May 2023 arXiv papers — page 18

Neau Maëlic, Paulo E. Santos, Anne-Gwenn Bosser, Cédric Buche

Learning to compose visual relationships from raw images in the form of scene graphs is a highly challenging task due to contextual dependencies, but it is essential in computer vision applications that depend on scene understanding. However, no current approaches in Scene Graph Generation (SGG) aim at providing useful graphs for downstream tasks. Instead, t

Ta Jiun Ting, Xiaocan Li, Scott Sanner, Baher Abdulhai

Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have become the prevailing models in the traffic prediction literature since they excel at extracting spatial correlations. In this work, we classify the components of successful GCNN pr

Suman Rath, Andres Intriago, Shamik Sengupta, Charalambos Konstantinou

Increased dependence of the maritime industry on information and communication networks has made shipboard power systems vulnerable to stealthy cyber-attacks. One such attack variant, called rootkit, can leverage system knowledge to hide its presence and allow remotely located malware handlers to gain complete control of infected subsystems. This paper prese

Chen Fan, Gaspard Choné-Ducasse, Mark Schmidt, Christos Thrampoulidis

The popularity of bi-level optimization (BO) in deep learning has spurred a growing interest in studying gradient-based BO algorithms. However, existing algorithms involve two coupled learning rates that can be affected by approximation errors when computing hypergradients, making careful fine-tuning necessary to ensure fast convergence. To alleviate this is

Miya Nakajima, Takahiro Saitoh, Tsuyoshi Kato

In recent years, laser ultrasonic visualization testing (LUVT) has attracted much attention because of its ability to efficiently perform non-contact ultrasonic non-destructive testing.Despite many success reports of deep learning based image analysis for widespread areas, attempts to apply deep learning to defect detection in LUVT images face the difficulty

Arshdeep Singh, Haohe Liu, Mark D. Plumbley

Sounds carry an abundance of information about activities and events in our everyday environment, such as traffic noise, road works, music, or people talking. Recent machine learning methods, such as convolutional neural networks (CNNs), have been shown to be able to automatically recognize sound activities, a task known as audio tagging. One such method, pr

Erhan Bayraktar, Song Yao

We study a stochastic control/stopping problem with a series of inequality-type and equality-type expectation constraints in a general non-Markovian framework. We demonstrate that the stochastic control/stopping problem with expectation constraints (CSEC) is independent of a specific probability setting and is equivalent to the constrained stochastic control

Frank Wanye, Vitaliy Gleyzer, Edward Kao, Wu-chun Feng

Stochastic block partitioning (SBP) is a community detection algorithm that is highly accurate even on graphs with a complex community structure, but its inherently serial nature hinders its widespread adoption by the wider scientific community. To make it practical to analyze large real-world graphs with SBP, there is a growing need to parallelize and distr

Yasusada Nambu, Koji Yamaguchi

We investigate entanglement of local spatial modes defined by a quantum field in a de Sitter universe. The introduced modes show dis-entanglement behavior when the separation between two regions where local modes are assigned becomes larger than the cosmological horizon. To understand the emergence of separability between these local modes, we apply the mono

Maruf Adewole, Jeffrey D. Rudie, Anu Gbadamosi, Oluyemisi Toyobo

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treat

Xiaozhi Zhang, Jeffrey G. Ulbrandt, Peco Myint, Andrei Fluerasu

Desorption of deposited species plays a role in determining the evolution of surface morphology during crystal growth when the desorption time constant is short compared to the time to diffuse to a defect site, step edge or kink. However, experiments to directly test the predictions of these effects are lacking. Novel techniques such as \emph{in-situ} cohere

Burt Totaro

Kawakami and the author showed that a projective variety with an int-amplified endomorphism of degree invertible in the base field satisfies Bott vanishing. That was a new way to analyze which varieties have nontrivial endomorphisms. In this paper, we extend that result to a logarithmic version of Bott vanishing for an endomorphism with a totally invariant d

Haifeng Dong, Min Hu

In recent years, progress in improving the bias stability of NMR gyroscopes has been hindered. Taking inspiration from the core idea of rotation modulation in the strapdown inertial navigation system, we propose a general approach to enhancing the bias stability of NMR gyroscopes that does not require consideration of the actual physical sources. The method

Héctor A. Chang-Lara

We establish the existence and uniqueness of viscosity solutions within a domain $\Omega\subseteq\mathbb R^n$ for a class of equations governed by elliptic and eikonal type equations in disjoint regions. Our primary motivation stems from the Hamilton-Jacobi equation that arises in the context of a stochastic optimal control problem.

Ivan Damnjanović

A nut graph is a non-trivial simple graph such that its adjacency matrix has a one-dimensional null space spanned by a full vector. It was recently shown by the authors that there exists a $d$-regular circulant nut graph of order $n$ if and only if $4 \mid d, \, 2 \mid n, \, d > 0$, together with $n \ge d + 4$ if $d \equiv_8 4$ and $n \ge d + 6$ if $8 \mid d

Qing Lyu, Marianna Apidianaki, Chris Callison-Burch

The representation space of pretrained Language Models (LMs) encodes rich information about words and their relationships (e.g., similarity, hypernymy, polysemy) as well as abstract semantic notions (e.g., intensity). In this paper, we demonstrate that lexical stylistic notions such as complexity, formality, and figurativeness, can also be identified in this

Tom Kahana, Daniel A. Bustamante Lopez, Dominik M. Juraschek

Rectification describes the generation of a quasistatic component from an oscillating field, such as an electric polarization in optical rectification, or a structural distortion in nonlinear phononic rectification. Here, we present a third fundamental process for magnetization, in which spin precession is rectified along the coordinates of a nonlinearly dri

Youngseog Chung, Aaron Rumack, Chirag Gupta

In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation. In this context, we introduce the notion of parity calibration, which captures the goal of calibrated forecasting for the increase-de

Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li

Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneously show failures on surprisingly trivial problems. This begs the question: Are these errors incidental, or do they signal more substantial limitations? In an attempt to demystify

Amel Korichi, Emmanuel Clément, Nicolas Dosme, Eric Legay

The AGATA tracking detector array represents a significant improvement over previous Compton suppressed arrays. The construction of AGATA led to numerous technological breakthroughs in order to meet the requirements and the challenges of building a mobile detector across Europe. This paper focuses on the design and implementation of the data acquisition syst

Jabir Chathanathil, Aneesh Ramaswamy, Vladimir S. Malinovsky, Dmitry Budker

Stimulated Raman Adiabatic Passage (STIRAP) is a widely used method for adiabatic population transfer in a multilevel system. In this work, we study STIRAP under novel conditions and focus on the fractional, F-STIRAP, which is known to create a superposition state with the maximum coherence. In both configurations, STIRAP and F-STIRAP, we implement pulse chi

Zhen Xiang, Zidi Xiong, Bo Li

Backdoor (Trojan) attack is a common threat to deep neural networks, where samples from one or more source classes embedded with a backdoor trigger will be misclassified to adversarial target classes. Existing methods for detecting whether a classifier is backdoor attacked are mostly designed for attacks with a single adversarial target (e.g., all-to-one att

Yang Song, Oscar Chaparro

Multiple approaches have been proposed to automatically recommend potential developers who can address bug reports. These approaches are typically designed to work for any bug report submitted to any software project. However, we conjecture that these approaches may not work equally well for all the reports in a project. We conducted an empirical study to va

Nan Wang, Ricardo G. Sanfelice

This paper proposes a stable sparse rapidly-exploring random trees (SST) algorithm to solve the optimal motion planning problem for hybrid systems. At each iteration, the proposed algorithm, called HySST, selects a vertex with the lowest cost among all the vertices within the neighborhood of a randomly selected sample and then extends the search tree by flow

Jozef H. Przytycki, Marithania Silvero

Our main result has topological, combinatorial and computational flavor. It is motivated by a fundamental conjecture stating that computing Khovanov homology of a closed braid of fixed number of strands has polynomial time complexity. We show that the independence simplicial complex $I(w)$ associated to the 4-braid diagram $w$ (and therefore its Khovanov spe

Greg Bodwin

In 2016, a breakthrough result of Chechik and Wulff-Nilsen [SODA '16] established that every $n$-node graph $G$ has a $(1+\varepsilon)(2k-1)$-spanner of lightness $O_{\varepsilon}(n^{1/k})$, and recent followup work by Le and Solomon [STOC '23] generalized the proof strategy and improved the dependence on $\varepsilon$. We give a new proof of this result, wi

Alexandros Gkillas, Dimitris Ampeliotis, Kostas Berberidis

In this study the problem of Federated Learning (FL) is explored under a new perspective by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL, such as the communication overhead due to the shar

Holger Bech Nielsen, Colin D. Froggatt

In the present article we put up for discussion the idea of there existing several versions, phases, of the vacuum, in the spirit in which we have long worked on this idea, namely the Multiple Point Criticality Principle, which also says that these different vacuum phases have the same energy density. We mention that we indeed predicted the Higgs mass to be

Clay D. Spence

A semiclassical approximation is derived by using a family of wavepackets to map arbitrary wavefunctions into phase space. If the Hamiltonian can be approximated as linear over each individual wavepacket, as often done when presenting Ehrenfest's theorem, the resulting approximation is a linear first-order partial differential equation on phase space, which

Théo N. Dionne, Alexandre Foley, Moïse Rousseau, David Sénéchal

Pyqcm is a Python/C++ library that implements a few quantum cluster methods with an exact diagonalization impurity solver. Quantum cluster methods are used in the study of strongly correlated electrons to provide an approximate solution to Hubbard-like models. The methods covered by this library are Cluster Perturbation Theory (CPT), the Variational Cluster

Ben Adcock, Nick Dexter, Sebastian Moraga

Over the last decade, approximating functions in infinite dimensions from samples has gained increasing attention in computational science and engineering, especially in computational uncertainty quantification. This is primarily due to the relevance of functions that are solutions to parametric differential equations in various fields, e.g. chemistry, econo

Alokendu Mazumder, Tirthajit Baruah, Akash Kumar Singh, Pagadla Krishna Murthy

Estimating the number of clusters and cluster structures in unlabeled, complex, and high-dimensional datasets (like images) is challenging for traditional clustering algorithms. In recent years, a matrix reordering-based algorithm called Visual Assessment of Tendency (VAT), and its variants have attracted many researchers from various domains to estimate the

Mingyang Zhou, Yi R. Fung, Long Chen, Christopher Thomas

Building cross-model intelligence that can understand charts and communicate the salient information hidden behind them is an appealing challenge in the vision and language(V+L) community. The capability to uncover the underlined table data of chart figures is a critical key to automatic chart understanding. We introduce ChartT5, a V+L model that learns how

Orchid Chetia Phukan, Arun Balaji Buduru, Rajesh Sharma

Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its applications in diverse fields. A current trend in methods used for SER is to leverage embeddings from pre-trained models (PTMs) as input features to downstream models. However, the use of embeddings from speaker recognition PTMs hasn't garnered much focus in comparison

Quinn Burke, Yohan Beugin, Blaine Hoak, Rachel King

Cloud file systems offer organizations a scalable and reliable file storage solution. However, cloud file systems have become prime targets for adversaries, and traditional designs are not equipped to protect organizations against the myriad of attacks that may be initiated by a malicious cloud provider, co-tenant, or end-client. Recently proposed designs le

Su-Youn Yoon

In this study, we developed an automated short answer grading (ASAG) model that provided both analytic scores and final holistic scores. Short answer items typically consist of multiple sub-questions, and providing an analytic score and the text span relevant to each sub-question can increase the interpretability of the automated scores. Furthermore, they ca

Yinong Zhou, Dmitri Leo M. Cordova, Griffin M. Milligan, Maxx Q. Arguilla

This study aims to investigate the interplay between chiral-induced spin-orbit coupling along the screw axis and antisymmetric spin-orbit coupling (ASOC) in the normal plane within a chiral crystal, using both general model analysis and first-principles simulations of InSeI, a chiral van der Waals crystal. While chiral molecules of light atoms typically exhi

Martin Larsson, Jonghwa Park, Johannes Wiesel

Let $\mu$ be a probability measure on $\mathbb{R}^d$ and $\mu_N$ its empirical measure with sample size $N$. We prove a concentration inequality for the optimal transport cost between $\mu$ and $\mu_N$ for radial cost functions with polynomial local growth, that can have superpolynomial global growth. This result generalizes and improves upon estimates of Fo

Richard B. Firestone

Three major planets, Venus, Earth, and Mercury formed out of the solar nebula. A fourth planetesimal, Theia, also formed near Earth where it collided in a giant impact, rebounding as the planet Mars. During this impact Earth lost ${\approx}4$\% of its crust and mantle that is now is found on Mars and the Moon. At the antipode of the giant impact, $\approx$60

Michael Grabchak, Xingnan Zhang

A multivariate extension of the Dickman distribution was recently introduced, but very few properties have been studied. We discuss several properties with an emphasis on simulation. Further, we introduce and study a multivariate extension of the more general class of Vervaat perpetuities and derive a number of properties and representations. Most of our res

Anil Yildiz, Esen Yel, Anthony L. Corso, Kyle H. Wray

One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen environments is often very costly and requires substantial data collection, it becomes computationally intractable to train the agent on every possible environment or task the AV may encounter. This paper i

Adam Machowczyk, Reiko Heckel

Given graphs as input, Graph Neural Networks (GNNs) support the inference of nodes, edges, attributes, or graph properties. Graph Rewriting investigates the rule-based manipulation of graphs to model complex graph transformations. We propose that, therefore, (i) graph rewriting subsumes GNNs and could serve as formal model to study and compare them, and (ii)

Alexey Fedyushkin, Arthur Puntus

This paper shows a method of symmetrization of an asymmetric flow of a vis-cous incompressible fluid in a flat diffuser using a weak periodic vibration ef-fect on the velocity input flow. The results are obtained for a viscous incom-pressible fluid by simulation based on numerical solving of the Navier-Stokes equations. The results of numerical simulation ha

Abhiram Mullapudi, Branko Kerkez

Dynamic control is emerging as an effective methodology for operating stormwater systems under stress from rapidly evolving weather patterns. Informed by rainfall predictions and real-time sensor measurements, control assets in the stormwater network can be dynamically configured to tune the behavior of the stormwater network to reduce the risk of urban floo

M. Liempi, L. Almonacid, D. R. G. Schleicher, A. Escala

The presence of supermassive black holes at redshift z > 6 raises some questions about their formation and growth in the early universe. Due to the construction of new telescopes like the ELT to observe and detect SMBHs, it will be useful to derive theoretical estimates for the population and to compare observations and model predictions in the future. In co

M. Trudu, M. Pilia, L. Nicastro, C. Guidorzi

Aims. Fast Radio Bursts are bright radio transients whose origin has not yet explained. The search for a multi-wavelength counterpart of those events can put a tight constrain on the emission mechanism and the progenitor source. Methods. We conducted a multi-wavelength observational campaign on FRB 20180916B between October 2020 and August 2021 during eight

Jihao Xin, Marco Canini, Peter Richtárik, Samuel Horváth

Distributed training enables large-scale deep learning, but suffers from high communication overhead, especially as models and datasets grow. Gradient compression, particularly quantization, is a promising approach to mitigate this bottleneck. However, existing quantization schemes are often incompatible with Allreduce, the dominant communication primitive i

Xiaocan Li, Ray Coden Mercurius, Ayal Taitler, Xiaoyu Wang

Perimeter control maintains high traffic efficiency within protected regions by controlling transfer flows among regions to ensure that their traffic densities are below critical values. Existing approaches can be categorized as either model-based or model-free, depending on whether they rely on network transmission models (NTMs) and macroscopic fundamental

Ashley M. Cook

We consider the problem of magnetic charges in $(2+1)$ dimensions for a torus geometry in real-space, subjected to an inverted Lorentz force due to an external electric field applied normal to the surface of the torus. We compute the Hall conductivity associated with transport of these charges for the case of negligible gapless excitations and global $\mathr

Mingchen Li, Yang Ye, Jeremy Yeung, Huixue Zhou

Contrastive learning has become a popular solution for few-shot Name Entity Recognization (NER). The conventional configuration strives to reduce the distance between tokens with the same labels and increase the distance between tokens with different labels. The effect of this setup may, however, in the medical domain, there are a lot of entities annotated a

Peilin Yu, Stephen H. Bach

Alfred is the first system for programmatic weak supervision (PWS) that creates training data for machine learning by prompting. In contrast to typical PWS systems where weak supervision sources are programs coded by experts, Alfred enables users to encode their subject matter expertise via natural language prompts for language and vision-language models. Al

Richard S. Bogart, Charles S. Baldner, Sarbani Basu, Rachel Howe

We present evidence of hitherto undiscovered global-scale oscillations in the near-surface shear layer of the Sun. These oscillations are seen as large scale variations of radial shear in both the zonal and meridional flows relative to their mean values. The variations cover all or most of a visible hemisphere, and reverse with a timescale on the order of a

Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu

Multivariate time series (MTS) imputation is a widely studied problem in recent years. Existing methods can be divided into two main groups, including (1) deep recurrent or generative models that primarily focus on time series features, and (2) graph neural networks (GNNs) based models that utilize the topological information from the inherent graph structur

Egor Voronetsky

Steinberg pro-groups are certain pro-groups used to analyze ordinary Steinberg groups locally in Zariski topology. In this paper we show that Steinberg pro-groups associated with general linear groups, odd unitary groups, and Chevalley groups satisfy a Zariski cosheaf property as crossed pro-modules over the base groups. Also, we prove an analogue of the sta

Polina Acheva, Konstantin Zaitsev, Vladimir Zavodilenko, Anton Losev

Attacks that control single-photon detectors in quantum key distribution using tailored bright illumination are capable of eavesdropping the secret key. Here we report an automated testbench that checks the detector's vulnerabilities against these attacks. We illustrate its performance by testing a free-running detector that includes a rudimentary countermea

Frédéric Déglise

These notes develop the foundations of Milnor-Witt K-theory for fields of arbitrary characteristic, without any perfectness assumptions. Extending the work of Morel and Feld, we establish all functorial properties of Milnor-Witt K-theory with careful attention to twists. A main new contribution is the computation of transfers in the general, in particular in

Federico Formica, Nicholas Petrunti, Lucas Bruck, Vera Pantelic

Automotive software development requires engineers to test their systems to detect violations of both functional and drivability requirements. Functional requirements define the functionality of the automotive software. Drivability requirements refer to the driver's perception of the interactions with the vehicle; for example, they typically require limiting

Yazan Obeidi

Data heterogeneity between clients remains a key challenge in Federated Learning (FL), particularly in the case of tabular data. This work presents Global Layers (GL), a novel partial model personalization method robust in the presence of joint distribution $P(X,Y)$ shift and mixed input/output spaces $X \times Y$ across clients. To the best of our knowledge

Yi Wu, Nan Jiang, Hung Viet Pham, Thibaud Lutellier

Security vulnerability repair is a difficult task that is in dire need of automation. Two groups of techniques have shown promise: (1) large code language models (LLMs) that have been pre-trained on source code for tasks such as code completion, and (2) automated program repair (APR) techniques that use deep learning (DL) models to automatically fix software

Salim B. Ivars, Yaroslav V. Kartashov, Pedro Fernández de Córdoba, J. Alberto Conejero

Taming the instabilities inherent to many nonlinear optical phenomena is of paramount importance for modern photonics. In particular, the so-called snake instability is universally known to severely distort localized wave stripes, leading to the occurrence of transient, short-lived dynamical states that eventually decay. The phenomenon is ubiquitous in nonli

Jesus-Pablo Toledo-Zucco, Daniel Sbarbaro, Joao Manoel Gomes da Silva

Sedimentation is a crucial phenomenon in recovering water from slurries by separating solid-liquid. Thickeners and sedimentation columns are equipments widely used in the process industry to reclaim water from process slurries. This contribution addresses the problem of controlling the turbidity of the recovered water in a sedimentation column by manipulatin

N. I. Stoilova, J. Van der Jeugt

We construct classes of $Z_2 \times Z_2$-graded Lie algebras corresponding to the classical Lie algebras, in terms of their defining matrices. For the $Z_2 \times Z_2$-graded Lie algebra of type $A$, the construction coincides with the previously known class. For the $Z_2 \times Z_2$-graded Lie algebra of type $B$, $C$ and $D$ our construction is new and giv

K. L. Luhman, P. Tremblin, S. M. Birkmann, E. Manjavacas

We present 1-5um spectroscopy of the young planetary mass companion TWA 27B (2M1207B) performed with NIRSpec on board the James Webb Space Telescope. In these data, the fundamental band of CH_4 is absent and the fundamental band of CO is weak. The nondetection of CH_4 reinforces a previously observed trend of weaker CH_4 with younger ages among L dwarfs, whi

Séverine Guillaume, Guillaume Wisniewski, Alexis Michaud

XLSR-53 a multilingual model of speech, builds a vector representation from audio, which allows for a range of computational treatments. The experiments reported here use this neural representation to estimate the degree of closeness between audio files, ultimately aiming to extract relevant linguistic properties. We use max-pooling to aggregate the neural r

Dingdong Yang, Yizhi Wang, Ali Mahdavi-Amiri, Hao Zhang

We present BRICS, a bi-level feature representation for image collections, which consists of a key code space on top of a feature grid space. Specifically, our representation is learned by an autoencoder to encode images into continuous key codes, which are used to retrieve features from groups of multi-resolution feature grids. Our key codes and feature gri

Tainara Silva Novaes, Kathleen Danielly Souza Lins, Adolfo Gustavo S. Seca Neto, Mariangela de Oliveira G. Setti

This article presents initiatives aimed at promoting female participation in STEM fields, with the goal of encouraging more women to pursue careers in these areas. One of these initiatives is the Em\'ilias - Arma\c{c}\~ao em Bits Project, which organizes workshops in schools. Additionally, a podcast has been created to foster interaction between young people

Sahar Tahmasebi, Sherzod Hakimov, Ralph Ewerth, Eric Müller-Budack

The increasing proliferation of misinformation and its alarming impact have motivated both industry and academia to develop approaches for fake news detection. However, state-of-the-art approaches are usually trained on datasets of smaller size or with a limited set of specific topics. As a consequence, these models lack generalization capabilities and are n

Gabriella Chronis, Kyle Mahowald, Katrin Erk

We study semantic construal in grammatical constructions using large language models. First, we project contextual word embeddings into three interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. We validate these interpretable spaces and then use them to automatically derive semantic characterizations of lexical i

Antoine Deza, Shmuel Onn, Sebastian Pokutta, Lionel Pournin

We investigate the following question: how close can two disjoint lattice polytopes contained in a fixed hypercube be? This question stems from various contexts where the minimal distance between such polytopes appears in complexity bounds of optimization algorithms. We provide nearly matching lower and upper bounds on this distance and discuss its exact com

Abhinav Goyal, Nikesh Garera

Automatic Speech Recognition (ASR) plays a crucial role in voice-based applications. For applications requiring real-time feedback like Voice Search, streaming capability becomes vital. While LSTM/RNN and CTC based ASR systems are commonly employed for low-latency streaming applications, they often exhibit lower accuracy compared to state-of-the-art models d

Beste Madran, Ender Abadoğlu

It is shown in \cite{yazar6} that a dynamical system defined by a nonvanishing vector field on an orientable three dimensional manifold is globally bi-Hamiltonian if and only if the first Chern class of the normal bundle of the given vector field vanishes, and the bi-Hamiltonian structure is globally compatible if and only if the Bott class of the complex co

Victor Livernoche, Vineet Jain, Yashar Hezaveh, Siamak Ravanbakhsh

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on an

Carlos Barreto, Olof Reinert, Tobias Wiesinger, Ulrik Franke

We study the impact of data sharing policies on cyber insurance markets. These policies have been proposed to address the scarcity of data about cyber threats, which is essential to manage cyber risks. We propose a Cournot duopoly competition model in which two insurers choose the number of policies they offer (i.e., their production level) and also the reso

H. A. P. Macedo, L. S. Brito, J. F. Jesus, M. E. S. Alves

Problems with the concordance cosmology $\Lambda$CDM as the cosmological constant problem, coincidence problems and Hubble tension has led to many proposed alternatives, as the $\Lambda(t)$CDM, where the now called $\Lambda$ cosmological term is allowed to vary due to an interaction with pressureless matter. Here, we analyze one class of these proposals, nam

Edgar Gevorgyan, Haoran Wang, Andrew Zimmer

In this note, we prove a rigidity result for proper holomorphic maps between unit balls that have many symmetries and which extend to $\mathcal{C}^2$-smooth maps on the boundary.

Wenhao Li, Hooman Barati Sedeh, Willie J. Padilla, Simiao Ren

Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating optical phenomena. While conventionally brute force optimization methods, relying on the iterative exploration of various geometries and materials, are employed to obtain the desire

Domenico Giuliano, Andrea Nava, Carmine Antonio Perroni, Manuel Bibes

We compute the spin-Hall conductance in a multiband model describing the two-dimensional electron gas formed at a LaAlO3/SrTiO3 interface in the presence of a finite concentration of impurities. Combining linear response theory with a systematic calculation of the impurity contributions to the self-energy, as well as to the vertex corrections of the relevant

Mohammad Farrokhi Derakhshandeh Ghouchan, Ali Akbar Yazdan Pour

Let $\mathbb{K}$ be a field and $n$ be a positive integer. Let $\Gamma =([n], E)$ be a simple graph, where $[n]=\{1,\ldots, n\}$. If $S=\mathbb{K}[x_1, \ldots, x_n, y_1, \ldots, y_n]$ is a polynomial ring, then the graded ideal \[ L_\Gamma^\mathbb{K}(2) = \left( x_{i}x_{j} + y_{i}y_{j} \colon \quad \{i, j\} \in E(\Gamma)\right) \subset S,\] is called the Lov

Roberto de A. Capistrano Filho, Boumediène Chentouf, Isadora Maria de Jesus

In this work, we are interested in a detailed qualitative analysis of the Kawahara equation, a model that has numerous physical motivations such as magneto-acoustic waves in a cold plasma and gravity waves on the surface of a heavy liquid. First, we design a feedback control law, which combines a damping component and another one of finite memory-type. Then,

Pranath Reddy Kumbam, Sohaib Uddin Syed, Prashanth Thamminedi, Suhas Harish

The advent of social media has given rise to numerous ethical challenges, with hate speech among the most significant concerns. Researchers are attempting to tackle this problem by leveraging hate-speech detection and employing language models to automatically moderate content and promote civil discourse. Unfortunately, recent studies have revealed that hate

Jiayi Wei, Greg Durrett, Isil Dillig

Developers often dedicate significant time to maintaining and refactoring existing code. However, most prior work on generative models for code focuses solely on creating new code, overlooking the distinctive needs of editing existing code. In this work, we explore a multi-round code auto-editing setting, aiming to predict edits to a code region based on rec

Tianjun Zhang, Yi Zhang, Vibhav Vineet, Neel Joshi

Current text-to-image generation models often struggle to follow textual instructions, especially the ones requiring spatial reasoning. On the other hand, Large Language Models (LLMs), such as GPT-4, have shown remarkable precision in generating code snippets for sketching out text inputs graphically, e.g., via TikZ. In this work, we introduce Control-GPT to

Pengfei Yu, Heng Ji

Large Language Models~(LLMs) struggle with providing current information due to the outdated pre-training data. Existing methods for updating LLMs, such as knowledge editing and continual fine-tuning, have significant drawbacks in generalizability of new information and the requirements on structured updating corpus. We identify the core challenge behind the

I. Sh. Kalimullin

We study the degrees of selector functions related to the degrees in which a rigid computable structure is relatively computably categorical. It is proved that for some structures such degrees can be represented as the unions of upper cones of c.e. degrees. In addition we show that there are non-c.e. upper cones realized as the degrees in which some computab

J. Lopez-Miralles, Sara E. Motta, S. Migliari, F. Jaron

The gamma-ray binary LS I+61 303 has been widely monitored at different wavelengths since its discovery more than sixty years ago. However, the nature of the compact object and the peculiar behavior of the system are still largely debated. Aimed at investigating the rapid X-ray variability of LS I+61 303, we have analysed all the archival RXTE/PCA data of th

Laura Ghezzi, Jooyoun Hong

A degree of a module $M$ is a numerical measure of information carried by $M$. We highlight some of Vasconcelos' outstanding contributions to the theory of degrees, bridging commutative algebra and computational algebra. We present several degrees he introduced and developed, including arithmetic degree, jdeg, homological degree, cohomological degrees, canon

Eduardo Paluzo-Hidalgo, Miguel A. Gutiérrez-Naranjo, Rocio Gonzalez-Diaz

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed weight and n

Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin

Learning to Optimize (L2O), a technique that utilizes machine learning to learn an optimization algorithm automatically from data, has gained arising attention in recent years. A generic L2O approach parameterizes the iterative update rule and learns the update direction as a black-box network. While the generic approach is widely applicable, the learned mod

Zichen Liu, Xuyuan Liu, Yanlong Wen, Guoqing Zhao

ICD coding is designed to assign the disease codes to electronic health records (EHRs) upon discharge, which is crucial for billing and clinical statistics. In an attempt to improve the effectiveness and efficiency of manual coding, many methods have been proposed to automatically predict ICD codes from clinical notes. However, most previous works ignore the

Mojtaba Valizadeh, Martin Berger

Regular expression inference (REI) is a supervised machine learning and program synthesis problem that takes a cost metric for regular expressions, and positive and negative examples of strings as input. It outputs a regular expression that is precise (i.e., accepts all positive and rejects all negative examples), and minimal w.r.t. to the cost metric. We pr

Riad Youmbai, Gang Chen

In this short note, it is proved that both the number of primitive characters and the number of quasi-primitive characters in a finite group $G$ is divisible by $|G:G'|$, where $G'$ is the derived subgroup of $G$.

Françoise Demengel

This paper is devoted to the proof of the existence of the principal eigenvalue and related eigenfunctions for fully nonlinear degenerate or singular uniformly elliptic equations posed in a punctured ball, in presence of a singular potential. More precisely, we analyze existence, uniqueness and regularity of solutions $( \bar\lambda_\gamma, u_\gamma)$ of the

Shuai Yang, Lixin Zhang, Feng Xia, Leyu Lin

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Individual-level diversity and system-level diversity are both important for industrial recommender systems. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based re

Lian Zhang, Jason M. Holmes, Zhengliang Liu, Sujay A. Vora

Purpose: To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, struc

Bowen Li, Jianfeng Lu

We introduce a sum-of-squares SDP hierarchy approximating the ground-state energy from below for quantum many-body problems, with a natural quantum embedding interpretation. We establish the connections between our approach and other variational methods for lower bounds, including the variational embedding, the RDM method in quantum chemistry, and the Anders

Rui Ni

Fragmentation of bubbles and droplets in turbulence produces a dispersed phase spanning a broad range of scales, encompassing everything from droplets in nanoemulsions to centimeter-sized bubbles entrained in breaking waves. Along with deformation, fragmentation plays a crucial role in enhancing interfacial area, with far-reaching implications across various

Lisandro A. Raviola, Mariano F. De Leo

We evaluate the performance of novel numerical methods for solving one-dimensional nonlinear fractional dispersive and dissipative evolution equations. The methods are based on affine combinations of time-splitting integrators and pseudo-spectral discretizations using Hermite and Fourier expansions. We show the effectiveness of the proposed methods by numeri

Brian Allen, Edward Bryden, Demetre Kazaras

Llarull's Theorem states that any Riemannian metric on the $n$-sphere which has scalar curv{\-}ature greater than or equal to $n(n-1)$, and whose distance function is bounded below by the unit sphere's, is isometric to the unit sphere. Gromov later posed the {\emph{Spherical Stability Problem}}, which probes the flexibility of this fact. We give a resolution

Wesley Andrés Watters, Abraham Loeb, Frank Laukien, Richard Cloete

(Abridged) Unidentified Aerial Phenomena (UAP) have resisted explanation and have received little formal scientific attention for 75 years. A primary objective of the Galileo Project is to build an integrated software and instrumentation system designed to conduct a multimodal census of aerial phenomena and to recognize anomalies. Here we present key motivat

Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa

We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image