July 2023 arXiv papers — page 112

Camillo Brena, Nicola Gigli

We give an alternative proof of the general chain rule for functions of bounded variation ([ADM90]), which allows to compute the distributional differential of $\varphi\circ F$, where $\varphi\in \mathrm{LIP}(\mathbb{R}^m)$ and $F\in\mathrm{BV}(\mathbb{R}^n,\mathbb{R}^m)$. In our argument we build on top of recently established links between `closability of

Mojgan Hashemian, Marta Couto, Samuel Mascarenhas, Ana Paiva

Can social power endow social robots with the capacity to persuade? This paper represents our recent endeavor to design persuasive social robots. We have designed and run three different user studies to investigate the effectiveness of different bases of social power (inspired by French and Raven's theory) on peoples' compliance to the requests of social rob

Gabriele Merlin, Vedant Nanda, Ruchit Rawal, Mariya Toneva

The pretrain-finetune paradigm usually improves downstream performance over training a model from scratch on the same task, becoming commonplace across many areas of machine learning. While pretraining is empirically observed to be beneficial for a range of tasks, there is not a clear understanding yet of the reasons for this effect. In this work, we examine

Kuan-Chun Chen, Cheng-Te Li, Kuo-Jung Lee

Neural Architecture Search (NAS) has shown promising capability in learning text representation. However, existing text-based NAS neither performs a learnable fusion of neural operations to optimize the architecture, nor encodes the latent hierarchical categorization behind text input. This paper presents a novel NAS method, Discretized Differentiable Neural

Zeno Pavanello, Laura Pirovano, Roberto Armellin

This work presents a sequential convex program method to compute fuel-optimal collision avoidance maneuvers for long-term encounters. The low-thrust acceleration model is used to account for the control, but the method can compute high-thrust maneuvers by increasing the maximum available acceleration. Dealing with the long-term conjunction poses additional c

Lujie Xia, Ziluo Ding, Rui Zhao, Jiyuan Zhang

Efficiently selecting an appropriate spike stream data length to extract precise information is the key to the spike vision tasks. To address this issue, we propose a dynamic timing representation for spike streams. Based on multi-layers architecture, it applies dilated convolutions on temporal dimension to extract features on multi-temporal scales with few

Laurent Bétermin, Ladislav Šamaj, Igor Travěnec

The Riemann zeta function $\zeta(s):= \sum_{n=1}^{\infty} 1/n^s$ can be interpreted as the energy per point of the lattice $\mathbb{Z}$, interacting pairwisely via the Riesz potential $1/r^s$. Given a parameter $\Delta\in (0,1]$, this physical model is generalized by considering the energy per point $E(s,\Delta)$ of a periodic one-dimensional lattice alterna

Samy Skander Bahoura

We consider variational problems with regular H{\"o}lderian weight or boundary singularity, and Dirichlet condition. We prove the boundedness of the volume of the solutions to these equations on analytic domains.

Michal Bartoszkiewicz, Jan Chorowski, Adrian Kosowski, Jakub Kowalski

We present Pathway, a new unified data processing framework that can run workloads on both bounded and unbounded data streams. The framework was created with the original motivation of resolving challenges faced when analyzing and processing data from the physical economy, including streams of data generated by IoT and enterprise systems. These required rapi

Zemin Pan, Wenqi Xiong, Jiaqi Dai, Yunhua Wang

Kagome lattices have an inherent two-dimensional nature. Despite previous realizations in the monolayer limit, their abilities to drive emergent electronic states such as correlated insulators have remained unobserved. Here, we report the experimental realization of a new structural phase of monolayer Mo33Te56, characterized by its virtually global uniformit

Pian Yu, Gianmarco Fedeli, Dimos V. Dimarogonas

This paper investigates the planning and control problems for multi-robot systems under linear temporal logic (LTL) specifications. In contrast to most of existing literature, which presumes a static and known environment, our study focuses on dynamic environments that can have unknown moving obstacles like humans walking through. Depending on whether local

Julian Moosmann, Hanna Mueller, Nicky Zimmerman, Georg Rutishauser

This paper deploys and explores variants of TinyissimoYOLO, a highly flexible and fully quantized ultra-lightweight object detection network designed for edge systems with a power envelope of a few milliwatts. With experimental measurements, we present a comprehensive characterization of the network's detection performance, exploring the impact of various pa

Cédric Gouvenelle, Maudhuy Flora, Thorin Florence

The arrival of digital platforms has revolutionized occupational health by giving the possibility to Occupational Health Services (SPSTI) to acquire databases to offer professionals new possibilities for action. However, in a sector of activity that has been questioning the development of multidisciplinarity for 20 years, the arrival of new tools can sometim

Daniel Schaub, Mark Spivakovsky

The Casas-Alvero conjecture predicts that every univariate polynomial over a field of characteristic zero having a common factor with each of its derivatives $H_i(f)$ is a power of a linear polynomial. One approach to proving the conjecture is to first prove it for polynomials of some small degree $d$, compile a list of bad primes for that degree (namely, th

Rasmus Nielsen, Andrea Crovetto, Alireza Assar, Ole Hansen

Selenium is experiencing renewed interest as a promising candidate for the wide bandgap photoabsorber in tandem solar cells. However, despite the potential of selenium-based tandems to surpass the theoretical efficiency limit of single junction devices, such a device has never been demonstrated. In this study, we present the first monolithically integrated s

V. M. Pergamenshchik, T. Bryk, A. Trokhymchuk

The canonical NLT partition function of a quasi-one dimensional (q1D) one-file system of equal hard disks [J. Chem Phys. 153, 144111 (2020)] provides an analytical description of the thermodynamics and ordering in this system (a pore) as a function of linear density Nd/L where d is the disk diameter. We derive the analytical formulae for the distance depende

David Ahmad Abboud, Damien Jacob

Monolithic applications used to be considered the standard for software development. However, due to the rapid evolution of technology and the increasing demand for scalability and flexibility, these applications have become increasingly inadequate for contemporary environment. In response to these challenges, developers have begun to adopt a microservice (M

Vladimiro Benedetti, Daniele Faenzi, Michele Bolognesi, L Manivel

Given a smooth genus three curve $C$, the moduli space of rank two stable vector bundles on C with trivial determinant embeds in $\mathbb{P}^8$ as a hypersurface whose singular locus is the Kummer threefold of $C$; this hypersurface is the Coble quartic. Gruson, Sam and Weyman realized that this quartic could be constructed from a general skew-symmetric four

Ze Chen, Ruichao Jiang, Javad Tavakoli, Yiqiang Zhao

In this article we show that Theorem 2 in Lie et al. (2023) is incorrect. Since Wombat Exchange, a decentralized exchange, is built upon Lie et al. (2023) and Theorem 2 is fundamental to Wombat Finance, we show that an undesirable phenomenon, which we call the robbed withdrawal, can happen as a consequence.

Zaicheng Zhang, Vincent Bertin, Martin Essink, Hao Zhang

The unsteady hydrodynamic drag exerted on an oscillating sphere near a planar wall is addressed experimentally, theoretically, and numerically. The experiments are performed by using colloidal-probe Atomic Force Microscopy (AFM) in thermal noise mode. The natural resonance frequencies and quality factors are extracted from the measurement of the power spectr

Meng Wang, Katsuhiro Tanaka, Shiro Sakai, Ziqian Wang

Anomalous Hall effect (AHE) emerged in antiferromagnetic metals shows intriguing physics and application potential. In contrast to certain noncollinear antiferromagnets, rutile RuO$_2$ has been proposed recently to exhibit a crystal-assisted AHE with collinear antiferromagnetism. However, in RuO$_2$, the on-site magnetic moment accompanying itinerant 4d elec

Qing-Ming Cheng, Guoxin Wei

In this paper, we study complete Vacuum Static Spaces. A complete classification of 3-dimensional complete Vacuum Static Spaces with non-negative scalar curvature and constant squared norm of Ricci curvature tensor is given by making use of the generalized maximum principle.

Florenc Demrozi, Cristian Turetta, Fadi Al Machot, Graziano Pravadelli

Human Activity Recognition (HAR) has become one of the leading research topics of the last decade. As sensing technologies have matured and their economic costs have declined, a host of novel applications, e.g., in healthcare, industry, sports, and daily life activities have become popular. The design of HAR systems requires different time-consuming processi

Lorenzo Gavassino, Marcelo M. Disconzi, Jorge Noronha

We show that linear superpositions of plane waves involving a single-valued, covariantly stable dispersion relation $\omega(k)$ always propagate outside the lightcone, unless $\omega(k) =a+b k$. This implies that there is no notion of causality for individual dispersion relations, since no mathematical condition on the function $\omega(k)$ (such as the front

Alarith Uhde, Tim zum Hoff, Marc Hassenzahl

Our interactions with technology do not just shape our individual experiences. They also affect people around us. Although previous research has addressed such "witness" experiences, the actual effect of interaction design on the witness experience remains largely unknown. In an online study (n = 407), we explored how witnesses perceive mid-air gesture-based

Jean Cauvin-Vila, Virginie Ehrlacher, Greta Marino, Jan-Frederik Pietschmann

We study some properties of a multi-species degenerate Ginzburg-Landau energy and its relation to a cross-diffusion Cahn-Hilliard system. The model is motivated by multicomponent mixtures where crossdiffusion effects between the different species are taken into account, and where only one species does separate from the others. Using a comparison argument, we

Roumen Tsekov

Trailing the modern ideas of sociophysics, a minimalistic thermodynamic model of society is proposed, which consists of three social ingredients: people, economy, and entropy. Employing the universal van der Waals equation of state, many important relationships are discovered, including laws of econophysics. A paramount finding is that the Second Law of ther

Robin Khanfir

The Horton-Strahler number, also known as the register function, provides a tool for quantifying the branching complexity of a rooted tree. We consider the Horton-Strahler number of critical Galton-Watson trees conditioned to have size $n$ and whose offspring distribution is in the domain of attraction of an $\alpha$-stable law with $\alpha\in [1, 2]$. We gi

Zoé Agathe-Nerine

We consider a population of Hawkes processes modeling the activity of $N$ interacting neurons. The neurons are regularly positioned on the circle $[-\pi, \pi]$, and the connectivity between neurons is given by a cosine kernel. The firing rate function is a sigmoid. The large population limit admits a locally stable manifold of stationary solutions. The main

Valentin Lemarié

We study on the whole space R d the compressible Euler system with damping coupled to the Poisson equation when the damping coefficient tends towards infinity. We first prove a result of global existence for the Euler-Poisson system in the case where the damping is large enough, then, in a second step, we rigorously justify the passage to the limit to the pa

Julien Bichon

Twisted separable functors generalize the separable functors of Nastasescu, Van den Bergh and Van Oystaeyen, and provide a convenient tool to compare various projective dimensions. We discuss when an adjoint functor is twisted separable, obtaining a version of Rafael's Theorem in the twisted case. As an application, we show that if $R$ is Hopf-Galois object

Pranav Agarwal, Aamer Abdul Rahman, Pierre-Luc St-Charles, Simon J. D. Prince

Transformers have significantly impacted domains like natural language processing, computer vision, and robotics, where they improve performance compared to other neural networks. This survey explores how transformers are used in reinforcement learning (RL), where they are seen as a promising solution for addressing challenges such as unstable training, cred

Yonah Conjungo Taumhas, Geneviève Dusson, Virginie Ehrlacher, Tony Lelièvre

In this article, we propose a reduced basis method for parametrized non-symmetric eigenvalue problems arising in the loading pattern optimization of a nuclear core in neutronics. To this end, we derive a posteriori error estimates for the eigenvalue and left and right eigenvectors. The practical computation of these estimators requires the estimation of a co

Anthony D Stephens, David R Walwyn

The use of wind and solar generation is fundamental to the decarbonisation of the United Kingdom electricity system. However, the optimal level of renewable energy as a proportion of total demand is still being debated. In this paper, several models, whose aims are to predict the efficiency of future system configurations, are explained. The models use histo

Sanghyun Kim, Seohyeon Jung, Balhae Kim, Moonseok Choi

Large-scale image generation models, with impressive quality made possible by the vast amount of data available on the Internet, raise social concerns that these models may generate harmful or copyrighted content. The biases and harmfulness arise throughout the entire training process and are hard to completely remove, which have become significant hurdles t

Andreas Fichtner, Coen Hofstede, Lars Gebraad, Andrea Zunino

Ice streams are major contributors to ice sheet mass loss and sea level rise. Effects of their dynamic behaviour are imprinted into seismic properties, such as wave speeds and anisotropy. Here we present results from the first Distributed Acoustic Sensing (DAS) experiment in a deep ice-core borehole in the onset region of the Northeast Greenland Ice Stream.

Andrés Gómez, José Neto

In many applications, when building linear regression models, it is important to account for the presence of outliers, i.e., corrupted input data points. Such problems can be formulated as mixed-integer optimization problems involving cubic terms, each given by the product of a binary variable and a quadratic term of the continuous variables. Existing approa

Wentao Ouyang, Rui Dong, Xiuwu Zhang, Chaofeng Guo

Conversion rate (CVR) prediction plays an important role in advertising systems. Recently, supervised deep neural network-based models have shown promising performance in CVR prediction. However, they are data hungry and require an enormous amount of training data. In online advertising systems, although there are millions to billions of ads, users tend to c

Wenlong Huang, Chen Wang, Ruohan Zhang, Yunzhu Li

Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major bottleneck. In this work, we aim to syn

James O' Neill, Sourav Dutta

We investigate the effects of post-training quantization and quantization-aware training on the generalization of Transformer language models. We present a new method called self-distilled quantization (SDQ) that minimizes accumulative quantization errors and outperforms baselines. We apply SDQ to multilingual models XLM-R-Base and InfoXLM-Base and demonstra

Jiliang Jing, Weike Deng, Sheng Long, Jieci Wang

By means of the scattering angles, we obtain an effective metric of spinless binaries with radiation-reaction effects up to fourth post-Minkowskian order, which is the foundation of the effective-one-body theory. We note that there are freedoms for the parameters of the effective metric because one equation corresponds to two parameters for each post-Minkows

Liat Nemirovsky-Levy, Uzi Pereg, Mordechai Segev

Quantum communication is based on the generation of quantum states and exploitation of quantum resources for communication protocols. Currently, photons are considered as the optimal carrier of information, because they enable long-distance transition with resilience to decoherence, and they are relatively easy to create and detect. Entanglement is a fundame

Tanjona Rabemananjara

We present a new perspective on the study of the behavior of the strong coupling $\alpha_s(Q^2)$ -- the fundamental coupling underlying the interactions between quarks and gluons as described by the Quantum Chromodynamics (QCD) -- in the low-energy infrared (IR) regime. We rely on the NNSF$\nu$ determination of neutrino-nucleus structure functions valid for

Khusrav Yorov, Mikhail Skopenkov, Helmut Pottmann

We study surfaces with a constant ratio of principal curvatures in Euclidean and simply isotropic geometries and characterize rotational, channel, ruled, helical, and translational surfaces of this kind under some technical restrictions (the latter two cases only in isotropic geometry). We use the interlacing of various methods of differential geometry, incl

Tanjona Rabemananjara

We present progress towards a unified framework enabling the simultaneous determination of the parton distribution functions (PDFs) of the proton, deuteron, and nuclei up to lead $(^{208}\rm{Pb})$. Our approach is based on the integration of the fitting framework underlying the nNNPDF3.0 determination of nuclear PDFs into that adopted for the NNPDF4.0 global

Sota Hirama, Masayuki Ohzeki

We propose an efficient algorithm that combines column generation and quantum annealing to solve binary quadratic problems. Binary quadratic problems are difficult to solve because they are NP-hard. An attempt to solve binary quadratic problems efficiently by column generation has been studied, but it demands successively solving quadratic unconstrained bina

William Banks, Asma Harcharras, Dominique Lecomte

The type $\tau$($\alpha$) of an irrational number $\alpha$ measures the extent to which rational numbers can closely approximate $\alpha$. More precisely, $\tau$($\alpha$) is the infimum over those t$\in$R for which |$\alpha$--h/k|<k^{--t--1} has at most finitely many solutions h,k$\in$Z, k>0. In this paper, we regard the type as a function $\tau$:R\Q$\right

Itai Dattner

This study introduces a novel mechanistic modeling and statistical framework for analyzing motion energy dynamics within psychotherapy sessions. We transform raw motion energy data into an interpretable narrative of therapist-patient interactions, thereby revealing unique insights into the nature of these dynamics. Our methodology is established through thre

Takuro Tanaka, Masami Sako, Mahito Chiba, Chul Lee

For searching a new chemical material which satisfies the target characteristic value, for example emission wavelength, many cut and trial of experiments/calculations are required since the chemical space is astronomically large (organic molecules generates >10^60 candidates). Extracting feature importance is a method to reduce the chemical space, and limiti

Junghyun Kim, Gi-Cheon Kang, Jaein Kim, Suyeon Shin

Language-Guided Robotic Manipulation (LGRM) is a challenging task as it requires a robot to understand human instructions to manipulate everyday objects. Recent approaches in LGRM rely on pre-trained Visual Grounding (VG) models to detect objects without adapting to manipulation environments. This results in a performance drop due to a substantial domain gap

Hossein Hosseinzadeh, Zeinab Sedaghatjoo

Conventionally, piecewise polynomials have been used in the boundary elements method (BEM) to approximate unknown boundary values. Since infinitely smooth radial basis functions (RBFs) are more stable and accurate than the polynomials for high dimensional domains, the unknown values are approximated by the RBFs in this paper. Therefore, a new formulation of

Harvey Lau

Traditional coverage grey-box fuzzers perform a breadth-first search of the state space of Program Under Test (PUT). This aimlessness wastes a lot of computing resources. Directed grey-box fuzzing focuses on the target of PUT and becomes one of the most popular topics of software testing. The early termination of unreachable test cases is a method to improve

Kanishka Bhattacharya, Tapan Jana, Amit Shaw, L. S. Ramachandra

Smoothed Particle Hydrodynamics (SPH) is plagued by the phenomenon of tensile instability, which is the occurrence of short wavelength zero energy modes resulting in unphysical clustering of particles. The root cause of the instability is the shape of derivative of the compactly supported kernel function which may yield negative stiffness in the particle int

Moo Jin Kim, Jiajun Wu, Chelsea Finn

Eye-in-hand cameras have shown promise in enabling greater sample efficiency and generalization in vision-based robotic manipulation. However, for robotic imitation, it is still expensive to have a human teleoperator collect large amounts of expert demonstrations with a real robot. Videos of humans performing tasks, on the other hand, are much cheaper to col

Yoshiaki Okumura

In this article, we prove that an asymptotic formula for the prime number race with respect to Fermat curves of prime degree is equivalent to part of the Deep Riemann Hypothesis (DRH), which is a conjecture on the convergence of partial Euler products of $L$-functions on the critical line. We also show that such an equivalence holds for some quotients of Fer

Hiroto Arima, Md. Riad Kasem, Hossein Sepehri-Amin, Fuyuki Ando

Applying a magnetic field to a solid changes its thermal-transport properties. Although such magneto-thermal-transport phenomena are usually small effects, giant magneto-thermal resistance has recently been observed in spintronic materials1,2 and superconductors3,4, opening up new possibilities in thermal management technologies. However, the thermal conduct

Wenxuan Wang, Guodong Ma, Yuke Li, Binbin Du

Multilingual speech recognition for both monolingual and code-switching speech is a challenging task. Recently, based on the Mixture of Experts (MoE), many works have made good progress in multilingual and code-switching ASR, but present huge computational complexity with the increase of supported languages. In this work, we propose a computation-efficient n

Zhe-Hao Zhang, Yuzhu Jiang, Hai-Qing Lin, Xi-Wen Guan

Using the Bethe ansatz (BA), we rigorously obtain non-equilibrium dynamics of an impurity with a large initial momentum $Q$ in the one-dimensional (1D) interacting bosonic medium. We show that magnon and exciton-like states obtained from the BA equations drastically determine the oscillation nature of the quantum flutter with the periodicity given by $\tau_{

Jun-Ting Hsieh, Pravesh K. Kothari, Aaron Potechin, Jeff Xu

In [Sau11,SPW13], Saunderson, Parrilo and Willsky asked the following elegant geometric question: what is the largest $m= m(d)$ such that there is an ellipsoid in $\mathbb{R}^d$ that passes through $v_1, v_2, \ldots, v_m$ with high probability when the $v_i$s are chosen independently from the standard Gaussian distribution $N(0,I_{d})$. The existence of such

Kamyar Azizzadenesheli, Trung Dang, Aranyak Mehta, Alexandros Psomas

We study a fundamental problem in optimization under uncertainty. There are $n$ boxes; each box $i$ contains a hidden reward $x_i$. Rewards are drawn i.i.d. from an unknown distribution $\mathcal{D}$. For each box $i$, we see $y_i$, an unbiased estimate of its reward, which is drawn from a Normal distribution with known standard deviation $\sigma_i$ (and an

Benjamin Poignard, Yoshikazu Terada

We consider the estimation of a sparse factor model where the factor loading matrix is assumed sparse. The estimation problem is reformulated as a penalized M-estimation criterion, while the restrictions for identifying the factor loading matrix accommodate a wide range of sparsity patterns. We prove the sparsistency property of the penalized estimator when

Kazusa Beppu, Jaakko V. I. Timonen

Highly concentrated active agents tend to exhibit turbulent flows, reminiscent of classical hydrodynamic turbulence, which has attracted considerable attention lately. Controlling the so-called active turbulence has long been a challenge, and the influence of external fields on such chaotic self-organization remains largely unexplored. Here we report on acti

Yichen Li, Yintong Huo, Zhihan Jiang, Renyi Zhong

Automated logging statement generation supports developers in documenting critical software runtime behavior. Given the great success in natural language generation and programming language comprehension, large language models (LLMs) might help developers generate logging statements, but this has not yet been investigated. To fill the gap, this paper perform

Agnimitra Sengupta, S. Ilgin Guler

Deep learning (DL) models for spatio-temporal traffic flow forecasting employ convolutional or graph-convolutional filters along with recurrent neural networks to capture spatial and temporal dependencies in traffic data. These models, such as CNN-LSTM, utilize traffic flows from neighboring detector stations to predict flows at a specific location of intere

Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu

Generating unlabeled data has been recently shown to help address the few-shot hypothesis adaptation (FHA) problem, where we aim to train a classifier for the target domain with a few labeled target-domain data and a well-trained source-domain classifier (i.e., a source hypothesis), for the additional information of the highly-compatible unlabeled data. Howe

Hanwu Li

In this paper, we study the backward stochastic differential equation (BSDE) with two nonlinear mean reflections, which means that the constraints are imposed on the distribution of the solution but not on its paths. Based on the backward Skorokhod problem with nonlinear constraints, we obtain the existence and uniqueness result by constructing a contraction

Agnimitra Sengupta, Sudeepta Mondal, Adway Das, S. Ilgin Guler

Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates,

Raja Sunkara, Tie Luo

We introduce YOGA, a deep learning based yet lightweight object detection model that can operate on low-end edge devices while still achieving competitive accuracy. The YOGA architecture consists of a two-phase feature learning pipeline with a cheap linear transformation, which learns feature maps using only half of the convolution filters required by conven

Xiaomeng Wang, Fengshi Tian, Xizi Chen, Jiakun Zheng

In this paper, we propose a high-precision SRAM-based CIM macro that can perform 4x4-bit MAC operations and yield 9-bit signed output. The inherent discharge branches of SRAM cells are utilized to apply time-modulated MAC and 9-bit ADC readout operations on two bit-line capacitors. The same principle is used for both MAC and A-to-D conversion ensuring high l

Yu-Chien Bo Ning

This paper explores the multiple testing problem for sparse high-dimensional data with binary outcomes. We propose novel empirical Bayes multiple testing procedures based on a spike-and-slab posterior and then evaluate their performance in controlling the false discovery rate (FDR). A surprising finding is that the procedure using the default conjugate prior

Seitaro Otsuki, Shintaro Ishikawa, Komei Sugiura

Although domestic service robots are expected to assist individuals who require support, they cannot currently interact smoothly with people through natural language. For example, given the instruction "Bring me a bottle from the kitchen," it is difficult for such robots to specify the bottle in an indoor environment. Most conventional models have been train

Masanobu Inubushi, Yoshitaka Saiki, Miki U. Kobayashi, Susumu Goto

Data assimilation (DA) reconstructing small-scale turbulent structures is crucial for forecasting and understanding turbulence. This study proposes a theoretical framework for DA based on ideas from chaos synchronization, in particular, the transverse Lyapunov exponents (TLEs). The analysis with TLEs characterizes a critical length scale, below which the tur

Vincent Pilaud, Daria Poliakova

The $(m,n)$-multiplihedron is a polytope whose faces correspond to $m$-painted $n$-trees, and whose oriented skeleton is the Hasse diagram of the rotation lattice on binary $m$-painted $n$-trees. Deleting certain inequalities from the facet description of the $(m,n)$-multiplihedron, we construct the $(m,n)$-Hochschild polytope whose faces correspond to $m$-l

Xuewei Wang, Qiang Jin, Shengyu Huang, Min Zhang

Dividing ads ranking system into retrieval, early, and final stages is a common practice in large scale ads recommendation to balance the efficiency and accuracy. The early stage ranking often uses efficient models to generate candidates out of a set of retrieved ads. The candidates are then fed into a more computationally intensive but accurate final stage

Andreas Metzger, Tristan Kley, Aristide Rothweiler, Klaus Pohl

Prescriptive business process monitoring provides decision support to process managers on when and how to adapt an ongoing business process to prevent or mitigate an undesired process outcome. We focus on the problem of automatically reconciling the trade-off between prediction accuracy and prediction earliness in determining when to adapt. Adaptations shoul

Konstantin Pichugin, Almas Sadreev, Evgeny Bulgakov

We perform optimization of Q-factor in the system of freestanding three/four/five/six coaxial subwavelength dielectric disks over all scales. Each parameter contributes almost one order of magnitude of the Q-factor due to multiple avoided crossings of resonances to give totally the unprecedented values for the Q-factors: $6.6\cdot10^4$ for the three, $4.8\cd

Roberto Doriguzzi-Corin, Luis Augusto Dias Knob, Luca Mendozzi, Domenico Siracusa

Programmable data planes offer precise control over the low-level processing steps applied to network packets, serving as a valuable tool for analysing malicious flows in the field of intrusion detection. Albeit with limitations on physical resources and capabilities, they allow for the efficient extraction of detailed traffic information, which can then be

Zeqing Zhang, Ruixing Jia, Youcan Yan, Ruihua Han

Proximity sensing detects an object's presence without contact. However, research has rarely explored proximity sensing in granular materials (GM) due to GM's lack of visual and complex properties. In this paper, we propose a granular-material-embedded autonomous proximity sensing system (GRAINS) based on three granular phenomena (fluidization, jamming, and

Chanda Grover Kamra, Indra Deep Mastan, Debayan Gupta

CLIPStyler demonstrated image style transfer with realistic textures using only a style text description (instead of requiring a reference style image). However, the ground semantics of objects in the style transfer output is lost due to style spill-over on salient and background objects (content mismatch) or over-stylization. To solve this, we propose Seman

Junjia Liu, Hengyi Sim, Chenzui Li, Fei Chen

Human bimanual manipulation can perform more complex tasks than a simple combination of two single arms, which is credited to the spatio-temporal coordination between the arms. However, the description of bimanual coordination is still an open topic in robotics. This makes it difficult to give an explainable coordination paradigm, let alone applied to roboti

Yunfei Guo, Tao Zhang, Wu Huang

Emotion recognition is an important research direction in artificial intelligence, helping machines understand and adapt to human emotional states. Multimodal electrophysiological(ME) signals, such as EEG, GSR, respiration(Resp), and temperature(Temp), are effective biomarkers for reflecting changes in human emotions. However, using electrophysiological sign

Kin-ya Oda, Juntaro Wada

We propose a novel formulation for a manifestly Lorentz-covariant spinor wave-packet basis. The traditional definition of the spinor wave packet is problematic due to its unavoidable mixing with other wave packets under Lorentz transformations. Our approach resolves this inherent mixing issue. The wave packet we develop constitutes a complete set, enabling t

Mark Durré, Jeremy Mould, Michael Brown, Tristan Reynolds

We present a near infrared spectroscopic atlas of nearby, bright early-type galaxies with radio emission, containing 163 galaxies observed by the Palomar 200" TripleSpec instrument, measuring the emission line fluxes for H, He, [Fe II] and H$_2$ and determined the nuclear excitation mechanisms. By stacking spectra, we deduced the H$_2$ excitation temperature

Sunny Kumar Singh, Nachiketa Sarkar, Deeptak Biswas

We have examined the chemical freeze-out surface of the heavy-ion collision experiments within an interacting hadron resonance gas model. By considering repulsive interaction among hadrons in the mean-field level, we have suitably parameterized the freeze-out surface by fitting the yield data of mid-rapidity for the most central collision, for the collision

Tianxiao Zhang, Kaidong Li, Xiangyu Chen, Cuncong Zhong

Aphids are one of the main threats to crops, rural families, and global food security. Chemical pest control is a necessary component of crop production for maximizing yields, however, it is unnecessary to apply the chemical approaches to the entire fields in consideration of the environmental pollution and the cost. Thus, accurately localizing the aphid and

Nayan Sharma, Ranjit Kumar Singh, Souvik Chatterjee, Prasanta K. Panigrahi

In this study, we present numerical investigations on a large Zeeman manifold in an electromagnetically induced transparency (EIT) medium, focusing on the D1 and D2 lines of 87 Rb as our model system. We examine two distinct models comprising 13 and 16 energy levels, respectively, using pump-probe spectroscopy with varying polarization of the light fields. A

Kieran J. Luken, Ray P. Norris, X. Rosalind Wang, Laurence A. F. Park

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GLEAM-X, VLASS and LoTSS is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs), and have a significantly higher average redshift

Chun Fu, Matias Quintana, Zoltan Nagy, Clayton Miller

Building energy prediction and management has become increasingly important in recent decades, driven by the growth of Internet of Things (IoT) devices and the availability of more energy data. However, energy data is often collected from multiple sources and can be incomplete or inconsistent, which can hinder accurate predictions and management of energy sy

Maryam Olyaee, Hadi Hashemi, Juan M. Romero-Jerez

The recently introduced independent fluctuating two-ray (IFTR) fading model, consisting of two specular components fluctuating independently plus a diffuse component, has proven to provide an excellent fit to different wireless environments, including the millimeter-wave band. However, the original formulations of the probability density function (PDF) and c

Rashmi Yadav, Rashmi Kamran, Pranav Jha, Abhay Karandikar

The upcoming Sixth Generation (6G) mobile communications system envisions supporting a variety of use cases with differing characteristics, e.g., very low to extremely high data rates, diverse latency needs, ultra massive connectivity, sustainable communications, ultra-wide coverage etc. To accommodate these diverse use cases, the 6G system architecture need

Kosuke Tatsumura, Ryo Hidaka, Jun Nakayama, Tomoya Kashimata

Financial portfolio construction problems are often formulated as quadratic and discrete (combinatorial) optimization that belong to the nondeterministic polynomial time (NP)-hard class in computational complexity theory. Ising machines are hardware devices that work in quantum-mechanical/quantum-inspired principles for quickly solving NP-hard optimization p

Kosuke Tatsumura, Ryo Hidaka, Jun Nakayama, Tomoya Kashimata

Pairs-trading is a trading strategy that involves matching a long position with a short position in two stocks aiming at market-neutral profits. While a typical pairs-trading system monitors the prices of two statistically correlated stocks for detecting a temporary divergence, monitoring and analyzing the prices of more stocks would potentially lead to find

Manish Kumar, Anisur Rahaman Molla

This paper studies the message complexity of authenticated Byzantine agreement (BA) in synchronous, fully-connected distributed networks under an honest majority. We focus on the so-called {\em implicit} Byzantine agreement problem where each node starts with an input value and at the end a non-empty subset of the honest nodes should agree on a common input

Yuhao Wang

Automated radiology report generation aims to generate radiology reports that contain rich, fine-grained descriptions of radiology imaging. Compared with image captioning in the natural image domain, medical images are very similar to each other, with only minor differences in the occurrence of diseases. Given the importance of these minor differences in the

Yuhao Wang

Contrastive language-image Pre-training (CLIP) [13] can leverage large datasets of unlabeled Image-Text pairs, which have demonstrated impressive performance in various downstream tasks. Given that annotating medical data is time-consuming and laborious, Image-Text Pre-training has promising applications in exploiting large-scale medical image and radiology

Andreani Petrou, Shinobu Hikami

In an attempt to generalise knot matrix models for non-torus knots, which currently remains an open problem, we derived formulas for the Harer-Zagier transform of the HOMFLY-PT polynomial for some infinite families of twisted hyperbolic knots. Among them, we found a family of Pretzel knots for which the transform has a fully factorised form, while for the re

Qingshuai Feng, You Peng, Wenjie Zhang, Xuemin Lin

The widespread use of graph data in various applications and the highly dynamic nature of today's networks have made it imperative to analyze structural trends in dynamic graphs on a continual basis. The shortest path is a fundamental concept in graph analysis and recent research shows that counting the number of shortest paths between two vertices is crucia

H. B. Benaoum, Luz Ángela García, Leonardo Castañeda

In this work, we introduce a parametrization of early dark energy that mimics radiation at early times and governs the present acceleration of the Universe. We show that such parametrization models non-linear electrodynamics in the early Universe and investigate the cosmological viability of the model. In our scenario, the early dark energy is encoded in the

Peter Yongho Kim, Junbeom Kwon, Sunghwan Joo, Sangyoon Bae

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing approaches for fMRI analysis utilize hand-crafted features, but the process of feature extraction risks losing essential information in fMRI scans. To address this challenge, we present SwiFT

C. S. Krishna

We propose a framework - Prompt, Generate, Train (PGT) - to efficiently develop a generative question-answering model for open-book question-answering over a proprietary collection of text documents. The framework adapts a retriever augmented generation (RAG) model to the target domain using supervised fine-tuning and reinforcement learning with synthetic fe