December 2023 arXiv papers — page 19

Amin Jalal Aghdasian, Amirhossein Heydarian Ardakani, Kianoush Aqabakee, Farzaneh Abdollahi

This paper proposes a novel approach by integrating sensor fusion with deep reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to develop an optimal control policy for self-driving cars. Our system employs a two-branch fusion method for vehicle image and tracking sensor data, leveraging the strengths of residual structures and identi

Kelsey A. Jackson, Carl A. Miller, Daochen Wang

In the wake of recent progress on quantum computing hardware, the National Institute of Standards and Technology (NIST) is standardizing cryptographic protocols that are resistant to attacks by quantum adversaries. The primary digital signature scheme that NIST has chosen is CRYSTALS-Dilithium. The hardness of this scheme is based on the hardness of three co

Vera Fischer, Lukas Schembecker, David Schrittesser

We introduce the notion of a tight cofinitary group, which captures forcing indestructibility of maximal cofinitary groups for a long list of partial orders, including Cohen, Sacks, Miller, Miller partition forcing and Shelah's poset for diagonalizing maximal ideal. Introducing a new robust coding technique, we establish the relative consistency of $\mathfra

Andrey Gelash, Sergey Dremov, Rustam Mullyadzhanov, Dmitry Kachulin

We investigate theoretically and numerically the dynamics of long-living oscillating coherent structures - bi-solitons - in the exact and approximate models for waves on the free surface of deep water. We generate numerically the bi-solitons of the approximate Dyachenko-Zakharov equation and fully nonlinear equations propagating without significant loss of e

Ilias Diakonikolas, Daniel M. Kane, Vasilis Kontonis, Christos Tzamos

We study the power of query access for the task of agnostic learning under the Gaussian distribution. In the agnostic model, no assumptions are made on the labels and the goal is to compute a hypothesis that is competitive with the {\em best-fit} function in a known class, i.e., it achieves error $\mathrm{opt}+\epsilon$, where $\mathrm{opt}$ is the error of

Pigar Biteng, Mathieu Caguiat, Dipok Deb, Mrinal Kanti Roychowdhury

Constrained quantization for a Borel probability measure refers to the idea of estimating a given probability by a discrete probability with a finite number of supporting points lying on a specific set. The specific set is known as the constraint of the constrained quantization. A quantization without a constraint is known as an unconstrained quantization, w

Takol Tangphati, Menglong Youk, Supakchai Ponglertsakul

We construct asymptotically flat, static spherically symmetric black holes with regular centre in $f(R,T)$ gravity coupled to nonlinear electrodynamics Lagrangian. We obtain generalized metric function of the Bardeen and Hayward black holes. The null, weak and strong energy conditions of these solutions are discussed. All the energy conditions hold outside t

Holger Severin Bovbjerg, Jesper Jensen, Jan Østergaard, Zheng-Hua Tan

In this paper, we propose the use of self-supervised pretraining on a large unlabelled data set to improve the performance of a personalized voice activity detection (VAD) model in adverse conditions. We pretrain a long short-term memory (LSTM)-encoder using the autoregressive predictive coding (APC) framework and fine-tune it for personalized VAD. We also p

Prabhat Agarwal, Shreya Singh

Meta-learning has emerged as an effective methodology to model several real-world tasks and problems due to its extraordinary effectiveness in the low-data regime. There are many scenarios ranging from the classification of rare diseases to language modelling of uncommon languages where the availability of large datasets is rare. Similarly, for more broader

Moritz Piening, Fabian Altekrüger, Johannes Hertrich, Paul Hagemann

The solution of inverse problems is of fundamental interest in medical and astronomical imaging, geophysics as well as engineering and life sciences. Recent advances were made by using methods from machine learning, in particular deep neural networks. Most of these methods require a huge amount of (paired) data and computer capacity to train the networks, wh

P Paryanto, Rakha Rahmadani Pratama, Roni Permana Saputra

Localization is a fundamental requirement for an autonomous vehicle system. One of the most often used systems for autonomous vehicle localization is the global positioning system (GPS). Nevertheless, the functionality of GPS is strongly dependent on the availability of satellites, making it unreliable in some situations. As a result, autonomous vehicles mus

Rongyu Zhang, Yulin Luo, Jiaming Liu, Huanrui Yang

The Mixture-of-Experts (MoE) approach has demonstrated outstanding scalability in multi-task learning including low-level upstream tasks such as concurrent removal of multiple adverse weather effects. However, the conventional MoE architecture with parallel Feed Forward Network (FFN) experts leads to significant parameter and computational overheads that hin

Iosif Sakos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos, Georgios Piliouras

A wide array of modern machine learning applications - from adversarial models to multi-agent reinforcement learning - can be formulated as non-cooperative games whose Nash equilibria represent the system's desired operational states. Despite having a highly non-convex loss landscape, many cases of interest possess a latent convex structure that could potent

Antonio Amariti, Simone Rota

We study 4d exceptional S-fold SCFTs obtained from the 6d $(2,0)$ theories of type $E_{6,7,8}$. We show that all but one of these theories are discrete gaugings of free theories because they do not admit a consistent charge lattice. We compute the 1-form symmetry of the only interacting theory, the $k=4$ exceptional S-fold SCFT of type $E_8$, and find that i

Jia Dong, Yao Yao, Liyan Lin, Yang Dong

Classifying hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a critical step in treatment selection and prognosis evaluation for patients with liver diseases. Traditional histopathological diagnosis poses challenges in this context. In this study, we introduce a novel polarization and radiomics feature fusion network, which combine

Lavanya Ratnabala, Robinroy Peter, E. Y. A. Charles

This research paper addresses the challenges of exploration and navigation in unknown environments from an evolutionary swarm robotics perspective. Path formation plays a crucial role in enabling cooperative swarm robots to accomplish these tasks. The paper presents a method called the sub-goal-based path formation, which establishes a path between two diffe

Wen-Di Guo, Qin Tan, Yu-Xiao Liu

Recently, a static spherically symmetric black hole solution was found in gravity nonminimally coupled a background Kalb-Ramond field. The Lorentz symmetry is spontaneously broken when the Kalb-Ramond field has a nonvanishing vacuum expectation value. In this work, we focus on the quasinormal modes and greybody factor of this black hole. The master equations

Lan Li, Bowen Tao, Lu Han, De-chuan Zhan

Differing from traditional semi-supervised learning, class-imbalanced semi-supervised learning presents two distinct challenges: (1) The imbalanced distribution of training samples leads to model bias towards certain classes, and (2) the distribution of unlabeled samples is unknown and potentially distinct from that of labeled samples, which further contribu

Aleksandar Tošić, Niki Hrovatin, Jernej Vičič

Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains. In this paper we introduce an innovative methodology designed to detect wash trading activities beyond surface-level transactions. Our approach integrates NFT ownership traces with the Ethereum Transaction Network, encompassing

Jiaxing Huang, Jingyi Zhang, Kai Jiang, Han Qiu

Traditional computer vision generally solves each single task independently by a dedicated model with the task instruction implicitly designed in the model architecture, arising two limitations: (1) it leads to task-specific models, which require multiple models for different tasks and restrict the potential synergies from diverse tasks; (2) it leads to a pr

A. Herrera, M. Origlia

In this survey we review recent results on left-invariant conformal Killing p-forms on Lie groups endowed with a left-invariant metric. We also mention interesting open questions that could lead into future research.

Weikang Jiang, Jinxian Wang, Jihong Guan, Shuigeng Zhou

Single-cell RNA sequencing (scRNA-seq) enables researchers to analyze gene expression at single-cell level. One important task in scRNA-seq data analysis is unsupervised clustering, which helps identify distinct cell types, laying down the foundation for other downstream analysis tasks. In this paper, we propose a novel method called Cluster-aware Iterative

Jay Kejriwal, Štefan Beňuš

The tendency of people to engage in similar, matching, or synchronized behaviour when interacting is known as entrainment. Many studies examined linguistic (syntactic and lexical structures) and paralinguistic (pitch, intensity) entrainment, but less attention was given to finding the relationship between them. In this study, we utilized state-of-the-art DNN

Qidong Zhao, Milind Chabbi, Xu Liu

Dynamic program analysis (also known as profiling) is well-known for its powerful capabilities of identifying performance inefficiencies in software packages. Although a large number of dynamic program analysis techniques are developed in academia and industry, very few of them are widely used by software developers in their regular software developing activ

Simone Cito, Alessandro Giacomini

In this paper we address the problem of the minimization of the $k$-th Robin eigenvalue $\lambda_{k,\beta}$ with parameter $\beta>0$ among bounded open Lipschitz sets with prescribed perimeter. The perimeter constraint allows us to naturally generalize the problem to a setting involving more general admissible geometries made up of sets of finite perimeter w

Himanshu Choudhary, Marwan Hassani

In today's urban landscape, traffic congestion poses a critical challenge, especially during outlier scenarios. These outliers can indicate abrupt traffic peaks, drops, or irregular trends, often arising from factors such as accidents, events, or roadwork. Moreover, Given the dynamic nature of traffic, the need for real-time traffic modeling also becomes cru

Pavlina K. Jordanova, Evelina Veleva, Milan Stehlik

This work continues the research done in Jordanova and Veleva (2023) where the history of the problem could be found. In order to obtain the structure distribution of the newly-defined Mixed Poisson process, here the operation "max" is replaced with "min". We start with the definition of Min-U-Exp distribution. Then, we compute its numerical characteristics

Anna Weber, Aurélien Pélissier, María Rodríguez Martínez

Recent advancements in immune sequencing and experimental techniques are generating extensive T cell receptor (TCR) repertoire data, enabling the development of models to predict TCR binding specificity. Despite the computational challenges due to the vast diversity of TCRs and epitopes, significant progress has been made. This paper discusses the evolution

Qingyao Li, Lingyue Fu, Weiming Zhang, Xianyu Chen

Online education platforms, leveraging the internet to distribute education resources, seek to provide convenient education but often fall short in real-time communication with students. They often struggle to address the diverse obstacles students encounter throughout their learning journey. Solving the problems encountered by students poses a significant c

E. G. K. M. Gamlath, Xiaonan Liu, Linyuan Lu, Xiaofan Yuan

In this paper, we prove that any simple $\{C_3,C_5\}$-free non-empty connected graph $G$ with LLY curvature bounded below by $\kappa>0$ has the order at most $2^{\frac{2}{\kappa}}$. This upper bound is achieved if and only if $G$ is a hypercube $Q_d$ and $\kappa=\frac{2}{d}$ for some integer $d\geq 1$.

Zaid Abdullah, Eva Lagunas, Steven Kisseleff, Frank Zeppenfeldt

The third generation partnership project (3GPP) has recently defined two frequency bands for direct access with satellites, which is a concrete step toward realizing the anticipated space-air-ground integrated networks. In addition, given the rapid increase in the numbers of satellites orbiting the Earth and emerging satellites applications, non-terrestrial

Constantin Podelski

We study the conormal geometry theta divisors of certain singular bielliptic curves. We apply these results to the boundary components $\mathscr{S}_\underline{d}$ of the bielliptic Prym locus. We obtain results on the Gauss map, compute the Chern-Mather class and the characteristic cycle of the intersection complex of the corresponding Prym theta divisor.

Nathan Ilten, Jake Levinson

We study embedded rational curves in projective toric varieties. Generalizing results of the first author and Zotine for the case of lines, we show that any degree $d$ rational curve in a toric variety $X$ can be constructed from a special affine-linear map called a degree $d$ Cayley structure. We characterize when the curves coming from a degree $d$ Cayley

Golam Mortuza Hossain, Susobhan Mandal

Neutron stars are known to have strong magnetic fields reaching as high as $10^{15}$ Gauss, besides having strongly curved interior spacetime. So for computing an equation of state for neutron-star matter, the effect of magnetic field as well as curved spacetime should be taken into account. In this article, we compute the equation of state for an ensemble o

Yuan Xu, Fujun Zhou, Yongsheng Li

Diffusive limit of the Vlasov-Poisson-Boltzmann system without angular cutoff in the framework of perturbation around global Maxwellian still remains open. By employing the weighted energy method with a newly introduced weight function $w_l(\alpha,\beta)$ and some novel treatments, we solve this problem for the full range of non-cutoff potentials $\gamma>-3$

Gulay Goktas Sever, Umut Demir, Abdullah Sadik Satir, Mustafa Cagatay Sahin

In this paper, we present a methodology for constructing data-driven maneuver generation models for agile aircraft that can generalize across a wide range of trim conditions and aircraft model parameters. Maneuver generation models play a crucial role in the testing and evaluation of aircraft prototypes, providing insights into the maneuverability and agilit

Suppanat Supanyo, Chanon Hasuwannakit, Sikarin Yoo-Kong, Lunchakorn Tannukij

We present an alternative framework to establish the neutrino mass scale from the Higgs mechanism in a minimalist approach, which does not introduce new scalar bosons or extend the symmetry group of the standard model (SM). A nonstandard form of the Higgs Lagrangian, constructed via the inverse problem of calculus of variations, is proposed. Only one dimensi

Rutwig Campoamor-Stursberg, Eduardo Fernandez-Saiz, Francisco J. Herranz

It is shown that the complex Bernoulli differential equations admitting the supplementary structure of a Lie-Hamilton system related to the book algebra $\mathfrak{b}_2$ can always be solved by quadratures, providing an explicit solution of the equations. In addition, considering the quantum deformation of Bernoulli equations, their canonical form is obtaine

Ruo Li, Yixiao Lu, Yanli Wang

This paper presents an asymptotic preserving (AP) implicit-explicit (IMEX) scheme for solving the quantum BGK equation using the Hermite spectral method. The distribution function is expanded in a series of Hermite polynomials, with the Gaussian function serving as the weight function. The main challenge in this numerical scheme lies in efficiently expanding

Alexander Migdal

This is the second paper in a cycle investigating the exact solution of loop equations in decaying turbulence. We perform numerical simulations of the Euler ensemble, suggested in the previous work, as a solution to the loop equations. We designed novel algorithms for simulation, which take a small amount of computer RAM so that only the CPU time grows linea

Igor Bragar, Łukasz Cywiński

We analyze in detail a procedure of entangling of two singlet-triplet ($S$-$T_{0}$) qubits operated in a regime when energy associated with the magnetic field gradient, $\Delta B_{z}$, is an order of magnitude smaller than the exchange energy, $J$, between singlet and triplet states [Shulman M. et al., Science 336, 202 (2012)]. We have studied theoretically

Tianxin Huang, Qingyao Liu, Xiangrui Zhao, Jun Chen

As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules. However, the static matching rules may deviate from actual shape differences. Although some works propose dynamically-up

Hyowon Wi, Yehjin Shin, Noseong Park

Time series imputation is one of the most fundamental tasks for time series. Real-world time series datasets are frequently incomplete (or irregular with missing observations), in which case imputation is strongly required. Many different time series imputation methods have been proposed. Recent self-attention-based methods show the state-of-the-art imputati

Seunggu Kang, WonJun Moon, Euiyeon Kim, Jae-Pil Heo

Zero-Shot Object Counting (ZSOC) aims to count referred instances of arbitrary classes in a query image without human-annotated exemplars. To deal with ZSOC, preceding studies proposed a two-stage pipeline: discovering exemplars and counting. However, there remains a challenge of vulnerability to error propagation of the sequentially designed two-stage proce

E. A. Lantsev, P. V. Andreev, A. V. Nokhrin, Yu. V. Blagoveshchenskiy

The study investigates the density, phase composition, microstructure and mechanical properties (microhardness, fracture toughness) of binderless WC + SiC and WC + SiC + C ceramics obtained by Spark Plasma Sintering (SPS). Nanopowders of a-WC produced by DC arc plasma chemical synthesis were used as raw materials. Powder compositions for sintering contained

Xiawei Li, Qingyuan Xu, Jing Zhang, Tianyi Zhang

3D point cloud semantic segmentation has a wide range of applications. Recently, weakly supervised point cloud segmentation methods have been proposed, aiming to alleviate the expensive and laborious manual annotation process by leveraging scene-level labels. However, these methods have not effectively exploited the rich geometric information (such as shape

G. N. Koutsokostas, S. Sypsas, O. Evnin, T. P. Horikis

We study a spherical, self-gravitating fluid model, which finds applications in cosmic structure formation. We argue that since the system features nonlinearity and gravity-induced dispersion, the emergence of solitons becomes possible. We thus employ a multiscale expansion method to study, in the weakly nonlinear regime, the evolution of small-amplitude per

Zishuo Zhao

We introduce an quantum entropy for bimodule quantum channels on finite von Neumann algebras, generalizing the remarkable Pimsner-Popa entropy. The relative entropy for Fourier multipliers of bimodule quantum channels establishes an upper bound of the quantum entropy. Additionally, we present the Araki relative entropy for bimodule quantum channels, revealin

Daniele Valeri, Di Yang

For systems of evolutionary partial differential equations the tau-structure is an important notion which originated from the deep relation between integrable systems and quantum field theories. We show that, under a certain non-degeneracy condition, existence of a tau-structure implies integrability. As an example, we apply this principle to provide a new p

Raffaela Capitanelli, Salvatore Fragapane

In this paper we study double obstacle problems involving $(p,q)-$Laplace type operators. In particular, we analyze the asymptotics of the solutions on fractal and pre-fractal boundary domains.

Alexey Sharapov, Evgeny Skvortsov, Richard Van Dongen

Chiral higher spin gravity is defined in terms of a strong homotopy algebra of pre-Calabi-Yau type (noncommutative Poisson structure). All structure maps are given by the integrals over the configuration space of concave polygons and the first two maps are related to the (Shoikhet-Tsygan-)Kontsevich Formality. As with the known formality theorems, we prove t

Chendi Qu, Jianping He, Xiaoming Duan

Designing controllers to generate various trajectories has been studied for years, while recently, recovering an optimal controller from trajectories receives increasing attention. In this paper, we reveal that the inherent linear quadratic regulator (LQR) problem of a moving agent can be reconstructed based on its trajectory observations only, which enables

Hefei Mei, Taijin Zhao, Shiyuan Tang, Heqian Qiu

Few-shot object detection (FSOD) aims to achieve object detection only using a few novel class training data. Most of the existing methods usually adopt a transfer-learning strategy to construct the novel class distribution by transferring the base class knowledge. However, this direct way easily results in confusion between the novel class and other similar

Klára Baksová, Olga Leskovjanová, Ladislav Mišta, Elizabeth Agudelo

Multi-copy activation of genuine multipartite entanglement (GME) is a phenomenon whereby multiple copies of biseparable but fully inseparable states can exhibit GME. This was shown to be generically possible in finite dimensions. Here, we extend this analysis to infinite dimensions. We provide examples of GME-activatable non-Gaussian states. For Gaussian sta

J. Perelló, F. Larroya, I. Bonhoure, F. Peter

Social physics is an active and diverse field in which many scientists with formal training in physics study a broad class of complex social phenomena. Social physics investigates societal problems but most often does not count on the active and conscious participation of the citizens. We here want to support the idea that citizen science, and more particula

Ashirbad Padhan, Tapan Mishra

Thouless charge pump enables a quantized transport of charge through an adiabatic evolution of the Hamiltonian exhibiting topological phase. While this charge pumping is known to be robust against the presence of weak disorder in the system, it often breaks down with the increase in disorder strength. In this work, however, we show that in a one dimensional

Valeriy G. Bardakov, Pravin Kumar, Mahender Singh

Twin groups are planar analogues of Artin braid groups and play a crucial role in the Alexander-Markov correspondence for the isotopy classes of immersed circles on the 2-sphere without triple and higher intersections. These groups admit diagrammatic representations, leading to maps obtained by the addition and deletion of strands. This paper explores Brunni

Chendi Qu, Jianping He, Xiaoming Duan, Jiming Chen

Inverse reinforcement learning (IRL) usually assumes the reward function model is pre-specified as a weighted sum of features and estimates the weighting parameters only. However, how to select features and determine a proper reward model is nontrivial and experience-dependent. A simplistic model is less likely to contain the ideal reward function, while a m

Rami Masri, Miroslav Kuchta, Beatrice Riviere

We propose and analyze discontinuous Galerkin (dG) approximations to 3D-1D coupled systems which model diffusion in a 3D domain containing a small inclusion reduced to its 1D centerline. Convergence to weak solutions of a steady state problem is established via deriving a posteriori error estimates and bounds on residuals defined with suitable lift operators

Rugved Katole, Deepak Mallya, Leena Vachhani, Arpita Sinha

In an environment with certain locations of higher priority, it is required to patrol these locations as frequently as possible due to their importance. However, the Non-Priority locations are often neglected during the task. It is necessary to balance the patrols on both kinds of sites to avoid breaches in security. We present a distributed online algorithm

Jeongwhan Choi, Hyowon Wi, Chaejeong Lee, Sung-Bae Cho

Contrastive learning (CL) has emerged as a promising technique for improving recommender systems, addressing the challenge of data sparsity by using self-supervised signals from raw data. Integration of CL with graph convolutional network (GCN)-based collaborative filterings (CFs) has been explored in recommender systems. However, current CL-based recommenda

Susanna Bisogni

The correlation between the X-ray and UV luminosities observed in quasars, spanning a wide redshift range and holding true for several decades in both spectral bands, suggests the presence of a universal mechanism governing the transfer of energy from the accretion disc to the hot corona. In this study, we leverage X-ray spectroscopic data extracted from the

Yoon-Seok Choun, Ki-Seok Kim, Sang-Jin Sin

We propose a method to constrain the scaling dimension of the operators of the strongly interacting systems (SIS) using the holographic setup. %where the (d+1)-dimensional black hole is used to describe the d-dimensional SIS. We demonstrate our method using the holographic superconductor theory. The idea is to consider the inside as well as the outside of th

Federico Errica, Henrik Christiansen, Viktor Zaverkin, Takashi Maruyama

Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is a dramatic increase in the overall computational costs. Recently, deep graph networks have been employed as efficient, data-driven models for predicting properties of complex syst

Tianyu Fang, Yijie Mao

In this work, we focus on solving non-smooth non-convex maximization problems in multi-group multicast transmission. Leveraging Karush-Kuhn-Tucker (KKT) optimality conditions and successive incumbent transcending (SIT) duality, we thoroughly analyze the optimal beamforming structure for a set of optimization problems characterized by a general utility-based

Aditya Vasudevan, Jorge Zorrilla Prieto, Sergei Zorkaltsev, Maciej Haranczyk

Local geometrical features of a porous material such as the shape and size of a pore or the curvature of a solid ligament often affect the macroscopic properties of the material, and their characterization is necessary to fully understand the structure-property relationships.In this contribution, we present an approach to automatically segment large porous s

Wojciech Rejchel, Paweł Teisseyre, Jan Mielniczuk

Learning from positive and unlabeled data (PU learning) is actively researched machine learning task. The goal is to train a binary classification model based on a training dataset containing part of positives which are labeled, and unlabeled instances. Unlabeled set includes remaining part of positives and all negative observations. An important element in

Gu Zhang, Pierre Glidic, Frederic Pierre, Igor Gornyi

The role of anyonic statistics stands as a cornerstone in the landscape of topological quantum techniques. While recent years have brought forth encouraging and persuasive strides in detecting anyons, a significant facet remains unexplored, especially in view of connecting anyonic physics to quantum information platforms -- whether and how entanglement can b

Jiahong Zhou, Shunhui Mao, Guoliang Yang, Bo Tang

Recommender systems aim to recommend the most suitable items to users from a large number of candidates. Their computation cost grows as the number of user requests and the complexity of services (or models) increases. Under the limitation of computation resources (CRs), how to make a trade-off between computation cost and business revenue becomes an essenti

Matthew T. Wheeler, Hayder Salman, Magnus O. Borgh

Symmetry-breaking quantum phase transitions lead to the production of topological defects or domain walls in a wide range of physical systems. In second-order transitions, these exhibit universal scaling laws described by the Kibble-Zurek mechanism, but for first-order transitions a similarly universal approach is still lacking. Here we propose a spinor Bose

Hanlin Gu, Xinyuan Zhao, Gongxi Zhu, Yuxing Han

Federated learning (FL) enables multiple clients to collaboratively learn a shared model without sharing their individual data. Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention. Differential privacy has emerged as a prevalent technique in FL, safeguarding the privacy of individual user data while imp

Toshiki Hiraoka, Ryo Kainuma, Keita Matsumoto, Kihiro T. Yamada

We performed time-resolved pump--probe measurements using rare-earth iron garnet \ce{Gd3/2Yb1/2BiFe5O12} as a two-sublattice ferrimagnet. We measured the initial phases of the magnetic resonance modes below and above the magnetization compensation temperature to clarify the sublattice selectivity of the inverse Faraday effect in ferrimagnets. A comparison of

Jakub Mosiński, Piotr Biliński, Thomas Merritt, Abdelhamid Ezzerg

Recently normalizing flows have been gaining traction in text-to-speech (TTS) and voice conversion (VC) due to their state-of-the-art (SOTA) performance. Normalizing flows are unsupervised generative models. In this paper, we introduce supervision to the training process of normalizing flows, without the need for parallel data. We call this training paradigm

Miaohui Wang

Blind Image Quality Assessment (BIQA) is essential for automatically evaluating the perceptual quality of visual signals without access to the references. In this survey, we provide a comprehensive analysis and discussion of recent developments in the field of BIQA. We have covered various aspects, including hand-crafted BIQAs that focus on distortion-specif

Suprio Bhar, Barun Sarkar

In this article, we study the existence and uniqueness problem for linear Stochastic PDEs involving a bilaplacian operator. Our results on the existence and uniqueness are obtained through an application of a Monotonicity inequality, which we also prove here. As an application of these results, we also obtain a probabilistic representation of the solution fo

Karan Gupta, Sumegh Roychowdhury, Siva Rajesh Kasa, Santhosh Kumar Kasa

In the In-Context Learning (ICL) setup, various forms of label biases can manifest. One such manifestation is majority label bias, which arises when the distribution of labeled examples in the in-context samples is skewed towards one or more specific classes making Large Language Models (LLMs) more prone to predict those labels. Such discrepancies can arise

İlknur Dönmez Phd, Mehmet Haklıdır Phd

A word may contain one or more hidden concepts. While the "animal" word evokes many images in our minds and encapsulates many concepts (birds, dogs, cats, crocodiles, etc.), the `parrot' word evokes a single image (a colored bird with a short, hooked beak and the ability to mimic sounds). In spoken or written texts, we use some words in a general sense and s

Devriş İşler, Elisa Cabana, Alvaro Garcia-Recuero, Georgia Koutrika

We present a novel technique for modulating the appearance frequency of a few tokens within a dataset for encoding an invisible watermark that can be used to protect ownership rights upon data. We develop optimal as well as fast heuristic algorithms for creating and verifying such watermarks. We also demonstrate the robustness of our technique against variou

Ari Pakman

Markov Chain Monte Carlo algorithms, the method of choice to sample from generic high-dimensional distributions, are rarely used for continuous one-dimensional distributions, for which more effective approaches are usually available (e.g. rejection sampling). In this work we present a counter-example to this conventional wisdom for the von Mises distribution

Ichio Kikuchi, Akihito Kikuchi

In this article, we discuss how a kind of hybrid computation, which employs symbolic, numeric, classic, and quantum algorithms, allows us to conduct Hartree-Fock electronic structure computation of molecules. In the proposed algorithm, we replace the Hartree-Fock equations with a set of equations composed of multivariate polynomials. We transform those polyn

Julian Holfeld

This paper examines the relevance of the Godot Engine in the indie game industry. The Godot Engine is a relatively new game engine from 2014 and competes with leading market players. To get to the bottom of its relevance, two major online sales platforms and the game engines that are commonly used, Steam and itch[dot]io, are examined. Mainly, these findings

Yukimura Izawa

We prsent a lepton flavor model with $S_4$ and $U(1)_{\mathrm{FN}}$ symmetry. The left-handed leptons are assigned as a triplet under the $S_4$ symmetry, the right-handed electron and muon as $S_4$ doublets, and the right-handed tauon as an $S_4$ singlet. We introduce three right-handed Majorana neutrinos that are charged under the $S_4$ symmetry and two sca

Sebastian Fuchs, Yuping Wang

A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a high similarity if the variables in the first group contain high predictive information about the behaviour of the variables in the other group and/or vice versa. The method presented here is model-free,

Sami Mabrouk, Othmen Ncib, Sihem Sendi

In this paper, first we recall the notion of Hom-Jordan superalgebras and study their representations. We define the Yang-Baxter equation in a Hom-Jordan superalgebra. Additionally, we extend the connections between $\mathcal {O}$-operators and skew-symmetric solutions Yang-Baxter equation of Hom-Jordan superalgebras (HJYBE). In which, we prove that a super

Christopher Adnel, Islem Rekik

Graph Neural Network (GNN) ushered in a new era of machine learning with interconnected datasets. While traditional neural networks can only be trained on independent samples, GNN allows for the inclusion of inter-sample interactions in the training process. This gain, however, incurs additional memory cost, rendering most GNNs unscalable for real-world appl

Ruikang Zhong, Xidong Mu, Yimeng Zhang, Mona Jabor

A conception of mobile edge generation (MEG) is proposed, where generative artificial intelligence (GAI) models are distributed at edge servers (ESs) and user equipment (UE), enabling joint execution of generation tasks. Various distributed deployment schemes of the GAI model are proposed to alleviate the immense network load and long user queuing times for

Carolin Mehlmann

Recently, a nonconforming surface finite element was developed to discretize 3d vector-valued compressible flow problems arising in climate modeling. In this contribution we derive an error analysis for this approach on a vector-valued Laplace problem, which is an important operator for fluid-equations on the surface. In our setup, the problem is approximate

Yunhe Wang, Hanting Chen, Yehui Tang, Tianyu Guo

The recent trend of large language models (LLMs) is to increase the scale of both model size (\aka the number of parameters) and dataset to achieve better generative ability, which is definitely proved by a lot of work such as the famous GPT and Llama. However, large models often involve massive computational costs, and practical applications cannot afford s

Yu. Dzhezherya, P. Polynchuk, A. Kravets, V. Korenivski

We investigate the switching of a magnetic nanoparticle comprising the middle free layer of a memory cell based on a double magnetic tunnel junction under the combined effect of spin-polarized current and weak on-chip magnetic field. We obtain the timing and amplitude parameters for the current and field pulses needed to achieve 100 ps range \emph{inertia-fr

Suprio Bhar, Barun Sarkar

In this article, we construct weak solutions for a class of Stochastic PDEs in the space of tempered distributions via Girsanov's theorem. It is to be noted that our drift and diffusion coefficients $(L,A)$ of the considered Stochastic PDE satisfy a Monotonicity type inequality, rather than Lipschitz conditions. As such, we can not follow the usual infinite

I. I. Mokhov

The predictability of extreme winter regimes in the regions of Northern Eurasia depending on El Ni\~no phenomena is assessed using data from long-term meteorological observations. The frequency of extremely warm and extremely cold winters is compared for different phases and different types of El Ni\~no events.

Luigi Del Debbio, Felix Erben, Jonathan Flynn, Rajnandini Mukherjee

We present a first numerical implementation of a massive nonperturbative renormalisation scheme, RI/mSMOM, in the study of heavy quarks using the domain-wall fermion action. In particular, we calculate renormalisation constants for fermion bilinears at non-vanishing heavy-quark masses and compare the approach to the continuum of the renormalised charm-quark

Alberto Debernardi Pinos

We obtain necessary and sufficient conditions on weights for a wide class of integral transforms to be bounded between weighted $L^p-L^q$ spaces, with $1\leq p\leq q\leq \infty$. The kernels $K(x,y)$ of such transforms are only assumed to satisfy upper bounds given by products of two functions, one in each variable. The obtained results are applicable to a n

Bo-Sture K. Skagerstam, Per K. Rekdal

In the present paper we construct a properly defined quantum state expressed in terms of elliptic Jacobi theta functions for the self-adjoint observables angular position $\theta$ and the corresponding angular momentum operator $L = -id/d\theta$. The quantum uncertainties $\Delta \theta$ and $\Delta L$ for the state are well-defined and are, e.g., shown to g

Samuel Rhys Cox

With growing capabilities of large language models (LLMs) comes growing affordances for human-like and context-aware conversational partners. On from this, some recent work has investigated the use of LLMs to simulate multiple conversational partners, such as to assist users with problem solving or to simulate an environment populated entirely with LLMs. Bey

Christoph Sendner, Lukas Petzi, Jasper Stang, Alexandra Dmitrienko

Ethereum smart contracts, which are autonomous decentralized applications on the blockchain that manage assets often exceeding millions of dollars, have become primary targets for cyberattacks. In 2023 alone, such vulnerabilities led to substantial financial losses exceeding a billion of US dollars. To counter these threats, various tools have been developed

Gauhar Abbas, Neelam Singh

We discuss a low-scale realization of the dark-technicolour paradigm, where the dark-technicolour scale is close to the electroweak scale. This scenario provides an ultraviolet completion of the standard HVM, and predicts a dark-Higgs with mass $ m_{\rm DH } = 95.4$ GeV. Moreover, the grand-unification scale in this framework can be as low as $1.18 \times 10

Mihailo Stojnic

We consider the classical \emph{spherical} perceptrons and study their capacities. The famous zero-threshold case was solved in the sixties of the last century (see, \cite{Wendel62,Winder,Cover65}) through the high-dimensional combinatorial considerations. The general threshold, $\kappa$, case though turned out to be much harder and stayed out of reach for t

Marcello Calvanese Strinati, Claudio Conti

Systems of coupled optical parametric oscillators (OPOs) forming an Ising machine are emerging as large-scale simulators of the Ising model. The advances in computer science and nonlinear optics have triggered not only the physical realization of hybrid (electro-optical) or all-optical Ising machines, but also the demonstration of quantum-inspired algorithms

Matthew Pugh, Jo Grundy, Corina Cirstea, Nick Harris

This paper is the first to construct and motivate a Machine Learning algorithm solely with Enriched Category Theory, supplementing evidence that Category Theory can provide valuable insights into the construction and explainability of Machine Learning algorithms. It is shown that a series of reasonable assumptions about a dataset lead to the construction of