October 2022 arXiv papers — page 38

Fotis Psallidas, Ashvin Agrawal, Chandru Sugunan, Khaled Ibrahim

Provenance encodes information that connects datasets, their generation workflows, and associated metadata (e.g., who or when executed a query). As such, it is instrumental for a wide range of critical governance applications (e.g., observability and auditing). Unfortunately, in the context of database systems, extracting coarse-grained provenance is a long-

Balázs Kovács

A numerical algorithm for mean curvature flow of closed mean convex surfaces with surgery is proposed. The method uses a finite element based mean curvature flow algorithm based on a coupled partial differential equation system which directly provides an approximation for mean curvature and outward unit normal. The proposed numerical surgery process closely

Brice Bakkali-Hassani, Jean Dalibard

In these lecture notes, we discuss the physics of a two-dimensional binary mixture of Bose gases at zero temperature, close to the point where the two fluids tend to demix. We are interested in the case where one of the two fluids (the bath) fills the whole space, while the other one (the minority component) contains a finite number of atoms. We discuss unde

Pu Ren, Chengping Rao, Su Chen, Jian-Xun Wang

There has been an increasing interest in integrating physics knowledge and machine learning for modeling dynamical systems. However, very limited studies have been conducted on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational co

Ioannis Zachos

We consider Shimura varieties associated to a unitary group of signature $(2,n-2)$. We give regular $p$-adic integral models for these varieties over odd primes $p$ which ramify in the imaginary quadratic field with level subgroup at $p$ given by the stabilizer of a selfdual lattice in the hermitian space. Our construction is given by an explicit resolution

Andrzej Dudek, Jarosław Grytczuk, Andrzej Ruciński

An ordered matching of size $n$ is a graph on a linearly ordered vertex set $V$, $|V|=2n$, consisting of $n$ pairwise disjoint edges. There are three different ordered matchings of size two on $V=\{1,2,3,4\}$: an alignment $\{1,2\},\{3,4\}$, a nesting $\{1,4\},\{2,3\}$, and a crossing $\{1,3\},\{2,4\}$. Accordingly, there are three basic homogeneous types of

Leonardo Fierro, Vesa Välimäki

The decomposition of sounds into sines, transients, and noise is a long-standing research problem in audio processing. The current solutions for this three-way separation detect either horizontal and vertical structures or anisotropy and orientations in the spectrogram to identify the properties of each spectral bin and classify it as sinusoidal, transient,

Anna T. Bui, Fabian L. Thiemann, Angelos Michaelides, Stephen J. Cox

Friction at water-carbon interfaces remains a major puzzle with theories and simulations unable to explain experimental trends in nanoscale waterflow. A recent theoretical framework -- quantum friction (QF)-- proposes to resolve these experimental observations by considering nonadiabatic coupling between dielectric fluctuations in water and graphitic surface

Amador Martin-Pizarro, Daniel Palacin, Julia Wolf

We introduce a relaxation of stability, called almost sure stability, which is insensitive to perturbations by subsets of Loeb measure $0$ in a non-standard finite group. We show that almost sure stability satisfies a stationarity principle in the sense of geometric stability theory for measure independent elements. We apply this principle to deduce the exis

Daniele Dorigoni, Michael B. Green, Congkao Wen, Haitian Xie

The use of supersymmetric localisation has recently led to modular covariant expressions for certain integrated correlators of half-BPS operators in $\mathcal{N} = 4$ supersymmetric Yang-Mills theory with a general classical gauge group $G_N$. Here we determine generating functions that encode such integrated correlators for any classical gauge group and pro

Andreas Nugaard Holm, Dustin Wright, Isabelle Augenstein

Uncertainty approximation in text classification is an important area with applications in domain adaptation and interpretability. One of the most widely used uncertainty approximation methods is Monte Carlo (MC) Dropout, which is computationally expensive as it requires multiple forward passes through the model. A cheaper alternative is to simply use the so

Martina Lippi, Paolo Di Lillo, Alessandro Marino

The requirements of modern production systems together with more advanced robotic technologies have fostered the integration of teams comprising humans and autonomous robots. However, along with the potential benefits also comes the question of how to effectively handle these teams considering the different characteristics of the involved agents. For this re

Md Tashfiq Bin Kashem, Raihan Sayeed Khan, ABM Hasan Talukder, Faruk Dirisaglik

We stabilize resistance of melt-quenched amorphous Ge2Sb2Te5 (a-GST) phase change memory (PCM) line cells by substantially accelerating resistance drift and bringing it to a stop within a few minutes with application of high electric field stresses. The acceleration of drift is clearly observable at electric fields > 26 MV/m at all temperatures (85 K - 300 K

Amin Bakhshandeh, Derek Frydel, Yan Levin

We present a theory that enables us to calculate the effective surface charge of colloidal particles and to efficiently obtain titration curves for different salt concentrations. The theory accounts for the shift of pH of solution due to the presence of 1:1 electrolyte. It also accounts self-consistently for the electrostatic potential produced by the deprot

Anton Arnold, Amit Einav, Tobias Wöhrer

The goal of this work is to introduce and investigate a generalised Fisher Information in the setting of linear Fokker-Planck equations. This functional, which depends on two functions instead of one, exhibits the same decay behaviour as the standard Fisher information, and allows us to investigate different parts of the Fokker-Planck solution via an appropr

Felix Draxler, Christoph Schnörr, Ullrich Köthe

Coupling-based normalizing flows (e.g. RealNVP) are a popular family of normalizing flow architectures that work surprisingly well in practice. This calls for theoretical understanding. Existing work shows that such flows weakly converge to arbitrary data distributions. However, they make no statement about the stricter convergence criterion used in practice

Hessah Albanwan, Rongjun Qin

Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robust DL methods are on satellite stereo images with varying radiometric and geometric configurations. This paper

Mattia Silvestri, Allegra De Filippo, Michele Lombardi, Michela Milano

The interplay between Machine Learning (ML) and Constrained Optimization (CO) has recently been the subject of increasing interest, leading to a new and prolific research area covering (e.g.) Decision Focused Learning and Constrained Reinforcement Learning. Such approaches strive to tackle complex decision problems under uncertainty over multiple stages, inv

A. V. Yanovsky, P. V. Pyshkin

We discuss how the ideal rechargeable energy accumulator can be made, and what are the limits for solid state energy storage. We show that in theory the spin batteries based on heavy fermions can surpass the chemical ones by energy capacitance. The absence of chemical reactions in spin batteries makes them more stable, also they don't need to be heated in co

E. S. Jennings, S. A. Jacobson, D. C. Rubie, Y. Nakajima

The liquid metal-liquid silicate partitioning of molybdenum and tungsten during core formation must be well-constrained in order to understand the evolution of Earth and other planetary bodies, in particular because the Hf-W isotopic system is used to date early planetary evolution. We combine 48 new high pressure and temperature experimental results with a

The STAR Collaboration

The chiral magnetic wave (CMW) has been theorized to propagate in the deconfined nuclear medium formed in high-energy heavy-ion collisions, and to cause a difference in elliptic flow ($v_{2}$) between negatively and positively charged hadrons. Experimental data consistent with the CMW have been reported by the STAR Collaboration at the Relativistic Heavy Ion

Marco Bornstein, Tahseen Rabbani, Evan Wang, Amrit Singh Bedi

The decentralized Federated Learning (FL) setting avoids the role of a potentially unreliable or untrustworthy central host by utilizing groups of clients to collaboratively train a model via localized training and model/gradient sharing. Most existing decentralized FL algorithms require synchronization of client models where the speed of synchronization dep

Stephanie M. Brown

Gravitational waves from compact binary coalescences are valuable for testing theories of gravity in the strong field regime. By measuring neutron star tidal deformability using gravitational waves from binary neutron stars, stringent constraints were placed on the equation of state of matter at extreme densities. Tidal Love numbers in alternative theories o

Xiangzhi Cao

In this paper, we will use the normalized intetral Ricci curvature to investigate Liouville type property of $ p $ harmonic function on Riemannian manifold. secondly, we will use the BiRic curvature to obtian Liuville theorem for $ p $ harmonic function or $ p $ harmonic 1 form. Lastly, we we will use the BiRic curvature to obtian Liouville theorem forharmon

Shaima'a Freewan, Mostafa Hayajneh

Let $A_i$ and $B_i$ be positive definite matrices for all $i=1,\cdots,m.$ It is shown that $$\left|\left|\sum_{i=1}^m(A_i^2\sharp B_i^2)^r\right|\right|_1\leq\left|\left|\left(\left(\sum_{i=1}^mA_i\right)^{\frac{pr}{_2}}\left(\sum_{i=1}^mB_i\right)^{pr}\left(\sum_{i=1}^mA_i\right)^{\frac{rp}{_2}}\right)^{\frac{1}{p}}\right|\right|_1,$$for all $p>0$ and for a

Georg Anagnostopoulos, Nikolas Geroliminis

Microscopic traffic flow models can be distinguished in lane-based or lane-free depending on the degree of lane-discipline. This distinction holds true only if motorcycles are neglected in lane-based traffic. In cities, as opposed to highways, this is an oversimplification and it would be more accurate to speak of hybrid situations, where lane discipline can

Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys

Sparse modelling or model selection with categorical data is challenging even for a moderate number of variables, because one parameter is roughly needed to encode one category or level. The Group Lasso is a well known efficient algorithm for selection continuous or categorical variables, but all estimates related to a selected factor usually differ. Therefo

Nicola Franchini, Sebastian H. Völkel

In this work we comment in more detail on what happens to the parametrized framework first presented by Cardoso et al. when there are departures from the Schwarzschild background metric, as well as possible deviations in the "dynamics". We treat possible deviations in the background metric with additional coefficients with respect to the original works. The

Sotiris Anagnostidis, Gregor Bachmann, Lorenzo Noci, Thomas Hofmann

Despite the empirical advances of deep learning across a variety of learning tasks, our theoretical understanding of its success is still very restricted. One of the key challenges is the overparametrized nature of modern models, enabling complete overfitting of the data even if the labels are randomized, i.e. networks can completely \textit{memorize} all gi

Wilson Patterson, Ivan Fernandez, Subash Neupane, Milan Parmar

Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier. The Digital Twin, which is typically described as consisting of a physical entity, a virtual counterpart, and the data connections

Hyunjin Shim

Antimicrobial resistance is a silent pandemic that is being exacerbated by the uncontrolled use of antibiotics. Since the discovery of penicillin, we have been largely dependent on microbe-derived small molecules to treat bacterial infections. However, the golden era of antibiotics is coming to an end as the emergence and spread of antimicrobial resistance a

Xin Qiu, Risto Miikkulainen

Population-based search has recently emerged as a possible alternative to Reinforcement Learning (RL) for black-box neural architecture search (NAS). It performs well in practice even though it is not theoretically well understood. In particular, whereas traditional population-based search methods such as evolutionary algorithms (EAs) draw much power from cr

Agulla Surya Bharath, Devanshu Singh Gaharwar, Kumar Appaiah, Debasattam Pal

Unitary matrix-valued functions of frequency are matrix all-pass systems, since they preserve the norm of the input vector signals. Typically, such systems are represented and analyzed using their unitary-matrix valued frequency domain characteristics, although obtaining rational realizations for matrix all-pass systems enables compact representations and ef

Filip Ficek

Nonlinear Schr\"odinger equations are usually investigated with the use of the variational methods that are limited to energy-subcritical dimensions. Here we present the approach based on the shooting method that can give the proof of existence of the ground states in critical and supercritical cases. We formulate the assumptions on the system that are suffi

Klemens Schumann, Luis Böttcher, Philipp Hälsig, Daniel Zelenak

In order to achieve the climate targets, electrification of individual mobility is essential. However, grid integration of electrical vehicles poses challenges for the electrical distribution network due to high charging power and simultaneity. To investigate these challenges in research studies, the network-referenced supply task needs to be modeled. Previo

Mathias Parger, Alexander Ertl, Paul Eibensteiner, Joerg H. Mueller

Training a sparse neural network from scratch requires optimizing connections at the same time as the weights themselves. Typically, the weights are redistributed after a predefined number of weight updates, removing a fraction of the parameters of each layer and inserting them at different locations in the same layers. The density of each layer is determine

Nitish Joshi, Xiang Pan, He He

The term `spurious correlations' has been used in NLP to informally denote any undesirable feature-label correlations. However, a correlation can be undesirable because (i) the feature is irrelevant to the label (e.g. punctuation in a review), or (ii) the feature's effect on the label depends on the context (e.g. negation words in a review), which is ubiquit

Maria Panoukidou, Simon Weir, Valerio Sorichetti, Yair Gutierrez Fosado

The process of polymer condensation, i.e. the formation of bonds between reactive end-groups, is ubiquitous in both industry and biology. Here we study generic systems undergoing polymer condensation in competition with cyclisation. Using a generalised Smoluchowski theory, molecular dynamics simulations and experiments using DNA and T4 ligase, we find that t

Zujian Dai, Gan Jin, Lixin He

The antiferromagnetic (AFM) topological insulator MnBi$_{2}$Te$_{4}$ was believed to have a topological surface state (TSS) with large band gap due to the ferromagnetic (FM) order on the surface, and be able to host the long-sought axion states. However, recent angle-resolved photoemission spectroscopy (ARPES) experiments indicate that the TSS is gapless, co

Pedro Chaves, Tiago Fonseca, Luis Lino Ferreira, Bernardo Cabral

Billions of interconnected Internet of Things (IoT) sensors and devices collect tremendous amounts of data from real-world scenarios. Big data is generating increasing interest in a wide range of industries. Once data is analyzed through compute-intensive Machine Learning (ML) methods, it can derive critical business value for organizations. Powerfulplatform

Zuhra Amiri, Boulat A. Bash, Janis Nötzel

Optical fiber transmission systems form the backbone of today's communication networks and will be of high importance for future networks as well. Among the prominent noise effects in optical fiber is phase noise, which is induced by the Kerr effect. This effect limits the data transmission capacity of these networks and incurs high processing load on the re

Jiacheng Ruan, Mingye Xie, Suncheng Xiang, Ting Liu

Recently, Visual Transformer (ViT) has been widely used in various fields of computer vision due to applying self-attention mechanism in the spatial domain to modeling global knowledge. Especially in medical image segmentation (MIS), many works are devoted to combining ViT and CNN, and even some works directly utilize pure ViT-based models. However, recent w

Wentu Song, Kui Cai

In this paper, we construct systematic $q$-ary two-deletion correcting codes and burst-deletion correcting codes, where $q\geq 2$ is an even integer. For two-deletion codes, our construction has redundancy $5\log n+O(\log q\log\log n)$ and has encoding complexity near-linear in $n$, where $n$ is the length of the message sequences. For burst-deletion codes,

Natan Katz, Uri Itai

The goodness of fit methods for classification problems relies traditionally on confusion matrices. This paper aims to enrich these methods with a risk evaluation and stability analysis tools. For this purpose, we present a parametric PDF framework.

M. Santimaria

Flavour-changing neutral-current processes, such as $b\to s \ell^+\ell^-$, are forbidden at tree level in the Standard Model and hence might receive comparatively large corrections from new particles. This document highlights recent measurements from LHCb on $b\to s \ell^+\ell^-$ and purely-leptonic decays, including tests of lepton flavour universality and

Efklidis Katsaros, Piotr K. Ostrowski, Krzysztof Włódarczak, Emilia Lewandowska

A microcamera firmly attached to a dental handpiece allows dentists to continuously monitor the progress of conservative dental procedures. Video enhancement in video-assisted dental interventions alleviates low-light, noise, blur, and camera handshakes that collectively degrade visual comfort. To this end, we introduce a novel deep network for multi-task vi

Yan-Xia Chang, Quan-Lin Li, Qing Wang, Xing-Shuo Song

In a practical Byzantine fault tolerance (PBFT) blockchain network, the voting nodes may always leave the network while some new nodes can also enter the network, thus the number of voting nodes is constantly changing. Such a new PBFT with dynamic nodes is called a dynamic PBFT. Clearly, the dynamic PBFT can more strongly support the decentralization and dis

E. Angelelli, A. Mor, M. G. Speranza

In bike sharing systems the quality of the service to the users strongly depends on the strategy adopted to reposition the bikes. The bike repositioning problem is in general very complex as it involves different interrelated decisions: the routing of the repositioning vehicles, the scheduling of their visits to the stations, the number of bikes to load or u

Giuseppe Ancona, Adriano Marmora

We study the Hodge standard conjecture for varieties over finite fields admitting a CM lifting, such as abelian varieties or products of K3 surfaces. For those varieties we show that the signature predicted by the conjecture holds true modulo $4$. This amounts to determining the discriminant and the Hilbert symbol of the intersection product. The first is ob

J. G. Kuhn, P. Patapis

The technological progress in spatial-light modulators (SLM) technology has made it possible to use those devices as programmable active focal-plane phase coronagraphic masks, opening the door to novel versatile and adaptive high-contrast imaging observation strategies. However, the scalar nature of the SLM-induced phase response is a potential hurdle when a

S. Takeuchi, F. Li, S. Iwasaki, J. Ning

Person re-identification is a key technology for analyzing video-based human behavior; however, its application is still challenging in practical situations due to the performance degradation for domains different from those in the training data. Here, we propose an environment-constrained adaptive network for reducing the domain gap. This network refines ps

Chunlin You, Qizhong Lin

For graphs $F$ and $H$, the Ramsey number $R(F, H)$ is the smallest positive integer $N$ such that any red/blue edge coloring of $K_N$ contains either a red $F$ or a blue $H$. Let $C_n$ be a cycle of length $n$ and $F_n$ be a fan consisting of $n$ triangles all sharing a common vertex. In this paper, we prove that for all sufficiently large $n$, \[ R(C_{2\lf

M. Santimaria, V. Carassiti, G. Ciullo, P. Di Nezza

The goal of LHCspin is to develop, in the next few years, innovative solutions and cutting-edge technologies to access spin physics in polarised fixed-target collisions at high energy, exploring the unique kinematic regime offered by LHC and exploiting new final states by means of the LHCb detector.

Louis Helmer, Andreas Penzkofer

The high energy consumption of proof of work-based distributed ledgers has become an important environmental concern. Bitcoin, for example, consumes as much energy in a year as a developed country. Alternative consensus mechanisms, such as proof of stake, have been shown to use drastically less energy than proof of work-based DLTs. For example, the IOTA DLT,

Steven A. Grosz, Joshua J. Engelsma, Rajeev Ranjan, Naveen Ramakrishnan

Minutiae matching has long dominated the field of fingerprint recognition. However, deep networks can be used to extract fixed-length embeddings from fingerprints. To date, the few studies that have explored the use of CNN architectures to extract such embeddings have shown extreme promise. Inspired by these early works, we propose the first use of a Vision

Akio Nakagawa

We prove finite field analogues of integral representations of Appell- Lauricella hypergeometric functions in many variables. We consider certain hypersurfaces having a group action and compute the numbers of rational points associated with characters of the group, which will be expressed in terms of Appell-Lauricella functions over finite fields.

Vighnesh S Bhat, Gandhodi Harshavardhan, A. Chockalingam

Reconfigurable intelligent surfaces (RIS) and orthogonal time-frequency space (OTFS) modulation have gained attention in recent wireless research. RIS technology aids communication by reflecting the incident electromagnetic waves towards the receiver, and OTFS modulation is effective in high-Doppler channels. This paper presents an early investigation of RIS

Junze Li, Mengjie Zhao, Yubo Xie, Antonis Maronikolakis

Humor is a magnetic component in everyday human interactions and communications. Computationally modeling humor enables NLP systems to entertain and engage with users. We investigate the effectiveness of prompting, a new transfer learning paradigm for NLP, for humor recognition. We show that prompting performs similarly to finetuning when numerous annotation

Tianci Liu, Tong Yang, Quan Zhang, Qi Lei

Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations. Notwithstanding, the existing optimization approaches use gradient descent largely without adapting to the non-convex nature of the problem and can be sensitive to initial values, impedi

Daniel Asimov, Florian Frick, Michael Harrison, Wesley Pegden

We show that if an open set in $\mathbb{R}^d$ can be fibered by unit $n$-spheres, then $d \geq 2n+1$, and if $d = 2n+1$, then the spheres must be pairwise linked, and $n \in \left\{ 0, 1, 3, 7 \right\}$. For these values of $n$, we construct unit $n$-sphere fibrations in $\mathbb{R}^{2n+1}$.

Congcong Le, Qiang Zhang, Cui Fan, Xianxin Wu

Twisted bilayer graphene (TBG) can host the moir\'{e} energy flat bands with two-fold degeneracy serving as a fruitful playground for strong correlations and topological phases. However, the number of degeneracy is not limited to two. Introducing a spatially alternative magnetic field, we report that the induced magnetic phase becomes an additional controlla

Harvendra Singh

We study the entanglement entropy of a subsystem in contact with symmetrical bath where the complete system lives on the boundary of AdS3 spacetime. The system-A is taken to be in the middle of the bath system-B and the full system is taken to be some fixed localized region of the boundary 2-dimensional CFT. We generally assume that the d.o.f.s in the total

Dubi Kelmer, Alex Kontorovich, Christopher Lutsko

We answer in the affirmative a question of Sarnak's from 2007, confirming that the Patterson-Sullivan base eigenfunction is the unique square-integrable eigenfunction of the hyperbolic Laplacian invariant under the group of symmetries of the Apollonian packing. Thus the latter has a maximal spectral gap. We prove further restrictions on the spectrum of the L

Dan Garber, Tsur Livney, Shoham Sabach

This paper considers a convex composite optimization problem with affine constraints, which includes problems that take the form of minimizing a smooth convex objective function over the intersection of (simple) convex sets, or regularized with multiple (simple) functions. Motivated by high-dimensional applications in which exact projection/proximal computat

Huan Hua, Jun Yan, Xi Fang, Weiquan Huang

The information bottleneck (IB) method is a feasible defense solution against adversarial attacks in deep learning. However, this method suffers from the spurious correlation, which leads to the limitation of its further improvement of adversarial robustness. In this paper, we incorporate the causal inference into the IB framework to alleviate such a problem

Semere Kiros Bitew, Amir Hadifar, Lucas Sterckx, Johannes Deleu

Multiple choice questions (MCQs) are widely used in digital learning systems, as they allow for automating the assessment process. However, due to the increased digital literacy of students and the advent of social media platforms, MCQ tests are widely shared online, and teachers are continuously challenged to create new questions, which is an expensive and

Brian E. Jackson, Jeong Hun Lee, Kevin Tracy, Zachary Manchester

We present a data-efficient algorithm for learning models for model-predictive control (MPC). Our approach, Jacobian-Regularized Dynamic-Mode Decomposition (JDMD), offers improved sample efficiency over traditional Koopman approaches based on Dynamic-Mode Decomposition (DMD) by leveraging Jacobian information from an approximate prior model of the system, an

Botao Fu, Renhong Gao, Jintian Lin, Ni Yao

In general, a high-Q microresonator can accommodate abundant whispering gallery modes (WGMs) with the mode number increasing with the dimensional sizes of the microresonator. Removing the unnecessary modes while reorganizing the remaining modes is of vital importance, which, however, has been proved challenging and usually results in a tradeoff with the Q of

Yacin Ameur, Christophe Charlier, Joakim Cronvall, Jonatan Lenells

We consider a two-dimensional point process whose points are separated into two disjoint components by a hard wall, and study the multivariate moment generating function of the corresponding disk counting statistics. We investigate the ``hard edge regime" where all disk boundaries are a distance of order $\frac{1}{n}$ away from the hard wall, where $n$ is th

M. Lopez-Corredoira, J. E. Betancort-Rijo, R. Scarpa, Z. Chrobakova

A specific modification of Newtonian dynamics known as MOND has been shown to reproduce the dynamics of most astrophysical systems at different scales without invoking non-baryonic dark matter (DM). There is, however, a long-standing unsolved problem when MOND is applied to rich clusters of galaxies in the form of a deficit (by a factor around two) of predic

Peter Hintz

We prove a strong localized gluing result for the general relativistic constraint equations (with or without cosmological constant) in $n\geq 3$ spatial dimensions. We glue an $\epsilon$-rescaling of an asymptotically flat data set $(\hat\gamma,\hat k)$ into the neighborhood of a point $\mathfrak{p}\in X$ inside of another initial data set $(X,\gamma,k)$, un

Yacin Ameur, Christophe Charlier, Joakim Cronvall

We study a class of radially symmetric Coulomb gas ensembles at inverse temperature $\beta=2$, for which the droplet consists of a number of concentric annuli, having at least one bounded ``gap'' $G$, i.e., a connected component of the complement of the droplet, which disconnects the droplet. Let $n$ be the total number of particles. Among other things, we d

Raffaele Marchesi, Nicolo Micheletti, Giuseppe Jurman, Venet Osmani

Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting the minority class using resampling (such as SMOTE) is a widely used approach due to the simplicity of the algorithms. However, these algorithms decrease data variability and may in

Heinz-Jürgen Schmidt, Christian Schröder

Small spin systems at the interface between analytical studies and experimental application have been intensively studied in recent decades. The spin ring consisting of four spins with uniform antiferromagnetic Heisenberg interaction is an example of a completely integrable system, in the double sense: quantum mechanical and classical. However, this does not

Chuanyan Zhou, Jie Ma, Fan Li, Yongming Li

Stack autoencoder (SAE), as a representative deep network, has unique and excellent performance in feature learning, and has received extensive attention from researchers. However, existing deep SAEs focus on original samples without considering the hierarchical structural information between samples. To address this limitation, this paper proposes a new SAE

Paweł Dłotko, Niklas Hellmer, Łukasz Stettner, Rafał Topolnicki

This paper adopts a tool from computational topology, the Euler characteristic curve (ECC) of a sample, to perform one- and two-sample goodness of fit tests. We call our procedure TopoTests. The presented tests work for samples of arbitrary dimension, having comparable power to the state-of-the-art tests in the one-dimensional case. It is demonstrated that t

Aleksandar Petrov, Carter Fang, Khang Minh Pham, You Hong Eng

Evaluating safety performance in a resource-efficient way is crucial for the development of autonomous systems. Simulation of parameterized scenarios is a popular testing strategy but parameter sweeps can be prohibitively expensive. To address this, we propose HiddenGems: a sample-efficient method for discovering the boundary between compliant and non-compli

Xiaoxun Gong, He Li, Nianlong Zou, Runzhang Xu

Combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-learning framework to represent density functional theory (DFT)

Tobias Leemann, Martin Pawelczyk, Christian Thomas Eberle, Gjergji Kasneci

We examine machine learning models in a setup where individuals have the choice to share optional personal information with a decision-making system, as seen in modern insurance pricing models. Some users consent to their data being used whereas others object and keep their data undisclosed. In this work, we show that the decision not to share data can be co

Guillermo Badia, David Clement Makinson

In this note we study a counterpart in predicate logic of the notion of 'logical friendliness', introduced into propositional logic in Makinson (2007). The result is a new consequence relation for predicate languages using first-order models. Although compactness and interpolation fail dramatically, other properties are preserved from the propositional case.

Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya, Owen Cornec

We propose KnowGL, a tool that allows converting text into structured relational data represented as a set of ABox assertions compliant with the TBox of a given Knowledge Graph (KG), such as Wikidata. We address this problem as a sequence generation task by leveraging pre-trained sequence-to-sequence language models, e.g. BART. Given a sentence, we fine-tune

Fang Yang, Zhenzhen Wang, Yonghe Liu, Shuai Yang

Controlling the interplay between localized spins and itinerant electrons at the oxide interfaces can lead to exotic magnetic states. Here we devise SrTiO3/LaTiO3/SrTiO3 heterostructures with varied thickness of the LaTiO3 layer (n monolayers) to investigate the magnetic interactions in the two-dimensional electron gas system. The heterostructures exhibit si

Pengtao Zhang, Junlin Zhang

New findings in natural language processing (NLP) demonstrate that the strong memorization capability contributes a lot to the success of Large Language Models (LLM). This inspires us to explicitly bring an independent memory mechanism into CTR ranking model to learn and memorize cross features' representations. In this paper, we propose multi-Hash Codebook

Junsong Fan, Zhaoxiang Zhang, Tieniu Tan

In this paper, we propose a new approach to applying point-level annotations for weakly-supervised panoptic segmentation. Instead of the dense pixel-level labels used by fully supervised methods, point-level labels only provide a single point for each target as supervision, significantly reducing the annotation burden. We formulate the problem in an end-to-e

Mousumi Mitra, Allen Benton, Md Sabbir Akhanda, Jie Qi

Half-Heusler (HH) phases have garnered much attention as thermally stable and non-toxic thermoelectric materials for power conversion. The most studied alloys to date utilize Hf, Zr, and Ti as the base components. These alloys can achieve a moderate dimensionless figure of merit, ZT, near 1. Recent studies have advanced the thermoelectric performance of HH a

Nicolas Broutin, Hui He, Minmin Wang

We consider a pruning of the inhomogeneous continuum random trees, as well as the cut trees that encode the genealogies of the fragmentations that come with the pruning. We propose a new approach to the reconstruction problem, which has been treated for the Brownian CRT in [Electron. J. Probab. vol. 22, 2017] and for the stable trees in [Ann. IHP B, vol 55,

Hossein Movasati

We give an explicit formula for the $27$ lines of a smooth cubic surface near the Fermat surface. Our formula involves convergent power series with coefficients in the extension of rational numbers with the sixth root of unity. Our main tool is the Artinian Gorenstein ring of socle two attached to such lines.

Francisco S. N. Lobo, José Pedro Mimoso, Jessica Santiago, Matt Visser

Redshift drift is the phenomenon whereby the observed redshift between an emitter and observer comoving with the Hubble flow in an expanding FLRW universe will slowly evolve -- on a timescale comparable to the Hubble time. In a previous article [JCAP 04 (2020) 043; arXiv 2001.11964] three of the current authors had performed a cosmographic analysis of the re

Lucas C. Tavano, Lucas K. Amin, Adolfo Gustavo Serra-Seca-Neto

With a wide range of libraries focused on the machine learning market, such as TensorFlow, NumPy, Pandas, Keras, and others, Python has made a name for itself as one of the main programming languages. In February 2021, Jos\'e Valim and Sean Moriarity published the first version of the Numerical Elixir (Nx) library, a library for tensor operations written in

Carlos Hernandez-Olivan, Javier Hernandez-Olivan, Jose R. Beltran

Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artificial intelligence models that imitate such processes. Music generation with Artificial Intelligence is an emerging field that is gaining much attention in the recent years. In thi

Jonathan Stallrich, Katherine Allen-Moyer, Bradley Jones

In practice, optimal screening designs for arbitrary run sizes are traditionally generated using the D-criterion with factor settings fixed at +/- 1, even when considering continuous factors with levels in [-1, 1]. This paper identifies cases of undesirable estimation variance properties for such D-optimal designs and argues that generally A-optimal designs

Ziluo Ding, Wanpeng Zhang, Junpeng Yue, Xiangjun Wang

We investigate the use of natural language to drive the generalization of policies in multi-agent settings. Unlike single-agent settings, the generalization of policies should also consider the influence of other agents. Besides, with the increasing number of entities in multi-agent settings, more agent-entity interactions are needed for language grounding,

Michele Benzi, Isabella Daidone, Chiara Faccio, Laura Zanetti-Polzi

Liquid water, besides being fundamental for life on Earth, has long fascinated scientists due to several anomalies. Different hypotheses have been put forward to explain these peculiarities. The most accredited one foresees the presence in the supercooled region of two phases at different densities: the low-density liquid phase and the high-density liquid ph

Sidharth Ranjan, Marten van Schijndel, Sumeet Agarwal, Rajakrishnan Rajkumar

We test the hypothesis that discourse predictability influences Hindi syntactic choice. While prior work has shown that a number of factors (e.g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature. Inspired by prior work on syntactic

Zitao Lin, Tianjun Gan, Sharon X. Wang, Avi Shporer

We report the discovery of three transiting low-mass companions to aged stars: a brown dwarf (TOI-2336b) and two objects near the hydrogen burning mass limit (TOI-1608b and TOI-2521b). These three systems were first identified using data from the Transiting Exoplanet Survey Satellite (TESS). TOI-2336b has a radius of $1.05\pm 0.04\ R_J$, a mass of $69.9\pm 2

Sidharth Ranjan, Marten van Schijndel, Sumeet Agarwal, Rajakrishnan Rajkumar

Word order choices during sentence production can be primed by preceding sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is driven by multiple different sources. Using a Hindi corpus of text productions, we model lexical priming with an n-gram cache model and we capture more abstract syntactic priming with an adaptive neural langu

Petrus Mikkola, Julien Martinelli, Louis Filstroff, Samuel Kaski

Bayesian optimization (BO) is a powerful framework for optimizing black-box, expensive-to-evaluate functions. Over the past decade, many algorithms have been proposed to integrate cheaper, lower-fidelity approximations of the objective function into the optimization process, with the goal of converging towards the global optimum at a reduced cost. This task

Sebastian Burciu, Sebastien Palcoux

This paper presents an abstract Isaacs property that involves the Fourier transform for fusion rings, which may be non-commutative, thus expanding upon the commutative version described in [12]. A categorical version of this property was subsequently introduced in [8] for any spherical fusion category, matching with our abstract version in the pseudo-unitary

M. Kioumarsipour, J. Sadeghi

There are two important different mechanisms, the imaginary potential and entropic force, to investigate the dissociation of heavy quarkonia. In this paper, we calculate these two quantities for static and moving quarkonia in the rotating black 3-brane Type IIB supergravity solution dual to N = 4 super Yang-Mills theory on the Coulomb branch (cSYM) at strong

Ngoc Hoang Anh Mai

Given a polynomial $f$ and a semi-algebraic set $S$, we provide a symbolic algorithm to find the equations and inequalities defining a semi-algebraic set $Q$ which is identical to the closure of the image of $S$ under $f$, i.e., \begin{equation} Q=\overline{f(S)}\,. \end{equation} Consequently, every polynomial optimization problem whose optimum value is fin