March 2024 arXiv papers — page 154

Matilde Baroni, Quoc-Huy Vu, Boris Bourdoncle, Eleni Diamanti

Nonlocal games play a crucial role in quantum information theory and have numerous applications in certification and cryptographic protocols. Kalai et al. (STOC 2023) introduced a procedure to compile a nonlocal game into a single-prover interactive proof, using a quantum homomorphic encryption scheme, and showed that their compilation method preserves the c

Prashant K. Jha, Patrick Diehl, Robert Lipton

This work considers the nodal finite element approximation of peridynamics, in which the nodal displacements satisfy the peridynamics equation at each mesh node. For the nonlinear bond-based peridynamics model, it is shown that, under the suitable assumptions on an exact solution, the discretized solution associated with the central-in-time and nodal finite

Julia Di, Zdravko Dugonjic, Will Fu, Tingfan Wu

Vision-based tactile sensors have recently become popular due to their combination of low cost, very high spatial resolution, and ease of integration using widely available miniature cameras. The associated field of view and focal length, however, are difficult to package in a human-sized finger. In this paper we employ optical fiber bundles to achieve a for

Abhinaya S. B., Aafaq Sabir, Anupam Das

Virtual Reality (VR) has witnessed a rising issue of harassment, prompting the integration of safety controls like muting and blocking in VR applications. However, the lack of standardized safety measures across VR applications hinders their universal effectiveness, especially across contexts like socializing, gaming, and streaming. While prior research has

Monirul Shaikh, Duo Wang, Saurabh Ghosh

Commencing from the centrosymmetric MnRMnSbO$_6$ compound, we explore the realm of magnetic polar double-double perovskite oxides characterized by significant ferroelectric polarization. Employing symmetry operations, first-principles methodologies, and Monte Carlo simulations, our investigation delves into the structural, magnetic, ferroelectric, and electr

Wenping Cui, Robert Marsland, Pankaj Mehta

Ecosystems are among the most interesting and well-studied examples of self-organized complex systems. Community ecology, the study of how species interact with each other and the environment, has a rich tradition. Over the last few years, there has been a growing theoretical and experimental interest in these problems from the physics and quantitative biolo

David Camarena, Francis-Yan Cyr-Racine

Some cosmic microwave background (CMB) data allow a cosmological scenario in which the free streaming of neutrinos is delayed until close to matter-radiation equality. Interestingly, recent analyses have revealed that large-scale structure (LSS) data also align with this scenario, discarding the possibility of an accidental feature in the CMB sky and calling

Rasul Abdusalamov, Jendrik Weise, Mikhail Itskov

The Mullins effect represents a softening phenomenon observed in rubber-like materials and soft biological tissues. It is usually accompanied by many other inelastic effects like for example residual strain and induced anisotropy. In spite of the long term research and many material models proposed in literature, accurate modeling and prediction of this comp

Agnes Luhtaru, Taido Purason, Martin Vainikko, Maksym Del

This study explores enhancing grammatical error correction (GEC) through artificial error generation (AEG) using language models (LMs). Specifically, we fine-tune Llama 2-based LMs for error generation and find that this approach yields synthetic errors akin to human errors. Next, we train GEC Llama models with the help of these artificial errors and outperf

Takahiro Nagaoka, Akihito Wachi

We prove the strong Lefschetz property for Artinian Gorenstein algebras generated by the relative invariants of prehomogeneous vector spaces of commutative parabolic type.

Ruoxi Zhu, Zifan Zhou, Jinyang Li, Jason Bonacum

We demonstrate a slow-light augmented unbalanced Mach-Zehnder interferometer (MZI) which can be used to enhance very significantly the sensitivity of measuring the frequency shift in a laser. The factor of enhancement depends on the group index of the slow-light medium, the degree of imbalance between the physical lengths of the two arms of the MZI, and the

Amitis Shidani, Devon Hjelm, Jason Ramapuram, Russ Webb

Contrastive learning typically matches pairs of related views among a number of unrelated negative views. Views can be generated (e.g. by augmentations) or be observed. We investigate matching when there are more than two related views which we call poly-view tasks, and derive new representation learning objectives using information maximization and sufficie

Royden Wagner, Omer Sahin Tas, Marvin Klemp, Carlos Fernandez

We present JointMotion, a self-supervised pre-training method for joint motion prediction in self-driving vehicles. Our method jointly optimizes a scene-level objective connecting motion and environments, and an instance-level objective to refine learned representations. Scene-level representations are learned via non-contrastive similarity learning of past

D. Fortune Kponou, Frejus A. A. Laleye, Eugene C. Ezin

In this paper, we introduce the Fongbe to French Speech Translation Corpus (FFSTC) for the first time. This corpus encompasses approximately 31 hours of collected Fongbe language content, featuring both French transcriptions and corresponding Fongbe voice recordings. FFSTC represents a comprehensive dataset compiled through various collection methods and the

Jae-Mo Lihm, Cheol-Hwan Park

The nonlinear Hall effect has attracted much attention due to the famous, widely adopted interpretation in terms of the Berry curvature dipole in momentum space. Using ab initio Boltzmann transport equations, we find a 60% enhancement in the nonlinear Hall effect of p-doped GeTe and its noticeable frequency dependence, qualitatively different from the predic

M. S. Sánchez, J. A. Oller, D. R. Entem

We study a family of separable potentials with and without added contact interactions by solving the associated Lippmann-Schwinger equation with two coupled partial waves. The matching of the resulting amplitude matrix with the effective-range expansion is studied in detail. When a counterterm is included in the potential we also carefully discuss its renorm

Mohamed E. M. K. Abdelaziz, Libaihe Tian, Thomas Lottner, Simon Reiss

Cardiovascular diseases (CVDs) and congenital heart diseases (CHD) pose significant global health challenges. Fluoroscopy-guided endovascular interventions, though effective, are accompanied by ionizing radiation concerns, especially in pediatric cases. Magnetic resonance imaging (MRI) emerges as a radiation-free alternative, offering superior soft tissue vi

Supranta S. Boruah, Pier Fiedorowicz, Eduardo Rozo

We update the field-level inference code KARMMA to enable tomographic forward-modelling of shear maps. Our code assumes a lognormal prior on the convergence field, and properly accounts for the cross-covariance in the lensing signal across tomographic source bins. We use mock weak lensing data from N-body simulations to validate our mass-mapping forward mode

Kisnney Almeida, Igor Lima

In this paper, we establish a new criterion for determining whether an Artin group is subgroup separable (LERF), building upon a criterion introduced in a previous work. Specifically, we prove an Artin group is LERF if and only if it does not contain certain induced subgraphs, thus providing a more direct generalization of the Metaftsis-Raptis criterion for

Lidice Cruz-Rodriguez, Diptesh Dey, Antonia Freibert, Philipp Stammer

Attosecond science has opened up new frontiers in our understanding of processes happening on the intrinsic timescale of electrons. The ability to manipulate and observe phenomena at the attosecond level has yielded groundbreaking insights into processes such as electron dynamics and the behavior of matter under extreme conditions. This interdisciplinary fie

Julian Runge, Igor Skokan, Gufeng Zhou, Koen Pauwels

As privacy-centric changes reshape the digital advertising landscape, deterministic attribution and measurement of advertising-related user behavior is increasingly constrained. In response, there has been a resurgence in the use of traditional probabilistic measurement techniques, such as media and marketing mix modeling (m/MMM), particularly among digital-

Artur Wolek, Isaac E. Weintraub, Alexander Von Moll, David Casbeer

Existing methods for avoiding dynamic engagement zones (EZs) and minimizing risk leverage the calculus of variations to obtain optimal paths. While such methods are deterministic, they scale poorly as the number of engagement zones increases. Furthermore, optimal-control based strategies are sensitive to initial guesses and often converge to local, rather th

Matheus Rakes, Maíra Chagas Morais, Leandro do Prado Ribeiro, Gabriel Rodrigues Palma

The impact of climate change has led to growing global concern about the interaction of temperature and xenobiotics in agricultural toxicological studies. Thus, for the first time, we evaluated the lethal, sublethal and transgerational effects of six insecticides used in the management of stink bug complex in soybean crops on the different life stages of the

Mengsha Hu, Jinzhou Li, Runxiang Jin, Chao Shi

As technological advancements continue to expand the capabilities of multi unmanned-aerial-vehicle systems (mUAV), human operators face challenges in scalability and efficiency due to the complex cognitive load and operations associated with motion adjustments and team coordination. Such cognitive demands limit the feasible size of mUAV teams and necessitate

Shijie Gao, Lauren Bramblett, Nicola Bezzo

Autonomous mobile robots (AMRs) equipped with high-quality cameras have revolutionized the field of inspections by providing efficient and cost-effective means of conducting surveys. The use of autonomous inspection is becoming more widespread in a variety of contexts, yet it is still challenging to acquire the best inspection information autonomously. In si

Imran Hayat, George Ilhwan Park

Pressure-gradient-induced separation of swept and unswept turbulent boundary layers, based on the DNS studies of Coleman et al. (J. Fluid Mech. 2018 & 2019), have been analyzed for various nonequilibrium effects. The goal is to isolate physical processes critical to near-wall flow modeling. The decomposition of skin friction into contributing physical terms,

Maarten V. de Hoop, Joonas Ilmavirta, Antti Kykkänen, Rafe Mazzeo

On gas giant planets the speed of sound is isotropic and goes to zero at the surface. Geometrically, this corresponds to a Riemannian manifold whose metric tensor has a conformal blow-up near the boundary. The blow-up is tamer than in asymptotically hyperbolic geometry: the boundary is at a finite distance. We study the differential geometry of such manifold

Wei Wan

The success of grain boundary (GB) plane orientation fundamental zone (FZ) has connected GB structures across multiple crystallographic characters with their properties in a unique insight, but quantitative understandings of the structure-property relationship therein are still lacking. Based on the well-known Read-Shockley relationship, a theoretical deriva

Claudio Garcia, Santosh Kumar Radha, Swagata Acharya, Walter R. L. Lambrecht

The electronic band structure of V$_2$O$_5$ is calculated using an all-electron quasiparticle self-consistent (QS) $GW$ method, including electron-hole ladder diagrams in the screening of $W$. The optical dielectric function calculated with the Bethe-Salpeter equation exhibits excitons with large binding energy, consistent with spectroscopic ellipsometry dat

Xinyu Jiang, Yibei Guo, Mengsha Hu, Ruoming Jin

Advanced by rich perception and precise execution, robots possess immense potential to provide professional and customized rehabilitation exercises for patients with mobility impairments caused by strokes. Autonomous robotic rehabilitation significantly reduces human workloads in the long and tedious rehabilitation process. However, training a rehabilitation

O. Băzăvan, S. Saner, D. J. Webb, E. M. Ainley

Quantum harmonic oscillators model a wide variety of phenomena ranging from electromagnetic fields to vibrations of atoms in molecules. Their excitations can be represented by bosons such as photons, single particles of light, or phonons, the quanta of vibrational energy. Linear interactions that only create and annihilate single bosons can generate coherent

Yan Wang, Sarah Chehade, Eugene Dumitrescu

Discovering pragmatic and efficient approaches to construct $\varepsilon$-approximations of quantum operators such as real (imaginary) time-evolution propagators in terms of the basic quantum operations (gates) is challenging. Prior $\varepsilon$-approximations are invaluable, in that they enable the compilation of classical and quantum algorithm modeling of

Shaktiranjan Mohanty, Brindaban Ojha, Minaxi Sharma, Subhankar Bedanta

The interlayer exchange coupling (IEC) between two ferromagnetic (FM) layers separated by a non-magnetic (NM) spacer layer gives rise to different types of coupling with the variation of spacer layer thickness. When the NM is metallic, the IEC is attributed to the well known Ruderman Kittel Kasuya Yosida (RKKY) interaction which shows an oscillatory decaying

Adrian de Wynter

We show that GPT-4's reasoning and planning capabilities extend to the 1993 first-person shooter Doom. This large language model (LLM) is able to run and play the game with only a few instructions, plus a textual description--generated by the model itself from screenshots--about the state of the game being observed. We find that GPT-4 can play the game to a

Moslem Zamani, François Glineur, Julien M. Hendrickx

Consider a sum of convex functions, where the only information known about each individual summand is the location of a minimizer. In this work, we give an exact characterization of the set of possible minimizers of the sum. Our results cover several types of assumptions on the summands, such as smoothness or strong convexity. Our main tool is the use of nec

Yu Xiang, Sai Haneesh Allu, Rohith Peddi, Tyler Summers

We introduce a new trajectory optimization method for robotic grasping based on a point-cloud representation of robots and task spaces. In our method, robots are represented by 3D points on their link surfaces. The task space of a robot is represented by a point cloud that can be obtained from depth sensors. Using the point-cloud representation, goal reachin

Akshat Ramachandran, Zishen Wan, Geonhwa Jeong, John Gustafson

Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low precision, and often require large silicon overhead and intensive quantization-aware training. In this study, we introduce Logarithmic Posits (LP), an adaptive, hardware-friendly da

Tea Martinić Bilać, Stjepan Meljanac, Salvatore Mignemi

We discuss the generalized Yang Poisson models. We construct generalizations of the Yang Poisson algebra related to $\mathfrak{o}(1,5)$ algebra discussed by Meljanac and Mignemi (2023). The exact realizations of this generalized algebra on canonical phase space are presented and the corresponding differential equations are solved in simple cases. Furthermore

Kheir-Eddine Otmani, Andrés Mateo-Gabín, Gonzalo Rubio, Esteban Ferrer

We explore the unsupervised clustering technique introduced in [25] to identify viscous/turbulent from inviscid regions in incompressible flows. The separation of regions allows solving the Navier-Stokes equations including Large Eddy Simulation closure models only in the viscous/turbulent ones, while solving the Euler equations in the remaining of the compu

Julian Braun, Maciej Buze

The elastic field around a crack opening is known to be described by continuum linearised elasticity in leading order. In this work, we explicitly develop the next term in the atomistic asymptotic expansion in the case of a Mode III crack in anti-plane geometry. The aim of such an expansion is twofold. First, we show that the well-known flexible boundary con

Joshua Cape

Varimax factor rotations, while popular among practitioners in psychology and statistics since being introduced by H. Kaiser, have historically been viewed with skepticism and suspicion by some theoreticians and mathematical statisticians. Now, work by K. Rohe and M. Zeng provides new, fundamental insight: varimax rotations provably perform statistical estim

Y. Li, S. Xu, T. H. Loeber, E. J. D. Vredenbregt

Scanning ion microscopy applications of novel focused ion beam (FIB) systems based on ultracold rubidium (Rb) and cesium (Cs) atoms were investigated via ion-induced electron and ion yields. Results measured on the Rb$^+$ and Cs$^+$ FIB systems were compared with results from commercially available gallium (Ga$^+$) systems to verify the merits of applying Rb

Zongzhe Du, David Stefanyszyn

We consider effective field theories (EFTs) of scalar fields with broken Lorentz boosts, which arise by taking the decoupling and flat-space limits of the EFT of inflation, and derive constraints that must be satisfied by the corresponding scattering amplitudes if there is an underlying non-linearly realised symmetry. We primarily concentrate on extended shi

Will Yeadon, Elise Agra, Oto-obong Inyang, Paul Mackay

This study evaluates $n = 300$ short-form physics essay submissions, equally divided between student work submitted before the introduction of ChatGPT and those generated by OpenAI's GPT-4. In blinded evaluations conducted by five independent markers who were unaware of the origin of the essays, we observed no statistically significant differences in scores

Ian Xul Belaustegui, Marcela Ordorica Arango, Román Rossi-Pool, Naomi Ehrich Leonard

An important problem in many areas of science is that of recovering interaction networks from simultaneous time-series of many interacting dynamical processes. A common approach is to use the elements of the correlation matrix or its inverse as proxies of the interaction strengths, but the reconstructed networks are necessarily undirected. Transfer entropy m

Marijana Butorac, Slaven Kožić, Arne Meurman, Mirko Primc

In this paper, we recall Lepowsky's and Wakimoto's product character formulas formulated in a new way by using arrays of specialized weighted crystals of negative roots for affine Lie algebras of type $C_l^{(1)}$, $D_{l+1}^{(2)}$ and $A_{2l}^{(2)}$. Lepowsky-Wakimoto's infinite periodic products appear as one side of (conjectured) Rogers-Ramanujan-type combi

Sean Anderson, João Pedro Hespanha

Data-driven control benefits from rich datasets, but constructing such datasets becomes challenging when gathering data is limited. We consider an offline experiment design approach to gathering data where we design a control input to collect data that will most improve the performance of a feedback controller. We show how such a control-oriented approach ca

Lucio Galeati, Khoa Lê, Avi Mayorcas

We consider interacting systems particle driven by i.i.d. fractional Brownian motions, subject to irregular, possibly distributional, pairwise interactions. We show propagation of chaos and mean field convergence to the law of the associated McKean--Vlasov equation, as the number of particles $N\to\infty$, with quantitative sharp rates of order $N^{-1/2}$. O

Hui June Zhu

Let A^d denote the coefficient space of all degree-d polynomials f in one variable for some d\ge 3. For any \bar{f} in A^d(\bar\F_p), a rank-\ell Artin-Schreier curve X_{\bar{f},\ell}: y^{p^\ell}-y= \bar{f} is called ordinary if its normalized Newton polygon achieves the infimum in A^d(\bar\F_p). Given \ell and a number field K, we show that there exists a Z

Amir Aghabiglou, Chung San Chu, Arwa Dabbech, Yves Wiaux

Radio-interferometric (RI) imaging entails solving high-resolution high-dynamic range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN's capability. These range from advanced proximal algorithms propelled by handcra

Amir M. Mansourian, Arya Jalali, Rozhan Ahmadi, Shohreh Kasaei

Deep learning models have achieved significant results across various computer vision tasks. However, due to the large number of parameters in these models, deploying them in real-time scenarios is a critical challenge, specifically in dense prediction tasks such as semantic segmentation. Knowledge distillation has emerged as a successful technique for addre

Tarek Bouazza, Robert Mahony, Tarek Hamel

Accurately estimating camera motion from image sequences poses a significant challenge in computer vision and robotics. Many computer vision methods first compute the essential matrix associated with a motion and then extract orientation and normalized translation as inputs to pose estimation, reconstructing the scene scale (that is unobservable in the epipo

Daniel Cariello

The complete reducibility property for bipartite states reduced the separability problem to a proper subset of positive under partial transpose states and was used to prove several theorems inside and outside entanglement theory. So far only three types of bipartite states were proved to possess this property. In this work, we provide some procedures to crea

Zhiang Li, Daisuke Mashima, Wen Shei Ong, Ertem Esiner

Programmable logic controllers (PLCs) are crucial devices for implementing automated control in various industrial control systems (ICS), such as smart power grids, water treatment systems, manufacturing, and transportation systems. Owing to their importance, PLCs are often the target of cyber attackers that are aiming at disrupting the operation of ICS, inc

Zheng Shen, Matteo Saveriano, Fares J. Abu-Dakka, Sami Haddadin

In the field of Learning from Demonstration (LfD), Dynamical Systems (DSs) have gained significant attention due to their ability to generate real-time motions and reach predefined targets. However, the conventional convergence-centric behavior exhibited by DSs may fall short in safety-critical tasks, specifically, those requiring precise replication of demo

Alessio Mazzetto

We present a new adaptive algorithm for learning discrete distributions under distribution drift. In this setting, we observe a sequence of independent samples from a discrete distribution that is changing over time, and the goal is to estimate the current distribution. Since we have access to only a single sample for each time step, a good estimation requir

Eduardo Camps-Moreno, Jorge Neves, Eliseo Sarmiento

We compute the minimum distance of the parameterized code of order 1 over an even cycle.

Chao Li, Meicong Li, Zhuli Zhang, Qiang Zhao

The effects of chlorine (Cl) and zinc (Zn) co-doping on the electronic structure and optical properties of the zinc blende ({\gamma}) phase of copper iodide ({\gamma}-CuI) scintillator material are investigated by using first-principles density functional theory calculations. The band structure, density of states, dielectric function, absorption coefficients

Sumesh P Thampi, Kevin Stratford, Oliver Henrich

Anisotropic particles are often encountered in different fields of soft matter and complex fluids. In this work, we present an implementation of the coupled hydrodynamics of solid ellipsoidal particles and the surrounding fluid using the lattice Boltzmann method. A standard link-based mechanism is used to implement the solid-fluid boundary conditions. We dev

Thomas M. Cross, David M. Benoit, Marco Pignatari, Brad K. Gibson

In this work we present a new approach to produce spectroscopic constants and model first-principles synthetic spectra for all molecules of astrophysical interest. We have generalized our previous diatomic molecule simulation framework, employing Transition-Optimised Shifted Hermite (TOSH) theory, thereby enabling the modelling of polyatomic rotational const

Daniel Nickelsen, Gernot Müller

We address the need for forecasting methodologies that handle large uncertainties in electricity prices for continuous intraday markets by incorporating parameter uncertainty and using a broad set of covariables. This study presents the first Bayesian forecasting of electricity prices traded on the German intraday market. Endogenous and exogenous covariables

Harald Steck, Chaitanya Ekanadham, Nathan Kallus

Cosine-similarity is the cosine of the angle between two vectors, or equivalently the dot product between their normalizations. A popular application is to quantify semantic similarity between high-dimensional objects by applying cosine-similarity to a learned low-dimensional feature embedding. This can work better but sometimes also worse than the unnormali

Sara Faridi, Tài Huy Hà, Takayuki Hibi, Susan Morey

Every multigraded free resolution of a monomial ideal I contains the Scarf multidegrees of I. We say I has a Scarf resolution if the Scarf multidegrees are sufficient to describe a minimal free resolution of I. The main question of this paper is which graphs G have edge ideal I(G) with a Scarf resolution? We show that I(G) has a Scarf resolution if and only

Yabo Zhang, Yuxiang Wei, Xianhui Lin, Zheng Hui

Text-to-image diffusion models (T2I) have demonstrated unprecedented capabilities in creating realistic and aesthetic images. On the contrary, text-to-video diffusion models (T2V) still lag far behind in frame quality and text alignment, owing to insufficient quality and quantity of training videos. In this paper, we introduce VideoElevator, a training-free

Giuseppe Procopio, Chiara Pezzotti, Massimiliano Giona

The phenomenon of ergodicity breaking of stochastic dynamics governed by Generalized Langevin Equations (GLE) in the presence of well-behaved exponentially decaying dissipative memory kernels, recently investigated by many authors (Phys. Rev. E {\bf 83} 062102 2011; Phys. Rev. E {\bf 98} 062140 2018; Eur. Phys. J. B {\bf 93} 184 2020), finds, in the dynamic

Mattia Calzi

Given a symmetric Siegel domain $\mathscr D$ and a positive plurihamonic function $f$ on $\mathscr D$, we study the largest positive Radon measure $\mu$ on the Silov boundary $\mathrm b \mathscr D$ of $\mathscr D$ whose Poisson integral $\mathscr P \mu$ is $\leq f$. If $\mathscr D$ has no tubular irreducible factors of rank $\geq 2$, we show that $\mathscr P

Anindya Mondal, Sauradip Nag, Xiatian Zhu, Anjan Dutta

Object counting is pivotal for understanding the composition of scenes. Previously, this task was dominated by class-specific methods, which have gradually evolved into more adaptable class-agnostic strategies. However, these strategies come with their own set of limitations, such as the need for manual exemplar input and multiple passes for multiple categor

Arijit Nag, Animesh Mukherjee, Niloy Ganguly, Soumen Chakrabarti

Large Language Models (LLMs) exhibit impressive zero/few-shot inference and generation quality for high-resource languages (HRLs). A few of them have been trained on low-resource languages (LRLs) and give decent performance. Owing to the prohibitive costs of training LLMs, they are usually used as a network service, with the client charged by the count of in

Aleksandr Mkrtchyan

We consider the problem of continuability into a sectorial domain for multiple power series and prove the multivariate analog of the Le Roy-Lindel\"of theorem, i.e. establish a connection between the growth of the holomorphic function interpolating the series coefficients in the imaginary subspace and the sectoral domain where the multiple power series is an

Chenhui Zhao, Liyue Shen

Precision medicine, such as patient-adaptive treatments assisted by medical image analysis, poses new challenges for segmentation algorithms in adapting to new patients, due to the large variability across different patients and the limited availability of annotated data for each patient. In this work, we propose a data-efficient segmentation algorithm, name

Sufyan Shehada, Manuel dos Santos Dias, Muayad Abusaa, Samir Lounis

Designing systems with large magnetic anisotropy energy (MAE) is desirable and critical for nanoscale magnetic devices. A recent breakthrough achieved the theoretical limit of the MAE for 3$d$ transition metal atoms by placing a single Co atom on a MgO(100) surface, a result not replicated by standard first-principles simulations. Our study, incorporating Hu

Massimiliano Giona, Giuseppe Procopio, Chiara Pezzotti

Using the initial-value formulation, a dynamic theory for systems evolving according to a Generalized Langevin Equation is developed, providing more restrictive conditions on the existence of equilibrium behavior and its fluctuation-dissipation implications. For systems fulfilling the property of local realizability, that for all the practical purposes corre

Zhuangwei Shi

Recently, lithium-ion batteries occupy a pivotal position in the realm of electric vehicles and the burgeoning new energy industry. Their performance is heavily dependent on three core states: remaining-useful-life (RUL), state-of-health (SOH), and state-of-charge (SOC). Given the remarkable success of Mamba (Structured state space sequence models with selec

Mattia Calzi

Given a bounded symmetric domain $D$, we study (positive) pluriharmonic functions on $D$ and investigate a possible analogue of the family of Clark measures associated with a holomorphic function from $D$ into the unit disc in $\mathbb C$.

Bingbing Wang, Bin Liang, Chun-Mei Feng, Wangmeng Zuo

In real-world conversations, the diversity and ambiguity of stickers often lead to varied interpretations based on the context, necessitating the requirement for comprehensively understanding stickers and supporting multi-tagging. To address this challenge, we introduce StickerTAG, the first multi-tag sticker dataset comprising a collected tag set with 461 t

Bin Liang, Bingbing Wang, Zhixin Bai, Qiwei Lang

Using stickers in online chatting is very prevalent on social media platforms, where the stickers used in the conversation can express someone's intention/emotion/attitude in a vivid, tactful, and intuitive way. Existing sticker retrieval research typically retrieves stickers based on context and the current utterance delivered by the user. That is, the stic

Mohit Bansil, Alpár R. Mészáros

In this paper we unveil novel monotonicity conditions applicable for Mean Field Games through the exploration of finite dimensional $canonical\ transformations$. Our findings contribute to establishing new global well-posedness results for the associated master equations, also in the case of potentially degenerate idiosyncratic noise. Additionally, we show t

Shouri Hu, Jiawei Li, Zhibo Cai

Bayesian optimization (BO) has shown impressive results in a variety of applications within low-to-moderate dimensional Euclidean spaces. However, extending BO to high-dimensional settings remains a significant challenge. We address this challenge by proposing a two-step optimization framework. Initially, we identify the effective dimension reduction (EDR) s

Vincent Delecroix, Pascal Hubert, Ferrán Valdez

This book explores infinite-type translation surfaces and is intended as an introductory text for graduate and PhD students, as well as a reference for more advanced researchers. Chapter 1 introduces the three definitions of translation surfaces and meticulously proves their equivalence. It is enriched with numerous examples that are revisited throughout the

Kazuki Hashimoto, Dmitri B. Horoshko, Mikhail I. Kolobov, Yoad Michael

Fourier-transform infrared spectroscopy (FTIR) is an indispensable analytical method that allows label-free identification of substances via fundamental molecular vibrations. However, the sensitivity of FTIR is often limited by the low efficiency of mid-infrared (MIR) photodetectors. SU(1,1) interferometry has previously enabled FTIR with undetected MIR phot

Huiming Sun, Jiacheng Guo, Zibo Meng, Tianyun Zhang

Vehicle detection in Unmanned Aerial Vehicle (UAV) captured images has wide applications in aerial photography and remote sensing. There are many public benchmark datasets proposed for the vehicle detection and tracking in UAV images. Recent studies show that adding an adversarial patch on objects can fool the well-trained deep neural networks based object d

Claudio Aravena-Plaza, Víctor Muñoz, Felipe A. Asenjo

From a classical analysis, it is shown that the nondiffractive accelerating gravitational Airy wave packets are solutions of Einstein equations for their linearized tensor modes in a Friedmann-Lema\^itre-Robertson-Walker cosmological background filled with a perfect fluid, with equations of state $w=1/3$ and $w=-1/3$. These solutions have finite energy, pres

John A. Fitzgerald, Philippe H. Trinh

In this dissertation, we seek to expand the scope of work done by Lustri and Chapman (2013) in modelling 3D flow past a point source, in order to account for more general flows, where the strength of such a source, $\delta$, is now $O(1)$. We find that in order to solve for the Stokes surfaces of this nonlinear system, we must develop a numerical scheme to s

Mubashir Noman, Muzammal Naseer, Hisham Cholakkal, Rao Muhammad Anwar

Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. Different from

Milad Ahanjideh, Martin Milanič, Mary Servatius

We continue the study of balanceable graphs, defined by Caro, Hansberg, and Montejano in 2021 as graphs $G$ such that any $2$-coloring of the edges of a sufficiently large complete graph containing sufficiently many edges of each color contains a balanced copy of $G$. While the problem of recognizing balanceable graphs was conjectured to be NP-complete by Da

Mako Bates, Joseph P. Near

Concurrent distributed systems are notoriously difficult to construct and reason about. Choreographic programming is a recent paradigm that describes a distributed system in a single global program called a choreography. Choreographies simplify reasoning about distributed systems and can ensure deadlock freedom by static analysis. In previous choreographic p

Yahao Lu, Yupei Lin, Han Wu, Xiaoyu Xian

Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. Recently, convolutional neural networks have achieved significant advantages in general object detection. With the development of Transformer, the scale of SIRST models is constantly increasing. Due to the limited training samples, performance has n

M Sabbir Salek, Mugdha Basu Thakur, Pardha Sai Krishna Ala, Mashrur Chowdhury

Automated vehicle (AV) platooning has the potential to improve the safety, operational, and energy efficiency of surface transportation systems by limiting or eliminating human involvement in the driving tasks. The theoretical validity of the AV platooning strategies has been established and practical applications are being tested under real-world conditions

Jehanne Dousse, Frédéric Jouhet, Isaac Konan

We provide combinatorial tools inspired by work of Warnaar to give combinatorial interpretations of the sum sides of the Andrews-Gordon and Bressoud identities. More precisely, we give an explicit weight- and length-preserving bijection between sets related to integer partitions, which provides these interpretations. In passing, we discover the $q$-series ve

Raphaël Ducatez, Alice Guionnet, Jonathan Husson

We establish large deviation principles for the largest eigenvalue of large random matrices with variance profiles. For $N \in \mathbb N$, we consider random $N \times N$ symmetric matrices $H^N$ which are such that $H_{ij}^{N}=\frac{1}{\sqrt{N}}X_{i,j}^{N}$ for $1 \leq i,j \leq N$, where the $X_{i,j}^{N}$ for $1 \leq i \leq j \leq N$ are independent and cen

Mohit Bansil, Alpár R. Mészáros

In this note we show how canonical transformations reveal hidden convexity properties for deterministic optimal control problems, which in turn result in global existence of $C^{1,1}_{loc}$ solutions to first order Hamilton--Jacobi--Bellman equations.

Wenya Luo, Zhidong Bai, Jiang Hu, Chen Wang

In this paper, we focus on the BDS test, which is a nonparametric test of independence. Specifically, the null hypothesis $H_{0}$ of it is that $\{u_{t}\}$ is i.i.d. (independent and identically distributed), where $\{u_{t}\}$ is a random sequence. The BDS test is widely used in economics and finance, but it has a weakness that cannot be ignored: over-reject

Richard Wünsch

Radiation transport plays a crucial role in star formation models, as certain questions within this field cannot be accurately addressed without taking it into account. Given the high complexity of the interstellar medium from which stars form, numerical simulations are frequently employed to model the star formation process. This study reviews recent method

Marc Corstanje, Frank van der Meulen, Moritz Schauer, Stefan Sommer

To date, most methods for simulating conditioned diffusions are limited to the Euclidean setting. The conditioned process can be constructed using a change of measure known as Doob's $h$-transform. The specific type of conditioning depends on a function $h$ which is typically unknown in closed form. To resolve this, we extend the notion of guided processes t

Yuxi Liu, Guibo Luo, Yuesheng Zhu

Medical image segmentation is crucial for clinical diagnosis. The Segmentation Anything Model (SAM) serves as a powerful foundation model for visual segmentation and can be adapted for medical image segmentation. However, medical imaging data typically contain privacy-sensitive information, making it challenging to train foundation models with centralized st

Abdolmahdi Bagheri, Mahdi Dehshiri, Babak Nadjar Araabi, Alireza Akhondi Asl

In the investigation of any causal mechanisms, such as the brain's causal networks, the assumption of causal sufficiency plays a critical role. Notably, neglecting this assumption can result in significant errors, a fact that is often disregarded in the causal analysis of brain networks. In this study, we propose an algorithmic identification approach for de

Muyao Wang, Wenchao Chen, Bo Chen

The forecasting of Multivariate Time Series (MTS) has long been an important but challenging task. Due to the non-stationary problem across long-distance time steps, previous studies primarily adopt stationarization method to attenuate the non-stationary problem of the original series for better predictability. However, existing methods always adopt the stat

R. Swami, G. Julie, S. Le-Denmat, G. Pernot

Scanning Thermal Microscopy (SThM) has become an important measurement tool for characterizing the thermal properties of materials at the nanometer scale. This technique requires a SThM probe that combines an Atomic Force Microscopy (AFM) probe and a very sensitive resistive thermometry; the thermometer being located at the apex of the probe tip allows the m

C. Alexandrou, S. Bacchio, A. De Santis, A. Evangelista

We present a lattice determination of the inclusive decay rate of the process $\tau\mapsto X_{us} \nu_\tau$ in which the $\tau$ lepton decays into a generic hadronic state $X_{us}$ with $\bar u s$ flavour quantum numbers. Our results have been obtained in $n_f=2+1+1$ iso-symmetric QCD with full non-perturbative accuracy, without any OPE approximation and, ex

Fintan McGee, Roderick McCall, Joan Baixauli

Augmented Reality (AR) provides a safe and low-cost option for hazardous safety training that allows for the visualization of aspects that may be invisible, such as radiation. Effectively visually communicating such threats in the environment around the user is not straightforward. This work describes visually encoding radiation using the spatial awareness m