October 2023 arXiv papers — page 67

I. F. Llovo, J. Mosqueira, F. Vidal

The confrontation between percolation processes and superconducting fluctuations to account for the observed enhanced in-plane electrical conductivity above but near $T_c$ in cuprates is revisited. The cuprates studied here, La$_{1.85}$Sr$_{0.15}$CuO$_4$, Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$, and Tl$_2$Ba$_2$Ca$_2$Cu$_3$O$_{10}$, have a different number of sup

Andrei Negulescu, Weijia Shang

In mobile wireless networks, livestreaming in high user density areas presents two typical challenges: the wireless bandwidth is depleted and the number of users is limited. In this study, a media distribution model utilizing peer to peer communications, Active Control in an Intelligent and Distributed Environment, is proposed for bandwidth efficient livestr

Uri Katz, Matan Vetzler, Amir DN Cohen, Yoav Goldberg

Recognizing entities in texts is a central need in many information-seeking scenarios, and indeed, Named Entity Recognition (NER) is arguably one of the most successful examples of a widely adopted NLP task and corresponding NLP technology. Recent advances in large language models (LLMs) appear to provide effective solutions (also) for NER tasks that were tr

Madoka Awada

In this paper, we show infinite series of $3$-designs in the extended quadratic residue codes over $\mathbb {F}_{r^2}$ for a prime $r$.

Arvind Arun Dev, Thomas Hermans, Bernard Doudin

The Rayleigh-Plateau instability (RPI) is a classical hydrodynamics phenomenon that prevents a jet of liquid to flow indefinitely within air or another liquid. Here, we show how adding a magnetic force field makes possible its suppression. Enclosing the jet in a ferrofluid held by magnetic forces allows flow focusing without sheath flow, which completely avo

Tatsumasa Suzuki

Karakurt and \c{S}avk computed the Ozsv\'ath-Szab\'o $d$-invariants of Brieskorn homology $3$-spheres arising as surgeries on almost simple linear graphs. In this paper, we refine their formula for these $d$-invariants. Furthermore, we present infinite families of examples for which the inequalities appearing in this refinement are equalities, as well as inf

Kun Wei, Bei Li, Hang Lv, Quan Lu

Automatic Speech Recognition (ASR) in conversational settings presents unique challenges, including extracting relevant contextual information from previous conversational turns. Due to irrelevant content, error propagation, and redundancy, existing methods struggle to extract longer and more effective contexts. To address this issue, we introduce a novel co

Bo Yuan, Danpei Zhao

Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. It breaks through the obstacle of one-way training on close sets and enables continuous adaptive learning on open-set conditions. In the recent decade, continual learning has been explored and applied in multiple fields espec

Martin W. Licht

We develop commuting finite element projections over smooth Riemannian manifolds. This extension of finite element exterior calculus establishes the stability and convergence of finite element methods for the Hodge-Laplace equation on manifolds. The commuting projections use localized mollification operators, building upon a classical construction by de Rham

Bae Jun Park, Naohito Tomita

In this paper, we study pointwise estimates for linear and multilinear pseudo-differential operators with exotic symbols in terms of the Fefferman-Stein sharp maximal function and Hardy-Littlewood type maximal function. Especially in the multilinear case, we use a multi-sublinear variant of the classical Hardy-Littlewood maximal function introduced by Lerner

Siyuan Li, Xun Wang, Rongchang Zuo, Kewu Sun

Imitation learning (IL) has achieved considerable success in solving complex sequential decision-making problems. However, current IL methods mainly assume that the environment for learning policies is the same as the environment for collecting expert datasets. Therefore, these methods may fail to work when there are slight differences between the learning a

Dahmane Dechicha

In this paper, we prove propagation of $\frac{1}{s}$-Gevrey regularity $(s \in (0, 1))$ and analyticity $(s=1)$ for the Vlasov-Navier-Stokes system on $\mathbb{T}^d \times \mathbb{R}^d$ (and $\mathbb{R}^d\times\mathbb{R}^d$) using a Fourier space method in analogy to the results proved for the Euler system in [Kukavica and Vicol, Proc. Amer. Math. Soc., 2009

Choon-Lin Ho

We give a brief overview of a simple and unified way, called the prepotential approach, to treat both exact and quasi-exact solvabilities of the one-dimensional Schr\"odinger equation. It is based on the prepotential together with Bethe ansatz equations. Unlike the the supersymmetric method for the exactly-solvable systems and the Lie-algebraic approach for

Lingyong Zeng, Xunwu Hu, Yazhou Zhou, Mebrouka Boubeche

Topological superconductors have drawn significant interest from the scientific community due to the accompanying Majorana fermions. Here, we report the discovery of electronic structure and superconductivity in high-entropy ceramics Ti0.2Zr0.2Nb0.2Mo0.2Ta0.2Cx (x = 1 and 0.8) combined with experiments and first-principles calculations. The Ti0.2Zr0.2Nb0.2Mo

Yibo Bai, Xiao-Lei Zhang, Xuelong Li

Recently, automatic speaker verification (ASV) based on deep learning is easily contaminated by adversarial attacks, which is a new type of attack that injects imperceptible perturbations to audio signals so as to make ASV produce wrong decisions. This poses a significant threat to the security and reliability of ASV systems. To address this issue, we propos

Dmitry V. Chubukov, Ivan A. Aleksandrov, Leonid V. Skripnikov, Alexander N. Petrov

We investigate the process of photon absorption by atoms or molecules shortly interacting with a laser beam in the dipole approximation. Assuming that the interaction time $\tau$ is much smaller than the lifetime of the corresponding excited state, we examine the absorption probability as a function of $\tau$. Besides, we incorporate Doppler broadening due t

Cătălin I. Cârstea, Matti Lassas, Tony Liimatainen, Leo Tzou

In this paper we consider an inverse problem of determining a minimal surface embedded in a Riemannian manifold. We show under a topological condition that if $\Sigma$ is a $2$-dimensional embedded minimal surface, then the knowledge of the Dirichlet-to-Neumann map associated to the minimal surface equation determines $\Sigma$ up to an isometry. Without the

Haiying Li, Tianshui Ma, Shuanhong Wang

Leibniz algebras can be seen as a ``non-commutative" analogue of Lie algebras. Nijenhuis operators on Leibniz algebras introduced by Cari\~{n}ena, Grabowski, and Marmo in [J. Phys. A: Math. Gen. 37(2004)] are (1, 1)-tensors with vanishing Nijenhuis torsion. Recently triangular Leibniz bialgebras were introduced by Tang and Sheng in [J. Noncommut. Geom. 16(20

Daizhan Cheng, Zhengping Ji

A generalized eigenvector of a hypermatrix, called the universal (U-) eigenvector, is proposed, which extended the notion of diagonal (D-) eigenvectors in the literature. Using the semi-tensor product, the homogeneous U-eigenequation can be converted into a general eigenequation of matrix $(A-\lambda B)x=0$. A general technique for solving this equation is p

Minxuan Lv, Chengwei Dai, Kun Li, Wei Zhou

Neural network models are vulnerable to adversarial examples, and adversarial transferability further increases the risk of adversarial attacks. Current methods based on transferability often rely on substitute models, which can be impractical and costly in real-world scenarios due to the unavailability of training data and the victim model's structural deta

Naoto Sakaki, Hideaki Miura, Kyo Yoshida, Yoshiyuki Tsuji

A method to identify a quantum vortex in a three-dimensional Gross-Pitaevskii simulation has been developed. A quantum vortex was identified by the use of eigenvalues and eigenvectors of the Hessian of the mass density, together with a condition to distinguish a point to constitute a swirling vortex from other confusing data points. This method has been veri

V. S. Kovtoniuk, E. V. Stolyarov, O. V. Kliushnichenko, A. A. Semenov

In quantum optics, measurement statistics -- for example, photocounting statistics -- are considered nonclassical if they cannot be reproduced with statistical mixtures of classical radiation fields. We have formulated a necessary and sufficient condition for such nonclassicality. This condition is given by a set of inequalities that tightly bound the convex

Ivana Kvapilíková, Ondřej Bojar

Even with the latest developments in deep learning and large-scale language modeling, the task of machine translation (MT) of low-resource languages remains a challenge. Neural MT systems can be trained in an unsupervised way without any translation resources but the quality lags behind, especially in truly low-resource conditions. We propose a training stra

Pratinav Seth, Rashi Goel, Komal Mathur, Swetha Vemulapalli

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts. Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentime

Fedor Bakharev, Aleksandr Enin, Sergey Matveenko, Dmitry Pavlov

We investigate one-phase flow in porous medium corresponding to a miscible displacement process in which the viscosity of the injected fluid is smaller than the viscosity in the reservoir fluid, which frequently leads to the formation of a mixing zone characterized by thin fingers. The mixing zone grows in time due to the difference in speed between its lead

Seong Won Cho, In Hak Lee, Youngwoong Lee, Sangheon Kim

The interplay between ferromagnetism and the non-trivial topology has unveiled intriguing phases in the transport of charges and spins. For example, it is consistently observed the so-called topological Hall effect (THE) featuring a hump structure in the curve of the Hall resistance (Rxy) vs. a magnetic field (H) of a heterostructure consisting of a ferromag

Zhiqi Tang, Rita Cunha, Tarek Hamel, Carlos Silvestre

This paper proposes a novel constructive barrier feedback for reactive collision avoidance between two agents. It incorporates this feature in a formation tracking control strategy for a group of 2nd-order dynamic robots defined in three-dimensional space. Using only relative measurements between neighboring agents, we propose an elegant decentralized contro

Kumar Saurav, Ashok Kumar Reddy Chavva

We consider a scheduling problem pertinent to a base station (BS) that serves heterogeneous users (UEs). We focus on a downlink model, with users that are either interested in large throughput, low latency (the time difference between the packet arrival at the BS, and reception at the UE), or low age-of-information (AoI, the difference between the current ti

Jin-Ping Zhu, Ying Qin, Zhen-Han-Tao Wang, Rui-Chong Hu

Super-Eddington accretion of neutron stars (NSs) has been suggested both observationally and theoretically. In this paper, we propose that NSs in close-orbit binary systems with companions of helium (He) stars, most of which systems form after the common-envelope phase, could experience super-Eddington stable Case BB/BC mass transfer (MT), and can sometimes

Jin-Ping Zhu

A population of binary stellar-mass black hole (BBH) mergers are believed to occur embedded in the accretion disk of active galactic nuclei (AGNs). In this {\em Letter}, we demonstrate that the jets from these BBH mergers can propagate collimatedly within the disk atmosphere along with a forward shock and a reverse shock forming at the jet head. Efficient pr

Yuto Watanabe, Hiroto Arima, Saori Kawaguchi-Imada, Hirokazu Kadobayashi

We investigate the thermal expansion and superconducting properties of a CuAl2-type (tetragonal) superconductor CoZr2 under high pressures. We perform high-pressure synchrotron X-ray diffraction in a pressure range of 2.9 GPa < P < 10.4 GPa and discover that CoZr2 exhibits volumetric negative thermal expansion under high pressures. Although the uniaxial posi

L. González-Sánchez, A. Veselinova, A. Martín Santa Daría, E. Yurtsever

We report new results from quantum calculations of energy-transfer processes taking place in interstellar environments and involving two newly observed molecular species: C$_5$N$^-$ and C$_7$N$^-$ in collision with He atoms and the p-H$_2$ molecules. These species are part of the anionic molecular chains labeled as cyanopolyynes which have been observed over

Jeffrey Shallit

We prove a recent conjecture of Sean A. Irvine about a nonlinear recurrence, using mechanized guessing and verification. The theorem-prover Walnut plays a large role in the proof.

Jianchao Zheng, Tuo Wu, Xiazhi Lai, Cunhua Pan

In this paper, we investigate a fluid antenna system (FAS)-assisted downlink non-orthogonal multiple access (NOMA) for short-packet communications. The base station (BS) adopts a single fixed antenna, while both the central user (CU) and the cell-edge user (CEU) are equipped with a FAS. Each FAS comprises $N$ flexible positions (also known as ports), linked

Maicol Caponi, Alessandro Carbotti, Francesco Sapio

In this paper we consider a dynamic model of fracture for viscoelastic materials, in which the constitutive relation, involving the Cauchy stress and the strain tensors, is given in an implicit nonlinear form. We prove the existence of a solution to the associated viscoelastic dynamic system on a prescribed time-dependent cracked domain via a discretisation-

Monica Ugaglia, Mikhail G. Katz

In this paper we offer a reconstruction of the evolution of Leibniz's thought concerning the problem of the infinite divisibility of bodies, the tension between actuality, unassignability and syncategorematicity, and the closely related question of the possibility of infinitesimal quantities, both in physics and in mathematics. Some scholars have argued that

Mingzhe Du, Anh Tuan Luu, Bin Ji, See-kiong Ng

The vast number of parameters in large language models (LLMs) endows them with remarkable capabilities, allowing them to excel in a variety of natural language processing tasks. However, this complexity also presents challenges, making LLMs difficult to train and inhibiting their ability to continuously assimilate new knowledge, which may lead to inaccuracie

Shamila Bayati

In this paper, we investigate which classes of monomial ideals have a quasi-additive property of homological shift ideals. More precisely, for a monomial ideal $I$ we are interested to find out whether $HS_{i+j}(I)\subseteq HS_i(HS_j(I))$. It turns out that $\mathbf{c}$-bounded principal Borel ideals as well as polymatroidal ideals satisfying strong exchange

Francesco Montagna, Nicoletta Noceti, Lorenzo Rosasco, Francesco Locatello

The use of simulated data in the field of causal discovery is ubiquitous due to the scarcity of annotated real data. Recently, Reisach et al., 2021 highlighted the emergence of patterns in simulated linear data, which displays increasing marginal variance in the casual direction. As an ablation in their experiments, Montagna et al., 2023 found that similar p

Sasha Boone, David Grant, Mark Hammond

We present an analytic theory for the resolution attainable via eclipse mapping of exoplanets, based on the Fourier components of the brightness distribution on the planetary disk. We find that the impact parameter determines which features can and cannot be seen, via the angle of the stellar edge relative to the axis of the orbit during the eclipse. We esti

Dongyang Yu

Lipkin-Meshkov-Glick (LMG) model is paradigmatic to study quantum phase transition in equilibrium or non-equilibrium systems and entanglement dynamics in a variety of disciplines. The generic LMG model usually incorporates two nonlinear interactions. While the classical dynamics of the single-nonlinear-inteaction LMG model is well understood through Jacobi e

Yuanyang Jiang

Inspired by [Pan22], we give a new proof that for an overconvergent modular eigenform $f$ of weight $1+k$ with $k\in\mathbb{Z}_{\ge1}$, assuming that its associated global Galois representation $\rho_{f}$ is irreducible, then $f$ is classical if and only if $\rho_{f}$ is de Rham at $p$. For the proof, we prove that theta operator $\theta^{k}$ coincides with

Zvi Stein, Adrian Stern

Despite the outstanding performance of deep neural networks, they are vulnerable to adversarial attacks. While there are many invisible attacks in the digital domain, most physical world adversarial attacks are visible. Here we present an invisible optical adversarial attack that uses a light source to dazzle a CMOS camera with a rolling shutter. We present

Yvain Bruned

In this work, we introduce Regularity Structures B-series which are used for describing solutions of singular stochastic partial differential equations (SPDEs). We define composition and substitutions of these B-series and as in the context of B-series for ordinary differential equations, these operations can be rewritten via products and Hopf algebras which

Ronni Amorim, Vinicius Rispoli, Sérgio Ulhoa, Kayo Araújo

In this article we analyze the behavior of a non-relativistic spinless particle near the event horizon of a Schwarzschild-like black hole. In this way, the Schr\"odinger covariant equation that describes the particle is obtained from the Galilean covariance technique. The Schr\"odinger equation in a Schwarzschild-like spacetime is solved analytically and its

Jia Zhang, Tiantian Shi, Jianxiang Miao, Deshui Yu

Lasing in the bad-cavity regime has promising applications in precision measurement and frequency metrology due to the reduced sensitivity of the laser frequency to cavity length fluctuations. Thus far, relevant studies have been mainly focused on conventional cavities whose finesse is high enough that the resonance linewidth is sufficiently narrow compared

Shwetang Dubey, Alok Ranjan Sahoo, Pavan Chakraborty

Visually impaired persons find it difficult to know about their surroundings while walking on a road. Walking sticks used by them can only give them information about the obstacles in the stick's proximity. Moreover, it is mostly effective in static or very slow-paced environments. Hence, this paper introduces a method to guide them in a busy street. To crea

Joji Benny, Supriyo Jana, Soumen Sarkar

In this paper, we characterize and study dynamical properties of cubic vector fields on the sphere $\mathbb{S}^2 = \{(x, y, z) \in \mathbb{R}^3 ~|~ x^2+y^2+z^2 = 1\}$. We start by classifying all degree three polynomial vector fields on $\mathbb{S}^2$ and determine which of them form Kolmogorov systems. Then, we show that there exist completely integrable cu

Zhiqian Lin, Jiangke Lin, Lincheng Li, Yi Yuan

Recent works based on convolutional encoder-decoder architecture and 3DMM parameterization have shown great potential for canonical view reconstruction from a single input image. Conventional CNN architectures benefit from exploiting the spatial correspondence between the input and output pixels. However, in 3D face reconstruction, the spatial misalignment b

Yuya Haraguchi, Hiroko Aruga Katori

The sample quality of the hyperhoneycomb lattice Kitaev magnet $\beta$-ZnIrO$_3$ was successfully improved by removing the maximum amount of moisture from the reaction ampoule. The X-ray diffraction structural analysis of the high-quality sample confirmed the presence of $P$2$_1$/$c$ superlattice peaks of the Fddd structure in the original structural model.

Sterling Ebel, Chris Kapulkin

We provide an axiomatic treatment of Quillen's construction of the model structure on topological spaces to make it applicable to a wider range of settings, including $\Delta$-generated spaces and pseudotopological spaces. We use this axiomatization to construct a model structure on the category of locales.

Niyaz Uddin Molla, Himanshu Chaudhary, Dhruv Arora, Farruh Atamurotov

We investigate the strong gravitational lensing phenomena caused by a black hole with a dark matter halo. In this study, we examine strong gravitational lensing with two significant dark matter models: the universal rotation curve model and the cold dark matter model. To do this, we first numerically estimate the strong lensing coefficients and strong deflec

Jinbiao Chen, Jiahai Wang, Zizhen Zhang, Zhiguang Cao

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first

Francesco Giuli

In this proceeding, we study the influence of theoretical systematic uncertainties due to the quark density on LHC experimental searches for $Z'$-bosons. Using an approach originally proposed in the context of the ABMP16 PDF set for the high-$x$ behaviour of the quark density, we presents results on differential cross section and Forward-Backward asymmetry o

Xiaoyu Yang, Yuzuru Inahama, Yong Xu

This work focuses on moderate deviations for two-time scale systems with mixed fractional Brownian motion. Our proof uses the weak convergence method which is based on the variational representation formula for mixed fractional Brownian motion. Throughout this paper, Hurst parameter of fractional Brownian motion is larger than $1/2$ and the integral along th

Anatolij K. Prykarpatski, Victor A. Bovdi

Poisson structures related with the affine Courant type algebroid are analyzed, including \ those related with cotangent bundles on Lie group manifolds. A special attantion is paid to Courant type algebroids and related R-structures \ on them, generated by suitably defined tensor mappings. \ There are constructed Lie-Poisson brackets invariant with respect t

Jinbiao Chen, Zizhen Zhang, Zhiguang Cao, Yaoxin Wu

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two

Haoran Wang, Qiuye Jin, Shiman Li, Siyu Liu

Deep learning has achieved widespread success in medical image analysis, leading to an increasing demand for large-scale expert-annotated medical image datasets. Yet, the high cost of annotating medical images severely hampers the development of deep learning in this field. To reduce annotation costs, active learning aims to select the most informative sampl

Nadav Borenstein, Phillip Rust, Desmond Elliott, Isabelle Augenstein

The digitisation of historical documents has provided historians with unprecedented research opportunities. Yet, the conventional approach to analysing historical documents involves converting them from images to text using OCR, a process that overlooks the potential benefits of treating them as images and introduces high levels of noise. To bridge this gap,

Zhenyi Lu, Wei Wei, Xiaoye Qu, XianLing Mao

Personalized dialogue systems aim to endow the chatbot agent with more anthropomorphic traits for human-like interactions. Previous approaches have explored explicitly user profile modeling using text descriptions, implicit derivation of user embeddings, or utilizing handicraft prompts for ChatGPT-like models. However, textual personas are limited in describ

Hendrik De Bie, Pan Lian, Frederick Maes

In this paper, we study the pointwise bounds for the kernel of the $(\kappa, a)$-generalized Fourier transform with $\kappa\equiv0$, introduced by Ben Sa\"id, Kobayashi and Orsted. We present explicit formulas for the case $a=4$, which show that the kernels can exhibit polynomial growth. Subsequently, we provide a polynomial bound for the even dimensional ke

Ruiying Lu, YuJie Wu, Long Tian, Dongsheng Wang

Unsupervised image Anomaly Detection (UAD) aims to learn robust and discriminative representations of normal samples. While separate solutions per class endow expensive computation and limited generalizability, this paper focuses on building a unified framework for multiple classes. Under such a challenging setting, popular reconstruction-based networks with

Kun Fang, Qinghua Tao, Xiaolin Huang, Jie Yang

Existing Out-of-Distribution (OoD) detection methods address to detect OoD samples from In-Distribution (InD) data mainly by exploring differences in features, logits and gradients in Deep Neural Networks (DNNs). We in this work propose a new perspective upon loss landscape and mode ensemble to investigate OoD detection. In the optimization of DNNs, there ex

Wei Lou, Xinyi Yu, Chenyu Liu, Xiang Wan

Cell segmentation for multi-modal microscopy images remains a challenge due to the complex textures, patterns, and cell shapes in these images. To tackle the problem, we first develop an automatic cell classification pipeline to label the microscopy images based on their low-level image characteristics, and then train a classification model based on the cate

Liyizhe Peng, Zixing Zhang, Tao Pang, Jing Han

The advent of large language models (LLMs) has gained tremendous attention over the past year. Previous studies have shown the astonishing performance of LLMs not only in other tasks but also in emotion recognition in terms of accuracy, universality, explanation, robustness, few/zero-shot learning, and others. Leveraging the capability of LLMs inevitably bec

Shiang Hu, Jie Ruan, Juan Hou, Pedro Antonio Valdes-Sosa

Plenty of artifact removal tools and pipelines have been developed to correct the EEG recordings and discover the values below the waveforms. Without visual inspection from the experts, it is susceptible to derive improper preprocessing states, like the insufficient preprocessed EEG (IPE), and the excessive preprocessed EEG (EPE). However, little is known ab

Zhibo Zhang, Pengfei Li, Ahmed Y. Al Hammadi, Fusen Guo

This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning. While EEG signal analysis has attracted attention because of the emergence of brain-computer interface (BCI) technology, it is difficult to create effici

Daoming Chen, Ning Wang, Feng Chen, Tony Pipe

This Paper proposes a novel Transformer-based end-to-end autonomous driving model named Detrive. This model solves the problem that the past end-to-end models cannot detect the position and size of traffic participants. Detrive uses an end-to-end transformer based detection model as its perception module; a multi-layer perceptron as its feature fusion networ

C. Y. Cui, H. H. Xu, J. Yang, X. G. Cui

Lightweight refractory high-entropy alloy coatings (RHEAcs) of NbxMo$_{0.5}$Ti$_{1.5}$Ta$_{0.2}$Cr (where $x=$ 1, 1.3, 1.5, and 2) were fabricated on the surface of 316L stainless steel using laser cladding (LC) technology. A comprehensive study was conducted to elucidate the effect of Nb content on the microstructure, microhardness and wear resistance of Nb

Yong Du, Jiahui Zhan, Xinzhe Li, Junyu Dong

In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while utilizing a separate module

Jinlai Ning, Michael Spratling

Tiny object detection has gained considerable attention in the research community owing to the frequent occurrence of tiny objects in numerous critical real-world scenarios. However, convolutional neural networks (CNNs) used as the backbone for object detection architectures typically neglect Nyquist's sampling theorem during down-sampling operations, result

Kai Xiao, Yuhao Wang, Chao Song, Bihui Zou

The morphing of 3D structures is suitable for i) future tunable material design for customizing material properties and ii) advanced manufacturing tools for fabricating 3D structures on a 2D plane. However, there is no inverse design method for topologically variable and volumetric morphing or morphing with shape locking, which limits practical engineering a

Shu Liu, Chaoping Xing

Determining the largest size, or equivalently finding the lowest redundancy, of q-ary codes for given length and minimum distance is one of the central and fundamental problems in coding theory. Inspired by the construction of Varshamov-Tenengolts (VT for short) codes via check-sums, we provide an explicit construction of nonlinear codes with lower redundanc

G. G. Plunk, P. Helander

The gyrokinetic theory of the residual flow, in the electrostatic limit, is revisited, with optimized stellarators in mind. We consider general initial conditions for the problem, and identify cases that lead to a non-zonal residual electrostatic potential, i.e. one having a significant component that varies within a flux surface. We investigate the behavior

Arthur S. de Sena, Jiguang He, Ahmed Al Hammadi, Chongwen Huang

The emerging concept of extremely-large holographic multiple-input multiple-output (HMIMO), beneficial from compactly and densely packed cost-efficient radiating meta-atoms, has been demonstrated for enhanced degrees of freedom even in pure line-of-sight conditions, enabling tremendous multiplexing gain for the next-generation communication systems. Most of

Shikun Feng, Lixin Yang, Yanwen Huang, Yuyan Ni

Molecular representation learning is fundamental for many drug related applications. Most existing molecular pre-training models are limited in using single molecular modality, either SMILES or graph representation. To effectively leverage both modalities, we argue that it is critical to capture the fine-grained 'semantics' between SMILES and graph, because

Ruilin Yuan, Leya Li, Yuanzhe Cai

Current item-item collaborative filtering algorithms based on artificial neural network, such as Item2vec, have become ubiquitous and are widely applied in the modern recommender system. However, these approaches do not apply to the large-scale item-based recommendation system because of their extremely long training time. To overcome the shortcoming that cu

Tianyu Yan, Zifu Wan, Pingping Zhang, Gong Cheng

In the remote sensing field, Change Detection (CD) aims to identify and localize the changed regions from dual-phase images over the same places. Recently, it has achieved great progress with the advances of deep learning. However, current methods generally deliver incomplete CD regions and irregular CD boundaries due to the limited representation ability of

G. Di Cesare, P. Zeidman, G. Lombardi, B. A. Urgen

In both human and monkeys the observation and execution of actions produced the activation of a network consisting of parietal and frontal areas. Although this network is involved in the encoding of the action goal, it does not consider the affective component of the action: vitality form (VF). Several studies showed that the observation and execution of act

Evangelos Matsinos

Four sets of global average temperature anomalies, altered so that they refer to pre-industrial temperature levels (baseline), as recommended by the Intergovernmental Panel on Climate Change, are analysed in this study. Expectation values of the time, at which the crossing of the $1.5^\circ$C threshold above the baseline will occur, are extracted from the tw

Minsun Gu, Moon Seop Hyun, Moonsup Han, Gyungtae Kim

Soaring demands of multi-stacked memory devices request urgent development of backside contact electrode technologies, such as high aspect ratio etching, metallization, and inspection methods. Especially the complex metal contact process should be monitored for each manufacturing step to filter the defective samples and to maintain the high yield of producti

Da Song, Xuan Xie, Jiayang Song, Derui Zhu

Over the past decade, Artificial Intelligence (AI) has had great success recently and is being used in a wide range of academic and industrial fields. More recently, LLMs have made rapid advancements that have propelled AI to a new level, enabling even more diverse applications and industrial domains with intelligence, particularly in areas like software eng

Niladri Modak, Swain Ashutosh, Shyamal Guchhait, Sayan Ghosh

Despite its non-Hermitian nature, the transverse optical beam shift exhibits both real eigenvalues and non-orthogonal eigenstates. To explore this unexpected similarity to typical PT (parity-time)-symmetric systems, we first categorize the entire parametric regime of optical beam shifts into Hermitian, PT-unbroken, and PT-broken phases. Besides experimentall

Mengnan Qi, Yufan Huang, Maoquan Wang, Yongqiang Yao

Automatic Program translation has enormous application value and hence has been attracting significant interest from AI researchers. However, we observe that current program translation models still make elementary syntax errors, particularly, when the target language does not have syntax elements in the source language. Metrics like BLUE, CodeBLUE and compu

Yeong Gwang Khim, Beomjin Park, Jin Eun Heo, Young Hun Khim

Titanium nitride (TiN) presents superior electrical conductivity with mechanical and chemical stability and compatibility with the semiconductor fabrication process. Here, we fabricated epitaxial and polycrystalline TiN (111) thin films on MgO (111), sapphire (001), and mica substrates at 640oC and room temperature by using a DC sputtering, respectively. The

Ryung Kim, Byoung Ki Choi, Kyeong Jun Lee, Hyuk Jin Kim

Vanadium diselenide (VSe2) has intriguing physical properties such as unexpected ferromagnetism at the two-dimensional limit. However, the experimental results for room temperature ferromagnetism are still controversial and depend on the detailed crystal structure and stoichiometry. Here we introduce crystal truncation rod (CTR) analysis to investigate the a

Ayan Sengupta, Md Shad Akhtar, Tanmoy Chakraborty

Multi-head self-attention-based Transformers have shown promise in different learning tasks. Albeit these models exhibit significant improvement in understanding short-term and long-term contexts from sequences, encoders of Transformers and their variants fail to preserve layer-wise contextual information. Transformers usually project tokens onto sparse mani

Hyuk Jin Kim, Minsu Chong, Tae Gyu Rhee, Yeong Gwang Khim

In situ reflective high-energy electron diffraction (RHEED) is widely used to monitor the surface crystalline state during thin-film growth by molecular beam epitaxy (MBE) and pulsed laser deposition. With the recent development of machine learning (ML), ML-assisted analysis of RHEED videos aids in interpreting the complete RHEED data of oxide thin films. Th

Kiminori Hattori, Ata Yamaguchi

In this paper, we derive a real-space topological invariant that involves all energy states in the system. This global invariant, denoted by $Q$, is always quantized to be 0 or 1, independent of symmetries. In terms of $Q$, we numerically investigate topological properties of the nonchiral Rice-Mele model including random onsite potentials to show that nontr

Charalambos Evangelou, Mihalis Maliakas, Dimitra-Dionysia Stergiopoulou

Consider the general linear group $G=GL_{n}(K)$ defined over an infinite field $K$ of positive characteristic $p$. We denote by $\Delta(\lambda)$ the Weyl module of $G$ which corresponds to a partition $\lambda$. Let $\lambda, \mu $ be partitions of $r$ and let $\gamma$ be partition with all parts divisible by $p$. In the first main result of this paper, we

Nguyen Huu Lam, Phuong Lien Nguyen, Byoung Ki Choi, Trinh Thi Ly

NiTe2, a type-II Dirac semimetal with strongly tilted Dirac band, has been explored extensively to understand its intriguing topological properties. Here, using density-functional theory (DFT) calculations, we report that the strength of spin-orbit coupling (SOC) in NiTe2 can be tuned by Se substitution. This results in negative shifts of the bulk Dirac poin

Yifan Luo, Yiming Tang, Chengfeng Shen, Zhennan Zhou

Prompt Engineering (PE) has emerged as a critical technique for guiding Large Language Models (LLMs) in solving intricate tasks. Its importance is highlighted by its potential to significantly enhance the efficiency and effectiveness of human-machine interaction. As tasks grow increasingly complex, recent advanced PE methods have extended beyond the limitati

Hongjiang Lei, Mingxu Yang, Ki-Hong Park, Nasir Saeed

This paper introduces a novel adaptive transmission scheme to amplify the prowess of coordinated direct and relay transmission (CDRT) systems rooted in non-orthogonal multiple access principles. Leveraging the maximum ratio transmission scheme, we seamlessly meet the prerequisites of CDRT while harnessing the potential of dynamic power allocation and directi

Xin Su, Wing Tat Leung, Wenyuan Li, Sai-Mang Pun

We develop a partially explicit time discretization based on the framework of constraint energy minimizing generalized multiscale finite element method (CEM-GMsFEM) for the problem of linear poroelasticity with high contrast. Firstly, dominant basis functions generated by the CEM-GMsFEM approach are used to capture important degrees of freedom and it is know

Rami Aly, Marek Strong, Andreas Vlachos

Fact verification systems assess a claim's veracity based on evidence. An important consideration in designing them is faithfulness, i.e. generating explanations that accurately reflect the reasoning of the model. Recent works have focused on natural logic, which operates directly on natural language by capturing the semantic relation of spans between an ali

E. Ahmadi-Azar, K. Atazadeh, A. Eghbali

Until now, various methods have been demonstrated to solve the Friedmann-Lama\'{\i}tre-Robertson-Walker (FLRW) equations in the spatially flat $(k=0)$ cosmological model. In this study, in order to solve the field equations of the spatially flat FLRW cosmological model in the presence of $\Lambda$, a new method based on the invariants of the symmetry groups

Seowoo Lee, Jiwon Youn, Hyungjin Kim, Mansu Kim

Purpose: This study aimed to develop an open-source multimodal large language model (CXR-LLAVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in large language models (LLMs) to potentially replicate the image interpretation skills of human radiologists Materials and Methods: For training, we collected 592,580 publicly available CXRs,

Xinyi Yu, Guanbin Li, Wei Lou, Siqi Liu

Nuclei segmentation is a fundamental but challenging task in the quantitative analysis of histopathology images. Although fully-supervised deep learning-based methods have made significant progress, a large number of labeled images are required to achieve great segmentation performance. Considering that manually labeling all nuclei instances for a dataset is

Sachit Kuhar, Shuo Cheng, Shivang Chopra, Matthew Bronars

Practical Imitation Learning (IL) systems rely on large human demonstration datasets for successful policy learning. However, challenges lie in maintaining the quality of collected data and addressing the suboptimal nature of some demonstrations, which can compromise the overall dataset quality and hence the learning outcome. Furthermore, the intrinsic heter

Rui Niu, Zhi-Chu Ma, Ji-Ming Chen, Chang Feng

In the near future, space-borne gravitational wave (GW) detector LISA can open the window of low-frequency band of GW and provide new tools to test gravity theories. In this work, we consider multi-parameter tests of GW generation and propagation where the deformation coefficients are varied simultaneously in parameter estimation and the principal component