July 2023 arXiv papers — page 40

Chull Hwan Song, Taebaek Hwang, Jooyoung Yoon, Shunghyun Choi

Many studies in vision tasks have aimed to create effective embedding spaces for single-label object prediction within an image. However, in reality, most objects possess multiple specific attributes, such as shape, color, and length, with each attribute composed of various classes. To apply models in real-world scenarios, it is essential to be able to disti

Kim-Ngan Le, Jörn Wichmann

The main objective of the present paper is to construct a new class of space-time discretizations for the stochastic $p$-Stokes system and analyze its stability and convergence properties. We derive regularity results for the approximation that are similar to the natural regularity of solutions. One of the key arguments relies on discrete extrapolation that

Grigory Mikhalkin, Kyler Siegel

We compute stationary gravitational descendants in symplectic ellipsoids of any dimension, and use these to derive a number of new recursive formula for punctured curve counts in symplectic manifolds with ellipsoidal ends. Along the way we develop a framework in which punctured curve counts can be explicitly computed using the standard complex structure on a

Tuan Duc Ngo, Binh-Son Hua, Khoi Nguyen

Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge in computer vision, where state-of-the-art methods are mainly based on full supervision. As annotating ground truth dense instance masks is tedious and expensive, solving 3DIS with weak supervision has become more practical. In this paper, we propose GaPro, a new instance segmentatio

Yi Cheng, Hehe Fan, Dongyun Lin, Ying Sun

The main challenge in video question answering (VideoQA) is to capture and understand the complex spatial and temporal relations between objects based on given questions. Existing graph-based methods for VideoQA usually ignore keywords in questions and employ a simple graph to aggregate features without considering relative relations between objects, which m

Cosimo Bambi

Black holes are one of the most fascinating predictions of general relativity. They are the natural product of the complete gravitational collapse of matter and today we have a body of observational evidence supporting the existence of black holes in the Universe. However, general relativity predicts that at the center of black holes there are spacetime sing

Sofia Valentina Sosa Fiscella, Michael T. Lam, Zaven Arzoumanian, Harsha Blumer

Noise characterization for pulsar-timing applications accounts for interstellar dispersion by assuming a known frequency-dependence of the delay it introduces in the times of arrival (TOAs). However, calculations of this delay suffer from mis-estimations due to other chromatic effects in the observations. The precision in modeling dispersion is dependent on

Langford White, Oskar Rynkiewicz, Duong Nguyen, Hung Nguyen

We introduce a new paradigm for game theory -- Bayesian satisfaction. This novel approach is a synthesis of the idea of Bayesian rationality introduced by Aumann, and satisfaction games. The concept of Bayesian rationality for which, in part, Robert Aumann was awarded the Nobel Prize in 2005, is concerned with players in a game acting in their own best inter

Henry K. Tran, Timothy C. Berkelbach

Vibrational heat-bath configuration interaction (VHCI) -- a selected configuration interaction technique for vibrational structure theory -- has recently been developed in two independent works [J. Chem. Phys. 154, 074104 (2021); Mol. Phys. 119, e1936250 (2021)], where it was shown to provide accuracy on par with the most accurate vibrational structure metho

Pratibha Verma

This article looks at the relationship between the discrete and the continuous Redner-Ben-Avraham-Kahng (RBK) coagulation models. On the basis of a priori estimation, a weak stability principle and the weak compactness in $L_1$ for the continuous RBK model is shown. By employing a sequence of discrete models to approximate the continuous one, we show that ho

Cheng Da, Peng Wang, Cong Yao

Due to the enormous technical challenges and wide range of applications, scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this tough problem, numerous innovative methods have been successively proposed, and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this w

Myeong-Ju Kim, Daegyu Lim, Gyeongjae Park, Kwanwoo Lee

The robust balancing capability of humanoids is essential for mobility in real environments. Many studies focus on implementing human-inspired ankle, hip, and stepping strategies to achieve human-level balance. In this paper, a robust balance control framework for humanoids is proposed. Firstly, a Model Predictive Control (MPC) framework is proposed for Capt

Duong Nguyen, Langford White, Hung Nguyen

We study the open question of how players learn to play a social optimum pure-strategy Nash equilibrium (PSNE) through repeated interactions in general-sum coordination games. A social optimum of a game is the stable Pareto-optimal state that provides a maximum return in the sum of all players' payoffs (social welfare) and always exists. We consider finite r

Justin Yang, Peter Bauer, Todd Harris, Changhyung Lee

Image quality assessment (IQA) is an active research area in the field of image processing. Most prior works focus on visual quality of natural images captured by cameras. In this paper, we explore visual quality of scanned documents, focusing on raster image areas. Different from many existing works which aim to estimate a visual quality score, we propose a

Zheng Chong, Xujie Zhang, Fuwei Zhao, Zhenyu Xie

The utilization of Large Language Models (LLMs) for the construction of AI systems has garnered significant attention across diverse fields. The extension of LLMs to the domain of fashion holds substantial commercial potential but also inherent challenges due to the intricate semantic interactions in fashion-related generation. To address this issue, we deve

Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) o

Kai Xiao, Haibo Yuan, Bowen Huang, Shuai Xu

In this paper, a total of approximately 2.6 million dwarfs were constructed as standard stars, with an accuracy of about 0.01-0.02 mag for each band, by combining spectroscopic data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope Data Release 7, photometric data from the corrected Gaia Early Data Release 3, and photometric metallicities. U

Shengguo Meng, Wankai Tang, Weicong Chen, Jifeng Lan

Reconfigurable intelligent surface (RIS) is a promising technology that can reshape the electromagnetic environment in wireless networks, offering various possibilities for enhancing wireless channels. Motivated by this, we investigate the channel optimization for multiple-input multiple-output (MIMO) systems assisted by RIS. In this paper, an efficient RIS

Jinxiang Liu, Chen Ju, Chaofan Ma, Yanfeng Wang

The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues. However, current fusion-based methods have the performance limitations due to the small receptive field of convolution and inadequate fusion of audio-visual features. To overcome these issues, we propose a novel \textbf{Au}dio-aware q

Christoph Böhm, Ramiro A. Lafuente

We show that a negative Einstein manifold admitting a proper isometric action of a connected unimodular Lie group with compact, possibly singular, orbit space splits isometrically as a product of a symmetric space and a compact negative Einstein manifold. The proof involves the theory of polar actions, Lie-theoretic arguments and maximum principles.

Kai-Kai Duan, Peng-Xiong Ma, Ke-Wen Zhang, Xiao-Yuan Huang

GRANDProto300 is a 300-antenna prototype array of the envisioned GRAND (Giant Radio Array for Neutrino Detection) project. The goal of GRANDProto300 is to detect radio signals emitted by cosmic ray-induced air showers, with energies ranging from $10^{16.5}$~eV to $10^{18.5}$~eV, which covers the transition region between Galactic and extragalactic sources. W

Zhen Jiang, Lefeng Zhou, Wei Li, Yudong Li

Topological phases have become an enabling role in exploiting new applications of nonlinear optics in recent years. Here we theoretically propose a valley photonic crystal resonator emulating topologically protected dissipative Kerr soliton combs. It is shown that topological resonator modes can be observed in the resonator. Moreover, we also simulate the dy

Emil Mendoza, Fabian Dunker, Marco Reale

In this paper house prices in Christchurch are analyzed over three distinct periods of time: post-2011 earthquake, pre-COVID-19 lockdown, and post-COVID-19 lockdown using the well-established hedonic price model. Results show that buyers, in periods that are temporally distant from the 2011 Christchurch earthquake, value the risk of potential earthquake dama

Ce Feng, Nuo Xu, Wujie Wen, Parv Venkitasubramaniam

Differential privacy is a widely accepted measure of privacy in the context of deep learning algorithms, and achieving it relies on a noisy training approach known as differentially private stochastic gradient descent (DP-SGD). DP-SGD requires direct noise addition to every gradient in a dense neural network, the privacy is achieved at a significant utility

Shota Kikuchi, Tatsuo Kobayashi, Kaito Nasu, Yusuke Yamada

We discuss how the moduli in modular flavor symmetric models dynamically select enhanced symmetry points at which the residual modular symmetry renders extra matter fields massless. The moduli dynamics non-perturbatively produces the extra matter particles, which gives (time-dependent) effective potential that traps the moduli to enhanced symmetry points. We

Gang Sun, Peter Harrowell

A structural order parameter for disordered configurations is defined, based, not on local topologies, but on the degree of local restraint imposed on each atom. This restraint parameter provides a clear distinction between a strong liquid (SiO2) and a fragile liquid (a binary Lennard-Jones mixture) without reference to dynamics. Where the fragile liquid exh

Sergey V. Sudoplatov

We study possibilities for semantic and syntactic rigidity, i.e., the rigidity with respect to automorphism group and with respect to definable closure. Variations of rigidity and their degrees are studied in general case, for special languages and for some natural operations with structures.

Sho Kubota, Hiroto Sekido, Kiyoto Yoshino

The interest of this paper is a characterization of graphs that induce periodic Grover walks with given periods. In previous studies, Yoshie has shown that the only graphs that induce odd periodic Grover walks are cycle graphs. However, this problem is largely unsolved for even periods. In this study, we show that regular graphs that induce $2l$-periodic Gro

Quankai Gao, Qiangeng Xu, Hao Su, Ulrich Neumann

We propose Strivec, a novel neural representation that models a 3D scene as a radiance field with sparsely distributed and compactly factorized local tensor feature grids. Our approach leverages tensor decomposition, following the recent work TensoRF, to model the tensor grids. In contrast to TensoRF which uses a global tensor and focuses on their vector-mat

Han Hu, Haolan Zhan, Yujin Huang, Di Liu

With the popularity of smartphones and tablets, users have become accustomed to using different devices for different tasks, such as using their phones to play games and tablets to watch movies. To conquer the market, one app is often available on both smartphones and tablets. However, although one app has similar graphic user interfaces (GUIs) and functiona

Elliot J. Carr, Giuseppe Pontrelli

Advances in material design has led to the rapid development of novel materials with increasing complexity and functions in bioengineering. In particular, functionally graded materials (FGMs) offer important advantages in various fields of application. In this work, we consider a heterogeneous reaction-diffusion model for an FGM spherical drug releasing syst

Xu Xu, Chao Zheng

Glickenstein \cite{Glickenstein} and Glickenstein-Thomas \cite{GT} introduced the discrete conformal structures on surfaces in an axiomatic approach and studied its classification. In this paper, we give a full classification of the discrete conformal structures on surfaces, which completes Glickenstein-Thomas' classification. As a result, we find some new c

Kota Ido, Mitsuaki Kawamura, Yuichi Motoyama, Kazuyoshi Yoshimi

$\mathcal{H}\Phi$ [$aitch$-$phi$] is an open-source software package of numerically exact and stochastic calculations for a wide range of quantum many-body systems. In this paper, we present the newly added functions and the implemented methods in vers. 2 and 3. In ver. 2, we implement spectrum calculations by the shifted Krylov method, and low-energy excite

Yuanhao Gong

Large language models have made significant progress in the past few years. However, they are either generic {\it or} field specific, splitting the community into different groups. In this paper, we unify these large language models into a larger map, where the generic {\it and} specific models are linked together and can improve each other, based on the use

Zi Wang, Xiaotong Yu, Chengyan Wang, Weibo Chen

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal du

Rebecca C. Steorts

The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, among others, has grown rapidly over the past decade. With this expansion, the statistical and methodological questions around data integration, or rather merging multiple data source

Eugene Y. S. Chua, Eddy Keming Chen

In Everettian quantum mechanics, justifications for the Born rule appeal to self-locating uncertainty or decision theory. Such justifications have focused exclusively on a pure-state Everettian multiverse, represented by a wave function. Recent works in quantum foundations suggest that it is viable to consider a mixed-state Everettian multiverse, represented

Masanori Hirano, Kentaro Minami, Kentaro Imajo

Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market conditions, such as market frictions, which are challenging to address within the traditional mathematical finance framework. Since deep hedging relies on market simulation, the und

Yanan Feng, Xiaohu Li, Tom J. Millar, Ryszard Szczerba

Whether SiC$_2$ is a parent species, that is formed in the photosphere or as a by-product of high-temperature dust formation, or a daughter species, formed in a chemistry driven by the photodestruction of parent species in the outer envelope, has been debated for a long time. Here, we analyze the ALMA observations of four SiC$_2$ transitions in the CSEs of t

Divam Gupta

Semantic segmentation plays a vital role in computer vision tasks, enabling precise pixel-level understanding of images. In this paper, we present a comprehensive library for semantic segmentation, which contains implementations of popular segmentation models like SegNet, FCN, UNet, and PSPNet. We also evaluate and compare these models on several datasets, o

Huy Q. Le, Minh N. H. Nguyen, Chu Myaet Thwal, Yu Qiao

Federated learning (FL) enables a decentralized machine learning paradigm for multiple clients to collaboratively train a generalized global model without sharing their private data. Most existing works simply propose typical FL systems for single-modal data, thus limiting its potential on exploiting valuable multimodal data for future personalized applicati

Xing Xu, Jianhua Guo, Shen Wang

GRANDProto300 is the planned 300-antenna pathfinder array of the Giant Radio Array for Neutrino Detection (GRAND), of which the first 100 detection units have been already produced. Its main goal is to demonstrate the viability of the detection of the radio emission from air showers initiated by inclined ultra-high-energy cosmic rays with energies of $10^{16

Diego Rengifo, Gabriel Téllez

When a system deviates from equilibrium, it is possible to manipulate and control it to drive it towards equilibrium within a finite time $t_f$, even reducing its natural relaxation time scale $\tau_{relax}$. Although numerous theoretical and experimental studies have explored these shortcut protocols, few have yielded analytical results for the probability

Nikolay A. Vinokurov

A derivation of the time-dependent Schr\"odinger equation from the time-independent one is considered. Instead of time, the coordinate of an additional degree of freedom, the clock, is introduced into the original time-independent Schr\"odinger equation. It is shown that the standard time-dependent Schr\"odinger equation can be obtained for the semiclassical

Wanyu Bian, Albert Jang, Fang Liu

This paper proposes a novel self-supervised learning method, RELAX-MORE, for quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll a model-based qMRI reconstruction into a deep learning framework, enabling the generation of highly accurate and robust MR parameter maps at imaging acceleration. Unlike conventional

Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo

We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of repeatable keypoints which is difficult to do especially in low-overlap scenarios. They seek correspondences over downsampled superpoints, which are then propagated to dense point

Mumtaz Hussain, Benjamin Ward

We prove a multidimensional weighted analogue of the well-known theorem of Kurzweil (1955) in the metric theory of inhomogeneous Diophantine approximation. Let $A$ be matrix of real numbers, $\Psi$ an $n$-tuple of monotonic decreasing functions, and let $W_{A}(\Psi)$ be the set of points that infinitely often lie in a $\Psi(q)$-neighbourhood of the sequence

Xueming Fu, Hao Zheng, Luyan Liu, Wenjuan Zhong

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due

Sanju Mandal, Molla Basir Ahamed

The Hankel determinant $H_{2,1}(F_{f^{-1}}/2)$ of logarithmic coefficients is defined as: \begin{align*} H_{2,1}(F_{f^{-1}}/2):= \begin{vmatrix} \Gamma_1 & \Gamma_2 \Gamma_2 & \Gamma_3 \end{vmatrix}=\Gamma_1\Gamma_3-\Gamma^2_2, \end{align*} where $\Gamma_1, \Gamma_2,$ and $\Gamma_3$ are the first, second and third logarithmic coefficients of inverse function

Satabdwa Majumdar, Ritesh Patel, Vaibhav Pant, Dipankar Banerjee

The primary objective of this study is to connect the coronal mass ejections (CMEs) to their source regions, primarily creating a CME source region (CSR) catalogue, and secondly probing into the influence the source regions have on different statistical properties of CMEs. We create a source region catalogue for 3327 CMEs from 1998 to 2017, thus capturing th

Ayushi Awasthi, O. S. K. S. Sastri

In this paper, we have made a comparative study of alpha-alpha scattering using different phenomenological models like Morse, double Gaussian, double Hulthen, Malfliet-Tjon and double exponential for the nuclear interaction and atomic Hulthen as screened coulomb potential. The phase equations for S, D and G channels have been numerically solved using 5th ord

A. Martina Neuman, Jason J. Bramburger

Graph neural networks (GNNs) have become powerful tools for processing graph-based information in various domains. A desirable property of GNNs is transferability, where a trained network can swap in information from a different graph without retraining and retain its accuracy. A recent method of capturing transferability of GNNs is through the use of grapho

Yuxuan Lei, Dingkang Yang, Mingcheng Li, Shunli Wang

Multimodal Sentiment Analysis (MSA) aims to mine sentiment information from text, visual, and acoustic modalities. Previous works have focused on representation learning and feature fusion strategies. However, most of these efforts ignored the disparity in the semantic richness of different modalities and treated each modality in the same manner. That may le

Chao Tang, Dehao Huang, Wenqi Ge, Weiyu Liu

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects, tasks, and grasps, existing methods incorporate semantic knowledge as priors into TOG pipelines. However, the existing semantic knowledge is typically constructed based on closed-w

Rishi Phatak, Dylan A. Shell

A useful capability is that of classifying some agent's behavior using data from a sequence, or trace, of sensor measurements. The sensor selection problem involves choosing a subset of available sensors to ensure that, when generated, observation traces will contain enough information to determine whether the agent's activities match some pattern. In genera

Yang Jiao, Kai Yang, Dongjin Song

Distributionally Robust Optimization (DRO), which aims to find an optimal decision that minimizes the worst case cost over the ambiguity set of probability distribution, has been widely applied in diverse applications, e.g., network behavior analysis, risk management, etc. However, existing DRO techniques face three key challenges: 1) how to deal with the as

Qing-Hua Zhang, Shao-Ming Fei

Entropic uncertainty relations demonstrate the intrinsic uncertainty of nature from an information-theory perspective. Recently, a quantum-memory-assisted entropic uncertainty relation for multiple measurements was proposed by Wu $et\ al.$ [Phys Rev A. 106. 062219 (2022)]. Interestingly, the quantum-memory-assisted entropic uncertainty relation for multiple

Divya Ahuja, Abhishek Banerjee, Surjeet Kour, Samarpita Ray

We consider representations of quivers taking values in monads or comonads over a Grothendieck category $\mathcal C$. We treat these as scheme like objects whose ``structure sheaf'' consists of monads or comonads. By using systems of adjoint functors between Eilenberg-Moore categories, we obtain a categorical framework of modules over monad quivers, and of c

V. M. Bastidas, H. L. Nourse, A. Sakurai, A. Hayashi

Equilibration plays a fundamental role in our understanding of statistical mechanics and the long-time dynamics of many-body systems. In quantum systems, the route to equilibration is intimately related to level repulsion and quantum signatures of chaos that are encoded in their unitary evolution. Chaotic quantum systems exhibit the level statistics characte

Attia Qammar, Abdenacer Naouri, Jianguo Ding, Huansheng Ning

Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients and data remains on their devices, only sharing the local model updates. With this feature, federated learning is considered a secure solution for data privacy issues. However, the typical FL structure relies on the client-s

Kimia Hemmatirad, Morteza Babaie, Jeffrey Hodgin, Liron Pantanowitz

Context: Analyzing digital pathology images is necessary to draw diagnostic conclusions by investigating tissue patterns and cellular morphology. However, manual evaluation can be time-consuming, expensive, and prone to inter- and intra-observer variability. Objective: To assist pathologists using computerized solutions, automated tissue structure detection

S. Q. Wang, J. B. Wang, D. Z. Li, J. M. Yao

Black widows are millisecond pulsars ablating their companions. The material blown from the companion blocks the radio emission, resulting in radio eclipses. The properties of the eclipse medium are poorly understood. Here, we present direct evidence of the existence of magnetic fields in the eclipse medium of the black widow PSR J2051$-$0827 using observati

Filippo Vittori, Chuan Fu Tan, Anna Laura Pisello, Adrian Chong

The holistic management of a building requires data from heterogeneous sources such as building management systems (BMS), Internet-of-Things (IoT) sensor networks, and building information models. Data interoperability is a key component to eliminate silos of information, and using semantic web technologies like the BRICK schema, an effort to standardize sem

Aritran Piplai, Anantaa Kotal, Seyedreza Mohseni, Manas Gaur

Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly advancing field that combines the subsymbolic strengths of (deep) neural networks and explicit, symbolic knowledge contained in knowledge graphs to enhance explainability and safety in AI systems. This approach addresses a key criticism of current generation systems, namely their inabilit

Barbara Maenhaut, Jeremy Mitchell, Anna Puskas

A connected 1-factorisation is a 1-factorisation of a hypergraph for which the union of each pair of distinct 1-factors is a connected hypergraph. A uniform 1-factorisation is a 1-factorisation of a hypergraph for which the union of each pair of distinct 1-factors is isomorphic to the same subhypergraph, and a uniform-connected 1-factorisation is a uniform 1

Valentin Alleaume, Miroslav Krstic

For the quite extensively developed PDE backstepping methodology for coupled linear hyperbolic PDEs, we provide a generalization from finite collections of such PDEs, whose states at each location in space are vector-valued, to previously unstudied infinite (continuum) ensembles of such hyperbolic PDEs, whose states are function-valued. The motivation for st

Vorrapan Chandee, Xiannan Li, Kaisa Matomäki, Maksym Radziwiłł

We prove an asymptotic formula for the eighth moment of Dirichlet $L$-functions averaged over primitive characters $\chi$ modulo $q$, over all moduli $q\leq Q$ and with a short average on the critical line. Previously the same result was shown conditionally on the Generalized Riemann Hypothesis by the first two authors.

Yi-Wei Qiu, Sheng-Qin Feng, Xue-Qiang Zhu

We study the properties of the shear viscosity coefficient of quark matter near the chiral phase transition at finite temperature and chemical potential, and the kinds of high temperature, high density and strong magnetic field background. The strong magnetic field induces anisotropy, that is, the quantization of Landau energy levels in phase space. If the m

Shripad V. Deshmukh, Srivatsan R, Supriti Vijay, Jayakumar Subramanian

As Reinforcement Learning (RL) agents are increasingly employed in diverse decision-making problems using reward preferences, it becomes important to ensure that policies learned by these frameworks in mapping observations to a probability distribution of the possible actions are explainable. However, there is little to no work in the systematic understandin

Bin Pei, Robert Hesse, Bjoern Schmalfuss, Yong Xu

This paper establishes the averaging method to a coupled system consisting of two stochastic differential equations which has a slow component driven by fractional Brownian motion (FBM) with less regularity $1/3< H \leq 1/2$ and a fast dynamics under additive FBM with Hurst-index $1/3< \hat H \leq 1/2$. We prove that the solution of the slow component conver

Joaquim Dias Garcia, Iago Leal, Raphael Chabar, Mario Veiga Pereira

Stochastic Dual Dynamic Programming (SDDP) is a widely used and fundamental algorithm for solving multistage stochastic optimization problems. Although SDDP has been frequently applied to solve risk-averse models with the Conditional Value-at-Risk (CVaR), it is known that the estimation of upper bounds is a methodological challenge, and many methods are comp

Renjie Zhao, Ziyue Zou, John D. Weeks, Pratyush Tiwary

Understanding nucleation from aqueous solutions is of fundamental importance in a multitude of fields, ranging from materials science to biophysics. The complex solvent-mediated interactions in aqueous solutions hamper the development of a simple physical picture elucidating the roles of different interactions in nucleation processes. In this work we make us

Jacob Abajian, Francesco Aprile, Robert C. Myers, Pedro Vieira

We describe solutions of asymptotically AdS$_3$ Einstein gravity that are sourced by the insertion of operators in the boundary CFT$_2$, whose dimension scales with the central charge of the theory. Previously, we found that the geometry corresponding to a black hole two-point function is simply related to an infinite covering of the Euclidean BTZ black hole

Akriti Verma, Shama Islam, Valeh Moghaddam, Adnan Anwar

Emotion regulation is the process of consciously altering one's affective state, that is the underlying emotional state such as happiness, confidence, guilt, anger etc. The ability to effectively regulate emotions is necessary for functioning efficiently in everyday life. Today, the pervasiveness of digital technology is being purposefully employed to modify

Henry Muccini, Barbara Russo, Eugenio Zimeo

IoT is changing the way Internet is used due to the availability of a large amount of data timely collected from every-day life objects. Designing applications in this new scenario poses new challenges. This extended abstract discusses them and presents the objective of the BeT project whose main aim is to introduce a reference architecture, a conceptual fra

Rakpong Kaewpuang, Minrui Xu, Dinh Thai Hoang, Dusit Niyato

Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources, such as quantum computers, to perform resource-intensive tasks. Like traditional cloud computing platforms, QCC providers can offer both reservation and on-demand plans for quantum resource provisioning to satisfy users' requirements. However, the fl

Robin K. S. Hankin

In this short article I introduce the frab package which provides an alternative interpretation of named vectors in the R programming language; it is available on CRAN. The underlying mathematical object is the free Abelian group.

Gretchen L. Matthews, Travis Morrison, Aidan W. Murphy

In this paper, we introduce curve-lifted codes over fields of arbitrary characteristic, inspired by Hermitian-lifted codes over $\mathbb{F}_{2^r}$. These codes are designed for locality and availability, and their particular parameters depend on the choice of curve and its properties. Due to the construction, the numbers of rational points of intersection be

George Vahala, Min Soe, Efstratios Koukoutsis, Kyriakos Hizanidis

It is well known that Maxwell equations can be expressed in a unitary Schrodinger-Dirac representation for homogeneous media. However, difficulties arise when considering inhomogeneous media. A Dyson map points to a unitary field qubit basis, but the standard qubit lattice algorithm of interleaved unitary collision-stream operators must be augmented by some

Borys Álvarez-Samaniego, Wilson P. Álvarez-Samaniego, Luis Rivera

A useful result is that if a bounded complex-valued path is Riemann-integrable, then its modulus is also Riemann-integrable. The extension of this last result to bounded paths taking values in a normed space is affirmed, as being true, in [3]. However, we show that if a bounded path taking values in a normed space is barely Riemann-integrable, then it is not

Glenn Bruns, Michael Haidar, Federico Rubino

We describe a method for the neural decoding of memory from EEG data. Using this method, a concept being recalled can be identified from an EEG trace with an average top-1 accuracy of about 78.4% (chance 4%). The method employs deep representation learning with supervised contrastive loss to map an EEG recording of brain activity to a low-dimensional space.

Mayana Pereira, Kevin Greene, Nilima Pisharody, Rahul Dodhia

The proliferation of misinformation and propaganda is a global challenge, with profound effects during major crises such as the COVID-19 pandemic and the Russian invasion of Ukraine. Understanding the spread of misinformation and its social impacts requires identifying the news sources spreading false information. While machine learning (ML) techniques have

Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun, Mohammad Sadegh Khorshidi

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured

Agnimitra Sengupta, S. Ilgin Guler, Vikash V. Gayah, Shannon Warchol

Vulnerable road users (VRUs), such as pedestrians and bicyclists, are at a higher risk of being involved in crashes with motor vehicles, and crashes involving VRUs also are more likely to result in severe injuries or fatalities. Signalized intersections are a major safety concern for VRUs due to their complex and dynamic nature, highlighting the need to unde

Jovan Žigić

Reduced-order models have long been used to understand the behavior of nonlinear partial differential equations (PDEs). Naturally, reduced-order modeling techniques come at the price of computational accuracy for a decrease in computation time. Optimization techniques are studied to improve either or both of these objectives and decrease the total computatio

Allmin Susaiyah, Aki Härmä, Milan Petković

In natural language generation (NLG), insight mining is seen as a data-to-text task, where data is mined for interesting patterns and verbalised into 'insight' statements. An 'over-generate and rank' paradigm is intuitively used to generate such insights. The multidimensionality and subjectivity of this process make it challenging. This paper introduces a sc

Xiaojin Bai, Siran Li, Xiangxiang Su

We formulate and prove compensated compactness theorems concerning the limiting behaviour of wedge products of weakly convergent differential forms on closed Riemannian manifolds \`{a} la Robbin--Rogers--Temple [Trans. Amer. Math. Soc. 303 (1987), 609--618]. The case of critical regularity exponents is considered, which generalises the div-curl lemma in Bria

Zafar Iqbal, Fengshuo Zu, Artem Musiienko, Emilio Gutierrez Partida

In perovskite solar cells (PSCs) energy levels alignment and charge extraction at the interfaces are the essential factors directly affecting the device performance. In this work, we present a modified interface between all-inorganic CsPbI$_3$ perovskite and its hole selective contact (Spiro-OMeTAD), realized by a dipole molecule trioctylphosphine oxide (TOP

Ji Qi, Tsz Wai Ko, Brandon C. Wood, Tuan Anh Pham

Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computational understanding and design of materials. However, MLIPs are only as accurate and robust as the data they are trained on. In this work, we present DImensionality-Reduced Encoded C

Allmin Susaiyah, Abhinay Pandya, Aki Härmä

We present a novel method for mining opinions from text collections using generative language models trained on data collected from different populations. We describe the basic definitions, methodology and a generic algorithm for opinion insight mining. We demonstrate the performance of our method in an experiment where a pre-trained generative model is fine

Abhiroop Sarkar, Robert Krook, Alejandro Russo, Koen Claessen

Trusted Execution Environments (TEEs) are hardware-enforced memory isolation units, emerging as a pivotal security solution for security-critical applications. TEEs, like Intel SGX and ARM TrustZone, allow the isolation of confidential code and data within an untrusted host environment, such as the cloud and IoT. Despite strong security guarantees, TEE adopt

Calderón Carlos, Cotrina John

In this work, we focus on the concept of projected solutions for generalized Nash equilibrium problems. We present new existence results by considering sets of strategies that are not necessarily compact. The relationship between projected solutions and Nash equilibria is studied for the generalized Nash game proposed by Rosen. Finally, we demonstrate that e

Aaron M. Miller, Joaquín E. Drut

Using schematic model potentials, we calculate exactly the virial coefficients of a classical gas up to sixth order and use them to assess the convergence properties of the virial expansion of basic thermodynamic quantities such as pressure, density, and compressibility. At sufficiently strong couplings, as expected, the virial expansion fails to converge. H

Ken K. W. Ma, Oğuz Türker, Alexander Seidel, Kun Yang

The interplay between different quantum phases plays an important role in strongly correlated systems, such as high-$T_c$ cuprates, quantum spin systems, and ultracold atoms. In particular, the application of effective field theory model and renormalization group analysis suggested that the coexistence of density wave (DW) and superfluid (SF) orders can lead

Stefanie Günther, N. Anders Petersson

We present an iterative scheme to estimate the minimal duration in which a quantum gate can be realized while satisfying hardware constraints on the control pulse amplitudes. The scheme performs a sequence of unconstrained numerical optimal control cycles that each minimize the gate fidelity for a given gate duration alongside an additional penalty term for

Tabita Catalán, Matías Courdurier, Axel Osses, René Botnar

Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelerated undersampled acquisitions. Several regularization approaches that exploit spatial-temporal redundancy have been proposed to reconstruct undersampled cardiac cine MRI. More rece

Matías I. Caruso, Javier Fernández, Cora Tori, Marcela Zuccalli

In this paper we propose a process of Lagrangian reduction and reconstruction for symmetric discrete-time mechanical systems acted on by external forces, where the symmetry group action on the configuration manifold turns it into a principal bundle. We analyze the evolution of momentum maps and Poisson structures under different conditions.

Koosha Nassiri Nazif, Frederick U. Nitta, Alwin Daus, Krishna C. Saraswat

Transition metal dichalcogenides (TMDs) show great promise as absorber materials in high-specific-power (i.e. high-power-per-weight) solar cells, due to their high optical absorption, desirable band gaps, and self-passivated surfaces. However, the ultimate performance limits of TMD solar cells remain unknown today. Here, we establish the efficiency limits of

Filippo Betello, Federico Siciliano, Pushkar Mishra, Fabrizio Silvestri

Sequential Recommender Systems (SRSs) are widely employed to model user behavior over time. However, their robustness in the face of perturbations in training data remains a largely understudied yet critical issue. A fundamental challenge emerges in previous studies aimed at assessing the robustness of SRSs: the Rank-Biased Overlap (RBO) similarity is not pa

Jannatul Ferdous, Rafiqul Islam, Maumita Bhattacharya, Md Zahidul Islam

Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However, what about malware. Malware is malicious software meant to penetrate private data, threaten a computer system, or gain u