July 2023 arXiv papers — page 11

Lifan Wu, Shan Luo, Dongxiao Song, Fan Yang

The next generation of wireless communication technology is anticipated to address the communication reliability challenges encountered in high-speed mobile communication scenarios. An Orthogonal Time Frequency Space (OTFS) system has been introduced as a solution that effectively mitigates these issues. However, OTFS is associated with relatively high pilot

Kirill Lazebnik

We characterize which planar graphs arise as the pullback, under a rational map $r$, of an analytic Jordan curve passing through the critical values of $r$. We also prove that such pullbacks are dense within the collection of $f^{-1}(\Sigma)$, where $f$ is a branched cover of the sphere and $\Sigma$ is a Jordan curve passing through the branched values of $f

Yunchuan Xiang, Qingquan Jiang, Xiaofei Lan

A likely starburst galaxy (SBG), IRAS 13052-5711, which is the most distant SBG candidate discovered to date, was found by analyzing 14.4 years of data from the Fermi large-area telescope (Fermi-LAT). This SBG's significance level is approximately 6.55$\sigma$ in the 0.1-500 GeV band. Its spatial position is close to that of 4FGL J1308.9-5730, determined fro

Sibo Tian, Minghui Zheng, Xiao Liang

Predicting human motion plays a crucial role in ensuring a safe and effective human-robot close collaboration in intelligent remanufacturing systems of the future. Existing works can be categorized into two groups: those focusing on accuracy, predicting a single future motion, and those generating diverse predictions based on observations. The former group f

Andrei Ivanov, Stefan Maria Ailuro

Polynomial regression is widely used and can help to express nonlinear patterns. However, considering very high polynomial orders may lead to overfitting and poor extrapolation ability for unseen data. The paper presents a method for constructing a high-order polynomial regression based on the Taylor map factorization. This method naturally implements multi-

Grey Nearing, Deborah Cohen, Vusumuzi Dube, Martin Gauch

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simulation models typically must be calibrated to long data records in each watershed. Using AI, we achieve reliabil

Matthew R. Walker, Saikat Bera, Marija Vucelja

We study connections between optimal transport and anomalous thermal relaxations. A prime example of anomalous thermal relaxations is the Mpemba effect, which occurs when a hot system overtakes an identical warm system and cools down faster. Conversely, optimal transport is a resource-efficient way to transport the source distribution to a target distributio

Symon Serbenyuk

The present article deals with properties of one class of functions with complicated local structure. These functions can be modeled by certain operators of digits. Such operators were considered by the author earlier (for example, see [27, 39] and references therein). This research is a generalization of investigations presented in the last-mentioned papers

Samadhan A. Pawar, P. R. Midhun, K. V. Reeja, Abin Krishnan

Thermoacoustic instability in turbulent combustion systems emerges from the complex interplay among the flame, flow, and acoustic subsystems. While the onset of thermoacoustic instability exhibits global order in system dynamics, the characteristics of local interactions between subsystems responsible for this order are not well understood. In this study, we

Peiwen Jiang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li

Semantic communication significantly reduces required bandwidth by understanding semantic meaning of the transmitted. However, current deep learning-based semantic communication methods rely on joint source-channel coding design and end-to-end training, which limits their adaptability to new physical channels and user requirements. Reconfigurable intelligent

Rajdeep Haldar, Qifan Song

Adversarial attacks are usually expressed in terms of a gradient-based operation on the input data and model, this results in heavy computations every time an attack is generated. In this work, we solidify the idea of representing adversarial attacks as a trainable function, without further gradient computation. We first motivate that the theoretical best at

Giorgi Tukhashvili, Paul J. Steinhardt

We show that it is possible for fermion condensation of the Nambu-Jona-Lasinio type to induce a non-singular bounce that smoothly connects a phase of slow contraction to a phase of expansion. A chiral condensate -- a non-zero vacuum expectation value of the spinor bilinear $\langle \bar{\Psi}\Psi \rangle$ -- can form spontaneously after a slow contraction ph

Zoe Cooperband, Robert Ghrist

Recent developments in applied algebraic topology can simplify and extend results in graphic statics - the analysis of equilibrium forces, dual diagrams, and more. The techniques introduced here are inspired by recent developments in cellular cosheaves and their homology. While the general theory has a few technical prerequisites (including homology and exac

Li-Hsiang Shen, Po-Chen Wu, Chia-Jou Ku, Yu-Ting Li

The joint uplink/downlink (JUD) design of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) is conceived in support of both uplink (UL) and downlink (DL) users. Furthermore, the dual STAR-RISs (D-STAR) concept is conceived as a promising architecture for 360-degree full-plane service coverage, including UL/DL users loc

Farhad Farzbod, Casey Holycross

Resonance ultrasound spectroscopy (RUS) is a non-destructive technique used to assess materials' elastic and anelastic properties. It involves measuring the frequencies of free vibrations in a carefully prepared sample to extract material properties. In this paper, we investigate the asymptotic behavior of eigenfrequencies. Our primary focus is on analyzing

Duván Cardona, Vishvesh Kumar, Michael Ruzhansky

In this paper we establish the $L^p$-$L^q$ estimates for global pseudo-differential operators on graded Lie groups. We provide both necessary and sufficient conditions for the $L^p$-$L^q$ boundedness of pseudo-differential operators associated with the global H\"ormander symbol classes on graded Lie groups, within the range $1<p\leq 2 \leq q<\infty$. Additio

Richard Brito, Shreya Shah

We study extreme mass-ratio binary systems in which a stellar mass compact object spirals into a supermassive black hole surrounded by a scalar cloud. Scalar clouds can form through superradiant instabilities of massive scalar fields around spinning black holes and can also serve as a proxy for dark matter halos. Our framework is fully relativistic and assum

Moshe Eliasof, Eldad Haber, Eran Treister

Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data. However, GNNs still face challenges in modeling complex phenomena that involve feature transportation. In this paper, we propose a novel GNN architecture inspired by Advection-Diffusion-Reaction systems, called ADR-GNN. Advection models feature t

A. Giannis, D. A. Kaltsas, G. N. Throumoulopoulos

Equilibrium equations for magnetically confined, axisymmetric plasmas are derived by means of the energy-Casimir variational principle in the context of Hall magnetohydrodynamics (MHD). This approach stems from the noncanonical Hamiltonian structure of Hall MHD, the simplest, quasineutral two-fluid model that incorporates contributions due to ion Hall drifts

Alexander Y. Sun, Zhi Li, Wonhyun Lee, Qixing Huang

Flood inundation forecast provides critical information for emergency planning before and during flood events. Real time flood inundation forecast tools are still lacking. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally

Andreea Iana, Goran Glavaš, Heiko Paulheim

Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e.g., topic, sentiment) between candidate news and user history or (2) by diversifying recommendations w.r.t. these aspects. This customization is achieved by ``hardcoding`` additional constraints into the NNR's architecture and/or training objectiv

Matias M. Reynoso, Florencia A. Deus

Choked gamma-ray bursts (CGRBs) are possible neutrino sources that have been proposed as capable of generating the flux detected by IceCube, since no accompanying gamma-ray signal is expected, as required by observations. We focus on obtaining the neutrino flux and flavor composition corresponding to CGRBs under different assumptions for the target photon de

David Cirauqui, Miguel Ángel García-March, José Ramón Martínez Saavedra, Maciej Lewenstein

Population Annealing, one of the currently state-of-the-art algorithms for solving spin-glass systems, sometimes finds hard disorder instances for which its equilibration quality at each temperature step is severely damaged. In such cases one can therefore not be sure about having reached the true ground state without vastly increasing the computational reso

Wali Ullah Khan, Eva Lagunas, Asad Mahmood, Zain Ali

Device-to-device (D2D) communications offers high spectral efficiency, low energy consumption and transmission latency. However, one of the main limitations of D2D communications is co-channel interference from underlaying wireless system. Reconfigurable intelligent surfaces (RIS) is a promising technology because it can manipulate the electromagnetic waves

Thomas A. Ivey

The method of moving frames (rep\`ere mobile) was used by Elie Cartan as a way of organizing the identification of differential invariants and solving equivalence problems. In this expository paper, we discuss how moving frames are used to determine differential invariants of curves and surfaces under the action of Euclidean, affine and conformal transformat

Muhammad Abdul Rahman, Muhammad Ahmad Waseem, Zubair Khalid, Muhammad Tahir

Any policy-level decision-making procedure and academic research involving the optimum use of resources for development and planning initiatives depends on accurate population density statistics. The current cutting-edge datasets offered by WorldPop and Meta do not succeed in achieving this aim for developing nations like Pakistan; the inputs to their algori

Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan, Marti Vives

The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems across both the classical and quantum regimes, the precise impact of noise on learning remains poorly understood. Focusing on supervised learning, we present a mathematical framework

Mohammadali Sefidi Esfahani, Mohammad Akbari

Social platforms have emerged as crucial platforms for distributing information and discussing social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks. Identifying unspecified events and detecting events without prior knowledge enables governments, aid agencies, and experts to respond swiftly and

Lingbo Mo, Shijie Chen, Ziru Chen, Xiang Deng

We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps. Covering a wide range of cooking and how-to tasks, we aim to deliver a collaborative and engaging dialogue experience. Equipped with language understanding, dialogue management, and response generation components

Maksim Levental, Alok Kamatar, Ryan Chard, Kyle Chard

Multi-Level Intermediate Representation (MLIR) is a novel compiler infrastructure that aims to provide modular and extensible components to facilitate building domain specific compilers. However, since MLIR models programs at an intermediate level of abstraction, and most extant frontends are at a very high level of abstraction, the semantics and mechanics o

Søren Fournais, Rupert L. Frank, Magnus Goffeng, Ayman Kachmar

We study the Pauli operator in a two-dimensional, connected domain with Neumann or Robin boundary condition. We prove a sharp lower bound on the number of negative eigenvalues reminiscent of the Aharonov-Casher formula. We apply this lower bound to obtain a new formula on the number of eigenvalues of the magnetic Neumann Laplacian in the semi-classical limit

Yin Liu, Austen Z. Fan, Jin-Yi Cai

$\operatorname{Holant}^*(f)$ denotes a class of counting problems specified by a constraint function $f$. We prove complexity dichotomy theorems for $\operatorname{Holant}^*(f)$ in two settings: (1) $f$ is any arity-3 real-valued function on input of domain size 3. (2) $f$ is any arity-3 $\{0,1\}$-valued function on input of domain size 4.

Katarzyna Szepietowska, Mateusz Troka, Monika Lichodziejewska-Niemierko, Michał Chmielewski

The presented study aims to assess the mechanical behaviour of the anterior abdominal wall based on an in vivo experiment on humans. Full-field measurement of abdominal wall displacement during changes of intra-abdominal pressure is performed using a digital image correlation (DIC) system. Continuous measurement in time enables the observation of changes in

Elena Caviglia, Luca Mesiti

We produce an indexed version of the Grothendieck construction. This gives an equivalence of categories between opfibrations over a fixed base in the 2-category of 2-copresheaves and 2-copresheaves on the Grothendieck construction of the fixed base. We also prove that this equivalence is pseudonatural in the base and that it restricts to discrete opfibration

Max T. M. Ng, Hani S. Mahmassani, Ömer Verbas, Taner Cokyasar

This study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flo

Zaedul Islam, A. Ben Hamza

Graph convolutional networks (GCNs) have proven to be an effective approach for 3D human pose estimation. By naturally modeling the skeleton structure of the human body as a graph, GCNs are able to capture the spatial relationships between joints and learn an efficient representation of the underlying pose. However, most GCN-based methods use a shared weight

Bo Yang

We designed and implemented a framework for creating extensible domain-specific languages that consists of library-defined keywords. First-class language features in other programming languages can be implemented as libraries with the help of our framework. The core concept in our framework is the type class Dsl, which can be considered as both the ad-hoc po

Rodrick Kuate Defo, Alejandro W. Rodriguez, Steven L. Richardson

The NV$^-$ color center in diamond has been extensively investigated for quantum sensing, computation, and communication applications. Nonetheless, charge-state decay from the NV$^-$ to its neutral counterpart the NV$^0$ detrimentally affects the robustness of the NV$^-$ center and remains to be fully overcome. In this work, we provide an $ab~initio$ formali

Tolulope Ogunremi, Kola Tubosun, Anuoluwapo Aremu, Iroro Orife

We introduce \`{I}r\`{o}y\`{i}nSpeech, a new corpus influenced by the desire to increase the amount of high quality, contemporary Yor\`{u}b\'{a} speech data, which can be used for both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) tasks. We curated about 23000 text sentences from news and creative writing domains with the open license CC-BY-4.0

Esra Uyar

The detector must be modeled in the most accurate way when Monte Carlo simulation method is used for efficiency calculation in gamma-ray spectrometric studies. This study aims to investigate the effect of the copper contact pin inside the detector on the efficiency of the HPGe detector for high gamma-ray energies. Simulated efficiencies were determined for 6

Zhanduo Tang, Ralf Rapp

A previously constructed $T$-matrix approach for studying the quark-gluon plasma (QGP) is improved by incorporating spin-dependent interactions between partons. These interactions arise from the relativistic corrections to the Cornell potential. We first study the vacuum spectroscopy of quarkonia with this potential and find that a significant admixture of a

Robert Dougherty-Bliss, Doron Zeilberger

We use Experimental Mathematics and Symbolic Computation (with Maple), to search for lots and lots of Perrin- and Lucas- style primality tests, and try to sort the wheat from the chaff. More impressively, we find quite a few such primality tests for which we can explicitly construct infinite families of pseudo-primes, rather, like in the cases of Perrin pseu

Dibyendu Adak, Gianmarco Manzini, Hashem M. Mourad, JeeYeon N. Plohr

We present a two-dimensional conforming virtual element method for the fourth-order phase-field equation. Our proposed numerical approach to the solution of this high-order phase-field (HOPF) equation relies on the design of an arbitrary-order accurate, virtual element space with $C^1$ global regularity. Such regularity is guaranteed by taking the values of

Manuela Magliocchetti, Paola Santini, Emiliano Merlin, Laura Pentericci

We investigate the clustering properties of 3<z<5 candidate passive galaxies from the Merlin et al. (2019) sample residing in the GOODS-North (35 sources) and GOODS-South (33 sources) fields. Within the large uncertainties due to the paucity of sources we do not detect clustering signal in GOODS-North, while this is present in GOODS-South, highlighting the i

Evangelos Kipouridis

In the $L_0$ Fitting Tree Metrics problem, we are given all pairwise distances among the elements of a set $V$ and our output is a tree metric on $V$. The goal is to minimize the number of pairwise distance disagreements between the input and the output. We provide an $O(1)$ approximation for $L_0$ Fitting Tree Metrics, which is asymptotically optimal as the

Li Li, Yang Zhang

We consider an inverse problem for a Westervelt type nonlinear wave equation with fractional damping. This equation arises in nonlinear acoustic imaging, and we show the forward problem is locally well-posed. We prove that the smooth coefficient of the nonlinearity can be uniquely determined, based on the knowledge of the source-to-solution map and a priori

Abdellah Ouaguid, Mohamed Ouzzif, Noreddine Abghour

Several solutions ensuring the dynamic detection of malicious activities on Android ecosystem have been proposed. These are represented by generic rules and models that identify any purported malicious behavior. However, the approaches adopted are far from being effective in detecting malware (listed or not) and whose form and behavior are likely to be diffe

Mandeep Kaur, Maguni Mahakhud, Ambresh Shivaji, Xiaoran Zhao

Future colliders aim to provide highly precise experimental measurements of the properties of the Higgs boson. In order to benefit from these precision machines, theoretical errors in the Higgs sector observables have to match at least the experimental uncertainties. The theoretical uncertainties in the Higgs sector observables can be reduced by including mi

Zengjie Zhang, Jayden Hong, Amir Soufi Enayati, Homayoun Najjaran

Reinforcement learning (RL) for motion planning of multi-degree-of-freedom robots still suffers from low efficiency in terms of slow training speed and poor generalizability. In this paper, we propose a novel RL-based robot motion planning framework that uses implicit behavior cloning (IBC) and dynamic movement primitive (DMP) to improve the training speed a

Zuyan Liu, Gaojie Lin, Congyi Wang, Min Zheng

With an enormous number of hand images generated over time, unleashing pose knowledge from unlabeled images for supervised hand mesh estimation is an emerging yet challenging topic. To alleviate this issue, semi-supervised and self-supervised approaches have been proposed, but they are limited by the reliance on detection models or conventional ResNet backbo

Yibo Wang, Yanbing Xue, Bo Liu, Musen Wen

Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems. Position bias in training data biases the ranking model, leading to increasingly unfair item rankings, click-through-rate (CTR), and conversion rate (CVR) predictions.

G. V. Aivazian, F. S. Stonyakin, D. A. Pasechnyuk, M. S. Alkousa

Some variant of the Frank-Wolfe method for convex optimization problems with adaptive selection of the step parameter corresponding to information about the smoothness of the objective function (the Lipschitz constant of the gradient). Theoretical estimates of the quality of the solution provided by the method are obtained in terms of adaptively selected par

André Mazzari, Gabriel Ruffolo, Carlos Vieira, Tassius Temistocles

Bell nonlocality and Kochen-Specker contextuality are among the main topics of foundations of quantum theory. Both of them are related to stronger-than-classical correlations, with the former usually referring to spatially separated systems while the latter considering a single system. In recent works, a unified framework for these phenomena was presented. T

Rafael A. Costa, Marcio Eisencraft

This work numerically examines the temporal and spectral properties of a quadratic map. The quadratic map described in this study has quadratic non-linearity, and its theoretical analysis poses a challenge. Additionally, this map can be used for a fixed parameter value in chaos-based communication systems. Therefore, it is important to understand and, if pos

Tom Richmond, Eliza Wajch

Hybrid topologies on the real line have been studied by various authors. Among the hybrid spaces, there are also Hattori spaces. However, some of the hybrid spaces are not homeomorphic to Hattori spaces. In this article, a common generalization of at least four kinds of the hybrid topologies on the real line is described. In the absence of the axiom of choic

Jouni Järvinen, Michiro Kondo

We consider fuzzy rough sets defined on De Morgan Heyting algebras. We present a theorem that can be used to obtain several correspondence results between fuzzy rough sets and fuzzy relations defining them. We characterize fuzzy rough approximation operators corresponding to compositions of reflexive, transitive, mediate, Euclidean and adjoint fuzzy relation

Ali Barzegar, Emilia Margoni, Daniele Oriti

We adopt a top-down approach to agency aimed at developing a minimalist, scalable and naturalized account of it. After providing a general definition, we explore some possible extensions and refinements, domain of applicability, as well as a comparison with other recent accounts of agency, and possible objections to our proposal. With respect to what we clas

Muhammad Arsalan Ali

Condensed matter physics plays a crucial role in modern scientific research and technological advancements, providing insights into the behavior of materials and their fundamental properties. Understanding complex phenomena and systems in condensed matter physics poses significant challenges due to their inherent intricacies. Over the years, computational ap

Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy

Testing deep learning-based systems is crucial but challenging due to the required time and labor for labeling collected raw data. To alleviate the labeling effort, multiple test selection methods have been proposed where only a subset of test data needs to be labeled while satisfying testing requirements. However, we observe that such methods with reported

Joao S. Silva, Eduardo V. Castro, Rubem Mondaini, María A. H. Vozmediano

We analyze the influence of disorder and strong correlations on the topology in two dimensional Chern insulators. A mean field calculation in the half-filled Haldane model with extended Hubbard interactions and Anderson disorder shows that disorder favors topology in the interacting case and extends the topological phase to a larger region of the Hubbard par

Pablo S. Cornaglia, Matias Nuñez, D. J. Garcia

In this study, we conduct a comprehensive theoretical analysis of a Fibonacci quasicrystalline stacking of ferromagnetic layers, potentially realizable using van der Waals magnetic materials. We construct a model of this magnetic heterostructure, which includes up to second neighbor interlayer magnetic interactions, that displays a complex relationship betwe

C. T. Kelley

We describe a three precision variant of Newton's method for nonlinear equations. We evaluate the nonlinear residual in double precision, store the Jacobian matrix in single precision, and solve the equation for the Newton step with iterative refinement with a factorization in half precision. We analyze the method as an inexact Newton method. This analysis s

Bedrettin Cetinkaya, Yucel Cimtay, Fatma Nazli Gunay, Gokce Nur Yilmaz

The changing level of haze is one of the main factors which affects the success of the proposed dehazing methods. However, there is a lack of controlled multi-level hazy dataset in the literature. Therefore, in this study, a new multi-level hazy color image dataset is presented. Color video data is captured for two real scenes with a controlled level of haze

Adrian Solyom, Michael Caouette-Mansour, Brandon Ruffolo, Patrick Braganca

We employ N-$V$ magnetometry to measure the stray field dynamics of a ferromagnetic permalloy nanowire driven by spin-orbit torques. Specifically, we observe the optically detected magnetic resonance (ODMR) signatures of both spontaneous DC-driven magnetic oscillations and phase-locking to a second harmonic drive, developing a simple macrospin model that cap

Jürgen Struckmeier, Claus Riedel

An exact invariant is derived for three-dimensional Hamiltonian systems of $N$ particles confined within a general velocity-independent potential. The invariant is found to contain a time-dependent function $f_{2}(t)$, embodying a solution of a linear third-order differential equation whose coefficients depend on the explicitly known trajectories of the part

Juraj Bodik, Valérie Chavez-Demoulin

We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs (DAGs) that represent the causal structure is a fundamental problem in science. Several results are known when the full DAG is identifiable from the distribution, such as assuming a nonlinear Gaussian data

Yujun Choi, Robert Joynt

Semiconductor qubit devices suffer from the drift of important device parameters as they are operated. The most important example is a shift in qubit operating frequencies. This effect appears to be directly related to the heating of the system as gate operations are applied. We show that the main features of this phenomenon can be explained by the two-level

Arkady Leiderman, Paul Szeptycki

$\Delta$-spaces have been defined by a natural generalization of a classical notion of $\Delta$-sets of reals to Tychonoff topological spaces; moreover, the class $\Delta$ of all $\Delta$-spaces consists precisely of those $X$ for which the locally convex space $C_p(X)$ is distinguished. The aim of this article is to better understand the boundaries of the c

Tianyi Yu

In his Ph.D. thesis, Sean Griffin introduced a family of ideals and found monomial bases for their quotient rings. These rings simultaneously generalize the Delta Conjecture coinvariant rings of Haglund-Rhoades-Shimozono and the cohomology rings of Springer fibers studied by Tanisaki and Garsia-Procesi. We recursively construct a Gr\"{o}bner basis of Griffin

Ana-Maria Bucur, Andreea Dincă, Mădălina Chitez, Roxana Rogobete

This paper presents the methodology and data used for the automatic extraction of the Romanian Academic Word List (Ro-AWL). Academic Word Lists are useful in both L2 and L1 teaching contexts. For the Romanian language, no such resource exists so far. Ro-AWL has been generated by combining methods from corpus and computational linguistics with L2 academic wri

Vi D. Ao, Duy V. Tran, Kien T. Pham, Duc M. Nguyen

We establish an analogy between the Fokker-Planck equation describing evolutionary landscape dynamics and the Schr\"{o}dinger equation which characterizes quantum mechanical particles, showing how a population with multiple genetic traits evolves analogously to a wavefunction under a multi-dimensional energy potential in imaginary time. Furthermore, we disco

Gang Chen

Although ubiquitous in nature and industrial processes, transport processes at the interface during evaporation and condensation are still poorly understood. Experiments have shown temperature discontinuities at the interface during evaporation and condensation but the experimentally reported interface temperature jump varies by two orders of magnitude. Even

N. I. Petrov

A spatial-frequency thin-film filter with inclusions of nanoparticles operating in the visible range of the spectrum is investigated. The effect of nanoparticles embedded in the central and lateral layers of the frustrated total internal reflection filter on the spectral characteristics, taking into account the frequency dispersion, is investigated. It is sh

Yaodong Tu, Gang Chen

Exergy represents the maximum useful work possible when a system at a specific state reaches equilibrium with the environmental dead state at temperature To. Correspondingly, the exergy difference between two states is the maximum work output when the system changes from one state to the other, assuming that during the processes, the system exchanges heat re

Junwen Lai, Jiangxu Li, Peitao Liu, Yan Sun

Recently, Lee et al. reported the experimental discovery of room-temperature ambient-pressure superconductivity in a Cu-doped lead-apatite (LK-99) (arXiv:2307.12008, arXiv:2307.12037). Remarkably, the superconductivity persists up to 400 K at ambient pressure. Despite strong experimental evidence, the electronic structure of LK-99 has not yet been studied. H

Viet Dac Lai, Chien Van Nguyen, Nghia Trung Ngo, Thuat Nguyen

A key technology for the development of large language models (LLMs) involves instruction tuning that helps align the models' responses with human expectations to realize impressive learning abilities. Two major approaches for instruction tuning characterize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), which are current

Boran Zhou, Ya-Hui Zhang

Weak Mott regime with finite U is a wonderful region to search for quantum spin liquid, but it is challenging to write down a wavefunction capturing both spin liquid and charge fluctuations. Conventional methods using complicated Jastrow factors have difficulties when the underlying spin liquid has a non-trivial projective symmetry group (PSG). To cure this

Colin Zhang, Yang Ha

Multiple sclerosis (MS) is a debilitating neurological disease affecting nearly one million people in the United States. Sphingosine-1-phosphate receptor 1, or S1PR1, is a protein target for MS. Siponimod, a ligand of S1PR1, was approved by the FDA in 2019 for MS treatment, but there is a demonstrated need for better therapies. To this end, we finetuned an a

Ronak Desai, Thomas Zhang, Ricky Oropeza, John J. Felice

Researchers in the field of ultra-intense laser science are beginning to embrace machine learning methods. In this study we consider three different machine learning methods -- a two-hidden layer neural network, Support Vector Regression and Gaussian Process Regression -- and compare how well they can learn from a synthetic data set for proton acceleration i

Hong-fu Chou, Vu Nguyen Ha, Hayder Al-Hraishawi, Luis Manuel Garces-Socarras

The ultra-secure quantum network leverages quantum cryptography to deliver unsurpassed data transfer security. In principle, the well-known quantum key distribution (QKD) achieves unconditional security, which raises concerns about the trustworthiness of 6G wireless systems in order to mitigate the gap between practice and theory. The long-distance satellite

Elouan Argouarc'h, François Desbouvries

Acceptance-rejection (AR), Independent Metropolis Hastings (IMH) or importance sampling (IS) Monte Carlo (MC) simulation algorithms all involve computing ratios of probability density functions (pdfs). On the other hand, classifiers discriminate labeled samples produced by a mixture of two distributions and can be used for approximating the ratio of the two

Michael Zurel, Lawrence Z. Cohen, Robert Raussendorf

Negativity in certain quasiprobability representations is a necessary condition for a quantum computational advantage. Here we define a quasiprobability representation exhibiting this property with respect to quantum computations in the magic state model. It is based on generalized Jordan-Wigner transformations, and it has a close connection to the probabili

Pangoth Santhosh Kumar, Kundrapu Supriya, Mallikharjuna Rao K, Taraka Satya Krishna Teja Malisetti

Lung disease is a common health problem in many parts of the world. It is a significant risk to people health and quality of life all across the globe since it is responsible for five of the top thirty leading causes of death. Among them are COVID 19, pneumonia, and tuberculosis, to name just a few. It is critical to diagnose lung diseases in their early sta

Scott C. Scharlach, Colton G. Morgan

We report an updated value for the distance to the planetary nebula NGC 6309 (the Box Nebula). The distance is found through two Kinematic Distance Methods (KDMs): the system of two equations reported in Zhu et al. 2013 and the Monte Carlo method reported by Wenger et al. 2018. We find the kinematic distance to NGC 6309 to be 4.1 kpc with an upper uncertaint

Naushad Ahmad Kamar, Mohammad Maghrebi

A large, or even infinite, local Hilbert space dimension poses a significant computational challenge for simulating quantum systems. In this work, we present a matrix product state (MPS)-based method for simulating one-dimensional quantum systems with a large local Hilbert space dimension, an example being bosonic systems with a large on-site population. To

Margherita Pagano

We discuss the role of primes of good reduction in the existence of the Brauer--Manin obstruction to weak approximation for varieties defined over number fields. Following Bright and Newton, we give some necessaries conditions on the ramification index that the prime ideal of the number field should satisfy in order to be involved in the Brauer--Manin obstru

Wenxiong Chen, Lingwei Ma, Yahong Guo

In this paper, we study the fully fractional master equation \begin{equation}\label{pdeq1} (\partial_t-\Delta)^s u(x,t) =f(x,t,u(x,t)),\,\,(x, t)\in \mathbb{R}^n\times \mathbb{R}. \end{equation} First we prove a Liouville type theorem for the homogeneous equation \begin{equation}\label{pdeq0} (\partial_t-\Delta)^s u(x,t) = 0,\,\,(x, t)\in \mathbb{R}^n\times

Eqab. M. Rabei, Mohamed. Al-Masaeed, Dumitru Baleanu

In this work, a conformable singular system with second-class constraints is discussed. The conformable Poisson bracket (CDB) of two functions is defined. and, the Dirac theory is developed to be applicable to conformable singular systems. In addition, to demonstrate the theory an illustrative example is given and solved, the results are found to be in agree

Andrés Anabalón, Hernán A. González, Aníbal Neira-Gallegos, Julio Oliva

We analyze the backreaction of a class of scalar field self-interactions with the possibility of evolving from an AdS vacuum to a fixed point where the scalar field potential vanishes. Exact solutions which interpolate between these regions, ranging from stationary black hole to dynamical spacetimes are constructed. Their surface charges are finite but non-i

Mengyi Yuan, Minjie Chen, Xiang Li

In recent years, self-supervised learning has emerged as a promising approach in addressing the issues of label dependency and poor generalization performance in traditional GNNs. However, existing self-supervised methods have limited effectiveness on heterophilic graphs, due to the homophily assumption that results in similar node representations for connec

Farhang Loran, Ali Mostafazadeh

The discovery of unidirectional invisibility and its broadband realization in optical media satisfying spatial Kramers-Kronig relations are important landmarks of non-Hermitian photonics. We offer a precise characterization of a higher-dimensional generalization of this effect and find sufficient conditions for its realization in the scattering of scalar wav

Yaroslav Zhumagulov, Denis Kochan, Jaroslav Fabian

Ex-so-tic van der Waals heterostructures take advantage of the electrically tunable layer polarization to swap proximity exchange and spin-orbit coupling in the electronically active region. Perhaps the simplest example is Bernal bilayer graphene (BBG) encapsulated by a layered magnet from one side and a strong spin-orbit material from the other. Taking WS$_

Charlotte Aten

Theorems from universal algebra such as that of Murski\u{i} from the 1970s have a striking similarity to universal approximation results for neural nets along the lines of Cybenko's from the 1980s. We consider here a discrete analogue of the classical notion of a neural net which places these results in a unified setting. We introduce a learning algorithm ba

Sindhura Gupta, Susovan Mukhopadhyay, Ambarnath Banerji, Sujit K. Biswas

The popularity of low-voltage ac distribution networks is increasing day by day. However, an efficient protection scheme for low-voltage ac distribution systems is still challenging. This paper introduces a protection scheme suitable for low-voltage grid connected and islanded ac microgrid based on local measurements in order to locate, identify and isolate

Soneya Binta Hossain, Antonio Filieri, Matthew B. Dwyer, Sebastian Elbaum

Defining test oracles is crucial and central to test development, but manual construction of oracles is expensive. While recent neural-based automated test oracle generation techniques have shown promise, their real-world effectiveness remains a compelling question requiring further exploration and understanding. This paper investigates the effectiveness of

Alexander O'Dell, Maria C. Babiuc Hamilton

The gravitational waves produced by binary neutron star mergers offer a unique window into matter behavior under extreme conditions. In this context, we model analytically the effect of matter on the gravitational waves from binary neutron star mergers. We start with a binary black hole system, leveraging the post-Newtonian formalism for the inspiral and the

Yordanka Velikova, Mohammad Farid Azampour, Walter Simson, Vanessa Gonzalez Duque

Anatomical segmentation of organs in ultrasound images is essential to many clinical applications, particularly for diagnosis and monitoring. Existing deep neural networks require a large amount of labeled data for training in order to achieve clinically acceptable performance. Yet, in ultrasound, due to characteristic properties such as speckle and clutter,

Begoña Alarcón, Sofia B. S. D. Castro, Isabel S. Labouriau

This is a complete study of the dynamics of polynomial planar vector fields whose linear part is a multiple of the identity and whose nonlinear part is a contracting homogeneous polynomial. The contracting nonlinearity provides the existence of an invariant circle and allows us to obtain a classification through a complete invariant for the dynamics, extendi

Francesco Manigrasso, Lia Morra, Fabrizio Lamberti

Neuro-symbolic integration aims at harnessing the power of symbolic knowledge representation combined with the learning capabilities of deep neural networks. In particular, Logic Tensor Networks (LTNs) allow to incorporate background knowledge in the form of logical axioms by grounding a first order logic language as differentiable operations between real te

David P. Kimsey, Mihai Putinar

The discrete data encoded in the power moments of a positive measure, fast decaying at infinity on euclidean space, is incomplete for recovery, leading to the concept of moment indeterminateness. On the other hand, classical integral transforms (Fourier-Laplace, Fantappi\`e, Poisson) of such measures are complete, often invertible via an effective inverse op