July 2023 arXiv papers — page 118

Yuxin Deng, Jiayi Ma

Attention-based graph neural networks have made great progress in feature matching learning. However, insight of how attention mechanism works for feature matching is lacked in the literature. In this paper, we rethink cross- and self-attention from the viewpoint of traditional feature matching and filtering. In order to facilitate the learning of matching a

Kadir Gümüş, Bin Chen, Thomas Bradley, Chigo Okonkwo

We introduce a simplified method for calculating the loss function for use in geometric shaping, allowing for the optimisation of high dimensional constellations. We design constellations up to 12D with 4096 points, with gains up to 0.31 dB compared to the state-of-the-art.

Johannes Storn

We introduce a numerical scheme that approximates solutions to linear PDE's by minimizing a residual in the $W^{-1,p'}(\Omega)$ norm with exponents $p> 2$. The resulting problem is solved by regularized Kacanov iterations, allowing to compute the solution to the non-linear minimization problem even for large exponents $p\gg 2$. Such large exponents remedy in

D. Depaoli, A. Chiavassa, D. Corti, F. Di Pierro

The Cherenkov Telescope Array (CTA) will be the next generation ground-based gamma-ray observatory. CTA consists of different telescope types of which the largest ones (Large-Sized Telescopes, LSTs) cover the lower energy range, between 20 GeV and 200 GeV. The first LST is currently being commissioned at the Roque de los Muchachos Observatory, La Palma, Cana

David Alonso-González, Dorian W. P. Amaral, Adriana Bariego-Quintana, David Cerdeno

We explore the complementarity of direct detection (DD) and spallation source (SS) experiments for the study of sterile neutrino physics. We focus on the sterile baryonic neutrino model: an extension of the Standard Model that introduces a massive sterile neutrino with couplings to the quark sector via a new gauge boson. In this scenario, the inelastic scatt

Sourav Manna, Ankur Das, Yuval Gefen, Moshe Goldstein

The $\nu = 2/3$ filling is the simplest paradigmatic example of a fractional quantum Hall state, which contains counter-propagating edge modes. These modes can be either in the unequilibrated regime or equilibrated to different extents, on top of a possible edge reconstruction. In the unequilibrated regime, two distinct renormalization group fixed points hav

Che Zhang, Ping'an Liu, Zhenyang Xiao, Haojun Fei

The study of human values is essential in both practical and theoretical domains. With the development of computational linguistics, the creation of large-scale datasets has made it possible to automatically recognize human values accurately. SemEval 2023 Task 4\cite{kiesel:2023} provides a set of arguments and 20 types of human values that are implicitly ex

Sourav Manna, Ankur Das, Yuval Gefen, Moshe Goldstein

Two-terminal conductance quantization in the context of quantum Hall (QH) physics is intimately related to the current carried by a discrete number of chiral edge modes. Upon pinching off a QH bar, one may engineer setups where some modes are fully transmitted (while the others are fully reflected), giving rise to the orthodox theory of quantized conductance

Aneta Maria Gójska, Karol Kozioł, Ewelina Agnieszka Miśta-Jakubowska, Adam Wasilewski

The numerical Monte-Carlo simulations of Cu-K$\alpha$ and Cu-K$\beta$ fluorescence lines induced by monoenergetic Am-241 radiation in copper have been presented. The simulations included modeling the K$\beta$/K$\alpha$ intensity ratios for various thicknesses of copper. The results obtained using the FLUKA code were compared to available experimental and the

Supriya Murali, Tina Walber, Christoph Schaefer, Sezen Lim

The think aloud method is an important and commonly used tool for usability optimization. However, analyzing think aloud data could be time consuming. In this paper, we put forth an automatic analysis of verbal protocols and test the link between spoken feedback and the stimulus using eye tracking and mouse tracking. The gained data - user feedback linked to

Bin Du, He Zhang, Xiangle Cheng, Lei Zhang

We seek the best traffic allocation scheme for the edge-cloud computing network that satisfies constraints and minimizes the cost based on burstable billing. First, for a fixed network topology, we formulate a family of integer programming problems with random parameters describing the various traffic demands. Then, to overcome the difficulty caused by the d

Rupali S. Jain, B. Surendranath Reddy, Wajid M. Shaikh

In this paper, we consider the unit graph $G(\mathbb{Z}_{n})$, where $n=p_{1}^{n_{1}} \text{ or } p_{1}^{n_{1}}p_{2}^{n_{2}} \text{ or } p_{1}^{n_{1}}p_{2}^{n_{2}}p_{3}^{n_{3}}$ and $p_{1}, p_{2}, p_{3}$ are distinct primes. For any prime $q$, we construct $q$-ary linear codes from the incidence matrix of the unit graph $G(\mathbb{Z}_{n})$ with their paramet

Csilla Bujtás, Sandi Klavžar, Jing Tian

If $G$ is a graph and $X\subseteq V(G)$, then $X$ is a total mutual-visibility set if every pair of vertices $x$ and $y$ of $G$ admits a shortest $x,y$-path $P$ with $V(P) \cap X \subseteq \{x,y\}$. The cardinality of a largest total mutual-visibility set of $G$ is the total mutual-visibility number $\mu_{\rm t}(G)$ of $G$. In this paper the total mutual-vis

Ryan Bowler, Geoffrey Goodell, Joe Revans, Gabriel Bizama

Central Bank Digital Currency (CBDC) is a novel form of money that could be issued and regulated by central banks, offering benefits such as programmability, security, and privacy. However, the design of a CBDC system presents numerous technical and social challenges. This paper presents the design and prototype of a non-custodial wallet, a device that enabl

Paula Reichert

This paper analyzes the ergodic hypothesis in the context of Boltzmann's late work in statistical mechanics, where Boltzmann lays the foundations for what is today known as the typicality account. I argue that, based on the concepts of stationarity (of the measure) and typicality (of the equilibrium state), the ergodic hypothesis, as an idealization, is a co

Martin Hasenbusch

We study the $3$-component $\phi^4$ model on the simple cubic lattice in presence of a cubic perturbation. To this end, we perform Monte Carlo simulations in conjunction with a finite size scaling analysis of the data. The analysis of the renormalization group (RG)-flow of a dimensionless quantity provides us with the accurate estimate $Y_4 - \omega_2 =0.000

Pablo Bueno, Horacio Casini, Oscar Lasso Andino, Javier Moreno

The entanglement entropy of an arbitrary spacetime region $A$ in a three-dimensional conformal field theory (CFT) contains a constant universal coefficient, $F(A)$. For general theories, the value of $F(A)$ is minimized when $A$ is a round disk, $F_0$, and in that case it coincides with the Euclidean free energy on the sphere. We conjecture that, for general

Sérgio F Chevtchenko, Elisson da Silva Rocha, Bruna Cruz, Ermeson Carneiro de Andrade

Energy storage solutions play an increasingly important role in modern infrastructure and lead-acid batteries are among the most commonly used in the rechargeable category. Due to normal degradation over time, correctly determining the battery's State of Health (SoH) and Remaining Useful Life (RUL) contributes to enhancing predictive maintenance, reliability

Cristian Camilo Pulgarín-Ospina, Rocío del Amor, Adrián Colomera, Julio Silva-Rodríguez

Digital pathology has become a standard in the pathology workflow due to its many benefits. These include the level of detail of the whole slide images generated and the potential immediate sharing of cases between hospitals. Recent advances in deep learning-based methods for image analysis make them of potential aid in digital pathology. However, a major li

Meelis Käärik, Anne Selart, Tuuli Puhkim, Liivika Tee

Several distributions and families of distributions are proposed to model skewed data, think, e.g., of skew-normal and related distributions. Lambert W random variables offer an alternative approach where, instead of constructing a new distribution, a certain transform is proposed (Goerg, 2011). Such an approach allows the construction of a Lambert W skewed

Kunal Suri, Prakhar Mishra, Saumajit Saha, Atul Singh

Finetuning Large Language Models helps improve the results for domain-specific use cases. End-to-end finetuning of large language models is time and resource intensive and has high storage requirements to store the finetuned version of the large language model. Parameter Efficient Fine Tuning (PEFT) methods address the time and resource challenges by keeping

Rixin Wu, Ran Wang, Jie Hao, Qiang Wu

Due to shortage of water resources and increasing water demands, the joint operation of multireservoir systems for balancing power generation, ecological protection, and the residential water supply has become a critical issue in hydropower management. However, the numerous constraints and nonlinearity of multiple reservoirs make solving this problem time-co

Yinghao Ma, Ruibin Yuan, Yizhi Li, Ge Zhang

Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trained on music recordings may have been mostly closed-sourced, recent speech models such as wav2vec2.0 have show

Grigori Olshanski

We study a spectral problem related to the finite-dimensional characters of the groups $Sp(2N)$, $SO(2N+1)$, and $SO(2N)$, which form the classical series $C$, $B$, and $D$, respectively. The irreducible characters of these three series are given by $N$-variate symmetric polynomials. The spectral problem in question consists in the decomposition of the chara

Edgar Benitez, Jesús López-Fidalgo

The state of the art related to parameter correlation in two-parameter models has been reviewed in this paper. The apparent contradictions between the different authors regarding the ability of D--optimality to simultaneously reduce the correlation and the area of the confidence ellipse in two-parameter models were analyzed. Two main approaches were found: 1

Anshul Gupta, Samy Tafasca, Jean-Marc Odobez

Predicting where a person is looking is a complex task, requiring to understand not only the person's gaze and scene content, but also the 3D scene structure and the person's situation (are they manipulating? interacting or observing others? attentive?) to detect obstructions in the line of sight or apply attention priors that humans typically have when obse

Sauro Succi, Wael Itani, Katepalli Sreenivasan, René Steijl

We present a pedagogical introduction to the current state of quantum computing algorithms for the simulation of classical fluids. Different strategies, along with their potential merits and liabilities, are discussed and commented on.

Cecilia Di Florio, Guido Governatori, Antonino Rotolo, Giovanni Sartor

This paper examines how a notion of stable explanation developed elsewhere in Defeasible Logic can be expressed in the context of formal argumentation. With this done, we discuss the deontic meaning of this reconstruction and show how to build from argumentation neighborhood structures for deontic logic where this notion of explanation can be characterised.

Vikrant Yadav, Santosh Kumar Yadav, Anil Kumar Yadav

In this paper, we investigate an extension of the standard $\Lambda$CDM model by allowing: a temporal evolution in the equation of state (EoS) of DM via Chevallier-Polarski-Linder parametrization, and the constant non-null sound speed. We also consider the properties of neutrinos, such as the effective neutrino mass and the effective number of neutrino speci

Jens Hönen, Johann L. Hurink, Bert Zwart

Within the last few years, the trend towards more distributed, renewable energy sources has led to major changes and challenges in the electricity sector. To ensure a stable electricity distribution in this changing environment, we propose a robust energy management approach to deal with uncertainty occurring in microgrids. For this, we combine robust optimi

Jean Bricmont

It is often claimed that there are three "realist" versions of quantum mechanics: the de Broglie-Bohm theory or Bohmian mechanics, the spontaneous collapse theories and the many worlds interpretation. We will explain why the two latter proposals suffer from serious defects coming from their ontology (or lack thereof) and that the many worlds interpretation i

Andrea Coccaro, Francesco Armando Di Bello, Stefano Giagu, Lucrezia Rambelli

Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as a trending technology for the triggering strategy of the upcoming high-luminosity program of the Large Hadron Collider

Zhao Liu, Quanchen Zou, Tian Yu, Xuan Wang

As machine learning gains prominence in various sectors of society for automated decision-making, concerns have risen regarding potential vulnerabilities in machine learning (ML) frameworks. Nevertheless, testing these frameworks is a daunting task due to their intricate implementation. Previous research on fuzzing ML frameworks has struggled to effectively

Denise Moussa, Germans Hirsch, Sebastian Wankerl, Christian Riess

Verifying the integrity of voice recording evidence for criminal investigations is an integral part of an audio forensic analyst's work. Here, one focus is on detecting deletion or insertion operations, so called audio splicing. While this is a rather easy approach to alter spoken statements, careful editing can yield quite convincing results. For difficult

Fadi Boutros, Marcel Klemt, Meiling Fang, Arjan Kuijper

Deep generative models have recently presented impressive results in generating realistic face images of random synthetic identities. To generate multiple samples of a certain synthetic identity, previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attr

Pierre Nunn, François Schwarzentruber

In this paper, we propose a modal logic in which counting modalities appear in linear inequalities. We show that each formula can be transformed into an equivalent graph neural network (GNN). We also show that each GNN can be transformed into a formula. We show that the satisfiability problem is decidable. We also discuss some variants that are in PSPACE.

Jean Bricmont

We will first define what is meant by ``hidden variables". Then, we will review various theorems proving the impossibility of theories introducing such variables and then show that the de Broglie-Bohm theory is not refuted by those theorems. We will also explain the relation between those theorems and nonlocality, with or without introducing Bell's inequalit

Marius Smytzek, Martin Eberlein, Batuhan Serce, Lars Grunske

Benchmarks are among the main drivers of progress in software engineering research. However, many current benchmarks are limited by inadequate system oracles and sparse unit tests. Our Tests4Py benchmark, derived from the BugsInPy benchmark, addresses these limitations. It includes 73 bugs from seven real-world Python applications and six bugs from example p

Alexander Cant, Bettina Eick

Let $\mathcal{T}$ denote the class of finitely generated torsion-free nilpotent groups. For a group $G$ let $F(G)$ be the set of isomorphism classes of finite quotients of $G$. Pickel proved that if $G \in \mathcal{T}$, then the set $\mathfrak{g}(G)$ of isomorphism classes of groups $H \in \mathcal{T}$ with $F(G)=F(H)$ is finite. We give an explicit descript

Hui Liu, Chengfeng Sun, Mei Li

The three-dimensional generalized tropical climate model with partial viscosity and damping is considered in this paper. Global well-posedness of solutions of the three-dimensional generalized tropical climate model with partial viscosity and damping is proved for $\alpha\geq\frac{3}{2}$ and $\beta\geq4$. Global smooth solution of the three-dimensional gener

Nicola Cavallucci

We give a detailed description of the possible limits in the equivariant-Gromov-Hausdorff sense of sequences $(X_j,G_j)$, where the $X_j$'s are proper, geodesically complete, uniformly packed, CAT$(0)$-spaces and the $G_j$'s are closed, totally disconnected, unimodular, uniformly cocompact groups of isometries. We show that the class of metric quotients $G/X

Danny D'Agostino, Ilija Ilievski, Christine Annette Shoemaker

Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this challenge, we propose a modification of the radial basis function neural network model by equipping its Gaussi

Indranil Ghosh, Robert I. McLachlan, David J. W. Simpson

This paper concerns the two-dimensional border-collision normal form -- a four-parameter family of piecewise-linear maps generalising the Lozi family and relevant to diverse applications. The normal form was recently shown to exhibit a chaotic attractor throughout an open region of parameter space. This was achieved by constructing a trapping region in phase

Myrthe Spronck, Bas Luttik

We present process-algebraic models of multi-writer multi-reader safe, regular and atomic registers. We establish the relationship between our models and alternative versions presented in the literature. We use our models to formally analyse by model checking to what extent several well-known mutual exclusion algorithms are robust for relaxed atomicity requi

Shunhao Ji, Zhongxiang Wang, Yi Xing, Dahai Yan

Intrigued by recent high-energy study results for nearby galaxies with gamma-ray emission and in particular NGC~1068 that has been detected as a neutrino-emitting source by the IceCube Neutrino Observatory, we conduct detailed analysis of the $\gamma$-ray data for the galaxies NGC~1068 and NGC~253, obtained with the Large Area Telescope onboard {\it the Ferm

Peng Yan, Ahmed Abdulkadir, Paul-Philipp Luley, Matthias Rosenthal

Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely interventions. Deep learning, with its capacity to discern non-trivial patterns within large datasets, plays a pivotal role in this process. Standard deep learning methods are suitab

Michael Przystupa, Faezeh Haghverd, Martin Jagersand, Samuele Tosatto

Movement primitives are trainable parametric models that reproduce robotic movements starting from a limited set of demonstrations. Previous works proposed simple linear models that exhibited high sample efficiency and generalization power by allowing temporal modulation of movements (reproducing movements faster or slower), blending (merging two movements i

Thomas Ahlefeldt, Daniel Ernst, Armin Goudarzi, Hans-Georg-Raumer

This paper presents an end-to-end approach for the assessment of pressurized and cryogenic wind tunnel measurements of an EMBRAER scaled full model close to real-world Reynolds numbers. The choice of microphones, measurement parameters, the design of the array, and the selection of flow parameters are discussed. Different wind tunnel conditions are proposed

M. V. Gonçalves-Faria, A. Pashkin, Q. Wang, H. C. Lei

Temperature- and fluence-dependent carrier dynamics of the magnetic Kagome metal Fe$_3$Sn$_2$ were studied using the ultrafast optical pump-probe technique. Two carrier relaxation processes ($\tau_1$ and $\tau_2$) and a laser induced coherent optical phonon were observed. By using the two-temperature model for metals, we ascribe the shorter relaxation $\tau_

Chuncheng Zhang, Tingting Liu, Zhihua Xie, Yu Wang

Following recent advancements in multimode fiber (MMF), miniaturization of imaging endoscopes has proven crucial for minimally invasive surgery in vivo. Recent progress enabled by super-resolution imaging methods with a data-driven deep learning (DL) framework has balanced the relationship between the core size and resolution. However, most of the DL approac

Nafiseh Soveizi, Fatih Turkmen

In this paper, we propose an architecture for a security-aware workflow management system (WfMS) we call SecFlow in answer to the recent developments of combining workflow management systems with Cloud environments and the still lacking abilities of such systems to ensure the security and privacy of cloud-based workflows. The SecFlow architecture focuses on

Adel Oulefki, Yassine Himeur, Thaweesak Trongtiraku, Kahina Amara

Solar Photovoltaic (PV) is increasingly being used to address the global concern of energy security. However, hot spot and snail trails in PV modules caused mostly by crakes reduce their efficiency and power capacity. This article presents a groundbreaking methodology for automatically identifying and analyzing anomalies like hot spots and snail trails in So

Aayush Kumar Sharma, Vineet Bhavikatti, Amogh Nidawani, Siddappaji

In this research paper, we delve into the topics of Speech Diarization and Automatic Speech Recognition (ASR). Speech diarization involves the separation of individual speakers within an audio stream. By employing the ASR transcript, the diarization process aims to segregate each speaker's utterances, grouping them based on their unique audio characteristics

Wen-Mei Guo, Bao-An Li, Gao-Chan Yong

Within an isospin- and momentum-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model, we investigate the hard photons emission from neutron-proton bremsstrahlung in reaction system of $^{208}Pb+^{208}Pb$ around Fermi energy. Effects of neutron skins in coordinate ($r$) space and proton skins in momentum ($k$) space on the time evolution, the angular

Paul Grimal, Hervé Le Borgne, Olivier Ferret, Julien Tourille

The progress in the generation of synthetic images has made it crucial to assess their quality. While several metrics have been proposed to assess the rendering of images, it is crucial for Text-to-Image (T2I) models, which generate images based on a prompt, to consider additional aspects such as to which extent the generated image matches the important cont

Francesco Grotto, Marco Romito, Milo Viviani

The continuation of point vortex dynamics after a vortex collapse is investigated by means of a regularization procedure consisting in introducing a small stochastic diffusive term, that corresponds to a vanishing viscosity. In contrast with deterministic regularization, in which a cutoff interaction selects in the limit a single trajectory of the system aft

Siyang Wang, Gustav Eje Henter, Joakim Gustafson, Éva Székely

Self-supervised learning (SSL) speech representations learned from large amounts of diverse, mixed-quality speech data without transcriptions are gaining ground in many speech technology applications. Prior work has shown that SSL is an effective intermediate representation in two-stage text-to-speech (TTS) for both read and spontaneous speech. However, it i

Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara, Salvador Lima López

This is an overview of the eleventh edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2023. BioASQ is a series of international challenges promoting advances in large-scale biomedical semantic indexing and question answering. This year, BioASQ consisted of new editions of the two established tasks b and

Chloë Hebborn, Melina L. Avila, Konstantinos Kravvaris, Gregory Potel

Indirect methods have become the predominant approach in experimental nuclear astrophysics for studying several low-energy nuclear reactions occurring in stars, as direct measurements of many of these relevant reactions are rendered infeasible due to their low reaction probability. Such indirect methods, however, require theoretical input that in turn can ha

Alex Goater, Justin I. Read, Noelia E. D. Noël, Matthew D. A. Orkney

Ultra-faint dwarf galaxies (UFDs) are commonly found in close proximity to the Milky Way and other massive spiral galaxies. As such, their projected stellar ellipticity and extended light distributions are often thought to owe to tidal forces. In this paper, we study the projected stellar ellipticities and faint stellar outskirts of tidally isolated ultra-fa

Yongcong Zhang, Yifei Xue, Ming Liao, Huiqing Zhang

Despite the increasing prevalence of rotating-style capture (e.g., surveillance cameras), conventional stereo rectification techniques frequently fail due to the rotation-dominant motion and small baseline between views. In this paper, we tackle the challenge of performing stereo rectification for uncalibrated rotating cameras. To that end, we propose Depth-

Kevin Hernandez-Diaz, Fernando Alonso-Fernandez, Josef Bigun

One weakness of machine-learning algorithms is the need to train the models for a new task. This presents a specific challenge for biometric recognition due to the dynamic nature of databases and, in some instances, the reliance on subject collaboration for data collection. In this paper, we investigate the behavior of deep representations in widely used CNN

Gaoyuan Cheng, Yuan Fang, Jie Xu, Derrick Wing Kwan Ng

This paper studies a multi-antenna networked integrated sensing and communications (ISAC) system, in which a set of multi-antenna base stations (BSs) employ the coordinated transmit beamforming to serve multiple single-antenna communication users (CUs) and perform joint target detection by exploiting the reflected signals simultaneously. To facilitate target

C. Coelho, M. Fernanda P. Costa, L. L. Ferrás

Due to their dynamic properties such as irregular sampling rate and high-frequency sampling, Continuous Time Series (CTS) are found in many applications. Since CTS with irregular sampling rate are difficult to model with standard Recurrent Neural Networks (RNNs), RNNs have been generalised to have continuous-time hidden dynamics defined by a Neural Ordinary

Kentaro Ohno, Nozomu Togawa

Ising machines are next-generation computers expected to efficiently sample near-optimal solutions of combinatorial optimization problems. Combinatorial optimization problems are modeled as quadratic unconstrained binary optimization (QUBO) problems to apply an Ising machine. However, current state-of-the-art Ising machines still often fail to output near-op

Amiran Gogatishvili, Bohumir Opic, Sergey Tikhonov, Walter Trebels

The paper provides a detailed study of crucial inequalities for smoothness and interpolation characteristics in rearrangement invariant Banach function spaces. We present a unified approach based on Holmstedt formulas to obtain these estimates. As examples, we derive new inequalities for moduli of smoothness and K-functionals in various Lorentz spaces.

Angela Sara Cacciapuoti, Jessica Illiano, Michele Viscardi, Marcello Caleffi

Entanglement distribution is a key functionality of the Quantum Internet. However, quantum entanglement is very fragile, easily degraded by decoherence, which strictly constraints the time horizon within the distribution has to be completed. This, coupled with the quantum noise irremediably impinging on the channels utilized for entanglement distribution, ma

Nathan Canen, Kyungchul Song

Counterfactual predictions are challenging when the policy variable goes beyond its pre-policy support. However, in many cases, information about the policy of interest is available from different ("source") regions where a similar policy has already been implemented. In this paper, we propose a novel method of using such data from source regions to predict

Yue Tian, Guanjun Liu

How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs) to the transaction fraud detection problem. Nevertheless, they encounter challenges in effectively learning spatial-temporal information du

Walter Bridges, Kathrin Bringmann

In this paper, we prove that the number of unimodal sequences of size $n$ is log-concave. These are coefficients of a mixed false modular form and have a Rademacher-type exact formula due to recent work of the second author and Nazaroglu on false theta functions. Log-concavity and higher Tur\'{a}n inequalities have been well-studied for (restricted) partitio

Eun-Kyung Cho, Minki Kim

In 2011, Henning, L\"{o}wenstein, and Rautenbach observed that the domination number of a graph is bounded from above by the product of the packing number and the maximum degree of the graph. We prove a stronger statement in subcubic graphs: the independent domination number is bounded from above by three times the packing number.

Indranil Biswas, Umesh V Dubey, Manish Kumar, A. J. Parameswaran

We consider several related examples of Fourier-Mukai transformations involving the quot scheme. A method of showing conservativity of these Fourier-Mukai transformations is described.

Yi Li, Honghao Lin, David P. Woodruff

We consider the randomized communication complexity of the distributed $\ell_p$-regression problem in the coordinator model, for $p\in (0,2]$. In this problem, there is a coordinator and $s$ servers. The $i$-th server receives $A^i\in\{-M, -M+1, \ldots, M\}^{n\times d}$ and $b^i\in\{-M, -M+1, \ldots, M\}^n$ and the coordinator would like to find a $(1+\epsil

Mark O. Goerbig

Volkov-Pankratov surface bands arise in smooth topological interfaces, i.e. interfaces between a topological and a trivial insulator, in addition to the chiral surface state imposed by the bulk-surface correspondence of topological materials. These two-dimensional bands become Landau-quantized if a magnetic field is applied perpendicular to the interface. I

Diego Barberena, Ana Maria Rey

We analyse the properties across steady state phase transitions of two all-to-all driven-dissipative spin models that describe possible dynamics of N two-level systems inside an optical cavity. We show that the finite size behaviour around the critical points can be captured correctly by carefully identifying the relevant non-linearities in the Holstein-Prim

Ting Wang, Yuzhi Zhou, Aihui Zhou

In this paper, we apply the effective potentials in the localization landscape theory (Filoche et al., 2012, Arnold et al., 2016) to study the spectral properties of the incommensurate systems. We uniquely develop a plane wave method for the effective potentials of the incommensurate systems and utilize that, the localization of the electron density can be i

Zhouhong Gu, Lin Zhang, Jiangjie Chen, Haoning Ye

Detectives frequently engage in information detection and reasoning simultaneously when making decisions across various cases, especially when confronted with a vast amount of information. With the rapid development of large language models~(LLMs), evaluating how these models identify key information and reason to solve questions becomes increasingly relevan

T. Kouchi, K. Yoshinaga, T. Asano, S. Nishioka

Isotope $^{121,123}$Sb nuclei with large electric quadrupole moments are applied to investigate the dynamics of orbital degrees of freedom in Sb-substituted iron(Fe)-based compounds. In the parent compound LaFe(As$_{0.6}$Sb$_{0.4}$)O, the nuclear spin relaxation rate $^{121,123}(T_{1}^{-1})$ at $^{121,123}$Sb sites was enhanced at structural transition tempe

Francesco Casini, Cristian Giardinà, Frank Redig

We study the density fluctuations at equilibrium of the multi-species stirring process, a natural multi-type generalization of the symmetric (partial) exclusion process. In the diffusive scaling limit, the resulting process is a system of infinite-dimensional Ornstein-Uhlenbeck processes that are coupled in the noise terms. This shows that at the level of eq

Ze Zheng, Kainan Chang, Jin Luo Cheng

Generating photogalvanic effects in centrosymmetric materials can provide new opportunities for developing passive photodetectors and energy harvesting devices. In this work, we investigate the photogalvanic effects in centrosymmetric two-dimensional materials, AA- and AB-stacked bilayer graphene, by applying an external gate voltage to break the symmetry. U

Etash Kumar Guha, Eugene Ndiaye, Xiaoming Huo

Given a sequence of observable variables $\{(x_1, y_1), \ldots, (x_n, y_n)\}$, the conformal prediction method estimates a confidence set for $y_{n+1}$ given $x_{n+1}$ that is valid for any finite sample size by merely assuming that the joint distribution of the data is permutation invariant. Although attractive, computing such a set is computationally infea

Safa Snoun, Noureddine Ghiloufi

In this paper, we start by introducing the modified Bergman-Dirichlet space $\mathcal D_m^2(\mathbb D_R,\mu^R_{\alpha,\beta})$ and then we study its asymptotic behavior when the parameter $\alpha$ goes to infinity and to $(-1)$ to obtain respectively the modified Bargmann-Dirichlet and the modified Hardy-Dirichlet spaces with their reproducing kernels. Final

Federico Simonetta, Ana Llorens, Martín Serrano, Eduardo García-Portugués

This paper presents a comprehensive investigation of existing feature extraction tools for symbolic music and contrasts their performance to determine the set of features that best characterizes the musical style of a given music score. In this regard, we propose a novel feature extraction tool, named musif, and evaluate its efficacy on various repertoires a

Till Schallau, Stefan Naujokat, Fiona Kullmann, Falk Howar

Scenario-based testing is envisioned as a key approach for the safety assurance of autonomous vehicles. In scenario-based testing, relevant (driving) scenarios are the basis of tests. Many recent works focus on specification, variation, generation and execution of individual scenarios. In this work, we address the open challenges of classifying sets of scena

Francesco Camilli, Daria Tieplova, Jean Barbier

We carry out an information-theoretical analysis of a two-layer neural network trained from input-output pairs generated by a teacher network with matching architecture, in overparametrized regimes. Our results come in the form of bounds relating i) the mutual information between training data and network weights, or ii) the Bayes-optimal generalization erro

Pieter M. Gunnink, Tim Ludwig, Rembert A. Duine

Spin torque oscillators are conventionally described by the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation. However, at the onset of oscillations, the predictions of the conventional LLGS equation differ qualitatively from experimental results and thus appear to be incomplete. In this work we show that taking charge conservation into account leads to a

Udo Schlegel, Daniel A. Keim

Explainable Artificial Intelligence (XAI) has gained significant attention recently as the demand for transparency and interpretability of machine learning models has increased. In particular, XAI for time series data has become increasingly important in finance, healthcare, and climate science. However, evaluating the quality of explanations, such as attrib

Yongxin Chen, Tryphon T. Georgiou, Michele Pavon

A paradigm put forth by E. Schr\"odinger in 1931/32, known as Schr\"odinger bridges, represents a formalism to pose and solve control and estimation problems seeking a perturbation from an initial control schedule (in the case of control), or from a prior probability law (in the case of estimation), sufficient to reconcile data in the form of marginal distri

Sebastian Falkensteiner, Rafael Sendra

In this paper, we give an algorithm for finding general rational solutions of a given first-order ODE with parametric coefficients that occur rationally. We present an analysis, complete modulo Hilbert's irreducibility problem, of the existence of rational solutions of the differential equation, with parametric coefficients, when the parameters are specializ

Annalisa Coriolano, Anna Moliterni, Francesco Todisco, Laura Polimeno

Hybrid two-dimensional perovskites hold considerable promise as semiconductors for a wide range of optoelectronic applications. Many efforts are addressed to exploit the potential of these materials by tailoring their characteristics. In this work, the optical properties and electronic band structure in three new Dion-Jacobson (DJ) perovskites (PVKs) are eng

Matthias Eckardt, Carles Comas, Jorge Mateu

Prompted by modern technologies in data acquisition, the statistical analysis of spatially distributed function-valued quantities has attracted a lot of attention in recent years. In particular, combinations of functional variables and spatial point processes yield a highly challenging instance of such modern spatial data applications. Indeed, the analysis o

Yonghui Yang, Zhengwei Wu, Le Wu, Kun Zhang

By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers have introduced Graph Contrastive Learning(GCL) techniques into CF to alleviate the sparse supervision issue, which first constructs contrastive views by data augmentations and then p

Junming Zhang, Haomeng Zhang, Ram Vasudevan, Matthew Johnson-Roberson

Most real-world 3D measurements from depth sensors are incomplete, and to address this issue the point cloud completion task aims to predict the complete shapes of objects from partial observations. Previous works often adapt an encoder-decoder architecture, where the encoder is trained to extract embeddings that are used as inputs to generate predictions fr

Hau Tian Teo, Subhaskar Mandal, Yang Long, Haoran Xue

It has recently been shown that the non-Hermitian skin effect can be suppressed by magnetic fields. In this work, using a two-dimensional tight-binding lattice, we demonstrate that a pseudomagnetic field can also lead to the suppression of the non-Hermitian skin effect. With an increasing pseudomagnetic field, the skin modes are found to be pushed into the b

Sushil Kumar Sahu, Abhiram Soori

Conductance of metallic heterostructures can be controlled by applying a gate voltage to a region in the transport channel. For sufficiently long phase coherent channels, oscillations appear in conductance versus chemical potential plot, which can be explained by Fabry-P\'erot interference. In this work, we study DC Josephson effect in a superconductor-norma

Stefan Junk

We consider the directed polymer model in the weak disorder phase under the assumption that the partition function is $L^p$-bounded for some $p>1+\frac{2}d$. We prove that the point-to-point partition function can be approximated by two point-to-plane partition functions at the startpoint and endpoint, and in particular that it is $L^p$-bounded as well. Some

Konstantia Zarkogianni, Edmund Dervakos, George Filandrianos, Theofanis Ganitidis

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,

Kun Li, Fan Zhang, Wei Guo

Deep learning technology has made great achievements in the field of image. In order to defend against malware attacks, researchers have proposed many Windows malware detection models based on deep learning. However, deep learning models are vulnerable to adversarial example attacks. Malware can generate adversarial malware with the same malicious function t

Nikola Kuzmanovski

Macaulay posets are posets in which an analog of the Kruskal-Katona Theorem holds. Macaulay rings (also called Macaulay-Lex rings) are rings in which an analog of Macaulay's Theorem for lex ideals holds. The study of both of these objects started with Macaulay almost a century ago. Since then, these two branches have developed separately over the past centur

Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli, Daniel Nikovski

In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components from it. We revisit the classical formulation of rigid body dynamics in order to extrapolate the physical dynamical components, such as inertial and gravitational components, from