May 2023 arXiv papers — page 7

Rahul Chakraborty, Katharina Boguslawski, Paweł Tecmer

Quantum embedding methods have recently developed significantly to model large molecular structures. This work proposes a novel wave function theory in density functional theory (WTF-in-DFT) embedding scheme based on pair-coupled cluster doubles (pCCD)-type methods. While pCCD can reliably describe strongly-correlated systems with mean-field-like computation

A. Schelle

A theoretical particle-number conserving quantum field theory based on the concept of imaginary time is presented and applied to the scenario of a coherent atomic laser field at ultra-cold temperatures. The proposed theoretical model describes the analytical derivation of the frequency comb spectrum for an atomic laser realized from modeling a coherent atomi

Xuan Liang, Tao Zou

With the rapid advancements in technology for data collection, the application of the spatial autoregressive (SAR) model has become increasingly prevalent in real-world analysis, particularly when dealing with large datasets. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable to handle the

Giuseppe Costantino, Sophie Giffard-Roisin, Mathilde Radiguet, Mauro Dalla Mura

Slow slip events (SSEs) originate from a slow slippage on faults that lasts from a few days to years. A systematic and complete mapping of SSEs is key to characterizing the slip spectrum and understanding its link with coeval seismological signals. Yet, SSE catalogues are sparse and usually remain limited to the largest events, because the deformation transi

Pedro E. Gória Silva, Adam Narbudowicz, Nicola Marchetti, Pedro H. J. Nardelli

In this paper, the privacy of wireless transmissions is improved through the use of an efficient technique termed dynamic directional modulation (DDM), and is subsequently assessed in terms of the measure of information leakage. Recently, a variation of DDM termed low-power dynamic directional modulation (LPDDM) has attracted significant attention as a promi

Xu-chang Guo, Hou-biao Li

In real-world tasks, there is usually a large amount of unlabeled data and labeled data. The task of combining the two to learn is known as semi-supervised learning. Experts can use logical rules to label unlabeled data, but this operation is costly. The combination of perception and reasoning has a good effect in processing such semi-supervised tasks with d

Alessio Micheli, Domenico Tortorella

Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features. Having deep models that can leverage longer-range interactions between nodes is hindered by the issues of over-smoothing and over-squashing. In particular, the latter is attributed to the graph topology which gu

Duarte Azevedo, Thomas Biekötter, P. M. Ferreira

In both Run 1 and Run 2 of the LHC, the CMS collaboration has observed an excess of events in the searches for low-mass Higgs bosons in the diphoton final state at a mass of about 95 GeV. After a recent update of the experimental analysis, in which the full Run 2 data collected at 13 TeV has been included and an improved experimental calibration has been app

Peter Schlosser

Superoscillations are a phenomenon in physics, where linear combinations of low-frequency plane waves interfere almost destructively in such a way that the resulting wave has a higher frequency than any of the individual waves. The evolution of superoscillatory initial datum under the time dependent Schr\"odinger equation is stable in free space, but in gene

Shang-Jie Jin, Ye-Zhu Zhang, Ji-Yu Song, Jing-Fei Zhang

In the coming decades, the space-based gravitational-wave (GW) detectors such as Taiji, TianQin, and LISA are expected to form a network capable of detecting millihertz GWs emitted by the mergers of massive black hole binaries (MBHBs). In this work, we investigate the potential of GW standard sirens from the Taiji-TianQin-LISA network in constraining cosmolo

Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen

The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have proposed algorithms to detect LLM-generated text and protect LLMs. In this paper, we investigate the robustness and reliability of these LLM de

Jonathan Barés, Manuel Cárdenas-Barrantes, Gustavo Pinzón, Edward andó

Very soft grain assemblies have unique shape-changing capabilities that allow them to be compressed far beyond the rigid jammed state by filling void spaces more effectively. However, accurately following the formation of these systems by monitoring the creation of new contacts, the changes in grain shape, and measuring grain-scale stresses is challenging. W

Fidele Twagirayezu, Abraham Ayirwanda, Albert Munyeshyaka, Solange Mukeshimana

The current work treats cosmological perturbation in a mixture of standard matter, Chaplygin gas as well as Gauss-bonnet fluids using a 1+3 covariant approach in the context of modified $f(G)$ gravity. We define the gradient variables to obtain linear perturbation equations. After scalar and redshift transformations, we consider both an original Chaplygin an

Xinyao Guo, Teng Zhang, Denis Martynov, Haixing Miao

The detection of high-frequency gravitational waves around kHz is critical to understanding the physics of binary neutron star mergers. A new interferometer design has been proposed in [Phys. Rev. X {\bf 13}, 021019 (2023)], featuring an L-shaped optical resonator as the arm cavity, which resonantly enhances kHz gravitational-wave signals. This new configura

Pedro E. Goria Silva, Pedro H. J. Nardelli, Arthur S. de Sena, Harun Siljak

This paper explores the use of semantic knowledge inherent in the cyber-physical system (CPS) under study in order to minimize the use of explicit communication, which refers to the use of physical radio resources to transmit potentially informative data. It is assumed that the acquired data have a function in the system, usually related to its state estimat

Linh The Nguyen, Thinh Pham, Dat Quoc Nguyen

We present XPhoneBERT, the first multilingual model pre-trained to learn phoneme representations for the downstream text-to-speech (TTS) task. Our XPhoneBERT has the same model architecture as BERT-base, trained using the RoBERTa pre-training approach on 330M phoneme-level sentences from nearly 100 languages and locales. Experimental results show that employ

Battulga Gankhuu

In this study, we introduce new estimation methods for the required rate of returns on equity and liabilities of private and public companies using the stochastic dividend discount model (DDM). To estimate the required rate of return on equity, we use the maximum likelihood method, the Bayesian method, and the Kalman filtering. We also provide a method that

Iva Bojic, Qi Chwen Ong, Shafiq Joty, Josip Car

Non-communicable diseases (NCDs) are a leading cause of global deaths, necessitating a focus on primary prevention and lifestyle behavior change. Health coaching, coupled with Question Answering (QA) systems, has the potential to transform preventive healthcare. This paper presents a human-Artificial Intelligence (AI) health coaching model incorporating a do

Jacobus G. M. van der Linden, Mathijs M. de Weerdt, Emir Demirović

Global optimization of decision trees has shown to be promising in terms of accuracy, size, and consequently human comprehensibility. However, many of the methods used rely on general-purpose solvers for which scalability remains an issue. Dynamic programming methods have been shown to scale much better because they exploit the tree structure by solving subt

J. S. Castellanos Durán, A. Korpi-Lagg, S. K. Solanki

In addition to the Evershed flow directed from the umbra towards the outer boundary of the sunspot, under special circumstances, a counter Evershed flow (CEF) in the opposite direction also occurs. We aim to characterize the proper motions and evolution of three CEFs observed by the Solar Optical Telescope onboard the Japanese Hinode spacecraft and the Helio

Chun Liu, Suqiang Ma, Zheng Li, Wei Yang

Zero-shot classification of image scenes which can recognize the image scenes that are not seen in the training stage holds great promise of lowering the dependence on large numbers of labeled samples. To address the zero-shot image scene classification, the cross-modal feature alignment methods have been proposed in recent years. These methods mainly focus

C. Gonzalez-Ballestero

Most quantum theorists are familiar with different ways of describing the effective quantum dynamics of a system coupled to external degrees of freedom, such as the Born-Markov master equation or the adiabatic elimination. Understanding the deep connection between these -- sometimes apparently unrelated -- methods can be a powerful tool, allowing us to deriv

Alexander J. Silenko

We suppose that the covariance of Lagrangians always taking place results in a distortion of any electric and magnetic field by a pseudoscalar field of dark matter axions and axion-like particles. As a result, electric and magnetic fields acquire oscillating magnetic and electric components, respectively. The Maxwell-like equations are rigorously derived. On

Giovanni Morello, Maria Moffa, Martina Montinaro, Annachiara Albanese

The optical gain of blue light-emitting electrospun polystyrene fibers doped with a linear multi-fragment molecular dye based on the combination of fluorine-carbazole functional units is investigated, with the aim of correlating emission properties and the specific material architecture made of either aligned or disordered fibers. Enhanced performance is fou

Luca Baracco, Olga Bernardi

In this paper we prove that a totally integrable strictly-convex symplectic billiard table, whose boundary has everywhere strictly positive curvature, must be an ellipse. The proof, inspired by the analogous result of Bialy for Birkhoff billiards, uses the affine equivariance of the symplectic billiard map.

Sadi Alawadi, Khalid Alkharabsheh, Fahed Alkhabbas, Victor Kebande

This paper proposes a Federated Learning Code Smell Detection (FedCSD) approach that allows organizations to collaboratively train federated ML models while preserving their data privacy. These assertions have been supported by three experiments that have significantly leveraged three manually validated datasets aimed at detecting and examining different cod

Haijun Xiong, Yunze Deng, Bin Feng, Xinggang Wang

Gait recognition, a growing field in biological recognition technology, utilizes distinct walking patterns for accurate individual identification. However, existing methods lack the incorporation of temporal information. To reach the full potential of gait recognition, we advocate for the consideration of temporal features at varying granularities and spans.

Tim Bürchner, Philipp Kopp, Stefan Kollmannsberger, Ernst Rank

Full waveform inversion (FWI) is an iterative identification process that serves to minimize the misfit of model-based simulated and experimentally measured wave field data, with the goal of identifying a field of parameters for a given physical object. The inverse optimization process of FWI is based on forward and backward solutions of the (elastic or acou

Jonas Schuhmacher, Fabio Gratl, Dario Izzo, Pablo Gómez

Recent advances in modeling density distributions, so-called neural density fields, can accurately describe the density distribution of celestial bodies without, e.g., requiring a shape model - properties of great advantage when designing trajectories close to these bodies. Previous work introduced this approach, but several open questions remained. This wor

Shu Hamanaka, Kazuki Yamamoto, Tsuneya Yoshida

Despite recent intensive research on topological aspects of open quantum systems, effects of strong interactions have not been sufficiently explored. In this paper, we demonstrate that complex-valued interactions induce the Liouvillian skin effect by analyzing a one-dimensional correlated model with two-body loss. We show that, in the presence of complex-val

Pedro E. G. Silva, Jules M. Moualeu, Pedro H. Nardelli, Rausley A. A. de Souza

The acquisition of accurate channel state information (CSI) is of utmost importance since it provides performance improvement of wireless communication systems. However, acquiring accurate CSI, which can be done through channel estimation or channel prediction, is an intricate task due to the complexity of the time-varying and frequency selectivity of the wi

Antonin Arsac, Aurore Lomet, Jean-Philippe Poli

Causality defines the relationship between cause and effect. In multivariate time series field, this notion allows to characterize the links between several time series considering temporal lags. These phenomena are particularly important in medicine to analyze the effect of a drug for example, in manufacturing to detect the causes of an anomaly in a complex

Anass Aghbalou, Guillaume Staerman

Hypothesis transfer learning (HTL) contrasts domain adaptation by allowing for a previous task leverage, named the source, into a new one, the target, without requiring access to the source data. Indeed, HTL relies only on a hypothesis learnt from such source data, relieving the hurdle of expansive data storage and providing great practical benefits. Hence,

Gabriel Raya, Luca Ambrogioni

Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data. In this paper, we show that the dynamics of these models exhibit a spontaneous symmetry breaking that divides the generative dynamics into two distinct phases: 1) A linear steady-state dynamics around a central fixed-point and 2) an attractor dynamic

Li Tang, Hai-Nan Lin, Xin Li

We reconstruct the extragalactic dispersion measure \ -- redshift relation (${\rm DM_E}-z$ relation) from well-localized fast radio bursts (FRBs) using Bayesian inference method. Then the ${\rm DM_E}-z$ relation is used to infer the redshift and energy of the first CHIME/FRB catalog. We find that the distributions of extragalactic dispersion measure and infe

Hugo Richard, Etienne Boursier, Vianney Perchet

Multiplayer bandits have recently been extensively studied because of their application to cognitive radio networks. While the literature mostly considers synchronous players, radio networks (e.g. for IoT) tend to have asynchronous devices. This motivates the harder, asynchronous multiplayer bandits problem, which was first tackled with an explore-then-commi

Denis Bernard

I present a study of the several contributions to the single-photon angular resolution of pair telescopes in the MeV energy range. I examine some test cases, the presently active {\sl Fermi} LAT, the ``pure-silicon'' projects ASTROGAM and AMEGO-X, and the emulsion-based project GRAINE.

Dávid Javorský, Ondřej Bojar, François Yvon

Many NLP tasks require to automatically identify the most significant words in a text. In this work, we derive word significance from models trained to solve semantic task: Natural Language Inference and Paraphrase Identification. Using an attribution method aimed to explain the predictions of these models, we derive importance scores for each input token. W

Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista Rios, Osman Semih Kayhan

The third edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop featured four data-impaired challenges, focusing on addressing the limitations of data availability in training deep learning models for computer vision tasks. The challenges comprised of four distinct data-impaired tasks, where participants were required t

Shichuan Chen, Yong Gao, Enping Zhou, Renxin Xu

Can pulsar-like compact objects release further huge free energy besides the kinematic energy of rotation? This is actually relevant to the equation of state of cold supra-nuclear matter, which is still under hot debate. Enormous energy is surely needed to understand various observations, such as $\gamma-$ray bursts, fast radio bursts and soft $\gamma-$ray r

Soner Albayrak, Paolo Benincasa, Carlos Duaso Pueyo

Unitarity of time evolution is one of the basic principles constraining physical processes. Its consequences in the perturbative Bunch-Davies wavefunction in cosmology have been formulated in terms of the cosmological optical theorem. In this paper, we re-analyse perturbative unitarity for the Bunch-Davies wavefunction, focusing on: 1) the role of the $i\eps

Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu

This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr\"odinger equation inspired by stochastic mechanics and generative diffusion models. Unlike existing approaches, which exhibit computational complexity that scales exponentially in the problem dimension, our method allows us to adapt to the latent low-d

Shohei Taniguchi, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo

We address the problem of biased gradient estimation in deep Boltzmann machines (DBMs). The existing method to obtain an unbiased estimator uses a maximal coupling based on a Gibbs sampler, but when the state is high-dimensional, it takes a long time to converge. In this study, we propose to use a coupling based on the Metropolis-Hastings (MH) and to initial

Manuel De Stefano, Fabiano Pecorelli, Dario Di Nucci, Fabio Palomba

Context. Quantum computing is becoming a reality, and quantum software engineering (QSE) is emerging as a new discipline to enable developers to design and develop quantum programs. Objective. This paper presents a systematic mapping study of the current state of QSE research, aiming to identify the most investigated topics, the types and number of studies,

Denis Aristov, Stepan Baryshev, Julian D. Töpfer, Helgi Sigurðsson

We report on the realization of all-optical planar microlensing for exciton-polariton condensates in semiconductor microcavities. We utilize spatial light modulators to structure a nonresonant pumping beam into a planoconcave lens-shape focused onto the microcavity plane. When pumped above condensation threshold, the system effectively becomes a directional

Florian K. Unseld, Marcel Meyer, Mateusz T. Mądzik, Francesco Borsoi

Semiconductor spin qubits have gained increasing attention as a possible platform to host a fault-tolerant quantum computer. First demonstrations of spin qubit arrays have been shown in a wide variety of semiconductor materials. The highest performance for spin qubit logic has been realized in silicon, but scaling silicon quantum dot arrays in two dimensions

D. R. Yafaev

Spectral properties of Jacobi operators $J$ are intimately related to an asymptotic behavior of the corresponding orthogonal polynomials $P_{n}(z)$ as $n\to\infty$. We study the case where the off-diagonal coefficients $a_{n}$ and, eventually, diagonal coefficients $ b_{n}$ of $J$ tend to infinity in such a way that the ratio $\gamma_{n}:=2^{-1}b_{n} (a_{n}a

Manoj Mandal, Rahul Sharma, Sabyasachi Pal, G. K. Jaisawal

The X-ray pulsar RX J0440.9+4431 went through a giant outburst in 2022 and reached a record-high flux of 2.3 Crab, as observed by Swift/BAT. We study the evolution of different spectral and timing properties of the source using NICER observations. The pulse period is found to decrease from 208 s to 205 s, and the pulse profile evolves significantly with ener

P. Sasorov, G. Bagdasarov, N. Bobrova, G. Grittani

We investigate the main physical processes that limit the repetition rate of capillary discharges used in laser accelerators of electrons theoretically and with computer simulations. We consider processes in the capillary. We assume that a cooling system independently maintains temperature balance of the capillary, as well as a gas supply system and a vacuum

Frederik S. B. Westerhout, Julian F. Schumann, Arkady Zgonnikov

Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles. Integrating cognitive processes and machine learning models has shown promise in other domains but is lacking in the trajectory forecasting of multiple traffic agents in large-scal

E. González-Álvarez, J. Kemmer, P. Chaturvedi, J. A. Caballero

We report the discovery of HN Lib b, a sub-Neptunian mass planet orbiting the nearby ($d \approx$ = 6.25 pc) M4.0 V star HN Lib detected by our CARMENES radial-velocity (RV) survey. We determined a planetary minimum mass of $M_\text{b}\sin i = $ 5.46 $\pm$ 0.75 $\text{M}_\oplus$ and an orbital period of $P_\text{b} = $ 36.116 $\pm$ 0.029 d, using $\sim$5 yr

Rodrigo A. González, Cristian R. Rojas, Siqi Pan, James S. Welsh

The Refined Instrumental Variable method for discrete-time systems (RIV) and its variant for continuous-time systems (RIVC) are popular methods for the identification of linear systems in open-loop. The continuous-time equivalent of the transfer function estimate given by the RIV method is commonly used as an initialization point for the RIVC estimator. In t

Anne-Marie Toparkus, Rafael Weißbach

In studies on lifetimes, occasionally, the population contains statistical units that are born before the data collection has started. Left-truncated are units that deceased before this start. For all other units, the age at the study start often is recorded and we aim at testing whether this second measurement is independent of the genuine measure of intere

Gábor Lugosi, Gergely Neu

We present a new framework for deriving bounds on the generalization bound of statistical learning algorithms from the perspective of online learning. Specifically, we construct an online learning game called the "generalization game", where an online learner is trying to compete with a fixed statistical learning algorithm in predicting the sequence of gener

Aydin Gezer, Busra Aktas, Olgun Durmaz

Let $(\acute{N},g,\nabla )$\ be a $2n$-dimensional quasi-statistical manifold that admits a pseudo-Riemannian metric $g$ (or $h)$ and a linear connection $\nabla $ with torsion. This paper aims to study an almost Hermitian structure $(g,L)$ and an almost anti-Hermitian structure $(h,L)$ on a quasi-statistical manifold that admit an almost complex structure $

Juha Harviainen, Kseniya Rychkova, Mikko Koivisto

The Bayesian network structure learning (BNSL) problem asks for a directed acyclic graph that maximizes a given score function. For networks with $n$ nodes, the fastest known algorithms run in time $O(2^n n^2)$ in the worst case, with no improvement in the asymptotic bound for two decades. Inspired by recent advances in quantum computing, we ask whether BNSL

Massimo Lanza de Cristoforis

We prove the validity of a regularizing property on the boundary of the double layer potential associated to the fundamental solution of a {\em nonhomogeneous} second order elliptic differential operator with constant coefficients in Schauder spaces of exponent greater or equal to two that sharpens classical results of N.M.~G\"{u}nter, S.~Mikhlin, V.D.~Kupra

Alexander Shevtsov, Despoina Antonakaki, Ioannis Lamprou, Polyvios Pratikakis

Twitter, as one of the most popular social networks, provides a platform for communication and online discourse. Unfortunately, it has also become a target for bots and fake accounts, resulting in the spread of false information and manipulation. This paper introduces a semi-automatic machine learning pipeline (SAMLP) designed to address the challenges assoc

Moritz Vandenhirtz, Laura Manduchi, Ričards Marcinkevičs, Julia E. Vogt

Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of the underlying decision rule. As a result, practitioners are often unaware of the biased decision-making of their classifiers. Such a biased model based on spurious correlations migh

Anna Chiara Lai, Monica Motta

Given a nonlinear control system, a target set, a nonnegative integral cost, and a continuous function $W$, we say that the system is globally asymptotically controllable to the target with W-regulated cost, whenever, starting from any point z, among the strategies that achieve classical asymptotic controllability we can select one that also keeps the cost l

Erhard Aichinger, Simon Grünbacher, Paul Hametner

We consider sparse polynomials in $N$ variables over a finite field, and ask whether they vanish on a set $S^N$, where $S$ is a set of nonzero elements of the field. We see that if for a polynomial $f$, there is $\mathbf{c}\in S^N$ with $f (\mathbf{c}) \neq 0$, then there is such a $\mathbf{c}$ in every sphere inside $S^N$, where the radius of the sphere is

Maria Giovanna Dainotti, Giada Bargiacchi, Aleksander Łukasz Lenart, Shigehiro Nagataki

Currently, the $\Lambda$ Cold Dark Matter model, which relies on the existence of cold dark matter and a cosmological constant $\Lambda$, best describes the Universe. However, we lack information in the high-redshift ($z$) region between Type Ia Supernovae (SNe Ia) (up to $z=2.26$) and the Cosmic Microwave Background ($z=1100$), an interval crucial to test c

Christof A. Bertram, Taryn A. Donovan, Alexander Bartel

One of the most relevant prognostication tests for tumors is cellular proliferation, which is most commonly measured by the mitotic activity in routine tumor sections. The goal of this systematic review is to scholarly analyze the methods and prognostic relevance of histologically measuring mitotic activity in canine tumors. A total of 137 articles that corr

Joonhyuk Yang, Dongpil Shin, Hye Won Chung

We consider the problem of graph matching, or learning vertex correspondence, between two correlated stochastic block models (SBMs). The graph matching problem arises in various fields, including computer vision, natural language processing and bioinformatics, and in particular, matching graphs with inherent community structure has significance related to de

Viola Siconolfi, Marlies Vantomme, Christopher Voll

We describe an effective procedure to compute the local subgroup zeta functions of the free class-$2$-nilpotent groups on $d$ generators, for all $d$. For $d=4$, this yields a new, explicit formula. For $d\in\{4,5\}$, we compute the topological subgroup zeta function. We also obtain general results about the reduced and topological subalgebra zeta functions.

A. S. Parvan

The exact and approximate hadron transverse momentum distributions for the Fermi-Dirac, Bose-Einstein and Maxwell-Boltzmann statistics of particles in the framework of the Tsallis statistics with escort probabilities (the Tsallis-3 statistics) have been derived. The classical and quantum transverse momentum distributions in the zeroth term approximation and

Ali Vosoughi, Shijian Deng, Songyang Zhang, Yapeng Tian

To increase the generalization capability of VQA systems, many recent studies have tried to de-bias spurious language or vision associations that shortcut the question or image to the answer. Despite these efforts, the literature fails to address the confounding effect of vision and language simultaneously. As a result, when they reduce bias learned from one

Levi Lingsch, Mike Y. Michelis, Emmanuel de Bezenac, Sirani M. Perera

The computational efficiency of many neural operators, widely used for learning solutions of PDEs, relies on the fast Fourier transform (FFT) for performing spectral computations. As the FFT is limited to equispaced (rectangular) grids, this limits the efficiency of such neural operators when applied to problems where the input and output functions need to b

Renxi Liu, Daye Zheng, Xinyuan Liang, Xinguo Ren

Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the exchange-correlation functional. By inclusion of the kinetic energy density $\tau$, the meta-generalized-gradient approximation (meta-GGA) family of functionals achieves better acc

Heng Dong, Junyu Zhang, Tonghan Wang, Chongjie Zhang

Robot design aims at learning to create robots that can be easily controlled and perform tasks efficiently. Previous works on robot design have proven its ability to generate robots for various tasks. However, these works searched the robots directly from the vast design space and ignored common structures, resulting in abnormal robots and poor performance.

Brian Alspach, Primoz Sparl

We investigate connected cubic vertex-transitive graphs whose edge sets admit a partition into a $2$-factor $\mathcal{C}$ and a $1$-factor that is invariant under a vertex-transitive subgroup of the automorphism group of the graph and where the quotient graph with respect to $\mathcal{C}$ is a cycle. There are two essentially different types of such cubic gr

Yi-jia Yang, Yu-qiang Liu, Chang-shui Yu

A quantum thermal diode is designed based on three pairwise coupled qubits, two connected to a common reservoir and the other to an independent reservoir. It is found that the internal couplings between qubits can enhance heat currents. If the two identical qubits uniformly couple with the common reservoir, the crossing dissipation will occur, leading to the

Amit Rothschild, Benjamin Assouline, Nadav Am Shalom, Nirel Bernstein

Measurements of ferromagnetic resonance (FMR) are pivotal to modern magnetism and spintronics. Recently, we reported on the Ferris FMR technique, which relies on large-amplitude modulation of the externally applied magnetic field. It was shown to benefit from high sensitivity while being broadband. The Ferris FMR also expanded the resonance linewidth such th

Shubhajit Roy, Shrutimoy Das, Binita Maity, Anant Kumar

Subgraph counting is a fundamental task for analyzing structural patterns in graph-structured data, with important applications in domains such as computational biology and social network analysis, where recurring motifs reveal functional and organizational properties. In this paper, we propose localized versions of the Weisfeiler-Leman (WL) algorithms to im

Kazimierz Musial

Let $(X, {\mathfrak A},P)$ and $(Y, {\mathfrak B},Q)$ be two probability spaces and $R$ be their skew product on the product $\sigma$-algebra ${\mathfrak A}\otimes\mfB$. Moreover, let $\{({\mathfrak A}_y,S_y)\colon y\in{Y}\}$ be a $Q$-disintegration of $R$ (if ${\mathfrak A}_y={\mathfrak A}$ for every $y\in{Y}$, then we have a regular conditional probability

Yuri A. Fadeyev

Calculations of stellar evolution at initial abundances of helium $Y=0.28$ and heavier elements $Z=0.014$ were done for stars with masses on the main sequence $1.7M_\odot\le M_\textrm{ZAMS}\le 5.2M_\odot$. Evolutionary sequences corresponding to the AGB stage were used for modelling the pulsation period decrease observed for almost two centuries in the Mira-

Subaru Yoshimoto, Yoshikazu Tabata, Takeshi Waki, Hiroyuki Nakamura

DyRu2Si2 is a frustrated magnet to exhibit multiple magnetic phase transition in zero and finite magnetic fields. We investigated and characterized the phase transition between the partially-ordered antiferromagnetic phases at zero field by ac susceptibility measurements. Detailed ac susceptibility measurements reveal the novel critical dynamics of the phase

Polina Turishcheva, Paul G. Fahey, Laura Hansel, Rachel Froebe

Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding of this system by allowing computational neuroscientists to create predictive models and bridge biological and

Geraud Nangue Tasse, Tamlin Love, Mark Nemecek, Steven James

An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common solution is for a human expert to define either a penalty in the reward function or a cost to be minimised when reaching unsafe states. However, this is non-trivial, since too small a penalty may lead to agents that reach unsafe sta

Kazimierz Musial

Let $X$ be a Banach space with RNP, $(\vO,\vS,\mu)$ be a complete probability space and $\vG:\vO\to{cb(X)}$ (nonempty, closed convex and bounded subsets of $X$) be a multifunction. Assume that $\vX\subset\vS$ is a $\sigma$-algebra and the multimeasure $M$ defined by the Pettis integral of $\vG$ be such that the restriction of $M$ to $\vX$ is of $\sigma$-fini

Michele Visinoni

We present a family of Virtual Element Methods for three-dimensional linear elasticity problems based on the Hellinger-Reissner variational principle. A convergence and stability analysis is developed. Moreover, using the hybridization technique and exploiting the information derived from this procedure, we show how to compute a better approximation for the

Qihang Sun

Sums of Kloosterman sums have deep connections with the theory of modular forms, and their estimation has many important consequences. Kuznetsov used his famous trace formula and got a power-saving estimate with respect to $x$ with implied constants depending on $m$ and $n$. Recently, in 2009, Sarnak and Tsimerman obtained a bound uniformly in $x$, $m$ and $

Dmitry Nikolaev, Collin F. Baker, Miriam R. L. Petruck, Sebastian Padó

This paper begins with the premise that adverbs are neglected in computational linguistics. This view derives from two analyses: a literature review and a novel adverb dataset to probe a state-of-the-art language model, thereby uncovering systematic gaps in accounts for adverb meaning. We suggest that using Frame Semantics for characterizing word meaning, as

Oscar E. González, Qihang Sun

Traces of singular moduli can be approximated by exponential sums of quadratic irrationals. Recently Andersen and Duke used theory of Maass forms to estimate generalized twisted traces with power-saving error bounds. We establish an asymptotic formula with effective error bounds for such traces. Our methods depend on an explicit bound for sums of Kloosterman

P. Maris, H. Le, A. Nogga, R. Roth

We present theoretical ground state energies and their uncertainties for p-shell nuclei obtained from chiral effective field theory internucleon interactions as a function of chiral order, fitted to two- and three-body data only. We apply a Similary Renormalization Group transformation to improve the numerical convergence of the many-body calculations, and d

H. Sabor Behmanush, M. Kucukaslan

In this paper, we will define $\mathcal{I}^{*}$-sequential topology on a topological space $(X,\tau)$ where $\mathcal{I}$ is an ideal of the subset of natural numbers $\mathbb{N}$. Besides the basic properties of the $\mathcal{I}^{*}$-sequential topology, we proved that $\mathcal{I}^{*}$-sequential topology is finer than $\mathcal{I}$-sequential topology. Fu

Patrick Lutz, Benjamin Siskind

Martin's Conjecture is a proposed classification of the definable functions on the Turing degrees. It is usually divided into two parts, the first of which classifies functions which are not above the identity and the second of which classifies functions which are above the identity. Slaman and Steel proved the second part of the conjecture for Borel functio

Jiakun Lei, Tao Meng, Kun Wang, Weijia Wang

This paper investigates the boresight alignment control problem under safety constraints and performance requirements, involving pointing-forbidden constraint, attitude angular velocity limitation, and pointing accuracy requirement. Meanwhile, the parameter uncertainty issue is taken into account simultaneously. To address this problem, we propose a modified

Ikboljon Sobirov

Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year. Clinicians use medical imaging modalities such as computed tomography and positron emission tomography to detect the presence of a tumor, and they combine that information with clinical

Jiakun Lei, Tao Meng, Kun Wang, Weijia Wang

This paper focuses on the spacecraft attitude control problem with intermittent actuator activation, taking into account the attitude rotation rate limitation and input saturation issue simultaneously. To address this problem, we first propose a composite event-trigger mechanism, which composed of two state-dependent trigger that governing the activation and

Cosmin I. Bercea, Michael Neumayr, Daniel Rueckert, Julia A. Schnabel

The introduction of diffusion models in anomaly detection has paved the way for more effective and accurate image reconstruction in pathologies. However, the current limitations in controlling noise granularity hinder diffusion models' ability to generalize across diverse anomaly types and compromise the restoration of healthy tissues. To overcome these chal

Adnan A. E. Hajomer, Cedric Bruynsteen, Ivan Derkach, Nitin Jain

Quantum key distribution (QKD) is a well-known application of quantum information theory that guarantees information-theoretically secure key exchange. As QKD becomes more and more commercially viable, challenges such as scalability, network integration, and high production costs need to be addressed. Photonic and electronic integrated circuits that can be p

D. Alberton, V. Lattanzi, C. Endres, V. M. Rivilla

Cyanides, ranging from three carbon atoms to PAHs, and alkenyl compounds are abundant in the interstellar medium (ISM). Aminoacrylonitrile (3-Amino-2-propenenitrile, H$_{2}$N-CH=CH-CN), an alkenyl cyanide, thus represents a promising candidate for new interstellar detection. A comprehensive spectroscopic laboratory investigation of aminoacrylonitrile in its

Junyu Zhou, Shuo Huang, Han Feng, Puyu Wang

In this paper, we are concerned with the generalization performance of non-parametric estimation for pairwise learning. Most of the existing work requires the hypothesis space to be convex or a VC-class, and the loss to be convex. However, these restrictive assumptions limit the applicability of the results in studying many popular methods, especially kernel

Ronakraj Gosalia, Robert Malaney, Ryan Aguinaldo, Jonathan Green

Precision navigation and timing, very-long-baseline interferometry, next-generation communication, sensing, and tests of fundamental physics all require a highly synchronized network of clocks. With the advance of highly-accurate optical atomic clocks, the precision requirements for synchronization are reaching the limits of classical physics (i.e. the stand

Christopher Williams, Fabian Falck, George Deligiannidis, Chris Holmes

U-Nets are a go-to, state-of-the-art neural architecture across numerous tasks for continuous signals on a square such as images and Partial Differential Equations (PDE), however their design and architecture is understudied. In this paper, we provide a framework for designing and analysing general U-Net architectures. We present theoretical results which ch

Bo Liu, Mengqing Zhan

In this paper, inspired by the spectral sequences constructed by signature operators with respect to the composition of fibrations, we define the "spectral sequences" for fiberwise Dirac operators and prove the equivariant family version of the adiabatic limit formula of eta invariants using the heat kernel method and the analytic localization techniques est

Flavio Di Martino, Franca Delmastro, Cristina Dolciotti

Malnutrition is a serious and prevalent health problem in the older population, and especially in hospitalised or institutionalised subjects. Accurate and early risk detection is essential for malnutrition management and prevention. M-health services empowered with Artificial Intelligence (AI) may lead to important improvements in terms of a more automatic,

Xinghong Pan

In this paper, we prove the global existence of analytical solutions to the compressible Oldroyd-B model without retardation near a non-vacuum equilibrium in ${\mathbb R}^n$ $(n=2,3)$. Zero retardation results in zero dissipation in the velocity equation, which is the main difficulty that prevents us to obtain the long time well-posedness of solutions. Throu

Yang Liu, Bao Wang, Hongwei Yu, Puxun Wu

The Hubble constant $H_0$ tension has emerged as the most serious crisis in modern cosmology, potentially indicating that the $\Lambda$CDM model may not describe our universe accurately. In this paper, we establish a new, cosmological-model-independent method to study the cosmic background dynamics. Using the latest Pantheon+ Type Ia supernova (SN Ia) sample