April 2023 arXiv papers — page 76

Andreas Fring, Takano Taira

We exactly solve a quantum Fermi accelerator model consisting of a time-independent non-Hermitian Hamiltonian with time-dependent Dirichlet boundary conditions. A Hilbert space for such systems can be defined in two equivalent ways, either by first constructing a time-independent Dyson map and subsequently unitarily mapping to fixed boundary conditions or by

Zhiqiang Nie, Jiankun Zhao, Qicheng Li, Yong Qin

Predicting the State-of-Health (SoH) of lithium-ion batteries is a fundamental task of battery management systems on electric vehicles. It aims at estimating future SoH based on historical aging data. Most existing deep learning methods rely on filter-based feature extractors (e.g., CNN or Kalman filters) and recurrent time sequence models. Though efficient,

Tommie A. Catanach, Niladri Das

The conventional approach to Bayesian decision-theoretic experiment design involves searching over possible experiments to select a design that maximizes the expected value of a specified utility function. The expectation is over the joint distribution of all unknown variables implied by the statistical model that will be used to analyze the collected data.

Siyue Zhang, Minrui Xu, Wei Yang Bryan Lim, Dusit Niyato

Recent breakthroughs in generative artificial intelligence have triggered a surge in demand for machine learning training, which poses significant cost burdens and environmental challenges due to its substantial energy consumption. Scheduling training jobs among geographically distributed cloud data centers unveils the opportunity to optimize the usage of co

Chenguang Duan, Yuling Jiao, Xiliang Lu, Jerry Zhijian Yang

In this paper, we focus on approximating a natural class of functions that are compositions of smooth functions. Unlike the low-dimensional support assumption on the covariate, we demonstrate that composition functions have an intrinsic sparse structure if we assume each layer in the composition has a small degree of freedom. This fact can alleviate the curs

Ulugbek Ergashev, Eduard C. Dragut, Weiyi Meng

As the content on the Internet continues to grow, many new dynamically changing and heterogeneous sources of data constantly emerge. A conventional search engine cannot crawl and index at the same pace as the expansion of the Internet. Moreover, a large portion of the data on the Internet is not accessible to traditional search engines. Distributed Informati

Yunpu Zhang, Changsheng You, Weijie Yuan, Fan Liu

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks, leading to a fundamental paradigm shift from conventional far-field communications towards the new near-field communications. Different from the existing works that mostly considered simultaneous

Zhenduo Wang, Zhichao Xu, Qingyao Ai, Vivek Srikumar

Conversational search has seen increased recent attention in both the IR and NLP communities. It seeks to clarify and solve users' search needs through multi-turn natural language interactions. However, most existing systems are trained and demonstrated with recorded or artificial conversation logs. Eventually, conversational search systems should be trained

Adam Dorian Wong

Domain Name System (DNS) is the backbone of the Internet. However, threat actors have abused the antiquated protocol to facilitate command-and-control (C2) actions, to tunnel, or to exfiltrate sensitive information in novel ways. The FireEye breach and Solarwinds intrusions of late 2020 demonstrated the sophistication of hacker groups. Researchers were eager

Shin'ichi Nojiri, Sergei D. Odintsov

Few parameters dependent generalised entropy includes Tsallis entropy, R{\'e}nyi entropy, Sharma-Mittal entropy, Barrow entropy, Kaniadakis entropy, etc as particular representatives. Its relation to physical systems is not always clear. In this paper, we propose the microscopic thermodynamic description for an arbitrary generalised entropy in terms of the p

Eyal Nitzan, Tirza Routtenberg, Joseph Tabrikian

In constrained parameter estimation, the classical constrained Cramer-Rao bound (CCRB) and the recent Lehmann-unbiased CCRB (LU-CCRB) are lower bounds on the performance of mean-unbiased and Lehmann-unbiased estimators, respectively. Both the CCRB and the LU-CCRB require differentiability of the likelihood function, which can be a restrictive assumption. Add

Quintin Fettes, Avinash Karanth, Razvan Bunescu, Brandon Beckwith

Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant benefits in terms of scalability, rapid development, modularity, and isolation. However, dependencies among microservices with uneven execution time may result in longer queues, idle

Hyunwoo Choi, Suryeon Kim, Seungwon Shin

Modern x86 processors support an AVX instruction set to boost performance. However, this extension may cause security issues. We discovered that there are vulnerable properties in implementing masked load/store instructions. Based on this, we present a novel AVX timing side-channel attack that can defeat address space layout randomization. We demonstrate the

Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille

Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we propose to leverage knowledge extracted from a diversified se

Tanusree Sharma, Yujin Kwon, Kornrapat Pongmala, Henry Wang

Decentralized Autonomous Organizations (DAOs) have emerged as a novel way to coordinate a group of (pseudonymous) entities towards a shared vision (e.g., promoting sustainability), utilizing self-executing smart contracts on blockchains to support decentralized governance and decision-making. In just a few years, over 4,000 DAOs have been launched in various

Benjamin Dozier, Jenya Sapir

We study properties of typical closed geodesics on expander surfaces of high genus, i.e. closed hyperbolic surfaces with a uniform spectral gap of the Laplacian. Under an additional systole lower bound assumption, we show almost every geodesic of length much greater than $\sqrt{g}\log g$ is non-simple. And we prove almost every closed geodesic of length much

Meng Li, Mingwei Zhang, Qing Wang, Hanni Ding

Threshold signatures are a fundamental cryptographic primitive used in many practical applications. As proposed by Boneh and Komlo (CRYPTO'22), TAPS is a threshold signature that is a hybrid of privacy and accountability. It enables a combiner to combine t signature shares while revealing nothing about the threshold t or signing quorum to the public and asks

Hengguang Li, Peimeng Yin

In this paper, we investigate a sixth order elliptic equation with the simply supported boundary conditions in a polygonal domain. We propose a new method that decouples the sixth order problem into a system of second order equations. Unlike the direct decomposition, which yields three Poisson problems but is restricted to polygonal domains with the largest

Tanner Andrulis, Joel S. Emer, Vivienne Sze

Processing-In-Memory (PIM) accelerators have the potential to efficiently run Deep Neural Network (DNN) inference by reducing costly data movement and by using resistive RAM (ReRAM) for efficient analog compute. Unfortunately, overall PIM accelerator efficiency is limited by energy-intensive analog-to-digital converters (ADCs). Furthermore, existing accelera

Shinichiro Akiyama, Yoshinobu Kuramashi

The critical endpoint of the (3+1)-dimensional $Z_3$ gauge-Higgs model at finite density is determined by the tensor renormalization group method. This work is an extension of the previous one on the $Z_2$ model. The vital difference between them is that the $Z_3$ model suffers from the sign problem, while the $Z_2$ model does not. We show that the tensor re

Saransh Singh, Richard Briggs, Martin G. Gorman, Lorin X. Benedict

We combine nanosecond laser shock compression with \emph{in-situ} picosecond X-ray diffraction to provide structural data on iron up to 275 GPa. We constrain the extent of hcp-liquid coexistence, the onset of total melt, and the structure within the liquid phase. Our results indicate that iron, under shock compression, melts completely by 258(8) GPa. A coord

Goran Senjanović, Michael Zantedeschi

We review minimal realistic grand unified models based on $SU(5)$ and $SO(10)$ gauge groups. The models with small Higgs representations and higher dimensional operators - under the assumption of no cancellations in proton decay amplitudes - predict nearby oases with new light particles. Two of them stand out: real fermion and scalar weak triplets. The forme

Nandeeka Nayak, Toluwanimi O. Odemuyiwa, Shubham Ugare, Christopher W. Fletcher

Over the past few years, the explosion in sparse tensor algebra workloads has led to a corresponding rise in domain-specific accelerators to service them. Due to the irregularity present in sparse tensors, these accelerators employ a wide variety of novel solutions to achieve good performance. At the same time, prior work on design-flexible sparse accelerato

Hideki Ozawa, Ryuta Yamamoto, Takeshi Fukuhara

We investigated the relaxation and excitation in a frustrated XY model realized by a Bose gas in Floquet-engineered optical triangular lattices. Periodically driving the position of the entire lattice structure enables the sign inversion of tunneling amplitudes, which, in the case of a triangular lattice, results in geometrical frustration of the local phase

José Oscar González Cervantes

This work presents a family of fiber bundles where the total spaces are associated with holomorphic functions on several complex variables and the basis spaces extend the notion of quaternionic slice regular functions of several quaternionic variables. This paper also shows how the fiber bundle theory justifies the domain these slice regular function in seve

Michael V. Reiss

Recent studies have demonstrated promising potential of ChatGPT for various text annotation and classification tasks. However, ChatGPT is non-deterministic which means that, as with human coders, identical input can lead to different outputs. Given this, it seems appropriate to test the reliability of ChatGPT. Therefore, this study investigates the consisten

Yuan Zhang, Ranbo Cheng, Yuanqing Xia

This paper addresses the problem of determining the minimum set of state variables in a network that need to be blocked from direct measurements in order to protect functional privacy with respect to {\emph{any}} output matrices. The goal is to prevent adversarial observers or eavesdroppers from inferring a linear functional of states, either vector-wise or

Jiachen T. Wang, Saeed Mahloujifar, Tong Wu, Ruoxi Jia

Bounding privacy leakage over compositions, i.e., privacy accounting, is a key challenge in differential privacy (DP). The privacy parameter ($\eps$ or $\delta$) is often easy to estimate but hard to bound. In this paper, we propose a new differential privacy paradigm called estimate-verify-release (EVR), which addresses the challenges of providing a strict

Bruno Tafur, Advait Sarkar

Fake news detection algorithms apply machine learning to various news attributes and their relationships. However, their success is usually evaluated based on how the algorithm performs on a static benchmark, independent of real users. On the other hand, studies of user trust in fake news has identified relevant factors such as the user's previous beliefs, t

Amr Soleiman, Salah G. Elgendi

In this paper, we study the unicorn's Landsberg problem from an intrinsic point of view. Precisely, we investigate a coordinate-free proof of Numata's theorem on Landsberg spaces of scalar curvature. In other words, following the pullback approach to Finsler geometry, we prove that all Landsberg spaces of dimension $n\geq 3$ of non-zero scalar curvature are

Jacob A. Siefert, Andrew F. Thompson, Jonah J. Glunt, Herschel C. Pangborn

This paper proposes a method for set-valued state estimation of nonlinear, discrete-time systems. This is achieved by combining graphs of functions representing system dynamics and measurements with the hybrid zonotope set representation that can efficiently represent nonconvex and disjoint sets. Tight over-approximations of complex nonlinear functions are e

Rui Liu, Bin Yin, Ziyi Cao, Qianchen Xia

Personalized news recommender systems help users quickly find content of their interests from the sea of information. Today, the mainstream technology for personalized news recommendation is based on deep neural networks that can accurately model the semantic match between news items and users' interests. In this paper, we present \textbf{PerCoNet}, a novel

Wytler Cordeiro dos Santos, Bruno Carmo Nunes, Ronni G. G. Amorim

In this work we discuss about the problem of an electrically charged particle placed on the symmetry axis of an electrically charged ring in a quantum viewpoint. This problem should be an expanded version of the usual quantum ring and quantum corral. For this purpose, we present a detailed and pedagogical review about a version of the quantum ring focusing i

Sahil Samar, Mia Chen, Jack Karpinski, Michael Ray

The Artificial Intelligence (AI) institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) is funded by the NSF to build the next generation of Cyberinfrastructure to render AI more accessible to everyone and drive its further democratization in the larger society. We describe our efforts to develop Jupyter Noteboo

Guangyu Nie, Changhoon Kim, Yezhou Yang, Yi Ren

Generative models have enabled the creation of contents that are indistinguishable from those taken from nature. Open-source development of such models raised concerns about the risks of their misuse for malicious purposes. One potential risk mitigation strategy is to attribute generative models via fingerprinting. Current fingerprinting methods exhibit a si

Siyu Wang, Xiaocong Chen, Quan Z. Sheng, Yihong Zhang

Recommendation models are typically trained on observational user interaction data, but the interactions between latent factors in users' decision-making processes lead to complex and entangled data. Disentangling these latent factors to uncover their underlying representation can improve the robustness, interpretability, and controllability of recommendatio

E. Soave, A. Canali, Zhu-Xiong Ye, M. Kreyer

We report on the preparation of a pure ultracold sample of bosonic DyK Feshbach molecules, which are composed of the fermionic isotopes 161Dy and 40K. Employing a magnetic sweep across a resonance located near 7.3 G, we produce up to 5000 molecules at a temperature of about 50 nK. For purification from the remaining atoms, we apply a Stern-Gerlach technique

Siyu Wang, Xiaocong Chen, Dietmar Jannach, Lina Yao

Reinforcement learning-based recommender systems have recently gained popularity. However, the design of the reward function, on which the agent relies to optimize its recommendation policy, is often not straightforward. Exploring the causality underlying users' behavior can take the place of the reward function in guiding the agent to capture the dynamic in

Seyed Mahdi Roostaiyan, Mohammad Mehdi Hosseini, Mahya Mohammadi Kashani, S. Hamid Amiri

In most image retrieval systems, images include various high-level semantics, called tags or annotations. Virtually all the state-of-the-art image annotation methods that handle imbalanced labeling are search-based techniques which are time-consuming. In this paper, a novel coupled dictionary learning approach is proposed to learn a limited number of visual

Jakub Matousek, Jindrich Dunik, Marek Brandner, Chan Gook Park

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel way of manipulating

Jiaxin Ge, Hongyin Luo, Siyuan Qian, Yulu Gan

Language-Image Pre-training has demonstrated promising results on zero-shot and few-shot downstream tasks by prompting visual models with natural language prompts. However, most recent studies only use a single prompt for tuning, neglecting the inherent step-to-step cognitive reasoning process that humans conduct in complex task settings, for example, when p

Yaxuan Zhu, Jianwen Xie, Ping Li

We propose the NeRF-LEBM, a likelihood-based top-down 3D-aware 2D image generative model that incorporates 3D representation via Neural Radiance Fields (NeRF) and 2D imaging process via differentiable volume rendering. The model represents an image as a rendering process from 3D object to 2D image and is conditioned on some latent variables that account for

Chiara Leadbeater, Nathan Fitzpatrick, David Muñoz Ramo, Alex J. W. Thom

We propose an imaginary time equivalent of the well-established Pauli gadget primitive for Trotter-decomposed real time evolution, using mid-circuit measurements on a single ancilla qubit. Imaginary time evolution (ITE) is widely used for obtaining the ground state of a system on classical hardware, computing thermal averages, and as a component of quantum a

Haidong Zhu, Wanrong Zheng, Zhaoheng Zheng, Ram Nevatia

Identifying humans with their walking sequences, known as gait recognition, is a useful biometric understanding task as it can be observed from a long distance and does not require cooperation from the subject. Two common modalities used for representing the walking sequence of a person are silhouettes and joint skeletons. Silhouette sequences, which record

Haidong Zhu, Zhaoheng Zheng, Wanrong Zheng, Ram Nevatia

This paper addresses the problem of human rendering in the video with temporal appearance constancy. Reconstructing dynamic body shapes with volumetric neural rendering methods, such as NeRF, requires finding the correspondence of the points in the canonical and observation space, which demands understanding human body shape and motion. Some methods use rigi

Renato Huzak, Pavao Mardešić, Maja Resman, Vesna Županović

We consider generic 1-parameter unfoldings of parabolic vector fields. It is known that the box dimension of orbits of their time-one maps is discontinuous at the bifurcation value. Here, we expand asymptotically the Lebesgue measure of the epsilon-neighborhoods of orbits of the time-one maps in a Chebyshev scale, uniformly with respect to the bifurcation pa

Anton A. Baykalov

Let $G$ be a finite simple group of Lie type and let $T$ be a maximal torus of $G$. It is well known that if the defining field of $G$ is large enough, then the normaliser of $T$ in $G$ is equal to the algebraic normaliser $N(G,T)$. We identify explicitly all the cases when $N_G(T)$ is not equal to $N(G,T).$

Matthew Lam

In this document we present a twistor correspondence for half-flat almost-Grassmannian structures on real and complex manifolds. We provide foundational results regarding local theory in the complex setting and a global correspondence when the underlying manifold is a real Grassmannian of 2-planes. Whereas twistor constructions typically involve moduli of cl

Zepeng Huai, Yuji Yang, Mengdi Zhang, Zhongyi Zhang

Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products can be described by a piece of text, especially when cold-start users or items have few interactions. However, two vital issues are still under-explored: (1) From the content modeli

Daqian Shi, Fausto Giunchiglia

The mainstream approach to the development of ontologies is merging ontologies encoding different information, where one of the major difficulties is that the heterogeneity motivates the ontology merging but also limits high-quality merging performance. Thus, the entity type (etype) recognition task is proposed to deal with such heterogeneity, aiming to infe

Muriel Figueredo Franco, Christian Omlin, Oliver Kamer, Eder John Scheid

Cybersecurity planning is challenging for digitized companies that want adequate protection without overspending money. Currently, the lack of investments and perverse economic incentives are the root cause of cyberattacks, which results in several economic impacts on companies worldwide. Therefore, cybersecurity planning has to consider technical and econom

Liu Leqi, Giulio Zhou, Fatma Kılınç-Karzan, Zachary C. Lipton

Personalized recommender systems suffuse modern life, shaping what media we read and what products we consume. Algorithms powering such systems tend to consist of supervised learning-based heuristics, such as latent factor models with a variety of heuristically chosen prediction targets. Meanwhile, theoretical treatments of recommendation frequently address

Abdullah Alnajim, Seyedmohammad Salehi, Chien-Chung Shen, Malcolm Smith

This tutorial paper analyzes the traffic characteristics of immersive experiences with extended reality (XR) technologies, including Augmented reality (AR), virtual reality (VR), and mixed reality (MR). The current trend in XR applications is to offload the computation and rendering to an external server and use wireless communications between the XR head-mo

Tuğrulcan Elmas

Twitter bots amplify target content in a coordinated manner to make them appear popular, which is an astroturfing attack. Such attacks promote certain keywords to push them to Twitter trends to make them visible to a broader audience. Past work on such fake trends revealed a new astroturfing attack named ephemeral astroturfing that employs a very unique bot

Ingo Czerwinski, Alexander Pott

Finding the maximum size of a Sidon set in $\mathbb{F}_2^t$ is of research interest for more than 40 years. In order to tackle this problem we recall a one-to-one correspondence between sum-free Sidon sets and linear codes with minimum distance greater or equal 5. Our main contribution about codes is a new non-existence result for linear codes with minimum d

Saugat Pandey, Alvitta Ottley

The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time-effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mi

M. Czarnecka, Y. Arakawa, A. Kocot, K. Merkel

Broadband dielectric spectroscopy was used to study the molecular orientational dynamics of the glass-forming, thioether-linked cyanobiphenyl liquid crystal dimers. As was expected theoretically, two different relaxation processes that contributed to the dielectric permittivity of dimers were observed. The low-frequency relaxation mode, m1, was attributed to

A. M. Vibhakar, D. D. Khalyavin, J. M. Moya, P. Manuel

Eu(Ga$_{1-x}$Al$_x$)$_4$ are centrosymmetric systems that have recently been identified as candidates to stabilise topologically non-trivial magnetic phases, such as skyrmion lattices. In this Letter, we present a high-resolution resonant x-ray and neutron scattering study on EuAl2Ga2 that provides new details of the complex coupling between the electronic o

Saksham Sharma, D. Ian Wilson

The sticky fluids found in pitcher plant leaf vessels can leave fractal-like filaments behind when dewetting from a substrate. To understand the origin of these filaments, we investigate the dynamics of a retreating thin-film of aqueous polyethylene oxide (PEO) solutions which partially wet polydimethyl siloxane (PDMS) substrates. Under certain conditions th

Belal Badawy, Romario Sameh Samir, Youssef Tarek, Mohammed Ahmed

Brain tumors are a complex and potentially life-threatening medical condition that requires accurate diagnosis and timely treatment. In this paper, we present a machine learning-based system designed to assist healthcare professionals in the classification and diagnosis of brain tumors using MRI images. Our system provides a secure login, where doctors can u

Rune Højlund, Emil Grovn, Sahar Pakdel, Kristian S. Thygesen

Recent theoretical and experimental studies suggest that van der Waals heterostructures with n- and p-doped bilayers of transition metal dichalcogenides are promising facilitators of exciton superfluidity. Exciton superfluidity in such bilayer systems is often modelled by solving a mean-field gap equation defined for only the conduction and valence band of t

Joao Paulo Almeida de Mendonca, Lorenzo A. Mariano, Emilie Devijver, Noel Jakse

During the past decades, approximate Kohn-Sham density-functional theory schemes garnered many successes in computational chemistry and physics; yet the performance in the prediction of spin state energetics is often unsatisfactory. By means of a machine-learning approach, an enhanced exchange and correlation functional is developed to describe adiabatic ene

Katrina Chen, Mingbin Feng, Tony S. Wirjanto

Time series anomaly detection (TSAD) plays a vital role in many industrial applications. While contrastive learning has gained momentum in the time series domain for its prowess in extracting meaningful representations from unlabeled data, its straightforward application to anomaly detection is not without hurdles. Firstly, contrastive learning typically req

Daniel Vedensky, Paul A. Parker, Scott H. Holan

The Household Pulse Survey (HPS), recently released by the U.S. Census Bureau, gathers timely information about the societal and economic impacts of coronavirus. The first phase of the survey was quickly launched one month after the beginning of the coronavirus pandemic and ran for 12 weeks. To track the immediate impact of the pandemic, individual responden

Siyuan Guo, Jonas Wildberger, Bernhard Schölkopf

The ability of an agent to do well in new environments is a critical aspect of intelligence. In machine learning, this ability is known as $\textit{strong}$ or $\textit{out-of-distribution}$ generalization. However, merely considering differences in data distributions is inadequate for fully capturing differences between learning environments. In the present

Christoph Adam, Chris Halcrow, Katarzyna Oles, Tomasz Romanczukiewicz

We apply the collective coordinate model framework to describe collisions of a kink and an antikink with nonzero total momentum, i.e., when the solitons possess different velocities. The minimal moduli space with only two coordinates (the mutual distance and the position of the center of mass) is of a wormhole type, whose throat shrinks to a point for symmet

Tina Nane, Roger Cooke

Computable solutions for expectations of Continuous Ranked Probability Scores are presented. After deriving a scale invariant version of these scores, a closed form for the convolutions of scores is presented. This closed form enables the testing experts' statistical accuracy. Results are compared with tests using a familiar Chi-square goodness of fit test u

Xiucai Ding, Jiahui Xie

In this paper, we study the largest eigenvalues of sample covariance matrices with elliptically distributed data. We consider the sample covariance matrix $Q=YY^*,$ where the data matrix $Y \in \mathbb{R}^{p \times n}$ contains i.i.d. $p$-dimensional observations $\mathbf{y}_i=\xi_iT\mathbf{u}_i,\;i=1,\dots,n.$ Here $\mathbf{u}_i$ is distributed on the unit

Pragathi Praveena, Bengisu Cagiltay, Michael Gleicher, Bilge Mutlu

Robotic technology can support the creation of new tools that improve the creative process of cinematography. It is crucial to consider the specific requirements and perspectives of industry professionals when designing and developing these tools. In this paper, we present the results from exploratory interviews with three cinematography practitioners, which

Kirsti D. Biggs, Julia Brandes

This paper studies the minimal conditions under which we can establish asymptotic formulae for Waring's problem and other additive problems that may be tackled by the circle method. We confirm in quantitative terms the well-known heuristic that a mean value estimate and an estimate of Weyl type, together with suitable distribution properties of the underlyin

Miguel Socolovsky

Black holes contradict the Nernst-Planck (N/P) version of the 3rd. law of thermodynamics, but agree with its unattainability (U) version. This happens without contradiction, because the N/P and U versions are not equivalent, namely, N/P implies U but U does not imply N/P. So, black holes obey the weaker version of the 3rd. law, but not the stronger one.

Tiago Andres Vaz, José Miguel Silva Dora, Luís da Cunha Lamb, Suzi Alves Camey

This article details the creation of a novel domain ontology at the intersection of epidemiology, medicine, statistics, and computer science. Using the terminology defined by current legislation, the article outlines a systematic approach to handling hospital data anonymously in preparation for its use in Artificial Intelligence (AI) applications in healthca

Basit Qureshi

The application of Artificial intelligence for teaching and learning in the academic sphere is a trending subject of interest in the computing education. ChatGPT, as an AI-based tool, provides various advantages, such as heightened student involvement, cooperation, accessibility and availability. This paper addresses the prospects and obstacles associated wi

Wenxiong Chen, Lingwei Ma

In this paper, we establish a generalized version of Gibbons' conjecture in the context of the master equation \begin{equation*} (\partial_t-\Delta)^s u(x,t)=f(t,u(x,t)) \,\, \mbox{in}\,\, \mathbb{R}^n\times\mathbb{R}. \end{equation*} We show that, for each $t\in\mathbb{R}$, the bounded entire solution $u(x,t)$ must be monotone increasing in one direction, a

Jakub Lang, Giovanni Garberoglio, Michal Przybytek, Malgorzata Jeziorska

The non-additive three-body interaction potential for helium was computed using the coupled-cluster theory and the full configuration interaction method. The obtained potential comprises an improved nonrelativistic Born--Oppenheimer energy and the leading relativistic and nuclear-motion corrections. The mean absolute uncertainty of our calculations due to th

Hao-Lan Xu, Alexander Zamolodchikov

We study the "three particle coupling" $\Gamma_{11}^{1}(\xi)$, in $2d$ Ising Field Theory in a magnetic field, as the function of the scaling parameter $\xi:=h/(-m)^{15/8}$, where $m \sim T_c-T$ and $h \sim H$ are scaled deviation from the critical temperature and scaled external field, respectively. The "$\varphi^3$ coupling" $\Gamma_{11}^1$ is defined in t

S. P. de Alwis

We discuss the logic of, and some puzzles in, the various approaches to thermodynamics of gravitational systems. In particular the blackhole, deSitter (dS), black hole in dS (SdS) and in Anti-deSitter SAdS backgrounds are considered. After reviewing the original calculations of Hawking and Gibbons we discuss an alternative Hamiltonian method. This justifies

Shamil Asgarli, Michael Hartglass, Daniel Ostrov, Byron Walden

Two possibly unfair $n$-sided dice, both labelled $1, 2, \ldots, n$, are rolled, and the sum is recorded. How should the dice's sides be weighted so that the resulting sum is closest to the uniform distribution on $2, 3, \ldots, 2n$? We answer this question by explicitly identifying the optimal pair of dice. This resolves a question raised by Gasarch and Kru

Bujiao Wu, Xiaoyang Wang, Xiao Yuan, Cupjin Huang

Errors are common issues in quantum computing platforms, among which leakage is one of the most challenging to address. This is because leakage, i.e., the loss of information stored in the computational subspace to undesired subspaces in a larger Hilbert space, is more difficult to detect and correct than errors that preserve the computational subspace. As a

Luca Piano, Filippo Gabriele Pratticò, Alessandro Sebastian Russo, Lorenzo Lanari

Instance-level object re-identification is a fundamental computer vision task, with applications from image retrieval to intelligent monitoring and fraud detection. In this work, we propose the novel task of damaged object re-identification, which aims at distinguishing changes in visual appearance due to deformations or missing parts from subtle intra-class

Hong-You Chen, Jike Zhong, Mingda Zhang, Xuhui Jia

Personalized federated learning (PFL) aims to harness the collective wisdom of clients' data while building personalized models tailored to individual clients' data distributions. Existing works offer personalization primarily to clients who participate in the FL process, making it hard to encompass new clients who were absent or newly show up. In this paper

Dr Alexandre Canet, Prof Meysam Qadrdan

The increased integration of variable renewable generation into the power systems, along with the phase-out of fossil-based power stations, necessitate procuring more flexibility from the demand sectors. The electrification of the residential heat sector is an option to decarbonise the heat sector in the United Kingdom. The inherent flexibility that is avail

Ramon Pires, Hugo Abonizio, Thales Sales Almeida, Rodrigo Nogueira

As the capabilities of language models continue to advance, it is conceivable that "one-size-fits-all" model will remain as the main paradigm. For instance, given the vast number of languages worldwide, many of which are low-resource, the prevalent practice is to pretrain a single model on multiple languages. In this paper, we add to the growing body of evid

Kenson Wesley R, Reena Monica P

Material discovery is a phenomenon practiced since the evolution of the world. The Discovery of materials had led to significant development in varied fields such as Science, Engineering and Technology etc., It had been a slow and long-drawn process, however, technological advancement had led to the rapid discovery of materials and the creation of a database

Trisha Guttal

This literature review involves the use of AI communication facilitators to detect mood disorders such as bipolar disorder, a psychiatric condition in which patients experience drastic mood shifts. Due to the ill-defined nature of the disorder, it is difficult for even a psychiatrist alone to be confident with their diagnosis. Changes in mental and mood stat

Emil Pitz, Kishore Pochiraju

Heterogeneity and uncertainty in a composite microstructure lead to either computational bottlenecks if modeled rigorously or to solution inaccuracies in the stress field and failure predictions if approximated. Although methods suitable for analyzing arbitrary and non-linear microstructures exist, their computational cost makes them impractical to use in la

Duc-Viet Vu

Let $X$ be a compact K\"ahler manifold and let $\mu$ be a non-pluripolar measure on $X$. We give a necessary and sufficient condition for $\mu$ so that the complex Monge-Amp\`ere equation (in a K\"ahler class in $X$) having $\mu$ as the right-hand side admits a bounded solution. This implies in particular a well-known conjecture of Kolodziej that a complex M

Florian Putz, Johanna Grigo, Thomas Weissmann, Philipp Schubert

Background: Tumor segmentation in MRI is crucial in radiotherapy (RT) treatment planning for brain tumor patients. Segment anything (SA), a novel promptable foundation model for autosegmentation, has shown high accuracy for multiple segmentation tasks but was not evaluated on medical datasets yet. Methods: SA was evaluated in a point-to-mask task for glioma

Yangyi Liu, Huan Liu, Liangyan Li, Zijun Wu

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset

N. L. Matsko

The work is devoted to the formation energy calculations of intrinsic defects in silicon based on the GW method and the Galitskii-Migdal formula. The two methods for calculating the electronic response function are applied. The first one uses direct integration over frequency to determine the response function. The diagonal form of the spectral function is t

Thomas Alazard, Claude Zuily

We study several different aspects of the energy equipartition principle for water waves. We prove a virial identity that implies that the potential energy is equal, on average, to a modified version of the kinetic energy. This is an exact identity for the complete nonlinear water wave problem, which is valid for arbitrary solutions. As an application, we ob

Anup Pradhan Sakhya, Sabin Regmi, Milo Sprague, Mazharul Islam Mondal

We performed a detailed study of the intrinsic electronic structure of YRu2Si2 employing angleresolved photoemission spectroscopy (ARPES) and density-functional theory (DFT) based firstprinciples calculations. Electrical and magnetic measurements were conducted on well-oriented highquality single crystals. Bulk physical measurements indicate that the compoun

Soumyarup Banerjee, Rajat Gupta, Rahul Kumar

In this article, we have studied transformation formulas of zeta function at odd integers over an arbitrary number field which in turn generalizes Ramanujan's identity for the Riemann zeta function. The above transformation leads to a new number field extension of Eisenstein series, which satisfies the transformation $z \mapsto -1/z$ like an integral weight

Vakul Goyle, Parvathy Krishnaswamy, Kannan Girija Ravikumar, Utsa Chattopadhyay

Neural Machine translation is a challenging task due to the inherent complex nature and the fluidity that natural languages bring. Nonetheless, in recent years, it has achieved state-of-the-art performance in several language pairs. Although, a lot of traction can be seen in the areas of multilingual neural machine translation (MNMT) in the recent years, the

Sooyeong Kim, Steve Kirkland

We study distinct $(0,1)$ matrices $A$ and $B$, called \textit{Gram mates}, such that $AA^T=BB^T$ and $A^TA=B^TB$. We characterize Gram mates where one can be obtained from the other by changing signs of some positive singular values. We classify Gram mates such that the rank of their difference is at most $2$. Among such Gram mates, we further produce equiv

Hang Fu, Hongming Nie

Let $p$ be a prime number, let $g(x)=x^{p^{2}}+p^{r+2}x^{p^{2}+1}$ with $r\in\mathbb{Z}_{\geq0}$, and let $\phi(x)=x+O(x^{2})$ be the B\"ottcher coordinate satisfying $\phi(g(x))=\phi(x)^{p^{2}}$. Salerno and Silverman conjectured that the radius of convergence of $\phi^{-1}(x)$ in $\mathbb{C}_{p}$ is $p^{-p^{-r}/(p-1)}$. In this article, we confirm that thi

Shirin Besati, Somasundaram Essakiappan, Madhav Manjrekar

One of the popular impedance-network converters are Y-source converters which along with their essential characteristics such as reducing the size of converter components, single-stage power transferring, fault tolerance, and wide voltage gain capabilities there are also some drawbacks that one of the most widespread is high leakage inductances which affect

Shifu Zhu, William N. Brandt, Fan Zou, Bin Luo

The Australia Telescope Large Area Survey (ATLAS) and the VLA survey in the XMM-LSS/VIDEO deep field provide deep ($\approx 15$ ${\mu}$Jybeam$^{-1}$) and high-resolution ($\approx$ 4.5--8 arcsec) radio coverage of the three XMM-SERVS fields (W-CDF-S, ELAIS-S1, and XMM-LSS). These data cover a total sky area of 11.3 deg$^2$ and contain $\approx 11000$ radio c

Chuanqi Chen, Nan Chen, Jin-Long Wu

Regime switching is ubiquitous in many complex dynamical systems with multiscale features, chaotic behavior, and extreme events. In this paper, a causation entropy boosting (CEBoosting) strategy is developed to facilitate the detection of regime switching and the discovery of the dynamics associated with the new regime via online model identification. The ca

Xinyi Li, Yongfeng Zhang, Edward C. Malthouse

Online news platforms often use personalized news recommendation methods to help users discover articles that align with their interests. These methods typically predict a matching score between a user and a candidate article to reflect the user's preference for the article. Some previous works have used language model techniques, such as the attention mecha