Krein-Milman property for $C$, one has the equivalence between convex point of continuity propert

Matthew Baas, Benjamin van Niekerk, Herman Kamper

Any-to-any voice conversion aims to transform source speech into a target voice with just a few examples of the target speaker as a reference. Recent methods produce convincing conversions, but at the cost of increased complexity -- making results difficult to reproduce and build on. Instead, we keep it simple. We propose k-nearest neighbors voice conversion

Matteo Vilucchio, Emanuele Troiani, Vittorio Erba, Florent Krzakala

We study robust linear regression in high-dimension, when both the dimension $d$ and the number of data points $n$ diverge with a fixed ratio $\alpha=n/d$, and study a data model that includes outliers. We provide exact asymptotics for the performances of the empirical risk minimisation (ERM) using $\ell_2$-regularised $\ell_2$, $\ell_1$, and Huber losses, w

Rashid Alawadhi

This thesis applies the Kerr-Schild and the Weyl double copy formalisms to study various concepts in the physics literature. First we apply both the Kerr-Schild and the Weyl double copy to solution generating transformations in General Relativity, where we identify Ehlers transformation as the double copy of electromagnetic duality transformation. Secondly,

Timo Dickel, Christine Hornung, Daler Amanbayev, Samuel Ayet San Andres

The novel technique of mean range bunching has been developed and applied at the projectile fragment separator FRS at GSI in four experiments of the FAIR phase-0 experimental program. Using a variable degrader system at the final focal plane of the FRS, the ranges of the different nuclides can be aligned, allowing to efficiently implant a large number of dif

Paul Bergold, Cesare Tronci

While Born-Oppenheimer molecular dynamics (BOMD) has been widely studied by resorting to powerful methods in mathematical analysis, this paper presents a geometric formulation in terms of Hamilton's variational principle and Euler-Poincar\'{e} reduction by symmetry. Upon resorting to the Lagrangian hydrodynamic paths made available by the Madelung transform,

Linzhe Huang, Arthur Jaffe, Zhengwei Liu, Jinsong Wu

We introduce $\mathfrak{F}$-positive elements in planar algebras. We establish the Perron-Frobenius theorem for $\mathfrak{F}$-positive elements. We study the existence and uniqueness of the Perron-Frobenius eigenspace. When it is not one-dimensional, we characterize its multiplicative structure. Moreover, we consider the Perron-Frobenius eigenspace as the s

Tao Zhang, Wu Huang

Prototype is widely used to represent internal structure of category for few-shot learning, which was proposed as a simple inductive bias to address the issue of overfitting. However, since prototype representation is normally averaged from individual samples, it can appropriately to represent some classes but with underfitting to represent some others that

Jing Wang, Aixin Sun, Hao Zhang, Xiaoli Li

Given a query, the task of Natural Language Video Localization (NLVL) is to localize a temporal moment in an untrimmed video that semantically matches the query. In this paper, we adopt a proposal-based solution that generates proposals (i.e., candidate moments) and then select the best matching proposal. On top of modeling the cross-modal interaction betwee

Shiva Choupanian, Wolfhard Moeller, Martin Seyring, Claudia Pacholski

Ion irradiation can cause burrowing of nanoparticles in substrates, strongly depending on the material properties and irradiation parameters. In this study, we demonstrate that the sinking process can be accomplished with ion irradiation of cube-shaped Ag nanoparticles on top of silicon; how ion channeling affects the sinking rate; and underline the importan

Giulio Pasqualetti, Oscar Bettermann, Nelson Darkwah Oppong, Eduardo Ibarra-García-Padilla

We characterize the equation of state (EoS) of the SU($N>2$) Fermi-Hubbard Model (FHM) in a two-dimensional single-layer square optical lattice. We probe the density and the site occupation probabilities as functions of interaction strength and temperature for $N = 3, 4$ and 6. Our measurements are used as a benchmark for state-of-the-art numerical methods i

Jakob Bossek, Dirk Sudholt

Quality diversity~(QD) is a branch of evolutionary computation that gained increasing interest in recent years. The Map-Elites QD approach defines a feature space, i.e., a partition of the search space, and stores the best solution for each cell of this space. We study a simple QD algorithm in the context of pseudo-Boolean optimisation on the ``number of one

Qiyu Kang, Kai Zhao, Yang Song, Sijie Wang

In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types. In this paper, we model the embedding update of a node feature as a Hamiltonian orbit over time. Since the Hamiltonian orbits generalize the exponential maps, this approach allows us to learn the underlying

Attila Joó

L. Soukup formulated an abstract framework in his introductory paper for proving theorems about uncountable graphs by subdividing them by an increasing continuous chain of elementary submodels. The applicability of this method relies on the preservation of a certain property (that varies from problem to problem) by the subgraphs obtained by subdividing the g

Yuval Dor, Yatir Halevi

We prove that the class of separably algebraically closed valued fields equipped with a distinguished Frobenius endomorphism $x \mapsto x^q$ is decidable, uniformly in $q$. The result is a simultaneous generalization of the work of Chatzidakis and Hrushovski (in the case of the trivial valuation) and the work of the first author and Hrushovski (in the case w

Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon

Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Our method relies on combining diffusion geometry, a central approach to manifold learning, and hyperbolic geometry. Specifically, using diffusion geometry, we build multi-scale dens

Anthony M. Smaldone, Gregory W. Kyro, Victor S. Batista

As the rapidly evolving field of machine learning continues to produce incredibly useful tools and models, the potential for quantum computing to provide speed up for machine learning algorithms is becoming increasingly desirable. In particular, quantum circuits in place of classical convolutional filters for image detection-based tasks are being investigate

Martin Hanik, Benjamin Ducke, Hans-Christian Hege, Friederike Fless

This paper explores a novel mathematical approach to extract archaeological insights from ensembles of similar artifact shapes. We show that by considering all the shape information in a find collection, it is possible to identify shape patterns that would be difficult to discern by considering the artifacts individually or by classifying shapes into predefi

Andras Suto

We prove two equilibrium properties of a system of interacting atoms in three or higher dimensional continuous space. (i) If the particles interact via pair potentials of a nonnegative Fourier transform, their self-organization into infinite permutation cycles is simultaneous with off-diagonal long-range order. If the cycle lengths tend to infinity not slowe

Maryam Samavaki, Arash Zarrin Nia, Santtu Söderholm, Sampsa Pursiainen

Background and Objective: This study aims to evaluate the dynamic effect of non-Newtonian cerebral arterial circulation on electrical conductivity distribution (ECD) in a realistic multi-compartment head model. It addresses the importance and challenges associated with electrophysiological modalities, such as transcranial electrical stimulation, electro-magn

Gaofei Shen, Afra Alishahi, Arianna Bisazza, Grzegorz Chrupała

Understanding which information is encoded in deep models of spoken and written language has been the focus of much research in recent years, as it is crucial for debugging and improving these architectures. Most previous work has focused on probing for speaker characteristics, acoustic and phonological information in models of spoken language, and for synta

Erik Schroedter, Björn Jakob Wurst, Jan-Philip Joost, Michael Bonitz

The quantum dynamics of correlated fermionic or bosonic many-body systems following external excitation can be successfully studied using nonequilibrium Green functions (NEGF) or reduced density matrix methods. Approximations are introduced via a proper choice of the many-particle selfenergy or decoupling of the BBGKY-hierarchy, respectively. These approxima

Jakob Bossek, Aneta Neumann, Frank Neumann

Evolutionary algorithms have been shown to obtain good solutions for complex optimization problems in static and dynamic environments. It is important to understand the behaviour of evolutionary algorithms for complex optimization problems that also involve dynamic and/or stochastic components in a systematic way in order to further increase their applicabil

Yushan Huang, Hamed Haddadi

Machine learning (ML) is moving towards edge devices. However, ML models with high computational demands and energy consumption pose challenges for ML inference in resource-constrained environments, such as the deep sea. To address these challenges, we propose a battery-free ML inference and model personalization pipeline for microcontroller units (MCUs). As

Stefan Leitner, M. Jehanzeb Mirza, Wei Lin, Jakub Micorek

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all

Runfa Chen, Jiaqi Han, Fuchun Sun, Wenbing Huang

Learning a shared policy that guides the locomotion of different agents is of core interest in Reinforcement Learning (RL), which leads to the study of morphology-agnostic RL. However, existing benchmarks are highly restrictive in the choice of starting point and target point, constraining the movement of the agents within 2D space. In this work, we propose

J. Salamania, F. Bock, L. J. S. Johnson, F. Tasnádi

We investigated the high temperature decomposition behavior of wurtzite phase Ti$_{1-x}$Al$_{x}$N films using experimental methods and first-principles calculations. Single phase metastable wurtzite Ti$_{1-x}$Al$_{x}$N (x = 0.65, 0.75, 085 and 0.95) solid solution films were grown by cathodic arc deposition using low duty cycle pulsed substrate-bias voltage.

Andreas Bjerre-Nielsen, Lykke Sterll Christensen, Mikkel Høst Gandil, Hans Henrik Sievertsen

Strategic incentives may lead to inefficient and unequal provision of public services. A prominent example is school admissions. Existing research shows that applicants "play the system" by submitting school rankings strategically. We investigate whether applicants also play the system by manipulating their eligibility at schools. We analyze this applicant d

Numan Saeed, Muhammad Ridzuan, Roba Al Majzoub, Mohammad Yaqub

Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment. Vision transformer (ViT)-based segmentation models have shown great performance in accomplishing this task. However, to build a powerful backbone, the self-attention block of ViT requires large-scale pre-training data. The