December 2023 arXiv papers — page 32

Rojin Zandi, Kian Behzad, Elaheh Motamedi, Hojjat Salehinejad

Despite the current surge of interest in autonomous robotic systems, robot activity recognition within restricted indoor environments remains a formidable challenge. Conventional methods for detecting and recognizing robotic arms' activities often rely on vision-based or light detection and ranging (LiDAR) sensors, which require line-of-sight (LoS) access an

L. Dubois, G. Thémèze, F. Nogrette, J. Dubail

One-dimensional Bose gases with contact repulsive interactions are characterized by the presence of infinite-lifetime quasiparticles whose momenta are called the `rapidities'. Here we develop a probe of the local rapidity distribution, based on the fact that rapidities are the asymptotic momenta of the particles after a long one-dimensional expansion. This i

Ola Mæhlen, Douglas Svensson Seth

By a bifurcation argument we prove that the capillary-gravity Whitham equation features asymmetrical periodic travelling wave solution of arbitrarily small amplitude. Such waves exist only in the weak surface tension regime $0<T<\frac{1}{3}$ and are necessarily bimodal; they are located at double bifurcation points satisfying a certain symmetry breaking cond

Slimane Adjerid, Tao Lin, Haroun Meghaichi

We present a high order immersed finite element (IFE) method for solving the elliptic interface problem with interface-independent meshes. The IFE functions developed here satisfy the interface conditions exactly and they have optimal approximation capabilities. The construction of this novel IFE space relies on a nonlinear transformation based on the Frenet

Amit Jena, Dileep Kalathil, Le Xie

This paper addresses the problem of Neural Network (NN) based adaptive stability certification in a dynamical system. The state-of-the-art methods, such as Neural Lyapunov Functions (NLFs), use NN-based formulations to assess the stability of a non-linear dynamical system and compute a Region of Attraction (ROA) in the state space. However, under parametric

Bram Grooten, Tristan Tomilin, Gautham Vasan, Matthew E. Taylor

The visual world provides an abundance of information, but many input pixels received by agents often contain distracting stimuli. Autonomous agents need the ability to distinguish useful information from task-irrelevant perceptions, enabling them to generalize to unseen environments with new distractions. Existing works approach this problem using data augm

Mayer Goldberg

This work presents and extends a known spigot-algorithm for computing square-roots, digit-by-digit, that is suitable for calculation by hand or an abacus, using only addition and subtraction. We offer an elementary proof of correctness for the original algorithm, then present a corresponding spigot-algorithm for computing cube-roots. Finally, we generalize t

Olga Podvigina

The Galerkin method is often employed for numerical integration of evolutionary equations, such as the Navier-Stokes equation or the magnetic induction equation. Application of the method requires solving an equation of the form $P(Av-f)=0$ at each time step, where $v$ is an element of a finite-dimensional space $V$ with a basis satisfying boundary condition

Leah Wrenn Berman, Gábor Gévay, Tomaz Pisanski

In this note we give a construction proving that the Gray graph, which is the smallest cubic semi-symmetric graph, is a unit-distance graph.

Christian Kuehn, Tobias Wöhrer

We investigate the mean-field dynamics of stochastic McKean differential equations with heterogeneous particle interactions described by large network structures. To express a wide range of graphs, from dense to sparse structures, we incorporate the recently developed graph limit theory of graphops into the limiting McKean--Vlasov equations. Global stability

Gerd Kortemeyer, Wolfgang Bauer

As a result of the pandemic, many physics courses moved online. Alongside, the popularity of internet-based problem-solving sites and forums rose. With the emergence of Large Language Models, another shift occurred. One year into the public availability of these models, how has online help-seeking behavior among introductory physics students changed, and wha

Tung Nguyen, Alex Scott, Paul Seymour

We prove the Erd\H{o}s-Hajnal conjecture for the five-vertex path $P_5$; that is, there exists $c>0$ such that every $n$-vertex graph with no induced $P_5$ has a clique or stable set of size at least $n^c$. This completes the verification of the Erd\H{o}s-Hajnal property of all five-vertex graphs. Our methods combine probabilistic and structural ideas with t

Yang Zheng, Chih-fan Pai, Yujie Tang

Direct policy search has achieved great empirical success in reinforcement learning. Many recent studies have revisited its theoretical foundation for continuous control, which reveals elegant nonconvex geometry in various benchmark problems, especially in fully observable state-feedback cases. This paper considers two fundamental optimal and robust control

Seyedeh Ameneh Bahadori, Zahra Shomali

In the present work, thermal transport and energy conversation in two thermoelectrically efficient candidates of Janus SnSSe and SnS$_2$ are investigated within the non-equilibrium Monte Carlo simulation of phonon Boltzmann equation. The phonon analysis is performed to determine the contributed phonons in heat transport. The results present that the dominant

Javier Aramayona, George Domat, Christopher J. Leininger

We classify surface Houghton groups, as well as their pure subgroups, up to isomorphism, commensurability, and quasi-isometry.

Olga Podvigina

We present three results on stability of rolls in Boussinesq convection in a plane horizontal layer with rigid boundaries that is rotating about an inclined axis with the angular velocity $\Omega=(\Omega_1,\Omega_2,\Omega_3)$. i. We call the full problem the set of equations governing the temporal behaviour of the flow and temperature for an arbitrary $\Omeg

Lambros Boukas, Antonios Tsokaros, Koji Uryu

Every numerical general relativistic investigation starts from the solution of the initial value equations at a given time. Astrophysically relevant initial values for different systems lead to distinct set of equations that obey specific assumptions tied to the particular problem. Therefore a robust and efficient solver for a variety of strongly gravitating

Jie Pan

Based on the construction of polytope functions and several results about them in [LP], we take a deep look on their mutation behaviors to find a link between a face of a polytope and a sub-cluster algebra of the corresponding cluster algebra. This find provides a way to induce a mutation sequence in a sub-cluster algebra from that in the cluster algebra in

Zsuzsanna Jankó, Attila Joó, Erel Segal-Halevi, Sheung Man Yuen

We investigate the problem of fairly dividing a divisible heterogeneous resource, also known as a cake, among a set of agents who may have different entitlements. We characterize the existence of a connected strongly-proportional allocation -- one in which every agent receives a contiguous piece worth strictly more than their proportional share. The characte

Yotam Dikstein, Irit Dinur

We introduce and study swap cosystolic expansion, a new expansion property of simplicial complexes. We prove lower bounds for swap coboundary expansion of spherical buildings and use them to lower bound swap cosystolic expansion of the LSV Ramanujan complexes. Our motivation is the recent work (in a companion paper) showing that swap cosystolic expansion imp

Carlos J. Sánchez Martínez, Johannes Feist, Francisco J. García-Vidal

The full information about the interaction between a quantum emitter and an arbitrary electromagnetic environment is encoded in the so-called spectral density. We present an approach for describing such interaction in any coupling regime, providing a Lindblad-like master equation for the emitter dynamics when coupled to a general nanophotonic structure. Our

A Akanbi

Environmental hazards like water and air pollution, extreme weather, or chemical exposures can affect human health in a number of ways, and it is a persistent apprehension in communities surrounded by mining operations. The application of modern technologies in the environmental monitoring of these Human-made hazards is critical, because while not immediatel

Konstantinos Balaskas, Andreas Karatzas, Christos Sad, Kostas Siozios

Deep Neural Networks (DNNs) have shown significant advantages in a wide variety of domains. However, DNNs are becoming computationally intensive and energy hungry at an exponential pace, while at the same time, there is a vast demand for running sophisticated DNN-based services on resource constrained embedded devices. In this paper, we target energy-efficie

Kumar Saurabh, Deepak Gajjala, Krishna Kaipa, Ranjana Vyas

Industrial cyber-physical systems (ICPS) are gradually integrating information technology and automating industrial processes, leading systems to become more vulnerable to malicious actors. Thus, to deploy secure Industrial Control and Production Systems (ICPS) in smart factories, cyber threats and risks must be addressed. To identify all possible threats, T

Konstantinos Balaskas, Florian Klemme, Georgios Zervakis, Kostas Siozios

One of the major barriers that CMOS devices face at nanometer scale is increasing parameter variation due to manufacturing imperfections. Process variations severely inhibit the reliable operation of circuits, as the operational frequency at the nominal process corner is insufficient to suppress timing violations across the entire variability spectrum. To av

Junayed Mahmud

Bug reports document unexpected behaviors in software, enabling developers to understand, validate, and fix bugs. Unfortunately, a significant portion of bug reports is of low quality, which poses challenges for developers in terms of addressing these issues. Prior research has delved into the information needed for documenting high-quality bug reports and e

Samuel Boissière, Paola Comparin, Lucas Li Bassi

We study the symplectic resolution of the Fano variety of lines on some singular cyclic cubic fourfolds, i.e. cubic fourfolds arising as cyclic 3:1 cover of $\mathbb{P}^4$ branched along a cubic threefold. In particular we are interested in the geometry of these varieties in the case of cyclic cubic fourfolds branched along a cubic threefold having one isola

Guan-Ting Lin, Prashanth Gurunath Shivakumar, Ankur Gandhe, Chao-Han Huck Yang

Large Language Models (LLMs) have demonstrated superior abilities in tasks such as chatting, reasoning, and question-answering. However, standard LLMs may ignore crucial paralinguistic information, such as sentiment, emotion, and speaking style, which are essential for achieving natural, human-like spoken conversation, especially when such information is con

Tian Xia, Jia Liu

This paper studies the chance constrained fractional programming with a random benchmark. We assume that the random variables on the numerator follow the Gaussian distribution, and the random variables on the denominator and the benchmark follow a joint discrete distribution. Under some mild assumptions, we derive a convex reformulation of chance constrained

Simon Becker, Lin Lin, Kevin D. Stubbs

One of the most remarkable theoretical findings in magic angle twisted bilayer graphene (TBG) is the emergence of ferromagnetic Slater determinants as exact ground states for the interacting Hamiltonian at the chiral limit. This discovery provides an explanation for the correlated insulating phase which has been experimentally observed at half filling. This

Zijian Long, Haiwei Dong, Abdulmotaleb El Saddik

Multi-access edge computing (MEC) is a promising solution to the computation-intensive, low-latency rendering tasks of the metaverse. However, how to optimally allocate limited communication and computation resources at the edge to a large number of users in the metaverse is quite challenging. In this paper, we propose an adaptive edge resource allocation me

Tian Xia, Jia Liu, Zhiping Chen

In this paper, we study the distributionally robust joint chance constrained Markov decision process. {Utilizing the logarithmic transformation technique,} we derive its deterministic reformulation with bi-convex terms under the moment-based uncertainty set. To cope with the non-convexity and improve the robustness of the solution, we propose a dynamical neu

Chenyang Liu, Keyan Chen, Zipeng Qi, Haotian Zhang

The existing methods for Remote Sensing Image Change Captioning (RSICC) perform well in simple scenes but exhibit poorer performance in complex scenes. This limitation is primarily attributed to the model's constrained visual ability to distinguish and locate changes. Acknowledging the inherent correlation between change detection (CD) and RSICC tasks, we be

Mohammad Mahmudul Alam, Edward Raff, Tim Oates

While deep learning has enjoyed significant success in computer vision tasks over the past decade, many shortcomings still exist from a Cognitive Science (CogSci) perspective. In particular, the ability to subitize, i.e., quickly and accurately identify the small (less than 6) count of items, is not well learned by current Convolutional Neural Networks (CNNs

Ehsan Faghih, Huiyang Zhou

With the rapid advancement of quantum computing technology, there is a growing need for new debugging tools for quantum programs. Recent research has highlighted the potential of assertions for debugging quantum programs. In this paper, we investigate assertions in quantum ternary systems, which are more challenging than those in quantum binary systems due t

Diego Ruano, Rodrigo San-José

Long quantum codes using projective Reed-Muller codes are constructed. Projective Reed-Muller codes are evaluation codes obtained by evaluating homogeneous polynomials at the projective space. We obtain asymmetric and symmetric quantum codes by using the CSS construction and the Hermitian construction, respectively. We provide entanglement-assisted quantum e

Glauco Amigo, Pablo Rivas Perea, Robert J. Marks

Biased datasets are ubiquitous and present a challenge for machine learning. For a number of categories on a dataset that are equally important but some are sparse and others are common, the learning algorithms will favor the ones with more presence. The problem of biased datasets is especially sensitive when dealing with minority people groups. How can we,

Yanming Zhang, Brette Fitzgibbon, Dino Garofolo, Akshith Kota

Causal networks are widely used in many fields, including epidemiology, social science, medicine, and engineering, to model the complex relationships between variables. While it can be convenient to algorithmically infer these models directly from observational data, the resulting networks are often plagued with erroneous edges. Auditing and correcting these

Eli Dugan, Klaus Mueller

This paper deals with developing techniques for the reconstruction of high-dimensional datasets given each bivariate projection, as would be found in a matrix scatterplot. A graph-based solution is introduced, involving clique-finding, providing a set of possible rows that might make up the original dataset. Complications are discussed, including cases where

Josie König, Max Pfeffer, Martin Stoll

To determine the optimal set of hyperparameters of a Gaussian process based on a large number of training data, both a linear system and a trace estimation problem must be solved. In this paper, we focus on establishing numerical methods for the case where the covariance matrix is given as the sum of possibly multiple Kronecker products, i.e., can be identif

James H. Davenport, Matthew England, Scott McCallum, Ali K. Uncu

This paper builds and extends on the authors' previous work related to the algorithmic tool, Cylindrical Algebraic Decomposition (CAD), and one of its core applications, Real Quantifier Elimination (QE). These topics are at the heart of symbolic computation and were first implemented in computer algebra systems decades ago, but have recently received renewed

Shijing Si, Siqing Zhou, Le Tang, Xiaoqing Cheng

ChatGPT's proficiency in handling modern standard languages suggests potential for its use in understanding ancient Chinese. This paper explores ChatGPT's capabilities on ancient Chinese via two tasks: translating ancient Chinese to modern Chinese and recognizing ancient Chinese names. A comparison of ChatGPT's output with human translations serves to evalua

Jens Marklof

This paper studies the logarithmic moments of the smallest denominator of all rationals in a shrinking interval with random center. Convergence follows from the more general results in [arXiv:2310.11251, Bull. Lond. Math. Soc., to appear], and the key point of this note is the derivation of explicit formulas for the moments of the limit distribution in dimen

Jon Ayerdi, Valerio Terragni, Gunel Jahangirova, Aitor Arrieta

Metamorphic testing is a popular approach that aims to alleviate the oracle problem in software testing. At the core of this approach are Metamorphic Relations (MRs), specifying properties that hold among multiple test inputs and corresponding outputs. Deriving MRs is mostly a manual activity, since their automated generation is a challenging and largely une

Vincent Martinetto, Karan Shah, Attila Cangi, Aurora Pribram-Jones

Electronic structure theory calculations offer an understanding of matter at the quantum level, complementing experimental studies in materials science and chemistry. One of the most widely used methods, density functional theory (DFT), maps a set of real interacting electrons to a set of fictitious non-interacting electrons that share the same probability d

Zicheng Zhang, Haoning Wu, Zhongpeng Ji, Chunyi Li

Recent advancements in Multi-modality Large Language Models (MLLMs) have demonstrated remarkable capabilities in complex high-level vision tasks. However, the exploration of MLLM potential in visual quality assessment, a vital aspect of low-level vision, remains limited. To address this gap, we introduce Q-Boost, a novel strategy designed to enhance low-leve

Atul S Vivek, Ranabir Dey, Harish N Dixit

Surfactant-laden thin liquid films overlaid on solid substrates are encountered in a variety of industrial and biological settings. As these films reach submicron thickness, they tend to become unstable owing to the influence of long-range dispersion forces. In the current study, we investigate how gravitational drainage affects the stability attributes of s

Jue Xuan Hao, Xiang Tang, Alexey Arefiev, Robert J. Kingham

Strong multi-kilotesla magnetic fields have various applications in high-energy density science and laboratory astrophysics, but they are not readily available. In our previous work [Y. Shi et al., Phys. Rev. Lett. 130, 155101 (2023)], we developed a novel approach for generating such fields using multiple conventional laser beams with a twist in the pointin

Gianni Franchi, Olivier Laurent, Maxence Leguéry, Andrei Bursuc

Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks, yet they often struggle with reliable uncertainty quantification - a critical requirement for real-world applications. Bayesian Neural Networks (BNN) are equipped for uncertainty estimation but cannot scale to large DNNs that are highly unstable to train. To address this challe

Gabriele Bruni, Luigi Piro, Yuan-Pei Yang, Salvatore Quai

Fast radio bursts (FRBs) are millisecond-duration, bright ($\sim$Jy) extragalactic bursts, whose production mechanism is still unclear. Recently, two repeating FRBs were found to have a physically associated persistent radio source of non-thermal origin. These two FRBs have unusually large Faraday rotation measure values likely tracing a dense magneto-ionic

Lu Xia, Stefano Massei

Adaptive first-order optimizers are fundamental tools in deep learning, although they may suffer from poor generalization due to the nonuniform gradient scaling. In this work, we propose AdamL, a novel variant of the Adam optimizer, that takes into account the loss function information to attain better generalization results. We provide sufficient conditions

Alexander Komech, Elena Kopylova

We prove the orbital stability of soliton solutions for 2D Maxwell--Lorentz system with extended charged particle. The solitons corresponds to the uniform motion and rotation of the particle. We reduce the corresponding Hamilton system by the canonical transformation via transition to a comoving frame. The solitons are the critical points of the reduced Hami

Anusha Vajapeyajula, Krishna Kumar

Underground duct banks carrying power cables dissipate heat to the surrounding soil. The amount of heat dissipated determines the current rating of cables, which in turn affects the sizing of the cables. The dissipation of heat through the surrounding soils happens through conduction and convection. The mode of heat transfer depends on the soil's thermal and

Max Koehler, Stefan Goetz

Transcranial magnetic stimulation (TMS) is a popular method for the noninvasive stimulation of neurons in the brain. It has become a standard instrument in experimental brain research and is approved for a range of diagnostic and therapeutic applications. Various applications have been established or approved for specific coil designs with their correspondin

Li Zheng, Hao Fei, Fei Li, Bobo Li

With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties in handling mult

Antonio Muñoz Mateo, Grigory E. Astrakharchik, Bruno Juliá-Díaz

A feasible experimental proposal to realize a non-dispersive quantum pendulum is presented. The proposed setup consists of an ultracold atomic cloud, featuring attractive interatomic interactions, loaded into a tilted ring potential. The classical and quantum domains are switched on by tuned interactions, and the classical dynamical stabilization of unstable

Andreas Postel

Context: Episodic accretion plays an important role during the early phases of star-formation. The main processes responsible for the episodic accretion events remain, however, unclear. Aims: Our main objective is to investigate the properties of FUors and EXors by analysing observational data, along with numerical hydrodynamics simulations of protostellar d

Lokesh Veeramacheneni, Moritz Wolter, Hildegard Kuehne, Juergen Gall

Modern metrics for generative learning like Fr\'echet Inception Distance (FID) and DINOv2-Fr\'echet Distance (FD-DINOv2) demonstrate impressive performance. However, they suffer from various shortcomings, like a bias towards specific generators and datasets. To address this problem, we propose the Fr\'echet Wavelet Distance (FWD) as a domain-agnostic metric

Tai Le-Gia, Jaehyun Ahn

Contrastive representation learning has emerged as an outstanding approach for anomaly detection. In this work, we explore the $\ell_2$-norm of contrastive features and its applications in out-of-distribution detection. We propose a simple method based on contrastive learning, which incorporates out-of-distribution data by discriminating against normal sampl

Alexander Komech, Anatoli Merzon

We describe Malyshev's method of automorphic functions in application to boundary value problems in angles and to diffraction by wedges. We give a consize survey of related results of A. Sommerfeld, S.L. Sobolev, J.B. Keller, G.E. Shilov and others.

Su Jia, Andrew Li, R. Ravi

Consider a single-product revenue-maximization problem where the seller monotonically decreases the price in $n$ rounds with an unknown demand model coming from a given family. Without monotonicity, the minimax regret is $\tilde O(n^{2/3})$ for the Lipschitz demand family and $\tilde O(n^{1/2})$ for a general class of parametric demand models. With monotonic

Fernando Granha Jeronimo, Nir Magrafta, Pei Wu

Pseudorandom states (PRS) are an important primitive in quantum cryptography. In this paper, we show that subset states can be used to construct PRSs. A subset state with respect to $S$, a subset of the computational basis, is \[ \frac{1}{\sqrt{|S|}}\sum_{i\in S} |i\rangle. \] As a technical centerpiece, we show that for any fixed subset size $|S|=s$ such th

Elena Kopylova, Alexander Komech

We consider the 2D Maxwell--Lorentz system which describes a rotating particle coupled to the Maxwell field. The system admits stationary soliton-type solutions. We prove the attraction to solitons for any finite energy solution relying on the conservation of angular momentum.

Minjae Kim, Sebastian Wolf

The collisional evolution of debris disks is expected to result in a characteristic wavy pattern of the grain size distributions, i.e., an under/overabundance of particles of specific sizes. This perturbed grain size distribution potentially leaves characteristic patterns in the spectral energy distribution (SED) of the disk system. We aim to quantify and un

Seyed Soroush Karimi Madahi, Bert Claessens, Chris Develder

Growth in the penetration of renewable energy sources makes supply more uncertain and leads to an increase in the system imbalance. This trend, together with the single imbalance pricing, opens an opportunity for balance responsible parties (BRPs) to perform energy arbitrage in the imbalance settlement mechanism. To this end, we propose a battery control fra

Douglas Schultz, Johannes Stephan, Julian Sieber, Trudie Yeh

This paper proposes a novel method for demand forecasting in a pricing context. Here, modeling the causal relationship between price as an input variable to demand is crucial because retailers aim to set prices in a (profit) optimal manner in a downstream decision making problem. Our methods bring together the Double Machine Learning methodology for causal i

Ruibo Wang, Mustafa A. Kishk, Mohamed-Slim Alouini

In recent years, LEO satellite constellations have become envisioned as a core component of the next-generation wireless communication networks. The successive establishment of mega satellite constellations has triggered further demands for satellite communication advanced features: high reliability and low latency. In this article, we first establish a mult

Mohammad Gholamzadeh, Behrooz Khadem

Image encryption is one of the most common and effective methods to secure digital images. Recently, Khalid M. Hosny presented an image encryption scheme based on 6D hyper chaotic mapping and Q-Fibonacci matrix, which, despite its remarkable theoretical and practical properties, has several weaknesses, including inaccuracy of black image encryption, inapprop

J. Kluson

In this short note we determine Hamiltonian for Weyl transverse gravity. We find primary, secondary and tertiary constraints and calculate Poisson brackets between them. We also show that gauge fixing in Weyl transverse gravity leads to the Hamiltonian for unimodular gravity.

Niels Kowalzig

We show that if an operad is at the same time a cosimplicial object such that the respective structure maps are compatible with the operadic composition in a natural way, then one obtains a Gerstenhaber algebra structure on cohomology, and if the operad is cyclic, even that of a BV algebra. In particular, if a cyclic opposite module over an operad with multi

Filipe S. P. Prates

This article uses a dataset of answers to questions to generate student similarity networks. Two similarity functions to determine the weights between each pair of students are used, one that assumes a power-law distribution of answers response times, and one that does not. The resulting networks are then clustered using different community finding algorithm

Shaolun Ruan, Zhiding Liang, Qiang Guan, Paul Griffin

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to u

Lyes Saad Saoud, Lakmal Seneviratne, Irfan Hussain

Underwater robotic vision encounters significant challenges, necessitating advanced solutions to enhance performance and adaptability. This paper presents MARS (Multi-Scale Adaptive Robotics Vision), a novel approach to underwater object detection tailored for diverse underwater scenarios. MARS integrates Residual Attention YOLOv3 with Domain-Adaptive Multi-

Rafaela Debastiani, Chantal Miriam Kurpiers, Enrico Domenico Lemma, Ben Breitung

While in situ experiments are gaining importance for the (mechanical) assessment of metamaterials or materials with complex microstructures, imaging conditions in such experiments are often challenging. The lab-based computed tomography system Xradia 810 Ultra allows for the in situ (time lapsed) mechanical testing of samples. However, the in situ loading se

Pierluigi Colli, Patrik Knopf, Giulio Schimperna, Andrea Signori

We investigate a new diffuse-interface model that describes creeping two-phase flows (i.e., flows exhibiting a low Reynolds number), especially flows that permeate a porous medium. The system of equations consists of a Brinkman equation for the volume averaged velocity field as well as a convective Cahn--Hilliard equation with dynamic boundary conditions for

Gen Shi, Hao Lu, Hui Hui, Jie Tian

The precise cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) data is crucial for clinically computer-aided diagnosis. However, the sparse distribution of cerebrovascular structures in TOF-MRA results in an exceedingly high cost for manual data labeling. The use of unlabeled TOF-MRA data holds the potential to enhance mo

Prabhat Agarwal, Akshat Jindal, Shreya Singh

Barriers to accessing mental health assessments including cost and stigma continues to be an impediment in mental health diagnosis and treatment. Machine learning approaches based on speech samples could help in this direction. In this work, we develop machine learning solutions to diagnose anxiety disorders from audio journals of patients. We work on a nove

Jingze Chen, Junfeng Yao, Qiqin Lin, Rongzhou Zhou

In the domain of supervised scene flow estimation, the process of manual labeling is both time-intensive and financially demanding. This paper introduces SSFlowNet, a semi-supervised approach for scene flow estimation, that utilizes a blend of labeled and unlabeled data, optimizing the balance between the cost of labeling and the precision of model training.

I. Ghosh, S. Gupta, S. Rana

Dengue incidence forecasting using hybrid models has been surging in the data rich world. Hybridization of statistical time series forecasting models and machine learning models are explored for dengue forecasting with different degrees of success. In this paper, we propose a multivariate expansion of the hybrid ARIMA-ARNN model. The main motivation is to pr

Marc Rovirola, Muhammad Waqas Khaliq, Travis Gustafson, Fiona Sosa

We use surface acoustic waves of 1 and 3 GHz in hybrid piezoelectric-magnetic systems with either cobalt or nickel as a magnetic layer to generate magnetoacoustic waves and directly image them using stroboscopic X-ray magnetic circular dichroism imaging. Our measurements visualize and quantify the amplitudes of both acoustic and magnetic components of the ma

Kaichen Zhou, Jia-Wang Bian, Jian-Qing Zheng, Jiaxing Zhong

Despite advancements in self-supervised monocular depth estimation, challenges persist in dynamic scenarios due to the dependence on assumptions about a static world. In this paper, we present Manydepth2, to achieve precise depth estimation for both dynamic objects and static backgrounds, all while maintaining computational efficiency. To tackle the challeng

Haichao Xu, Xingpao Suo

The Discrete Fourier Transform (DFT) is widely utilized for signal analysis but is plagued by spectral leakage, leading to inaccuracies in signal approximation. Window functions play a crucial role in mitigating spectral leakage by providing weighting mechanisms for discrete signals. In this paper, we introduce a novel window type based on exponential functi

S. Sivaprasad Kumar, Neha Verma

In the present investigation, we introduce a new subclass of starlike functions defined by $\mathcal{S}^{*}_{\tau}:=\{f\in \mathcal{A}:zf'(z)/f(z) \prec 1+\arctan z=:\tau(z)\}$, where $\tau(z)$ maps the unit disk $\mathbb {D}:= \{z\in \mathbb{C}:|z|<1\}$ onto a strip domain. We derive structural formulae, growth, and distortion theorems for $\mathcal{S}^{*}_

Srijan Saket, Olivier Jeunen, Md. Danish Kalim

Emerging short-video platforms like TikTok, Instagram Reels, and ShareChat present unique challenges for recommender systems, primarily originating from a continuous stream of new content. ShareChat alone receives approximately 2 million pieces of fresh content daily, complicating efforts to assess quality, learn effective latent representations, and accurat

Lucas Hall, Leonard Huang, Jacek Krajczok, Mariusz Tobolski

The Stone-von Neumann Theorem is a fundamental result which unified the competing quantum mechanical models of matrix mechanics and wave mechanics. It's mechanism of proof ultimately involved the study of unitary group representations on a Hilbert space. In this article, we continue the broad generalization set out in arxiv:1903.09351 and arxiv:2109.08997, a

Yu Cai, Tianyu Shen, Shi-Sheng Huang, Hua Huang

Depth completion, aiming to predict dense depth maps from sparse depth measurements, plays a crucial role in many computer vision related applications. Deep learning approaches have demonstrated overwhelming success in this task. However, high-precision depth completion without relying on the ground-truth data, which are usually costly, still remains challen

Felix Joos, Richard Lang, Nicolás Sanhueza-Matamala

We study conditions under which a given hypergraph is randomly robust Hamiltonian, which means that a random sparsification of the host graph contains a Hamilton cycle with high probability. Our main contribution provides nearly optimal results whenever the host graph is Hamilton connected in a locally robust sense, which translates to a typical induced subg

Sami Nadif, Essaid Sabir, Halima Elbiaze, Abdelkrim Haqiq

Grant free access, in which each Internet of Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant free access with multi packet reception capabilities, with an emph

Tom Benhamou

We continue the study of the pseudo-intersection property with respect to an ideal introduced in \cite{TomNatasha2}. Our theory applies to the study of the Tukey types of general sums of ultrafilters, which, as evidenced by the results of this paper, can be quite complex. It also applies to construct a large class of ultrafilter $\mathcal{C}$ over $\omega$ s

Mira Gergácz, Ákos Kereszturi

Before the seasonal polar ice cap starts to expand towards lower latitudes on Mars, small frost patches may condensate out during the cold night and they may remain on the surface even during the day in shady areas. If ice in these areas can persist before the arrival of the contiguous ice cap, they may remain after the recession of it too, until the irradia

Jia-Wen Li, Gang Su, Bo Gu

To realize room temperature ferromagnetic semiconductors is still a challenge in spintronics. Recent experiments have obtained two-dimensional (2D) room temperature ferromagnetic metals, such as monolayer MnSe2. In this paper, we proposed a way to obtain room temperature ferromagnetic semiconductors through metal-semiconductor transition. By the density func

Mingwei Li, Jiachen Tao, Zongxin Yang, Yi Yang

Reconstructing the human body from single-view videos plays a pivotal role in the virtual reality domain. One prevalent application scenario necessitates the rapid reconstruction of high-fidelity 3D digital humans while simultaneously ensuring real-time rendering and interaction. Existing methods often struggle to fulfill both requirements. In this paper, we

Kazusa Beppu, Kaito Matsuura, Yusuke T. Maeda

Dense systems of active matter exhibit highly dynamic collective motion characterized by intermingled vortices, referred to as active turbulence. The interaction between these vortices is key to controlling turbulent dynamics, and a promising approach for revealing the rules governing their interaction is geometric confinement. In this study, we investigate

Frédéric Cérou, Patrick Héas, Mathias Rousset

This paper considers the classical problem of sampling with Monte Carlo methods a target rare event distribution defined by a score function that is very expensive to compute. We assume we can build using evaluations of the true score, an approximate surrogate score certified with error bounds. This work proposes a fully adaptive algorithm to sequentially sa

Dariusz Bugajewski, Piotr Maćkowiak

In this paper we are going to prove a very general fixed point theorem for mappings acting in partial metric spaces. In that theorem we impose some conditions on behavior of considered mappings on orbits and a condition relating orbits of points of small size.

Mingzheng Zhu, Hao Fu, Jun Wu, Chi Zhang

As the leading candidate of quantum error correction codes, surface code suffers from significant overhead, such as execution time. Reducing the circuit's execution time not only enhances its execution efficiency but also improves fidelity. However, finding the shortest execution time is NP-hard. In this work, we study the surface code mapping and scheduling

Chen Yang, Kailing Wang, Yuehao Wang, Qi Dou

Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems. Existing methods either compromise on rendering quality or are excessively computationally intensive, often demanding dozens of hours to perform, which significantly hinders their practical application. In this paper, we introduce Fast O

Zhangli Lu, Chuqi Lei, Kaili Wang, Libo Qin

Accurate and robust prediction of drug-target interactions (DTIs) plays a vital role in drug discovery. Despite extensive efforts have been invested in predicting novel DTIs, existing approaches still suffer from insufficient labeled data and cold start problems. More importantly, there is currently a lack of studies focusing on elucidating the mechanism of

Keiji Nakatsugawa, Xiao Hu

Klein's paradox refers to the transmission of a relativistic particle through a high potential barrier. Although it has a simple resolution in terms of particle-to-antiparticle tunneling (Klein tunneling), debates on its physical meaning seem lasting partially due to the lack of direct experimental verification. In this article, we point out that honeycomb-t

L. G. Martins, M. V. Flamarion, R. Ribeiro-Jr

The movement of water waves is a topic of interest to researchers from different areas. While their propagation is described by Euler equations, there are instances where simplified models can also provide accurate approximations. A well-known reduced model employed to study the wave dynamics is the Boussinesq model. Despite being extensively studied, to our