May 2023 arXiv papers — page 52

Jiatu Li, Igor Carboni Oliveira

While there has been progress in establishing the unprovability of complexity statements in lower fragments of bounded arithmetic, understanding the limits of Je\v{r}\'abek's theory $APC_1$ (2007) and of higher levels of Buss's hierarchy $S^i_2$ (1986) has been a more elusive task. Even in the more restricted setting of Cook's theory PV (1975), known results

Natalia Vassileva Vesselinova

The main contribution reported in the paper is a novel paradigm through which mobile cellular traffic forecasting is made substantially more accurate. Specifically, by incorporating freely available road metrics we characterise the data generation process and spatial dependencies. Therefore, this provides a means for improving the forecasting estimates. We e

Sunkyoung Kim, Dayeon Ki, Yireun Kim, Jinsik Lee

Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning. Existing approaches typically construct monolingual in-context examples, either in the source or target language. However, translating entire in-context examples into the target language might compromise contextual integrity and be

A. Di Teodoro, E. Contreras

In this work, we construct a modified version of the Einstein field equations for a vacuum and spherically symmetric spacetime in terms of the Riemann-Louville fractional derivative. The main difference between our approach and other works is that we ensure that both the classical differential equations and the classical solutions are exactly recovered in th

Bogdan David, Madalina Erascu

Optimization solvers based on methods from constraint programming (OR-Tools, Chuffed, Gecode), optimization modulo theory (Z3), and mathematical programming (CPLEX) are successfully applied nowadays to solve many non-trivial examples. However, for solving the problem of automated deployment in the Cloud of component-based applications, their computational re

Carsten Henkel, Ron Folman

It has been a long-standing goal to bring massive objects into a superposition of different locations in real space, not only to confirm quantum theory in new regimes, but also to explore the interface with gravity. The main challenge is usually thought to arise from forces or scattering due to environmental fields and particles that decohere the large objec

Rhiannon Griffiths

In an unpublished preprint \cite{batanin}, Batanin conjectures that it is possible to take `slices' of a globular operad, thereby isolating the algebraic structure in each dimension. It was further hypothesised that the slices of a globular operad for some theory of higher category contain essential information about those higher categories, namely whether o

Daniel Kelshaw, Luca Magri

Manifolds discovered by machine learning models provide a compact representation of the underlying data. Geodesics on these manifolds define locally length-minimising curves and provide a notion of distance, which are key for reduced-order modelling, statistical inference, and interpolation. In this work, we first analyse existing methods for computing lengt

Anja Delić, Matej Grcić, Siniša Šegvić

Most approaches to dense anomaly detection rely on generative modeling or on discriminative methods that train with negative data. We consider a recent hybrid method that optimizes the same shared representation according to cross-entropy of the discriminative predictions, and negative log likelihood of the predicted energy-based density. We extend that work

Matteo Baggioli

Mixing theoretical topological structures with cutting-edge simulation methods, a recent study in Nature Communications has finally confirmed the existence of topological defects in glasses and their crucial role for plasticity.

Kobbi Nissim, Uri Stemmer, Eliad Tsfadia

In adaptive data analysis, a mechanism gets $n$ i.i.d. samples from an unknown distribution $D$, and is required to provide accurate estimations to a sequence of adaptively chosen statistical queries with respect to $D$. Hardt and Ullman (FOCS 2014) and Steinke and Ullman (COLT 2015) showed that in general, it is computationally hard to answer more than $\Th

Hongyin Luo, Yung-Sung Chuang, Yuan Gong, Tianhua Zhang

Large language models (LLMs) have been significantly improved by instruction fine-tuning, but still lack transparency and the ability to utilize up-to-date knowledge and information. In this work, we propose search-augmented instruction learning (SAIL), which grounds the language generation and instruction following abilities on complex search results genera

Gui-Qiang G. Chen, Alexander Cliffe, Feimin Huang, Song Liu

We present a rigorous approach and related techniques to construct global solutions of the 2-D Riemann problem with four-shock interactions for the Euler equations for potential flow. With the introduction of three critical angles: the vacuum critical angle from the compatibility conditions, the detachment angle, and the sonic angle, we clarify all configura

Qian Zhang, Yuzhu Han

In this paper, a critical fourth-order Kirchhoff type elliptic equation with a subcritical perturbation is studied. The main feature of this problem is that it involves both a nonlocal coefficient and a critical term, which bring essential difficulty for the proof of the existence of weak solutions. When the dimension of the space is smaller than or equals t

Koyena Pal, Seyed Ali Bahrainian, Laura Mercurio, Carsten Eickhoff

Hospital discharge documentation is among the most essential, yet time-consuming documents written by medical practitioners. The objective of this study was to automatically generate hospital discharge summaries using neural network summarization models. We studied various data preparation and neural network training techniques that generate discharge summar

Kenneth Dere, Giulio Del Zanna, Peter Young, Enrico Landi

The CHIANTI atomic database provides sets of assessed data used for simulating spectral observations of astrophysical plasmas. This article describes updates that will be released as version~10.1 of the database. A key component of CHIANTI is the provision of ionization and recombination rates that are used to compute the ionization balance of a plasma over

Caitlin Grasso, Josh Bongard

Empowerment -- a domain independent, information-theoretic metric -- has previously been shown to assist in the evolutionary search for neural cellular automata (NCA) capable of homeostasis when employed as a fitness function. In our previous study, we successfully extended empowerment, defined as maximum time-lagged mutual information between agents' action

Yao Rong, Xiangyu Wei, Tianwei Lin, Yueyu Wang

Augmenting LiDAR input with multiple previous frames provides richer semantic information and thus boosts performance in 3D object detection, However, crowded point clouds in multi-frames can hurt the precise position information due to the motion blur and inaccurate point projection. In this work, we propose a novel feature fusion strategy, DynStaF (Dynamic

Hanqi Su, Binyang Song, Faez Ahmed

Accurate vehicle rating prediction can facilitate designing and configuring good vehicles. This prediction allows vehicle designers and manufacturers to optimize and improve their designs in a timely manner, enhance their product performance, and effectively attract consumers. However, most of the existing data-driven methods rely on data from a single mode,

Zheng Chang, Shuchen Weng, Peixuan Zhang, Yu Li

Language-based colorization produces plausible and visually pleasing colors under the guidance of user-friendly natural language descriptions. Previous methods implicitly assume that users provide comprehensive color descriptions for most of the objects in the image, which leads to suboptimal performance. In this paper, we propose a unified model to perform

Tanveer Hussain, Juan Gallego-Calderon, S M Shafiul Alam

The increasing integration of renewable energy resources in evolving bulk power system (BPS) is impacting the system inertia. Type-5 wind turbine generation has the potential to behave like a traditional synchronous generator and can help improve system inertia. Hydraulic torque converter (TC) and gearbox with torque limiting feature are integral parts of a

Tao Yu, Toni J. B. Liu, Albert Tseng, Christopher De Sa

Hyperbolic space has proven to be well-suited for capturing hierarchical relations in data, such as trees and directed acyclic graphs. Prior work introduced the concept of entailment cones, which uses partial orders defined by nested cones in the Poincar\'e ball to model hierarchies. Here, we introduce the ``shadow cones" framework, a physics-inspired entail

Nikita V. Tepliakov, Ruize Ma, Johannes Lischner, Efthimios Kaxiras

Half-metals have been envisioned as active components in spintronic devices by virtue of their completely spin-polarized electrical currents. Actual materials hosting half-metallic phases, however, remain scarce. Here, we predict that recently fabricated heterojunctions of zigzag nanoribbons embedded in two-dimensional hexagonal boron nitride are half-semime

Wei Zhou, Qian Wang, Weiwei Jin, Xinzhe Shi

Recently, graph-based and Transformer-based deep learning networks have demonstrated excellent performances on various point cloud tasks. Most of the existing graph methods are based on static graph, which take a fixed input to establish graph relations. Moreover, many graph methods apply maximization and averaging to aggregate neighboring features, so that

Zhen-Ru Zhang, Chuanqi Tan, Haiyang Xu, Chengyu Wang

Fine-tuning large pre-trained language models on various downstream tasks with whole parameters is prohibitively expensive. Hence, Parameter-efficient fine-tuning has attracted attention that only optimizes a few task-specific parameters with the frozen pre-trained model. In this work, we focus on prefix tuning, which only optimizes continuous prefix vectors

Arghya Choudhury, Sourav Mitra, Arpita Mondal, Subhadeep Mondal

In this work, we explore a well motivated beyond the Standard Model scenario, namely, R-parity violating Supersymmetry, in the context of light neutrino masses and mixing. We assume that the R-parity is only broken by the lepton number violating bilinear term. We try to fit two non-zero neutrino mass square differences and three mixing angle values obtained

Matt Franchi, J. D. Zamfirescu-Pereira, Wendy Ju, Emma Pierson

Large-scale policing data is vital for detecting inequity in police behavior and policing algorithms. However, one important type of policing data remains largely unavailable within the United States: aggregated police deployment data capturing which neighborhoods have the heaviest police presences. Here we show that disparities in police deployment levels c

Melvin Wong, Yew-Soon Ong, Abhishek Gupta, Kavitesh K. Bali

Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI. However, such models often fail to connect the generated outputs and desired target concepts/preferences implied by the prompts. Current research addressing this limitation has largely focused on enhancing the

Farida Mohsen, Hamada R. H. Al-Absi, Noha A. Yousri, Nady El Hajj

The rising prevalence of type 2 diabetes mellitus (T2DM) necessitates the development of predictive models for T2DM risk assessment. Artificial intelligence (AI) models are being extensively used for this purpose, but a comprehensive review of their advancements and challenges is lacking. This scoping review analyzes existing literature on AI-based models fo

Graham Manuell, Joshua L. Wrigley

We give an expository, and hopefully approachable, account of the Joyal-Tierney result that every topos can be represented as a topos of sheaves on a localic groupoid. We give an explicit presentation of a representing localic groupoid for the classifying topos of a given geometric theory and discuss links with the topological groupoids of Forssell.

Richard Gao, Michael Deistler, Jakob H. Macke

Simulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive simulations, or when the model cannot exactly reproduce the observed data (i.e., is misspecified), targeting the Bayesian posterior may be overly restrictive. Generalized Bayesian In

Pepijn Wissing, Edwin R. van Dam

Complex unit gain graphs may exhibit various kinds of symmetry. In this work, we explore structural symmetry, spectral symmetry and sign-symmetry in such graphs, and their respective relations to one-another. Our main result is a construction that transforms an arbitrary complex unit gain graph into infinitely many switching-distinct ones whose spectral symm

Anna Ben-Hamou, Vasiliki Velona

We introduce a random recursive tree model with two communities, called balanced community modulated random recursive tree, or BCMRT in short. In this setting, pairs of nodes of different type appear sequentially. Each node of the pair decides independently to attach to their own type with probability 1-q, or to the other type with probability q, and then ch

Yuliya Mishura, Anton Yurchenko-Tytarenko

In this paper, we construct consistent statistical estimators of the Hurst index, volatility coefficient, and drift parameter for Bessel processes driven by fractional Brownian motion with $H<1/2$. As an auxiliary result, we also prove the continuity of the fractional Bessel process. The results are illustrated with simulations.

Tyler Lawson

We prove that a lax $\mathbb{E}_{n+1}$-monoidal functor from $\mathcal V$ to $\mathcal W$ induces a lax $\mathbb{E}_n$-monoidal functor from $\mathcal V$-enriched $\infty$-categories to $\mathcal W$-enriched $\infty$-categories in the sense of Gepner--Haugseng. We prove this as part of a general-purpose interaction with the Boardman--Vogt tensor product $\ot

Lorenzo Basile, Nikos Karantzas, Alberto d'Onofrio, Luca Manzoni

Despite their impressive performance in classification tasks, neural networks are known to be vulnerable to adversarial attacks, subtle perturbations of the input data designed to deceive the model. In this work, we investigate the correlation between these perturbations and the implicit bias of neural networks trained with gradient-based algorithms. To this

Xiaomeng Chen, Wei Jiang, Themistoklis Charalambous, Ling Shi

This paper addresses the problem of distributed optimization, where a network of agents represented as a directed graph (digraph) aims to collaboratively minimize the sum of their individual cost functions. Existing approaches for distributed optimization over digraphs, such as Push-Pull, require agents to exchange explicit state values with their neighbors

Shree Hari Sureshbabu, Dylan Herman, Ruslan Shaydulin, Joao Basso

Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization. The challenge of parameter optimization is particularly acute in the case of weighted problems, for which the eigenvalue

J. C. Bohorquez, R. Chinnarasu, J. Isaacs, D. Booth

We demonstrate reduction of the dc polarizability of Cesium atom Rydberg states in a 77 K environment utilizing microwave field dressing. In particular we reduce the polarizability of $52P_{3/2}$ states which have resonances at 5.35 GHz to $51D_{5/2}$, suitable for interfacing Rydberg atoms to superconducting resonators in a cryogenic environment. We measure

Nathan Vance, Jeremy Speth, Benjamin Sporrer, Patrick Flynn

Remote Photoplethysmography (rPPG), or the remote monitoring of a subject's heart rate using a camera, has seen a shift from handcrafted techniques to deep learning models. While current solutions offer substantial performance gains, we show that these models tend to learn a bias to pulse wave features inherent to the training dataset. We develop augmentatio

Yuya Hattori, Keisuke Sagisaka, Shunsuke Yoshizawa, Yuki Tokumoto

Topological surface states of Bi-doped PbSb2Te4 [Pb(Bi0.20Sb0.80)2Te4] are investigated through analyses of quasiparticle interference (QPI) patterns observed by scanning tunneling microscopy. Interpretation of the experimental QPI patterns in the reciprocal space is achieved by numerical QPI simulations using two types of surface density of states produced

Navdeep Rana, Rayan Chatterjee, Sunghan Ro, Dov Levine

We study the effects of inertia in dense suspensions of polar swimmers. The hydrodynamic velocity field and the polar order parameter field describe the dynamics of the suspension. We show that a dimensionless parameter $R$ (ratio of the swimmer self-advection speed to the active stress invasion speed) controls the stability of an ordered swimmer suspension.

Joonhun Lee, Myeongho Jeon, Myungjoo Kang, Kyunghyun Park

We propose Feature-aligned N-BEATS as a domain-generalized time series forecasting model. It is a nontrivial extension of N-BEATS with doubly residual stacking principle (Oreshkin et al. [45]) into a representation learning framework. In particular, it revolves around marginal feature probability measures induced by the intricate composition of residual and

Peihu Duan, Tao Liu, Yuezu Lv, Guanghui Wen

Cooperative behavior design for multi-agent systems with collective tasks is a critical issue in promoting swarm intelligence. This paper investigates cooperative control for a multi-channel system, where each channel is managed by an agent expected to self-organize a controller to stabilize the system collaboratively by communicating with neighbors in a net

Sungnyun Kim, Junsoo Lee, Kibeom Hong, Daesik Kim

In this study, we aim to enhance the capabilities of diffusion-based text-to-image (T2I) generation models by integrating diverse modalities beyond textual descriptions within a unified framework. To this end, we categorize widely used conditional inputs into three modality types: structure, layout, and attribute. We propose a multimodal T2I diffusion model,

Wenjian Hao, Zehui Lu, Zihao Liang, Tianyu Zhou

This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which combines Bellman's principle of optimality with the policy gradient approach to improve the convergence rate. This paper provides theoretic

Kiarash Banihashem, Leyla Biabani, Samira Goudarzi, MohammadTaghi Hajiaghayi

Maximizing a monotone submodular function under cardinality constraint $k$ is a core problem in machine learning and database with many basic applications, including video and data summarization, recommendation systems, feature extraction, exemplar clustering, and coverage problems. We study this classic problem in the fully dynamic model where a stream of i

Farooq Shaikh, Elias Bou-Harb, Aldin Vehabovic, Jorge Crichigno

The Internet of Things(IoT) paradigm provides persistent sensing and data collection capabilities and is becoming increasingly prevalent across many market sectors. However, most IoT devices emphasize usability and function over security, making them very vulnerable to malicious exploits. This concern is evidenced by the increased use of compromised IoT devi

Changsub Kim, Christina Bell, Jake Evans, Jonathan Greenfield

Progress in superconducting device and detector technologies over the past decade have realized practical applications in quantum computers, detectors for far-infrared telescopes, and optical communications. Superconducting thin film materials, however, have remained largely unchanged, with aluminum still being the material of choice for superconducting qubi

Jan Achterhold, Philip Tobuschat, Hao Ma, Dieter Buechler

In this paper, we present a method for table tennis ball trajectory filtering and prediction. Our gray-box approach builds on a physical model. At the same time, we use data to learn parameters of the dynamics model, of an extended Kalman filter, and of a neural model that infers the ball's initial condition. We demonstrate superior prediction performance of

Wenjian Hao, Paulo C. Heredia, Shaoshuai Mou

This paper presents a data-driven method to find a closed-loop optimal controller, which minimizes a specified infinite-horizon cost function for systems with unknown dynamics. Suppose the closed-loop optimal controller can be parameterized by a given class of functions, hereafter referred to as the policy. The proposed method introduces a novel gradient est

Julian F. Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober, Gustav Markkula

The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable models predicting human behavior are essential for overcoming this issue. While data-driven models are commonly used to this end, they can be vulnerable in safety-critical edge case

Tetsu Kasanishi, Masaru Isonuma, Junichiro Mori, Ichiro Sakata

Automatic literature review generation is one of the most challenging tasks in natural language processing. Although large language models have tackled literature review generation, the absence of large-scale datasets has been a stumbling block to the progress. We release SciReviewGen, consisting of over 10,000 literature reviews and 690,000 papers cited in

Andrew L. Miller

The third observing run of advanced LIGO, Virgo and KAGRA brought unprecedented sensitivity towards a variety of quasi-monochromatic, persistent gravitational-wave signals. Continuous waves allow us to probe not just the existence of canonical asymmetrically rotating neutron stars, but also different forms of dark matter, thus showing the wide-ranging astrop

Ruiqi Liu, Meng Hua, Ke Guan, Xiping Wang

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On o

Chenming Tang, Xiuyu Wu, Yunfang Wu

Model ensemble has been in widespread use for Grammatical Error Correction (GEC), boosting model performance. We hypothesize that model ensemble based on the perplexity (PPL) computed by pre-trained language models (PLMs) should benefit the GEC system. To this end, we explore several ensemble strategies based on strong PLMs with four sophisticated single mod

He Zhu, Chong Zhang, Junjie Huang, Junran Wu

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. Under such observation, we tend to investigate the feas

Anson Ka Long Yip, Patrick Chi-Kit Cheong, Tjonnie Guang Feng Li

Strong magnetic fields make neutron stars potential sources of detectable electromagnetic and gravitational-wave signals. Hence, inferring these magnetic fields is critical to understand the emissions of neutron stars. However, due to the lack of direct observational evidence, the interior magnetic field configuration remains ambiguous. Here, for the first t

Yuming Fu, Fei Yang

Let $f_\theta(z)=e^{2\pi i\theta}z+z^2$ be the quadratic polynomial having an indifferent fixed point at the origin. For any bounded type irrational number $\theta\in\mathbb{R}\setminus\mathbb{Q}$ and any rational number $\nu\in\mathbb{Q}$, we prove that $f_\theta$ and $f_\nu$ are conformally mateable, and that the mating is unique up to conjugacy by a M\"{o

Louis Vaslin, Vincent Barra, Julien Donini

In recent years, interest has grown in alternative strategies for the search for New Physics beyond the Standard Model. One envisaged solution lies in the development of anomaly detection algorithms based on unsupervised machine learning techniques. In this paper, we propose a new Generative Adversarial Network-based auto-encoder model that allows both anoma

Yuchang Jiang, Vivien Sainte Fare Garnot, Konrad Schindler, Jan Dirk Wegner

Data imbalance is ubiquitous when applying machine learning to real-world problems, particularly regression problems. If training data are imbalanced, the learning is dominated by the densely covered regions of the target distribution and the learned regressor tends to exhibit poor performance in sparsely covered regions. Beyond standard measures like oversa

Hang Yu, Zhenxing Dou, Zhiwei Chen, Xiaomeng Yan

Datasets with sheer volume have been generated from fields including computer vision, medical imageology, and astronomy whose large-scale and high-dimensional properties hamper the implementation of classical statistical models. To tackle the computational challenges, one of the efficient approaches is subsampling which draws subsamples from the original lar

Matthew C. B. Zaremsky

We construct examples of finitely presented simple groups whose Dehn functions are at least exponential. To the best of our knowledge, these are the first such examples known. Our examples arise from R\"over-Nekrashevych groups, using carefully calibrated self-similar representations of Baumslag-Solitar groups.

Yiyang Li, Xinting Huang, Wei Bi, Hai Zhao

Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these obstacles, an effective way is to pre-train a model that understands the discourse structure of multi-party dialogues, namely,

Cornelius Schröder, Jakob H. Macke

Many scientific models are composed of multiple discrete components, and scientists often make heuristic decisions about which components to include. Bayesian inference provides a mathematical framework for systematically selecting model components, but defining prior distributions over model components and developing associated inference schemes has been ch

Stephan Helfrich, Arne Herzel, Stefan Ruzika, Clemens Thielen

We study the approximation of general multiobjective optimization problems with the help of scalarizations. Existing results state that multiobjective minimization problems can be approximated well by norm-based scalarizations. However, for multiobjective maximization problems, only impossibility results are known so far. Countering this, we show that all mu

Rodrigo Aldana-Lopez, Eduardo Sebastian, Rosario Aragues, Eduardo Montijano

The outer Lowner-John method is widely used in sensor fusion applications to find the smallest ellipsoid that can approximate the intersection of a set of ellipsoids, described by positive definite covariance matrices modeling the quality of each sensor. We propose a distributed algorithm to solve this problem when these matrices are defined over the network

Xinhang Liu, Jiaben Chen, Shiu-hong Kao, Yu-Wing Tai

Novel view synthesis via Neural Radiance Fields (NeRFs) or 3D Gaussian Splatting (3DGS) typically necessitates dense observations with hundreds of input images to circumvent artifacts. We introduce Deceptive-NeRF/3DGS to enhance sparse-view reconstruction with only a limited set of input images, by leveraging a diffusion model pre-trained from multiview data

Sebastian Buschow, Jan Keller, Sabrina Wahl

Heatwaves are known to arise from the interplay between large-scale climate variability, synoptic weather patterns and regional to local scale surface processes. While recent research has made important progress for each individual contributing factor, ways to properly incorporate multiple or all of them in a unified analysis are still lacking. In this study

Concepción Domínguez, Ricardo Gázquez, Juan Miguel Morales, Salvador Pineda

The Maximal Covering Location Problem (MCLP) is a classical location problem where a company maximizes the demand covered by placing a given number of facilities, and each demand node is covered if the closest facility is within a predetermined radius. In the cooperative version of the problem (CMCLP), it is assumed that the facilities of the decision maker

Domenico Trotta, Oreste Pezzi, David Burgess, Luis Preisser

The complex interaction between shocks and plasma turbulence is extremely important to address crucial features of energy conversion in a broad range of astrophysical systems. We study the interaction between a supercritical, perpendicular shock and pre-existing, fully-developed plasma turbulence, employing a novel combination of magnetohydrodynamic (MHD) an

Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic

We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs. Our method is based on the popular solution concept of Shapley values extended to stochastic cooperative games, resulting in explanations that are random variables. The GP explanations generated using our approach sati

Stephan Helfrich, Stefan Ruzika, Clemens Thielen

Convex approximation sets for multiobjective optimization problems are a well-studied relaxation of the common notion of approximation sets. Instead of approximating each image of a feasible solution by the image of some solution in the approximation set up to a multiplicative factor in each component, a convex approximation set only requires this multiplica

Geon Heo, Junseok Seo, Steven Euijong Whang

Personalized privacy becomes critical in deep learning for Trustworthy AI. While Differentially Private Stochastic Gradient Descent (DP-SGD) is widely used in deep learning methods supporting privacy, it provides the same level of privacy to all individuals, which may lead to overprotection and low utility. In practice, different users may require different

Daichi Takeuchi

In this paper, we introduce a new class of $\ell$-adic sheaves, which we call quadratic $\ell$-adic sheaves, on connected unipotent commutative algebraic groups over finite fields. They are sheaf-theoretic enhancements of quadratic forms on finite abelian groups in the spirit of the function-sheaf dictionary. We show that a certain finite Heisenberg group ac

Alejandro N. Diaz, Youngsoo Choi, Matthias Heinkenschloss

This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear-manifold by training a shallow, sparse autoencoder using FOM snapshot data. These NM-ROMs can be advantageous over linear-subspace ROMs (LS-ROMs) for problems with slowly decaying Kolmogor

Dubi Kelmer, Alex Kontorovich, Christopher Lutsko

We study the sup-norm and mean-square-norm problems for Eisenstein series on certain arithmetic hyperbolic orbifolds, producing sharp exponents for the modular surface and Picard 3-fold. The methods involve bounds for Epstein zeta functions, and counting restricted values of indefinite quadratic forms at integer points.

Takeshi Morita

We consider a low energy effective theory of $p$-branes in a $D$-dimensional spacetime, and impose two conditions: 1) the theory is scale invariant, and 2) the electric-magnetic dual $(D-p-4)$-branes exist and they obey the same type of interactions to the $p$-branes. (We also assume other natural conditions such as Lorentz invariance but not string theory,

Jianpei Geng, Tetyana Shalomayeva, Mariia Gryzlova, Amlan Mukherjee

Charge state instabilities have been a bottleneck for the implementation of solid-state spin systems and pose a major challenge to the development of spin-based quantum technologies. Here we investigate the stabilization of negatively charged nitrogen-vacancy (NV$^-$) centers in phosphorus-doped diamond at liquid helium temperatures. Photoionization of phosp

Zheng Hu, Shi-Min Cai, Jun Wang, Tao Zhou

The existing collaborative recommendation models that use multi-modal information emphasize the representation of users' preferences but easily ignore the representation of users' dislikes. Nevertheless, modelling users' dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users' dislikes should be integrated into th

David E. Fernandes, Mário G. Silveirinha

In this work, we develop a homogenization model to determine the effective response of a metallic nanowire array embedded in an electric gyrotropic material. We study the interaction of electromagnetic waves with the metamaterial and demonstrate that the nanowire array can greatly enhance the nonreciprocal response of the gyrotropic substrate. In particular,

Yifan Shi, Yingqi Liu, Yan Sun, Zihao Lin

Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems. However, almost all existing works have to face large communication burdens and the risk of disruption if the central server fails. Only limited efforts have been used in a decentraliz

Ilyas Fatkhullin, Alexander Tyurin, Peter Richtárik

Due to the high communication overhead when training machine learning models in a distributed environment, modern algorithms invariably rely on lossy communication compression. However, when untreated, the errors caused by compression propagate, and can lead to severely unstable behavior, including exponential divergence. Almost a decade ago, Seide et al [20

Kiran Kokilepersaud, Stephanie Trejo Corona, Mohit Prabhushankar, Ghassan AlRegib

This paper presents a novel positive and negative set selection strategy for contrastive learning of medical images based on labels that can be extracted from clinical data. In the medical field, there exists a variety of labels for data that serve different purposes at different stages of a diagnostic and treatment process. Clinical labels and biomarker lab

Yi-Zhi Huang

Let $V$ be a $C_2$-cofinite vertex operator algebra without nonzero elements of negative weights. We prove the conjecture that the spaces spanned by analytic extensions of pseudo-$q$-traces ($q=e^{2\pi i\tau}$) shifted by $-\frac{c}{24}$ of products of geometrically-modified (logarithmic) intertwining operators among grading-restricted generalized $V$-module

Alejandro Naser-Pastoriza, Gregory Chockler, Alexey Gotsman

We study implementations of basic fault-tolerant primitives, such as consensus and registers, in message-passing systems subject to process crashes and a broad range of communication failures. Our results characterize the necessary and sufficient conditions for implementing these primitives as a function of the connectivity constraints and synchrony assumpti

Jana Kierdorf, Ribana Roscher

Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important. However, accurately determining harvest-readiness can be challenging due to the cauliflower head being covered by its canopy. While deep learning enables automated harvest-readiness estimation, errors can occur due to field-variabili

Xiaojin Zhang, Wenjie Li, Yiming Li, Wei Chen

We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility. The algorithm is applicable to arbitrary privacy measurements that maps from the distortion to a real value. It can achieve personalized utility-privacy trade-off for each mo

Elena Bachini, Veit Krause, Ingo Nitschke, Axel Voigt

We consider two-phase fluid deformable surfaces as model systems for biomembranes. Such surfaces are modeled by incompressible surface Navier-Stokes-Cahn-Hilliard-like equations with bending forces. We derive this model using the Lagrange-D'Alembert principle considering various dissipation mechanisms. The highly nonlinear model is solved numerically to expl

Clément Calvino, Lucille Furgerot, Emmanuel Poizot, Pascal Bailly du Bois

A prediction model for the turbulent kinetic energy (TKE) induced by tidal-currents is proposed as a function of the barotropic velocity only, along with a robust method evaluating the different parameters involved using Acoustic Doppler Current Profiler (ADCP) measurements from Alderney Race. We find that the model is able to reproduce correctly the TKE pro

Zheng Hu, Satoshi Nakagawa, Shi-Min Cai, Fuji Ren

Real-world multinational e-commerce companies, such as Amazon and eBay, serve in multiple countries and regions. Some markets are data-scarce, while others are data-rich. In recent years, cross-market recommendation (XMR) has been proposed to bolster data-scarce markets by leveraging auxiliary information from data-rich markets. Previous XMR algorithms have

Johann Faouzi, Olivier Colliot

In this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest-neighbor methods, linear and logistic regressions, support vector machines and tree-based algorithms. We also describe the problem of overfitting as well as strate

Akihiro Ozawa, Koji Kobayashi, Kentaro Nomura

We theoretically study the spin Hall effect in a simple tight-binding model of stacked-kagome Weyl semimetal Co3Sn2S2 with ferromagnetic ordering. We focus on the two types of the spin Hall current: one flowing in the in-plane direction with respect to the kagome lattice (in-plane spin Hall current), and one flowing in the stacking direction (out-of-plane sp

A. Alonso-Herrero, S. Garcia-Burillo, M. Pereira-Santaella, T. Shimizu

We present new ALMA observations of the CO(3-2) transition and 854micron continuum at 0.06-0.3" resolution, together with new VLT/SINFONI observations of NGC7172. This is a luminous (bolometric luminosity of ~10^44 erg/s) Seyfert galaxy that belongs to the Galaxy Activity, Torus, and Outflow Survey (GATOS). The CO(3-2) observations reveal the presence of a h

Cristina Bazgan, Arne Herzel, Stefan Ruzika, Clemens Thielen

It is well known that, under very weak assumptions, multiobjective optimization problems admit $(1+\varepsilon,\dots,1+\varepsilon)$-approximation sets (also called $\varepsilon$-Pareto sets) of polynomial cardinality (in the size of the instance and in $\frac{1}{\varepsilon}$). While an approximation guarantee of $1+\varepsilon$ for any $\varepsilon>0$ is t

Guy Kornowski, Gilad Yehudai, Ohad Shamir

Overparameterized neural networks (NNs) are observed to generalize well even when trained to perfectly fit noisy data. This phenomenon motivated a large body of work on "benign overfitting", where interpolating predictors achieve near-optimal performance. Recently, it was conjectured and empirically observed that the behavior of NNs is often better described

Lijie Chen, Zhenjian Lu, Igor C. Oliveira, Hanlin Ren

A randomized algorithm for a search problem is *pseudodeterministic* if it produces a fixed canonical solution to the search problem with high probability. In their seminal work on the topic, Gat and Goldwasser posed as their main open problem whether prime numbers can be pseudodeterministically constructed in polynomial time. We provide a positive solution

Nicolas Blanco

In this thesis, we develop the theory of bifibrations of polycategories. We start by studying how to express certain categorical structures as universal properties by generalising the shape of morphism. We call this phenomenon representability and look at different variations, namely the correspondence between representable multicategories and monoidal categ

Chunpu Xu, Jing Li, Piji Li, Min Yang

Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a

Matteo Rinaldi, Matous Mrovec, Anton Bochkarev, Yury Lysogorskiy

The Atomic Cluster Expansion (ACE) provides a formally complete basis for the local atomic environment. ACE is not limited to representing energies as a function of atomic positions and chemical species, but can be generalized to vectorial or tensorial properties and to incorporate further degrees of freedom (DOF). This is crucial for magnetic materials with