December 2023 arXiv papers — page 24

Gaëtan Borot, Maksim Karev, Danilo Lewański

The general relation between Chekhov-Eynard-Orantin topological recursion and the intersection theory on the moduli space of curves, the deformation techniques in topological recursion, and the polynomiality properties with respect to deformation parameters can be combined to derive vanishing relations involving intersection indices of tautological classes.

Fangqing Chen

This paper designs a servo control system based on sliding mode control for the shape control of elastic objects. In order to solve the effect of non-smooth and asymmetric control saturation, a Gaussian-based continuous differentiable asymmetric saturation function is used for this goal. The proposed detection approach runs in a highly real-time manner. Mean

Kun Lan, Haoran Li, Haolin Shi, Wenjun Wu

Recently, 3D Gaussian, as an explicit 3D representation method, has demonstrated strong competitiveness over NeRF (Neural Radiance Fields) in terms of expressing complex scenes and training duration. These advantages signal a wide range of applications for 3D Gaussians in 3D understanding and editing. Meanwhile, the segmentation of 3D Gaussians is still in i

Yingpeng Wen, Weijiang Yu, Fudan Zheng, Dan Huang

Previous post-processing studies on rainfall forecasts using numerical weather prediction (NWP) mainly focus on statistics-based aspects, while learning-based aspects are rarely investigated. Although some manually-designed models are proposed to raise accuracy, they are customized networks, which need to be repeatedly tried and verified, at a huge cost in t

Durmuş Demir

The ultraviolet cutoff on a quantum field theory can be interpreted as a condensate of the affine curvature such that while the maximum of the affine action gives the power-law corrections, its minimum leads to the emergence of gravity. This mechanism applies also to fundamental strings as their spinless unstable ground levels can be represented by the scala

Konstantinos Kogkalidis, Jean-Philippe Bernardy, Vikas Garg

We introduce a novel positional encoding strategy for Transformer-style models, addressing the shortcomings of existing, often ad hoc, approaches. Our framework provides a flexible mapping from the algebraic specification of a domain to an interpretation as orthogonal operators. This design preserves the algebraic characteristics of the source domain, ensuri

Siqi Lai, Zhao Xu, Weijia Zhang, Hao Liu

Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion. Traditional TSC methods, primarily based on transportation engineering and reinforcement learning (RL), often struggle with generalization abilities across varied traffic scenarios and lack interpretability. This

Hyenkyun Woo

This article presents a new polynomial parameterized sigmoid called SIGTRON, which is an extended asymmetric sigmoid with Perceptron, and its companion convex model called SIGTRON-imbalanced classification (SIC) model that employs a virtual SIGTRON-induced convex loss function. In contrast to the conventional $\pi$-weighted cost-sensitive learning model, the

Daisuke Inoue, Seiichiro Onari, Hiroshi Kontani

In the magic angle twisted bilayer graphene (MATBG), non-Fermi liquid like transport phenomena are universally observed. To understand their origin, we perform the self-consistent analysis of the self-energy due to SU(4) valley + spin fluctuations induced by the electron-electron correlation. In the SU(4) fluctuation mechanism, the fifteen channels of fluctu

Tamaghna Chowdhury, Sagnik Chatterjee, Dibyasankar Das, Ivan Timokhin

Transition-metal dichalcogenides (TMDs) host tightly bound quasi-particles called excitons. Based on spin and momentum selection rules, these excitons can be either optically bright or dark. In tungsten-based TMDs, momentum-forbidden dark exciton is the energy ground state and therefore it strongly affect the emission properties. In this work, we brighten th

Xingxing Yang, Jie Chen, Zaifeng Yang

NIR-to-RGB spectral domain translation is a challenging task due to the mapping ambiguities, and existing methods show limited learning capacities. To address these challenges, we propose to colorize NIR images via a multi-scale progressive feature embedding network (MPFNet), with the guidance of grayscale image colorization. Specifically, we first introduce

Yunqi Gu, Tao Zhou, Yizhe Zhang, Yi Zhou

Medical image segmentation plays a crucial role in computer-aided diagnosis. However, existing methods heavily rely on fully supervised training, which requires a large amount of labeled data with time-consuming pixel-wise annotations. Moreover, accurately segmenting lesions poses challenges due to variations in shape, size, and location. To address these is

Takeru Yokota

Addressing high-dimensional partial differential equations to derive effective actions within the functional renormalization group is formidable, especially when considering various field configurations, including inhomogeneous states, even on lattices. We leverage physics-informed neural networks (PINNs) as a state-of-the-art machine learning method for sol

Henri Tertilt, Jonas Mensing, Marlon Becker, Wilfred G. van der Wiel

Nonlinear behavior in the hopping transport of interacting charges enables reconfigurable logic in disordered dopant network devices, where voltages applied at control electrodes tune the relation between voltages applied at input electrodes and the current measured at an output electrode. From kinetic Monte Carlo simulations we analyze the critical nonlinea

Felix Dollack, Kiyoshi Kiyokawa, Huakun Liu, Monica Perusquia-Hernandez

The congruence between affective experiences and physiological changes has been a debated topic for centuries. Recent technological advances in measurement and data analysis provide hope to solve this epic challenge. Open science and open data practices, together with data analysis challenges open to the academic community, are also promising tools for solvi

Bruno da Ré, Damian Szmuc, Emmanuel Chemla, Paul Égré

Given a three-valued definition of validity, which choice of three-valued truth tables for the connectives can ensure that the resulting logic coincides exactly with classical logic? We give an answer to this question for the five monotonic consequence relations $st$, $ss$, $tt$, $ss\cap tt$, and $ts$, when the connectives are negation, conjunction, and disj

Gennaro Auricchio, Jie Zhang, Mengxiao Zhang

In this paper, we study of the $m$-Capacitated Facility Location Problem ($m$-CFLP) on the line from a Bayesian Mechanism Design perspective and propose a novel class of mechanisms: the \textit{Extended Ranking Mechanisms} (ERMs). We first show that an ERM is truthful if and only if it satisfies a system of inequalities that depends on the capacities of the

Alejandro Ramos, Takuya Uemura, Daichi Amagata, Ryo Shirai

Order Dependencies (ODs) have many applications, such as query optimization, data integration, and data cleaning. Although many works addressed the problem of discovering OD (and its variants), they do not consider datasets with missing values, a standard observation in real-world datasets. This paper introduces the novel notion of Embedded ODs (eODs) to dea

Jitendra Dhakar, Ram Prakash Bharti

This study has numerically investigated the charge-heterogeneity effects in the electroviscous flow of symmetric ($1$:$1$) electrolyte liquid through a uniform slit microfluidic device. The Poisson's, Nernst-Planck (N-P), Navier-Stokes (N-S), and continuity equations are solved using the finite element method (FEM) to obtain the flow fields, such as total el

Sivaram P

We adapt the PDE approach of Guo-Phong-Tong and Guo-Phong-Tong-Wang [17, 18] to prove an $L^\infty$ estimate for transverse complex Monge-Amp\`ere equations on homologically orientable transverse K\"ahler manifolds. As an application, we obtain a purely PDE proof of the regularity of Calabi-Yau cone metrics on Q-Gorenstein T-varieties.

Zhenguo Wang, Xian-Hui Ge, Shuta Ishigaki

Recent experiments strongly indicate deep connections between transports of strange metal and high $T_c$ superconductors. For instance, it is known that the dependence of the zero-temperature phase stiffness on the critical superconducting temperature becomes linear in underdoped materials. In this paper, we investigate relation meticulously between the phas

D. Minniti, N. Matsunaga, J. G. Fernandez-Trincado, S. Otsubo

Context. The Galactic centre is hazardous for stellar clusters because of the strong tidal force. Supposedly, many clusters were destroyed and contributed stars to the crowded stellar field of the bulge and the nuclear stellar cluster. However, it is hard to develop a realistic model to predict the long-term evolution of the complex inner Galaxy, and observi

Hyewon Han, Bogeun Gwak

We explored the impact of mass fluctuations on anti-de Sitter black holes in higher dimensions, particularly focusing on their effects on thermodynamic properties and null trajectories of the black holes. Our findings indicate that mass oscillations lead to perturbations in thermodynamic variables and geodesics. These result in the second-order fluctuations

Arash Dehghan, Mucahit Cevik, Merve Bodur

This paper explores the integration of Automated Guided Vehicles (AGVs) in warehouse order picking, a crucial and cost-intensive aspect of warehouse operations. The booming AGV industry, accelerated by the COVID-19 pandemic, is witnessing widespread adoption due to its efficiency, reliability, and cost-effectiveness in automating warehouse tasks. This paper

Okyanus Oral, Figen S. Oktem

Near-field radar imaging systems are used in a wide range of applications such as concealed weapon detection and medical diagnosis. In this paper, we consider the problem of reconstructing the three-dimensional (3D) complex-valued reflectivity distribution of the near-field scene by enforcing regularization on its magnitude. We solve this inverse problem by

Hang Du, Guoshun Nan, Sicheng Zhang, Binzhu Xie

Multimodal Sarcasm Understanding (MSU) has a wide range of applications in the news field such as public opinion analysis and forgery detection. However, existing MSU benchmarks and approaches usually focus on sentence-level MSU. In document-level news, sarcasm clues are sparse or small and are often concealed in long text. Moreover, compared to sentence-lev

Xiaoxiang Chai, Xueyuan Wan

In odd dimensions, we prove a scalar curvature rigidity for parabolic convex polytopes in hyperbolic space enclosed by linear planes in the Poincare upper half-space model and convex with respect to the conformally related flat metric. Our method is based on spinor techniques and relies on the recent smoothing constructions of Brendle-Wang. We also prove a L

V. R. Shaginyan, A. Z. Msezane, G. S. Japaridze

The recent paper (Science 382, 907 (2023)) is devoted to measurements of shot noise to probe excitations in nanowires of the heavy fermion (HF) metal $\rm YbRh_2Si_2$. The authors observed that shot noise is strongly suppressed, and claim that the suppression cannot be attributed to either electron-phonon or electron-electron interactions in a Fermi liquid.

Alexey Golovnev, A. N. Semenova, V. P. Vandeev

We study conformal transformations in the most general parity-preserving models of the New General Relativity type. Then we apply them to analysis of cosmological perturbations in the (simplest) spatially flat cosmologies. Strong coupling issues around Minkowski spacetime are seen for many special cases of these models. At the same time, the behaviour of the

Juyoung Yun

This research embarks on pioneering the integration of gradient sampling optimization techniques, particularly StochGradAdam, into the pruning process of neural networks. Our main objective is to address the significant challenge of maintaining accuracy in pruned neural models, critical in resource-constrained scenarios. Through extensive experimentation, we

Michael Potter, Stefano Maxenti, Michael Everett

Survival Analysis (SA) models the time until an event occurs, with applications in fields like medicine, defense, finance, and aerospace. Recent research indicates that Neural Networks (NNs) can effectively capture complex data patterns in SA, whereas simple generalized linear models often fall short in this regard. However, dataset uncertainties (e.g., nois

Zhijie Shen, Chunyu Lin, Junsong Zhang, Lang Nie

Existing panoramic layout estimation solutions tend to recover room boundaries from a vertically compressed sequence, yielding imprecise results as the compression process often muddles the semantics between various planes. Besides, these data-driven approaches impose an urgent demand for massive data annotations, which are laborious and time-consuming. For

Sichun Luo, Bowei He, Haohan Zhao, Wei Shao

Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has employed specialized \textit{prompts} to leverage the in-context learning capabilities of LLMs for recommendation purposes. More recent studies have utilized instruction tuning tec

Lu Chen, Yabo Yang

In this paper, we are concerned with the critical Hardy-Sobolev equation \begin{equation*} -\Delta_{p}u = \frac{u^{p^{*}_s-1}}{|x|^{s}}, \ \ x\in \mathbb{R}^n \end{equation*} where $p^{*}_s = \frac{(n-s)p}{n-p}$ denotes the critical Hardy-Sobolev exponent. We classify the positive solutions of this equation for $0 < s < \frac{p-1}{p}$ and $\frac{(2s+n+1)+\sq

Gongjin Lan, Qi Hao

This paper aims to provide a quick review of the methods including the technologies in detail that are currently reported in industry and academia. Specifically, this paper reviews the end-to-end planning, including Tesla FSD V12, Momenta 2023, Horizon Robotics 2023, Motional RoboTaxi 2022, Woven Planet (Toyota): Urban Driver, and Nvidia. In addition, we rev

Sanghun Jung, JoonHo Lee, Xiangyun Meng, Byron Boots

Reliable estimation of terrain traversability is critical for the successful deployment of autonomous systems in wild, outdoor environments. Given the lack of large-scale annotated datasets for off-road navigation, strictly-supervised learning approaches remain limited in their generalization ability. To this end, we introduce a novel, image-based self-super

Aryan Jadon, Avinash Patil

The effectiveness of recommendation systems is pivotal to user engagement and satisfaction in online platforms. As these recommendation systems increasingly influence user choices, their evaluation transcends mere technical performance and becomes central to business success. This paper addresses the multifaceted nature of recommendations system evaluation b

Jiarui Zhang, Ruixu Geng, Xiaolong Du, Yan Chen

Passive non-line-of-sight (NLOS) imaging has witnessed rapid development in recent years, due to its ability to image objects that are out of sight. The light transport condition plays an important role in this task since changing the conditions will lead to different imaging models. Existing learning-based NLOS methods usually train independent models for d

Zheng-Rong Liu, Rui Chen, Bin Zhou

Floquet topological insulators have been widely investigated in lower-dimensional systems. However, Floquet topological insulators induced by time-periodic driving in higher-dimensional systems remain unexplored. In this work, we study the effects of time-periodic driving in a four-dimensional (4D) normal insulator, focusing on topological phase transitions

Yuhang Liu, Daowan Peng, Wei Wei, Yuanyuan Fu

Recently, neural module networks (NMNs) have yielded ongoing success in answering compositional visual questions, especially those involving multi-hop visual and logical reasoning. NMNs decompose the complex question into several sub-tasks using instance-modules from the reasoning paths of that question and then exploit intermediate supervisions to guide ans

Nicolas Gillis, Paul Van Dooren

The target stationary distribution problem (TSDP) is the following: given an irreducible stochastic matrix $G$ and a target stationary distribution $\hat \mu$, construct a minimum norm perturbation, $\Delta$, such that $\hat G = G+\Delta$ is also stochastic and has the prescribed target stationary distribution, $\hat \mu$. In this paper, we revisit the TSDP

Chollakorn Nimpattanavong, Thai Van Nguyen, Ibrahim Khan, Ruck Thawonmas

This paper proposes a delay mechanism to mitigate the impact of latency differences in the gRPC framework--a high-performance, open-source universal remote procedure call (RPC) framework--between different programming languages on the performance of agents in DareFightingICE, a fighting game research platform. The study finds that gRPC latency differences be

Md Sohel Mondal, Dov Fields, Vladimir S. Malinovsky, Siddhartha Santra

Large-scale quantum networks, necessary for distributed quantum information processing, are posited to have quantum entangled systems between distant network nodes. The extent and quality of distributed entanglement in a quantum network, that is its functionality, depends on its topology, edge-parameter distributions and the distribution protocol. We uncover

Anirban Basak, Amir Dembo, Allan Sly

Fixing $\beta \ge 0$ and an integer $q \ge 2$, consider the ferromagnetic $q$-Potts measures $\mu_n^{\beta,B}$ on finite graphs ${\sf G}_n$ on $n$ vertices, with external field strength $B \ge 0$ and the corresponding random cluster measures $\varphi^{q,\beta,B}_{n}$. Suppose that as $n \to \infty$ the uniformly sparse graphs ${\sf G}_n$ converge locally to

Zahra Seyedi, Farhad Rahmati, Mohammad Ali, Ximeng Liu

Edge storage presents a viable data storage alternative for application vendors (AV), offering benefits such as reduced bandwidth overhead and latency compared to cloud storage. However, data cached in edge computing systems is susceptible to intentional or accidental disturbances. This paper proposes a decentralized integrity auditing scheme to safeguard da

Qinghui Lu, Zhen Du, Zenghui Zhang

Conventional orthogonal frequency division multiplexing (OFDM) waveform design in integrated sensing and communications (ISAC) systems usually selects the channels with high-frequency responses to transmit communication data, which does not fully consider the possible interference in the environment. To mitigate these adverse effects, we propose an optimizat

Nasir Ali, Hafiz Muhammad Afzal Siddiqui, Muhammad Imran Qureshi

This article investigates the concept of dominant metric dimensions in zero divisor graphs (ZD-graphs) associated with rings. Consider a finite commutative ring with unity, denoted as R, where nonzero elements x and y are identified as zero divisors if their product results in zero (x.y=0). The set of zero divisors in ring R is referred to as L(R). To analyz

Zan Yu, Lianzeng Zhang

The Gerber-Shiu function is a classical research topic in actuarial science.However, exact solutions are only available in the literature for very specific cases where the claim amounts follow distributions such as the exponential distribution. This presents a longstanding challenge, particularly from a computational perspective. For the classical risk proce

Jinxiang Song, Vincent Lauinger, Christian Häger, Jochen Schröder

We propose a novel frequency-domain blind equalization scheme for coherent optical communications. The method is shown to achieve similar performance to its recently proposed time-domain counterpart with lower computational complexity, while outperforming the commonly used CMA-based equalizers.

Meng Ge, Yizhou Peng, Yidi Jiang, Jingru Lin

This paper summarizes our team's efforts in both tracks of the ICMC-ASR Challenge for in-car multi-channel automatic speech recognition. Our submitted systems for ICMC-ASR Challenge include the multi-channel front-end enhancement and diarization, training data augmentation, speech recognition modeling with multi-channel branches. Tested on the offical Eval1

Monu Singh, Santabrata Das

We examine the effect of variable viscosity parameter ($\alpha$) in relativistic, low angular momentum advective accretion flow around rotating black holes. Following the recent simulation studies of magnetohydrodynamic disk that reveal the radial variation of $\alpha(r)$, we theoretically investigate the properties of the global transonic accretion flow con

S. B. Korolev, E. N. Bashmakova, T. Yu. Golubeva

The paper addresses the construction an error correction code for quantum calculations based on squeezed Fock states. It is shown that the use of squeezed Fock states makes it possible to satisfy the Knill-Laflamme (KL) criteria for bosonic error correction codes. It is shown that the first squeezed Fock state corrects both photon loss and dephasing errors b

Jianyu Xu, Yu-Xiang Wang

We study an online contextual dynamic pricing problem, where customers decide whether to purchase a product based on its features and price. We introduce a novel approach to modeling a customer's expected demand by incorporating feature-based price elasticity, which can be equivalently represented as a valuation with heteroscedastic noise. To solve the probl

Ambra Nanni, Sergio Cristallo, Darko Donevski, Michał J. Michałowski

Aims. We investigate the role of photo-evaporation of dust exposed to the radiation field from hot young stars and planetary nebulae (PNe) as a possible destruction mechanism of dust grains in the interstellar medium (ISM). Methods. We estimate photo-evaporation induced by the feedback of individual or clustered young stars, of PNe and in the presence of a v

Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li

Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness. Existing methods for achieving this alignment often involves employing reinforcement learning from human feedback (RLHF) to fine-tune LLMs based on human labels assessing the rel

Shu-Cheng Chang, Chin-Tung Wu, Liuyang Zhang

In this article, we recapture the Smale conjecture on a Sasakian $3$-sphere via the Legendrian mean curvature flow. More precisely,~we deform the area-preserving contactomorphism (symplectomorphism) of Sasakian $3$-spheres to an isometry via the Legendrian mean curvature flow on the Legendrian graph in $\mathbb{S}^{2}\times \mathbb{S}^{3}$. By using the mono

Artem Betlei, Mariia Vladimirova, Mehdi Sebbar, Nicolas Urien

The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users. The bidding procedure is highly complex due to various factors such as market competition, user behavior, and the diverse objectives of advertisers. In this paper we consider the problem at the level of user timel

Daniel Barzilai, Ohad Shamir

It is by now well-established that modern over-parameterized models seem to elude the bias-variance tradeoff and generalize well despite overfitting noise. Many recent works attempt to analyze this phenomenon in the relatively tractable setting of kernel regression. However, as we argue in detail, most past works on this topic either make unrealistic assumpt

Bhushan Chaudhary, Anubha Pandey, Deepak Bhatt, Darshika Tiwari

Addressing bias in the trained machine learning system often requires access to sensitive attributes. In practice, these attributes are not available either due to legal and policy regulations or data unavailability for a given demographic. Existing bias mitigation algorithms are limited in their applicability to real-world scenarios as they require access t

Yuqi Zheng, Ruidong Yan, Bin Jia, Rui Jiang

In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can fully utilize the advantages of the two algorithms and significantly improve the performance of car following. However, it is challenging for the traditional hybrid strategy based on fixed coefficients to adapt to mixed traffic flow sc

Joshua N. Benabou, Adriano Testa, Chen Heinrich, Henry S. Grasshorn Gebhardt

The bispectrum, the three-point correlation in Fourier space, is a crucial statistic for studying many effects targeted by the next-generation galaxy surveys, such as primordial non-Gaussianity (PNG) and general relativistic (GR) effects on large scales. In this work we develop a formalism for the bispectrum in the Spherical Fourier-Bessel (SFB) basis - a na

Jan Butora, Patrick Bas

If the extraction of sensor fingerprints represents nowadays an important forensic tool for sensor attribution, it has been shown recently that images coming from several sensors were more prone to generate False Positives (FP) by presenting a common "leak". In this paper, we investigate the possible cause of this leak and after inspecting the EXIF metadata

Andrzej Okolow

We consider a specific Hamiltonian formulation of the Teleparallel Equivalent of General Relativity, where the canonical variables are expressed by means of differential forms. We show that some ``position'' variables of this formulation can be always gauge-transformed to zero. In this gauge the constraints of the theory become simpler, and the other ``posit

Huiqun Jiang, Yue Liu

Let $\dot{\mathscr{B}}\triangleq \dot{\mathscr{B}}_{\dot{H}, \mu}$ denote an arbitrary signed bipartite graph with $\dot{H}$ as a star complement for an eigenvalue $\mu$, where $\dot{H}$ is a totally disconnected graph of order $s$. In this paper, by using Hadamard and Conference matrices as tools, the maximum order of $\dot{\mathscr{B}}$ and the extremal gr

Xiangru Li, Xiaoyu Zhang, Shengchun Xiong, Yulong Zheng

This paper investigates the problem of estimating three stellar atmospheric physical parameters and thirteen elemental abundances for medium-resolution spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). Typical characteristics of these spectra are their huge scale, wide range of spectral signal-to-noise ratios, and uneven distri

Natalia P. Bondarenko

In this paper, we revisit McLaughlin's inverse problem, which consists in the recovery of the fourth-order differential operator from the eigenvalues and two sequences of weight numbers. We for the first time prove the uniqueness for solution of this problem. Moreover, we obtain the interpretation of McLaughlin's problem in the framework of the general inver

Hitesh Poddar, Akhileswar Chowdary, Theodore S. Rappaport, Marwa Chafii

The next generation of wireless communication is expected to harness the potential of the sub-THz bands to achieve exceptional performance and ubiquitous connectivity. However, network simulators such as ns-3 currently lack support for channel models above 100 GHz. This limits the ability of researchers to study, design, and evaluate systems operating above

Anna Beliakova, Ivelina Bobtcheva, Marco De Renzi, Riccardo Piergallini

In this paper, we give a new direct proof of a result by Bobtcheva and Piergallini that provides finite algebraic presentations of two categories, denoted $3\mathrm{Cob}$ and $4\mathrm{HB}$, whose morphisms are manifolds of dimension $3$ and $4$, respectively. More precisely, $3\mathrm{Cob}$ is the category of connected oriented $3$-dimensional cobordisms be

Hang Chen, Yuchuan Jang, Weijie Zhou, Cristian Meo

Individuals, despite having varied life experiences and learning processes, can communicate effectively through languages. This study aims to explore the efficiency of language as a communication medium. We put forth two specific hypotheses: First, discrete messages are more effective than continuous ones when agents have diverse personal experiences. Second

Khushboo Dange, Vaishali Roondhe, Alok Shukla

In this work, we have systematically investigated the structural, electronic, vibrational and optical properties of the edge-functionalized hg-C3N4 quantum dots with the aim of exploring their possible applications in solar cells and other optoelectronic devices such as light-emitting diodes. The functional groups considered in this work are methyl (-CH$_3$)

Yusuke Tanimura, Myung-Ki Cheoun

Effects of the center-of-mass correction together with the nucleon electromagnetic form factors on the nuclear charge radius are systematically studied with a relativistic Hartree-Bogoliubov model. Both one- and two-body parts of the CM correction are taken into account. It is found that the one- and two-body CM corrections, and the spin-orbit effect origina

Ayse Kotil, Fedor Simkovic, Martin Leib

We develop a qubit routing algorithm with polynomial classical run time for the Quantum Approximate Optimization Algorithm (QAOA). The algorithm follows a two step process. First, it obtains a near-optimal solution, based on Vizing's theorem for the edge coloring problem, consisting of subsets of the interaction gates that can be executed in parallel on a fu

Joel Fotso Tachago, Hubert Nnang

Stochastic-periodic homogenization is studied for the Maxwell equations with nonlinear and periodic electric conductivity. It is shown by the stochastic-two-scale convergence method that the sequence of solutions of a class of highly oscillatory problems converges to the solution of a homogenized Maxwell equation.

Sangmin Woo, Byeongjun Park, Hyojun Go, Jin-Young Kim

Recent progress in single-image 3D generation highlights the importance of multi-view coherency, leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale images. However, the aspect of novel-view diversity remains underexplored within the research landscape due to the ambiguity in converting a 2D image into 3D content, where numero

Johannes Sandberg, Thomas Voigtmann, Emilie Devijver, Noel Jakse

Neural network potentials are a powerful tool for atomistic simulations, allowing to accurately reproduce \textit{ab initio} potential energy surfaces with computational performance approaching classical force fields. A central component of such potentials is the transformation of atomic positions into a set of atomic features in a most efficient and informa

Franck Arnold Tchinda, Joel Fotso Tachago, Joseph Dongho

We extend the concept of two-scale convergence on forms in Orlicz-Sobolev's spaces and we describe the homogenization for a family of integral functionals with convex and nonstandard growth integrands defined on the tangent bundle of a Remannian manifold.

Tomoya Nakatani, Prabhanjan D. Kulkarni, Hirofumi Suto, Keisuke Masuda

Recent advances in the study of materials with topological electronic band structures have revealed magnetic materials exhibiting giant anomalous Hall effects (AHE). The giant AHE has not only attracted the research interest in its mechanism but also opened up the possibility of practical application in magnetic sensors. In this article, we describe simulati

Irina Despirak, Pavel Setsko, Andris Lubchich, Rajkumar Hajra

We analyzed intense geomagnetically induced currents (GICs) recorded during a complex space weather event observed on 23-24 April 2023. Two geomagnetic storms characterized by SYM/H intensities of -179 nT and -233 nT was caused by southward IMG Bz of -25 nT in the sheath fields and -33 nT in the magnetic cloud (MC) fields. GIC observations were divided into

Amar Aryan

Core-collapse supernovae (CCSNe) are catastrophic astrophysical phenomena that occur during the last evolutionary stages of massive stars having initial masses of around 8 M$_{\odot}$ or more. These calamitous events play a pivotal role in enriching our Universe with heavy elements and are also responsible for the birth of Neutron stars and stellar mass Blac

Rahul Gupta

Gamma-ray bursts (GRBs) are fascinating sources studied in modern astronomy. They are extremely luminous electromagnetic explosions in the Universe observed from cosmological distances. These unique characteristics provide a marvellous chance to study the evolution of massive stars and probe the rarely explored early Universe. In addition, the central source

Grigorios A. Pavliotis, Sebastian Reich, Andrea Zanoni

We consider the problem of estimating unknown parameters in stochastic differential equations driven by colored noise, which we model as a sequence of Gaussian stationary processes with decreasing correlation time. We aim to infer parameters in the limit equation, driven by white noise, given observations of the colored noise dynamics. We consider both the m

Rajnandini Sharma, Pawan Kumar Ojha, Simran Sahoo, Rijul Roychowdhury

Magnonics has shown the immense potential of compatibility with CMOS devices and the ability to be utilized in futuristic quantum computing. Therefore, the magnonic crystals, both metallic and insulating, are under extensive exploration. The presence of high spin-orbit interaction induced by the presence of rare-earth elements in thulium iron garnet (TmIG) i

Seungchan Lim, Sumin Kim, Sung-Hee Lee

This paper presents a novel method for reconstructing 3D garment models from a single image of a posed user. Previous studies that have primarily focused on accurately reconstructing garment geometries to match the input garment image may often result in unnatural-looking garments when deformed for new poses. To overcome this limitation, our approach takes a

Dana Cohen Hochberg, Hayit Greenspan, Raja Giryes

The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the high costs associated with labeling, resulting in degraded performances of deep learning methods. Self-supervised learning is frequently adopted to mitigate the reliance on massive

Shengmin Zhang, Zhencai Shen

Let $G$ be a finite group and $H$ be a subgroup of $G$. Then $H$ is called a weakly $S\Phi$-supplemented subgroup of $G$, if there exists a subgroup $T$ of $G$ such that $G =HT$ and $H \cap T \leq \Phi (H) H_{sG}$, where $H_{sG}$ denotes the subgroup of $H$ generated by all subgroups of $H$ which are $S$-permutable in $G$. Let $p$ be a prime, $S$ be a $p$-gr

Junwen Guo, Guobao Xiao, Shiping Wang, Jun Yu

Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, in this paper we propose Graph Context Transformation Network (GCT-Net) enhancing context information to conduct consensus guidance for progress

Hongjie Li, Yao Guo, Xianwei Zheng, Hanjiang Xiong

This paper introduces a learnable Deformable Hypothesis Sampler (DeformSampler) to address the challenging issue of noisy depth estimation for accurate PatchMatch Multi-View Stereo (MVS). We observe that the heuristic depth hypothesis sampling modes employed by PatchMatch MVS solvers are insensitive to (i) the piece-wise smooth distribution of depths across

Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shooredeli

To benefit from the modeling capacity of deep models in system identification, without worrying about inference time, this study presents a novel training strategy that uses deep models only at the training stage. For this purpose two separate models with different structures and goals are employed. The first one is a deep generative model aiming at modeling

Annalisa Buffa, Ondine Chanon, Denise Grappein, Rafael Vázquez

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are considered not relevant for the approximation of the solution of a given PDE. In this work, the focus is on P

Andrea Secchi, Filippo Troiani

Strain represents an ubiquitous feature in semiconductor heterostructures, and can be engineered by different means in order to improve the properties of various devices, including advanced MOSFETs and spin-based qubits. However, its treatment within the envelope function framework is well established only for the homogeneous case, thanks to the theory of Bi

Kazim Ergun, Rishikanth Chandrasekaran, Tajana Rosing

Federated learning (FL) enables a loose set of participating clients to collaboratively learn a global model via coordination by a central server and with no need for data sharing. Existing FL approaches that rely on complex algorithms with massive models, such as deep neural networks (DNNs), suffer from computation and communication bottlenecks. In this pap

Noam Ben-Moshe, Kenta Tsutsui, Shany Biton, Leif Sörnmo

Introduction: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term, ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the perf

Jingpu Yang, Helin Wang, Qirui Zhao, Zhecheng Shi

Reinforcement Learning (RL), recognized as an efficient learning approach, has achieved remarkable success across multiple fields and applications, including gaming, robotics, and autonomous vehicles. Classical single-agent reinforcement learning grapples with the imbalance of exploration and exploitation as well as limited generalization abilities. This met

Hyun Kang, Dohae Lee, Myungjin Shin, In-Kwon Lee

Recent advancements in Text-to-Image (T2I) diffusion models have demonstrated impressive success in generating high-quality images with zero-shot generalization capabilities. Yet, current models struggle to closely adhere to prompt semantics, often misrepresenting or overlooking specific attributes. To address this, we propose a simple, training-free approac

Alexander Wires

The study of extensions realizing affine datum is specialized to central extensions in varieties with a difference term which leads to generalizations of several classical theorems on central extensions from group theory. We establish a 1-dimensional Hochschild-Serre sequence for a central extension equipped with affine datum. This is used to develop a Schur

On-Hei Solomon Lo, Cheng Wang, Huan Zhou, Xuding Zhu

Assume $G$ is a graph and $k$ is a positive integer. Let $f$ from $V(G)$ to $ N$ be defined as $f(v)$ is the minimum of $k$ and $d(v)$. If $G$ is $f$-DP-colourable (respectively, $f$-choosable), then we say $G$ is $k$-truncated degree DP-colourable (respectively, $k$-truncated degree-choosable). Hutchinson proved that 2-connected maximal outerplanar graphs o

Song Bao, Junsen Wang, Shin-ichiro Yano, Yanyan Shangguan

In $d$-electron systems, there can also be intricate interplay between Kondo coupling and magnetic interactions as that in $f$-electron systems, but the underlying mechanism remains elusive. Here, using inelastic neutron scattering, we investigate the temperature evolution of the low-energy spin waves (magnons) in a metallic van der Waals ferromagnet Fe$_{3-

Jingyao Li, Pengguang Chen, Bin Xia, Hong Xu

Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks. However, their performance tends to falter when confronted with more challenging programming problems. We observe that conventional models often generate solutions as monolithic code blocks, restricting their effectiveness in tackling intricate

Keli Zhang, Changping Yu, Peiqing Liu, Xinliang Li

In this paper, a permeable surface nondimensional FW-H (Ffowcs Williams-Hawkings) acoustics analogy post-processing code with convective effect and AoA (angle of attack) corrections, OpenCFD-FWH, has been eveloped. OpenCFD-FWH is now used as post processing code of our finite volume CFD solver OpenCFD-EC (Open Computational Fluid Dynamic code for Engineering

Aryan Esmailpour, Sanjay Krishnan, Stavros Sintos

Data partitioning that maximizes/minimizes the Shannon entropy, or more generally the R\'enyi entropy is a crucial subroutine in data compression, columnar storage, and cardinality estimation algorithms. These partition algorithms can be accelerated if we have a data structure to compute the entropy in different subsets of data when the algorithm needs to de