February 2024 arXiv papers — page 169

Yujing Sun, Caiyi Sun, Yuan Liu, Yuexin Ma

Human has an incredible ability to effortlessly perceive the viewpoint difference between two images containing the same object, even when the viewpoint change is astonishingly vast with no co-visible regions in the images. This remarkable skill, however, has proven to be a challenge for existing camera pose estimation methods, which often fail when faced wi

Zhiqiang Miao, Liqiang Qi, Juan Zhang, Ang Li

The understanding of neutron star equation of state hinges on a comprehensive analysis of multi-messenger, multi-wavelength data. The recent scrutiny of PSR J0030+0451 data by NICER introduces complexities, unveiling a tension with another X-ray observation of the central compact object in HESS J1731-347, specifically concerning the mass-radius constraint of

Fabian Holzberger, Markus Muhr, Barbara Wohlmuth

Endovascular coil embolization is one of the primary treatment techniques for cerebral aneurysms. Although it is a well established and minimally invasive method, it bears the risk of sub-optimal coil placement which can lead to incomplete occlusion of the aneurysm possibly causing recurrence. One of the key features of coils is that they have an imprinted n

Xiaoheng Jiang, Feng Yan, Yang Lu, Ke Wang

Surface defect inspection plays an important role in the process of industrial manufacture and production. Though Convolutional Neural Network (CNN) based defect inspection methods have made huge leaps, they still confront a lot of challenges such as defect scale variation, complex background, low contrast, and so on. To address these issues, we propose a jo

Hartmut Führ, Mahya Ghandehari

We consider the problem of characterizing the Sobolev wavefront set of a tempered distribution $u\in\mathcal{S}'(\mathbb{R}^{d})$ in terms of its continuous wavelet transform, with the latter being defined with respect to a suitably chosen dilation group $H\subset{\rm GL}(\mathbb{R}^{d})$. We derive necessary and sufficient criteria for elements of the Sobol

Hoda Torabi, Hamzeh Khazaei, Marin Litoiu

With the advent of 5G networks and the rise of the Internet of Things (IoT), Content Delivery Networks (CDNs) are increasingly extending into the network edge. This shift introduces unique challenges, particularly due to the limited cache storage and the diverse request patterns at the edge. These edge environments can host traffic classes characterized by v

Shashank Suman, Elaganuru Bashaiah, Resmi M, Ramachandrarao Yalla

We experimentally demonstrate the in-situ characterization of optical micro/nano fibers (MNFs).The MNF (test fiber, TF) is positioned on a microfiber (probe fiber, PF) and simulated for the scattering loss at various PF and TF diameters. The TF is fabricated using chemical etching technique. The PF is a conventional single-mode fiber with an outer diameter o

Karthik V., Amal Medhi

We use a convolutional restricted Boltzmann machine (CRBM) neural network to construct a variational wave function (WF) for the Hubbard model on a square lattice and study it using the variational Monte Carlo (VMC) method. In the wave function, the CRBM acts as a correlation factor to a mean-field BCS state. The number of variational parameters in the WF doe

Martin Hanke

We consider the conductivity problem for a homogeneous body with an inclusion of a different, but known, conductivity. Our interest concerns the associated shape derivative, i.e., the derivative of the corresponding electrostatic potential with respect to the shape of the inclusion. For a smooth inclusion it is known that the shape derivative is the solution

Olivier Bokanowski, Xavier Warin

We study deterministic optimal control problems for differential games with finite horizon. We propose new approximations of the strategies in feedback form, and show error estimates and a convergence result of the value in some weak sense for one of the formulations. This result applies in particular to neural networks approximations. This work follows some

Yehui Tang, Kai Han, Fangcheng Liu, Yunsheng Ni

The power of large language models (LLMs) has been demonstrated through numerous data and computing resources. However, the application of language models on mobile devices is facing huge challenge on the computation and memory costs, that is, tiny language models with high performance are urgently required. Limited by the highly complex training process, th

Alfredo Fernandez, Ankur Mali

In this paper, we introduce the Hyperbolic Tangent Exponential Linear Unit (TeLU), a novel neural network activation function, represented as $f(x) = x{\cdot}tanh(e^x)$. TeLU is designed to overcome the limitations of conventional activation functions like ReLU, GELU, and Mish by addressing the vanishing and, to an extent, the exploding gradient problems. Ou

Chengjia Chen, Qiyuan Pan, Jiliang Jing

We study geometrically thick non-self gravitating equilibrium tori orbiting a Schwarzschild black hole immersed in swirling universes. This solution is axially symmetric and non-asymptotically flat, and its north and south hemispheres spin in opposite directions. Due to repulsive effects arising from the swirl of the background spacetime, the equilibrium tor

Jiaji Zhang, Carlos L. Benavides-Riveros, Lipeng Chen

The accurate (or even approximate) solution of the equations that govern the dynamics of dissipative quantum systems remains a challenging task for quantum science. While several algorithms have been designed to solve those equations with different degrees of flexibility, they rely mainly on highly expensive iterative schemes. Most recently, deep neural netw

Zhiwen Zhang

In light of recent work in the global well-posedness of solutions for an ionic Vlasov-Poisson system, as demonstrated by Griffin-Pickering and Iacobelli, the current work focuses on the moment propagation of the corresponding system in quasi-neutral regime. Such moment propagation result relies on an estimate of $Q_*(t)=|V(t;0,x,v)-V(0;0,x,v)|$, where $V(s;t

Jianhua Yang, Tao Li

There are two seemingly unrelated puzzles about the cuprate superconductors. The first puzzle concerns the strong non-BCS behavior around $x_{c}$, the end point of the superconducting dome on the overdoped side, where the cuparte is believed to be well described by the fermi liquid theory. This is the most evident in the observed $\rho_{s}(0)-T_{c}$ scaling

Peder Lyngby, Kristian Sommer Thygesen

We perform extensive density functional theory (DFT) calculations to determine the stability and elementary properties of 4249 previously unexplored monolayer crystals. The monolayers comprise the most stable subset (energy within 0.1 eV/atom of the convex hull) of a larger portfolio of two-dimensional (2D) materials recently discovered using a deep generati

Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares

We study incremental constituent parsers to assess their capacity to output trees based on prefix representations alone. Guided by strictly left-to-right generative language models and tree-decoding modules, we build parsers that adhere to a strong definition of incrementality across languages. This builds upon work that asserted incrementality, but that mos

S. Cobzaş

Roughly speaking, Ekeland's Variational Principle (EkVP) (J. Math. Anal. Appl. 47 (1974), 324--353) asserts the existence of strict minima of some perturbed versions of lower semicontinuous functions defined on a complete metric space. Later, Pando Georgiev (J. Math. Anal. Appl. \textbf{131} (1988), no.~1, 1--21) and Tomonari Suzuki (J. Math. Anal. Appl. \te

Yang Xiao, Rohan Kumar Das

Sound event detection (SED) is essential for recognizing specific sounds and their temporal locations within acoustic signals. This becomes challenging particularly for on-device applications, where computational resources are limited. To address this issue, we introduce a novel framework referred to as dual knowledge distillation for developing efficient SE

Yashvi Sharma, Jesper Sollerman, Shrinivas R. Kulkarni, Takashi J. Moriya

Multi-peaked supernovae with precursors, dramatic light-curve rebrightenings, and spectral transformation are rare, but are being discovered in increasing numbers by modern night-sky transient surveys like the Zwicky Transient Facility (ZTF). Here, we present the observations and analysis of SN 2023aew, which showed a dramatic increase in brightness followin

Chongwu Xie, Konghao Sun, Kang-Da Wu, Chuan-Feng Li

Dissipative Rydberg gases are an outstanding platform for the investigation of many-body quantum open systems. Despite the wealth of existing studies, the non-equilibrium dynamics of dissipative Rydberg gases are rarely examined or harnessed from the perspective of non-Hermitian physics, which is but intrinsic to open systems. Here we report the experimental

Panomporn Poojon, Aeree Chung, Thiem Hoang, Junhyun Baek

We present the results of the single-dish observations using the Korean VLBI Network to search for anomalous microwave emission (AME) in nearby galaxies. The targets were selected from MApping the dense moLecular gAs in the sTrongest stAr-formiNg Galaxies (MALATANG), a legacy survey project of the James Clerk Maxwell Telescope. The MALATANG galaxies are good

Jian-Te Wang, Jing-Xin Liu, Hai-Tao Ding, Peng He

We propose a feasible scheme to realize a four-band Stiefel-Whitney insultor (SWI) with spin-orbit coupled ultracold atoms in an optical Raman lattice. Four selected spin states are coupled by carefully designed Raman lasers, to generate the desired spin-orbit interactions with spacetime inversion symmetry. We map out a phase diagram with respect to the expe

Manil T. Mohan, Shri Lal Raghudev Ram Singh

A boundary control problem for the following generalized Korteweg-de Vries-Burgers-Huxley equation: $$u_t=\nu u_{xx}-\mu u_{xxx}-\alpha u^{\delta}u_x+\beta u(1-u^{\delta})(u^{\delta}-\gamma), \ x\in[0,1], \ t>0,$$ where $\nu,\mu,\alpha,\beta>0,$ $\delta\in[1,\infty)$, $\gamma\in(0,1)$ subject to Neumann boundary conditions is considered in this work. We firs

Tianyu Zhao, Zhaojun Wang, Manming Shu, Jingxiang Zhang

Over the past decade, structured illumination microscopy (SIM) has found its niche in super-resolution (SR) microscopy due to its fast imaging speed and low excitation intensity. However, due to the significantly higher light dose compared to wide-field microscopy and the time-consuming post-processing procedures, long-term, real-time, super-resolution obser

Franziska Eberle, Felix Hommelsheim, Alexander Lindermayr, Zhenwei Liu

Querying complex models for precise information (e.g. traffic models, database systems, large ML models) often entails intense computations and results in long response times. Thus, weaker models which give imprecise results quickly can be advantageous, provided inaccuracies can be resolved using few queries to a stronger model. In the fundamental problem of

Daisuke Kurisu, Yasumasa Matsuda

This paper develops a general asymptotic theory of series estimators for spatial data collected at irregularly spaced locations within a sampling region $R_n \subset \mathbb{R}^d$. We employ a stochastic sampling design that can flexibly generate irregularly spaced sampling sites, encompassing both pure increasing and mixed increasing domain frameworks. Spec

Yixiang Shan, Zhengbang Zhu, Ting Long, Qifan Liang

The performance of offline reinforcement learning (RL) is sensitive to the proportion of high-return trajectories in the offline dataset. However, in many simulation environments and real-world scenarios, there are large ratios of low-return trajectories rather than high-return trajectories, which makes learning an efficient policy challenging. In this paper

Jia Li, Lu Wang, Lei Zhang, Beibei Wang

Reconstructing objects with realistic materials from multi-view images is problematic, since it is highly ill-posed. Although the neural reconstruction approaches have exhibited impressive reconstruction ability, they are designed for objects with specific materials (e.g., diffuse or specular materials). To this end, we propose a novel framework for robust g

Yingru Li, Jiawei Xu, Lei Han, Zhi-Quan Luo

We propose HyperAgent, a reinforcement learning (RL) algorithm based on the hypermodel framework for exploration in RL. HyperAgent allows for the efficient incremental approximation of posteriors associated with an optimal action-value function ($Q^\star$) without the need for conjugacy and follows the greedy policies w.r.t. these approximate posterior sampl

Rupchand Sutradhar, D C Dalal

In this work, a hepatitis B virus infection dynamics model is proposed including the spatial dependence of viruses. The existence of traveling waves for the proposed model is established through the application of the celebrated Gersgorin theorem. The procedure followed to establish the existence of a traveling wave solution is innovative and probably the fi

Can Jin, Tong Che, Hongwu Peng, Yiyuan Li

Generalization remains a central challenge in machine learning. In this work, we propose Learning from Teaching (LoT), a novel regularization technique for deep neural networks to enhance generalization. Inspired by the human ability to capture concise and abstract patterns, we hypothesize that generalizable correlations are expected to be easier to imitate.

Salwa Mostafa, Mohammed S. Elbamby, Mohamed K. Abdel-Aziz, Mehdi Bennis

To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently expre

Masaki J. S. Yang

In this letter, we perform chiral perturbative diagonalization of the type-I seesaw mechanism by hierarchical singular values $\lambda_{i}$ of the Dirac mass matrix $m_{D}$ up to the next-to-leading order (NLO). Since the mass matrix of right-handed neutrinos $M_{R}$ has parity symmetries under $\lambda_{i} \leftrightarrow - \lambda_{i}$, the singular values

Viktor Plusnin, Nadezhda Khoroshavkina, Ali Abonakour, Arsenii Onuchin

This study investigates the dynamics of non-spatial specializations in hippocampal place cells during exposure to novel environments. Hippocampal place cells, known for their role in spatial mapping, exhibit multi-modal responses to sensory cues. The research focuses on understanding how these cells adapt their specialization in response to novel stimuli, sp

Zi-Long Man, Cheng-Rui Shu, Yan-Rui Liu, Hong Chen

We systematically investigate the mass spectrum and two-body open-charm strong decays of charmonium states in a coupled-channel model where the $^3P_0$ quark-antiquark pair creation mechanism is employed. The results of masses, mass shifts, proportions of the $c\bar{c}$ component, and open-charm decay widths are provided. The $S$-$D$ wave mixing angles and d

Shicheng Xu, Liang Pang, Jun Xu, Huawei Shen

The results of information retrieval (IR) are usually presented in the form of a ranked list of candidate documents, such as web search for humans and retrieval-augmented generation for large language models (LLMs). List-aware retrieval aims to capture the list-level contextual features to return a better list, mainly including reranking and truncation. Rera

Djulustan Nikiforov

In this paper, a multiscale approach with partially explicit time discretization is proposed. The idea is to use a partially explicit time scheme, considering a filtration problem in a fractured medium, where the implicit scheme is used for nodes whose subdomains contain fractures, and the explicit scheme is used for all others. In this way, it is possible t

Jaerin Lee, JoonKyu Park, Sungyong Baik, Kyoung Mu Lee

Image restoration models are typically trained with a pixel-wise distance loss defined over the RGB color representation space, which is well known to be a source of blurry and unrealistic textures in the restored images. The reason, we believe, is that the three-channel RGB space is insufficient for supervising the restoration models. To this end, we augmen

Siegfried Boecherer, Toshiyuki Kikuta

We prove that weights of two Siegel modular forms of nonquadratic nebentypus should satisfy some congruence relations if these modular forms are congruent to each other. Applying this result, we prove that there are no mod $p$ singular forms of nonquadratic nebentypus. Here we consider the case where the Fourier coefficients of the modular forms are algebrai

Wei Song, Pei Li, Man Wang

To address the challenges of low detection accuracy and high false positive rates of transmission lines in UAV (Unmanned Aerial Vehicle) images, we explore the linear features and spatial distribution. We introduce an enhanced stochastic Hough transform technique tailored for detecting transmission lines in complex backgrounds. By employing the Hessian matri

Zhao Sun, Zhencen He, Zhuohang He, Junxiang Wu

We present a nitrogen-targeting-Proton-Carbon-Alpha-Therapy method, abbreviated as Proton-CAT, which partially converts protons into carbon-12 and $\alpha$ particles through nuclear reactions between protons and nitrogen-15. Monte Carlo simulations validated the effectiveness of the Proton-CAT, and the study specifically focused on the distribution of relati

Lucas Amorim, Nicolai Haydn, Sandro Vaienti

We obtain quenched hitting distributions to be compound Poissonian for a certain class of random dynamical systems. The theory is general and designed to accommodate non-uniformly expanding behavior and targets that do not overlap much with the region where uniformity breaks. Based on annealed and quenched polynomial decay of correlations, our quenched resul

Jiahe Pan, Jonathan Eden, Denny Oetomo, Wafa Johal

Teleoperation is increasingly recognized as a viable solution for deploying robots in hazardous environments. Controlling a robot to perform a complex or demanding task may overload operators resulting in poor performance. To design a robot controller to assist the human in executing such challenging tasks, a comprehensive understanding of the interplay betw

Akira Furukawa

This study determines the volume-fraction reduction of sheared fragile glass-forming liquids. We consider a group of hypothetical systems that consist of particles with anisotropic particle-size modulations yet have almost the same average particle configuration as actual systems under shear flow. Our molecular dynamics (MD) simulations demonstrate that one

Tomoharu Terayama, Akira Furukawa

Dense non-Brownian suspensions exhibit significant shear thinning, although a comprehensive understanding of the full scope of this phenomenon remains elusive. This study numerically reveals intimate heterogenous coupled dynamics between many-body particle motions and solvent hydrodynamics in shear-thinning non-Brownian suspensions. We demonstrate the spatia

Firehun Dullo, Jesil Jose, Gregory Bouquet, Zeljko Skokic

A conventional refractive lens surface can act as a positive (converging) or negative (diverging) lens, but the same surface cannot act as both. We show that a geometric phase metasurface lens can have the unique property of acting both as a positive or negative lens upon transmission through its front or rear side, respectively. This offers certain freedom

Luca Della Libera, Cem Subakan, Mirco Ravanelli

The increasing success of deep neural networks has raised concerns about their inherent black-box nature, posing challenges related to interpretability and trust. While there has been extensive exploration of interpretation techniques in vision and language, interpretability in the audio domain has received limited attention, primarily focusing on post-hoc e

Benjamin Harper, Behnam Tonekaboni, Bahar Goldozian, Martin Sevior

Quantum computing has the potential to provide solutions to problems that are intractable on classical computers, but the accuracy of the current generation of quantum computers suffer from the impact of noise or errors such as leakage, crosstalk, dephasing, and amplitude damping among others. As the access to quantum computers is almost exclusively in a sha

Antony Orth, Oliver Pitts, Costel Flueraru, Terrence Stewart

Dynamic wavefront aberrations negatively impact a wide range of optical applications including astronomy, optical free-space telecommunications and bio-imaging. Wavefront errors can be compensated by an adaptive optics system comprised of a deformable mirror and wavefront sensor connected by a control loop. For satellite optical communications (SatCom), wave

Helena Simunkova, Eva Kolibalova, Lukas Kalina, Tomas Lednicky

Electrodeposition of tantalum coating was performed from an ionic liquid, BMP[Tf2N], in the presence of dissolved anhydrous TaF5 and LiF. Superficial X-ray photoelectron spectroscopy supplemented by an argon ion etching and depth profiling has proven the presence of the tantalum metal inside a thin coating deposited via a porous anodic alumina template. Addi

James Y. Huang, Sailik Sengupta, Daniele Bonadiman, Yi-An Lai

Large Language Models (LLMs) are nowadays expected to generate content aligned with human preferences. Current work focuses on alignment at model training time, through techniques such as Reinforcement Learning with Human Feedback (RLHF). However, it is unclear if such methods are an effective choice to teach alignment objectives to the model. First, the ina

Aryan Agrawal

This paper introduces a novel paradigm for depression detection and treatment using advanced Large Language Models (LLMs): Generative Pre-trained Transformer 4 (GPT-4), Llama 2 chat, and Gemini. These LLMs are fine-tuned with specialized prompts to diagnose, explain, and suggest therapeutic interventions for depression. A unique few-shot prompting method enh

Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong

Efficiently serving large language models (LLMs) requires batching of many requests to reduce the cost per request. Yet, with larger batch sizes and longer context lengths, the key-value (KV) cache, which stores attention keys and values to avoid re-computations, significantly increases memory demands and becomes the new bottleneck in speed and memory usage.

Ye Zhang

In this paper we provide another way to deduce the Loomis-Whitney inequality on higher dimensional Heisenberg groups $\mathbb{H}^n$ based on the one on the first Heisenberg group $\mathbb{H}^1$ and the known nonlinear Loomis-Whitney inequality (which has more projections than ours). Moreover, we generalize the result to the case of corank $1$ Carnot groups a

Naoya Ando, Anri Yonezaki

We study the complexity of horizontality in the twistor space $\hat{E}$ associated with an oriented vector bundle $E$ of rank $4$ with a positive-definite metric over a torus. If the horizontality has finite complexity of degree $d>2$ for an element of a fiber of $\hat{E}$, then the complexity is expressed in terms of a finite subgroup of $SO(3)$ ([3]). In t

Seongsu Kim, Sungsoo Ahn

This work studies machine learning for electron density prediction, which is fundamental for understanding chemical systems and density functional theory (DFT) simulations. To this end, we introduce the Gaussian plane-wave neural operator (GPWNO), which operates in the infinite-dimensional functional space using the plane-wave and Gaussian-type orbital bases

Viktoriia Kornich

We study theoretically a setup consisting of excitons formed in two valleys, with proximity-induced Cooper pairing, different in the conduction and valence bands. Due to the combination of a Coulomb interaction with superconducting proximity effects, Cooper pairing between electrons from conduction and valence bands from different valleys is formed. This fin

Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao

Graph representation learning on vast datasets, like web data, has made significant strides. However, the associated computational and storage overheads raise concerns. In sight of this, Graph condensation (GCond) has been introduced to distill these large real datasets into a more concise yet information-rich synthetic graph. Despite acceleration efforts, e

Zhitong Xu, Haitao Wang, Jeff M Phillips, Shandian Zhe

A long-standing belief holds that Bayesian Optimization (BO) with standard Gaussian processes (GP) -- referred to as standard BO -- underperforms in high-dimensional optimization problems. While this belief seems plausible, it lacks both robust empirical evidence and theoretical justification. To address this gap, we present a systematic investigation. First

Chi Truong, Matteo Malavasi, Han Li, Stefan Trueck

Assessing climate risk and its potential impacts on our cities and economies is of fundamental importance. Extreme weather events, such as hurricanes, floods, and storm surges can lead to catastrophic damages. We propose a flexible approach based on real options analysis and extreme value theory, which enables the selection of optimal adaptation pathways for

Dawei Zhai, Zuzhang Lin, Wang Yao

Supersymmetry (SUSY) of Hamiltonian dictates double degeneracy between a pair of superpartners (SPs) transformed by supercharge, except at zero energy where modes remain unpaired in many cases. Here we explore a SUSY of complete isospectrum between SPs -- with paired zero modes -- realized by 2D electrons in zero-flux periodic gauge fields, which can describ

William Y. C. Chen, Amy M. Fu

We came across an unexpected connection between a remarkable grammar of Dumont for the joint distribution of $(\exc, \fix)$ over $S_n$ and a beautiful theorem of Diaconis-Evans-Graham on successions and fixed points of permutations. With the grammar in hand, we demonstrate the advantage of the grammatical calculus in deriving the generating functions, where

Tomohiro Taniguchi

An experimental demonstration of a parametric oscillation of a magnetization in a ferromagnet was performed recently by applying a microwave voltage, indicating the potential to be applied in a switching method in non-volatile memories. In the previous works, the modulation of a perpendicular magnetic anisotropy field produced by the microwave voltage was sm

Ryuichiro Hataya, Yoshinobu Kawahara

Gradient-based hyperparameter optimization methods update hyperparameters using hypergradients, gradients of a meta criterion with respect to hyperparameters. Previous research used two distinct update strategies: optimizing hyperparameters using global hypergradients obtained after completing model training or local hypergradients derived after every few mo

Axel Gross, Zhaozhou Li, Yong-Zhong Qian

For a spherical dark matter halo with isotropic velocity distribution, the phase space distribution function (DF), the energy distribution, and the density profile form a set of self-consistent description of its equilibrium state, and knowing one is sufficient to determine the other two. The Navarro-Frenk-White density profile (NFW profile) is known to be a

Yang Sui, Huy Phan, Jinqi Xiao, Tianfang Zhang

In the exciting generative AI era, the diffusion model has emerged as a very powerful and widely adopted content generation and editing tool for various data modalities, making the study of their potential security risks very necessary and critical. Very recently, some pioneering works have shown the vulnerability of the diffusion model against backdoor atta

Yihao Huang, Kaiyuan Yu, Qing Guo, Felix Juefei-Xu

In recent years, LiDAR-camera fusion models have markedly advanced 3D object detection tasks in autonomous driving. However, their robustness against common weather corruption such as fog, rain, snow, and sunlight in the intricate physical world remains underexplored. In this paper, we evaluate the robustness of fusion models from the perspective of fusion s

Amin Esmaeili, Yutaka Fujita

We use two-dimensional particle-in-cell simulations to investigate the generation and evolution of the magnetic field associated with the propagation of a jet for various initial conditions. We demonstrate that, in general, the magnetic field is initially grown by the Weibel and Mushroom instabilities. However, the field is saturated by the Alfv'en current l

Andrey Davydov, Alexey Sidnev, Artsiom Sanakoyeu, Yuhua Chen

When enough annotated training data is available, supervised deep-learning algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sources, such as databases of body shapes, to learn priors. Unfortunately, such sources are not always availa

Hao Zhu, Kefan Jin, Rui Gao, Jialin Wang

Existing trajectory planning methods are struggling to handle the issue of autonomous track swinging during navigation, resulting in significant errors when reaching the destination. In this article, we address autonomous trajectory planning problems, which aims at developing innovative solutions to enhance the adaptability and robustness of unmanned systems

Bowen Lei, Yeseul Jeon, Rajarshi Guhaniyogi, Aaron Scheffler

Understanding relationships across multiple imaging modalities is central to neuroimaging research. We introduce the Integrative Variational Autoencoder (InVA), the first hierarchical VAE framework for image-on-image regression in multimodal neuroimaging. Unlike standard VAEs, which are not designed for predictive integration across modalities, InVA models o

Bumsoo Kim, Abdul Muqeet, Kyuchul Lee, Sanghyun Seo

Face re-aging is a prominent field in computer vision and graphics, with significant applications in photorealistic domains such as movies, advertising, and live streaming. Recently, the need to apply face re-aging to non-photorealistic images, like comics, illustrations, and animations, has emerged as an extension in various entertainment sectors. However,

Raha Moraffah, Huan Liu

Surrogate-based black-box attacks have exposed the heightened vulnerability of DNNs. These attacks are designed to craft adversarial examples for any samples with black-box target feedback for only a given set of samples. State-of-the-art surrogate-based attacks involve training a discriminative surrogate that mimics the target's outputs. The goal is to lear

Guan-Ren Wang, Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li

We propose a Riemannian gradient descent with the Poincar\'e metric to compute the order-$\alpha$ Augustin information, a widely used quantity for characterizing exponential error behaviors in information theory. We prove that the algorithm converges to the optimum at a rate of $\mathcal{O}(1 / T)$. As far as we know, this is the first algorithm with a non-a

Pengqi Li, Tianhao Wang, Lantian Li, Askar Hamdulla

Which phonemes convey more speaker traits is a long-standing question, and various perception experiments were conducted with human subjects. For speaker recognition, studies were conducted with the conventional statistical models and the drawn conclusions are more or less consistent with the perception results. However, which phonemes are more important wit

Zezhong Zhang, Guangxu Zhu, Junting Chen, Shuguang Cui

In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually represented by the spatial signal power strength over the geographical environment, known as a radio map. In this paper, we pre

Tomas H. Rutherford, Jesse van de Sande, Scott M. Croom, Lucas M. Valenzuela

Slow rotator galaxies are distinct amongst galaxy populations, with simulations suggesting that a mix of minor and major mergers are responsible for their formation. A promising path to resolve outstanding questions on the type of merger responsible, is by investigating deep imaging of massive galaxies for signs of potential merger remnants. We utilise deep

Gouranga Mallik, Rahul Biswas, Thirupathi Gudi

In this article, we consider a local projection stabilisation for a Hybrid High-Order (HHO) approximation of the Oseen problem. We prove an existence-uniqueness result under a stronger SUPG-like norm. We improve the stability and provide error estimation in stronger norm for convection dominated Oseen problem. We also derive an optimal order error estimate u

Vedant Chauhan, Chetan Arora, Hourieh Khalajzadeh, John Grundy

Context: Human-centric software design and development focuses on how users want to carry out their tasks rather than making users accommodate their software. Software users can have different genders, ages, cultures, languages, disabilities, socioeconomic statuses, and educational backgrounds, among many other differences. Due to the inherently varied natur

Masoud Salehi, Nikoo Javadpour, Brietta Beisner, Mohammadamin Sanaei

Despite the widespread adoption of Virtual Reality (VR) technology, cybersickness remains a barrier for some users. This research investigates head movement patterns as a novel physiological marker for cybersickness detection. Unlike traditional markers, head movements provide a continuous, non-invasive measure that can be easily captured through the sensors

Haoran Li, Jiahua Shi, Huaming Chen, Bo Du

Artificially generated induced pluripotent stem cells (iPSCs) from somatic cells play an important role for disease modeling and drug screening of neurodegenerative diseases. Astrocytes differentiated from iPSCs are important targets to investigate neuronal metabolism. The astrocyte differentiation progress can be monitored through the variations of morpholo

Peter Sidajaya, Valerio Scarani

As a consequence of Bell's theorem, the statistics of measurements on some entangled states cannot be simulated with local hidden variables alone. The amount of communication that must be supplied is an intuitive quantifier of nonclassicality. While it is obvious that this amount can be very large in general, it has been surprisingly difficult to find simple

Shanshan Rodriguez, Leo Rodriguez, Zhenzhong Xing, Connor McMillin

We provide a unique and novel extension of da Costa's calculation of a quantum mechanically constrained particle. This is achieved by analyzing the perturbative back reaction of the quantum confined particle's eigenstates and spectra upon the geometry of the curved surface itself, thereby addressing the problem of shape optimization in this model. We do this

Mohadeseh Azari, Paul Polakos, Kaushik P. Seshadreesan

The Gottesman-Kitaev-Preskill (GKP) code, being information theoretically near optimal for quantum communication over Gaussian thermal-loss optical channels, is likely to be the encoding of choice for advanced quantum networks of the future. Quantum repeaters based on GKP-encoded light have been shown to support high end-to-end entanglement rates across larg

Zhiyu Zhang, David Bombara, Heng Yang

We study online learning in adversarial nonstationary environments. Since the future can be very different from the past, a critical challenge is to gracefully forget the history while new data comes in. To formalize this intuition, we revisit the discounted regret in online convex optimization, and propose an adaptive (i.e., instance optimal), FTRL-based al

Sushmita Gupta, Pallavi Jain, A. Mohanapriya, Vikash Tripathi

Allocating conflicting jobs among individuals while respecting a budget constraint for each individual is an optimization problem that arises in various real-world scenarios. In this paper, we consider the situation where each individual derives some satisfaction from each job. We focus on finding a feasible allocation of conflicting jobs that maximize egali

Sicong Xie, Qunwei Li, Weidi Xu, Kaiming Shen

Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are irrelevant to the target item as noises, which limits the performance of target-related time cycle modeli

Hwa Jeong Lee, Yoonsang Lee, Chanmin Lee, Yeseo Park

We give a list of minimal grid diagrams of the 13 crossing prime nonalternating knots which have arc index 13. There are 9,988 prime knots with crossing number 13. Among them 4,878 are alternating and have arc index 15. Among the other nonalternating knots, 49, 399, 1,412 and 3,250 have arc index 10, 11, 12, and 13, respectively. We used the Dowker-Thistleth

Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang

As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. We provide a taxonomy of existing works on LLM-Agent pl

Lu Wang, Marcelo Fabián Ciappina, Thomas Brabec, Xiaojun Liu

The increasing interest in chiral light stems from its spiral trajectory along the propagation direction, facilitating the interaction between different polarization states of light and matter. Despite tremendous achievements in chiral light-related research, the generation and control of chiral pulse have presented enduring challenges, especially at the ter

Changqing Teng, Guanglian Li

The rough Bergomi (rBergomi) model can accurately describe the historical and implied volatilities, and has gained much attention in the past few years. However, there are many hidden unknown parameters or even functions in the model. In this work, we investigate the potential of learning the forward variance curve in the rBergomi model using a neural SDE. T

Ming Jin, Yifan Zhang, Wei Chen, Kexin Zhang

Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications. Although large language models (LLMs) have recently made significant strides, the development of artificial general intelligence (AGI) equipped with time series analysis capabilities remains in its nascent phase. Most existing time ser

Yaoyao Chen, Hailiang Liu, Nianyu Yi, Peimeng Yin

In this paper, we present several unconditionally energy-stable invariant energy quadratization (IEQ) finite element methods (FEMs) with linear, first- and second-order accuracy for solving both the Cahn-Hilliard equation and the Allen-Cahn equation. For time discretization, we compare three distinct IEQ-FEM schemes that position the intermediate function in

Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey

Physics-informed neural networks (PINNs) offer a promising avenue for tackling both forward and inverse problems in partial differential equations (PDEs) by incorporating deep learning with fundamental physics principles. Despite their remarkable empirical success, PINNs have garnered a reputation for their notorious training challenges across a spectrum of

Hyeonah Kim, Minsu Kim, Sanghyeok Choi, Jinkyoo Park

The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design. Recent advances in deep learning-based generative methods have shown promise but face the issue of sample efficiency due to the computational expense of evaluating the reward function. This paper proposes a novel algorithm for sam

Xuan Zuo, Zhi-Yuan Fan, Huai-Bing Zhu, Jie Li

Exciton optomechanics, bridging cavity exciton polaritons and optomechanics, opens new opportunities for the study of light-matter strong interactions and nonlinearities, due to the rich nonlinear couplings among excitons, phonons, and photons. Here, we propose to entangle two exciton modes in an exciton-optomechanics system, which consists of a semiconducto

Jia-Wei Ying, Peng Zhao, Wei Zhong, Ming-Ming Du

Quantum secure direct communications (QSDC) can directly transmit secret messages through a quantum channel without keys. The imperfect photon source is a major obstacle for QSDC's practical implementation. The unwanted vacuum state and multiphoton components emitted from imperfect photon source largely reduce QSDC's secrecy message capacity and even threate

Dimitrious Schreiber, Zhaowei Yu, Taylor Henderson, Derek Chen

Computed Tomography (CT) image guidance enables accurate and safe minimally invasive treatment of diseases, including cancer and chronic pain, with needle-like tools via a percutaneous approach. The physician incrementally inserts and adjusts the needle with intermediate images due to the accuracy limitation of free-hand adjustment and patient physiological