July 2023 arXiv papers — page 120

Luming Yao, Lun Zhang

The tacnode process is a universal determinantal point process arising from non-intersecting particle systems and tiling problems. It is the aim of this work to explore the integrable structure and large gap asymptotics for the gap probability of the thinned/unthinned tacnode process over $(-s,s)$. We establish an integral representation of the gap probabili

E. J Ferrer, W. Gyory, V. de la Incera

We study the stability against thermal phonon fluctuations of the magnetic dual chiral density wave (MDCDW) phase, an inhomogeneous phase arising in cold dense QCD in a magnetic field. Following a recent study that demonstrated the absence of the Landau-Peierls (LP) instability from this phase, we calculate the (threshold) temperature at which the phonon flu

Lei Zeng, Ying-Hao Ye, Ming-Xin Dong, Wei-Hang Zhang

Inherent spin angular momentum (SAM) and orbital angular momentum (OAM) which manifest as polarization and spatial degrees of freedom (DOF) of photons, hold a promise of large capability for applications in classical and quantum information processing. To enable these photonic spin and orbital dynamic properties strongly coupled with each other, Poincar\'{e}

Giang Huynh, Xiaoying Zhuang, Hoang-Giang Bui, G. Meschke

This paper studies elasto-plastic large deformation behavior of thin shell structures using the isogeometric computational approach with the main focus on the efficiency in modelling the multi-patches and arbitrary material formulations. In terms of modelling, we employ the bending strip method to connect the patches in the structure. The incorporation of be

Vinit S. Unni, Ashish Mittal, Preethi Jyothi, Sunita Sarawagi

RNN-Transducers (RNN-Ts) have gained widespread acceptance as an end-to-end model for speech to text conversion because of their high accuracy and streaming capabilities. A typical RNN-T independently encodes the input audio and the text context, and combines the two encodings by a thin joint network. While this architecture provides SOTA streaming accuracy,

Or Raz

We construct a family of independent sets for finite, atomic, and graded lattices, extending the well-known cryptomorphism between geometric lattices and matroids. This construction leads to an embedding theorem into geometric lattices that preserves the set of atoms. We then apply these results to adjoint matroids, providing new characterizations of adjoint

Jesse Stevens, Daniel N. Wilke, Itumeleng Setshedi

Linear latent variable models such as principal component analysis (PCA), independent component analysis (ICA), canonical correlation analysis (CCA), and factor analysis (FA) identify latent directions (or loadings) either ordered or unordered. The data is then projected onto the latent directions to obtain their projected representations (or scores). For ex

Tomoaki Nakamura, Akira Taniguchi, Tadahiro Taniguchi

This paper proposes a generative probabilistic model integrating emergent communication and multi-agent reinforcement learning. The agents plan their actions by probabilistic inference, called control as inference, and communicate using messages that are latent variables and estimated based on the planned actions. Through these messages, each agent can send

Mehmet Berkay On, Farshid Ashtiani, David Sanchez-Jacome, Daniel Perez-Lopez

A variety of topological Hamiltonians have been demonstrated in photonic platforms, leading to fundamental discoveries and enhanced robustness in applications such as lasing, sensing, and quantum technologies. To date, each topological photonic platform implements a specific type of Hamiltonian with inexistent or limited reconfigurability. Here, we propose a

Stefan Ivanov, Nikola Stanchev

Curvature properties of the characteristic connection on an integrable $G_2$ manifold are investigated. We consider integrable $G_2$ manifold of constant type, i.e. the scalar product of the exterior derivative of the $G_2$ form with its Hodge dual is a constant. We show that on an integrable $G_2$ manifold of constant type with $G_2$-instanton characteristi

Yi Liao, Xiao-Bo Gong

For one-dimension Brownian motion in the confined system with the size $L$, the mean-squared displacement(MSD) defined by $\left \langle (x-x_0)^2 \right\rangle$ should be proportional to $t^{α(t)}$. The power $α(t)$ should range from $1$ to $0$ over time, and the MSD turns from $2Dt$ to $c L^2$, here the coefficient $c$ independent of $t$, $D$ being the dif

S. Aljbaae, J. Murcia-Pineros, A. F. B. A. Prado, R. V. Moraes

In recent years (2000-2021), human-space activities have been increasing faster than ever. More than 36000 Earth' orbiting objects, all larger than 10 cm, in orbit around the Earth, are currently tracked by the European Space Agency (ESA). Around 70\% of all cataloged objects are in Low-Earth Orbit (LEO). Aerodynamic drag provides one of the main sources of

Stefan Ivanov, Nikola Stanchev

It is observed that on a compact almost complex Calabi-Yau with torsion 6-manifold the Nijenhuis tensor is parallel with respect to the torsion connection. If the torsion is closed then the space is a compact generalized gradient Ricci soliton. In this case, the torsion connection is Ricci-flat if and only if either the norm of the torsion or the Riemannian

Yanyang Wang, Wei Li, Feiyi Liu, Jianmin Shen

Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily

Cong Wang, Di Kang, Yan-Pei Cao, Linchao Bao

Rendering photorealistic and dynamically moving human heads is crucial for ensuring a pleasant and immersive experience in AR/VR and video conferencing applications. However, existing methods often struggle to model challenging facial regions (e.g., mouth interior, eyes, hair/beard), resulting in unrealistic and blurry results. In this paper, we propose {\fu

Yue Huang, Dongli Shi, Xiaolu Zhang, Xiang Ma

Gravitational Wave High-energy Electromagnetic Counterpart All-sky Monitor (GECAM), consisting of two micro-satellites, is designed to detect gamma-ray bursts associated with gravitational-wave events. Here, we introduce the real-time burst alert system of GECAM, with the adoption of the BeiDou-3 short message communication service. We present the post-trigg

Ayush Sekhari, Karthik Sridharan, Wen Sun, Runzhe Wu

We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback. While imitation learning has been empirically successful, much of prior work assumes access to noiseless expert feedback which is not practical in many applications. In fact, when one only has access to noisy expert feedback, algorithms that rely on purely offl

Omar Antolín-Camarena, Luis Eduardo García-Hernández, Luis Jorge Sánchez Saldaña

For a topological group $G$ let $E_{\textsf{com}}(G)$ be the total space of the universal transitionally commutative principal $G$-bundle as defined by Adem--Cohen--Torres-Giese. So far this space has been most studied in the case of compact Lie groups; but in this paper we focus on the case of infinite discrete groups. For a discrete group $G$, the space $E

Ghanshyam Verma, Shovon Sengupta, Simon Simanta, Huan Chen

Personalized recommender systems play a crucial role in direct marketing, particularly in financial services, where delivering relevant content can enhance customer engagement and promote informed decision-making. This study explores interpretable knowledge graph (KG)-based recommender systems by proposing two distinct approaches for personalized article rec

Zixuan Ma, Haojie Wang, Jingze Xing, Liyan Zheng

Deep neural networks (DNNs) are of critical use in different domains. To accelerate DNN computation, tensor compilers are proposed to generate efficient code on different domain-specific accelerators. Existing tensor compilers mainly focus on optimizing computation efficiency. However, memory access is becoming a key performance bottleneck because the comput

Ce Sui, Xiaosheng Zhao, Tao Jing, Yi Mao

The ability to compress observational data and accurately estimate physical parameters relies heavily on informative summary statistics. In this paper, we introduce the use of mutual information (MI) as a means of evaluating the quality of summary statistics in inference tasks. MI can assess the sufficiency of summaries, and provide a quantitative basis for

Suk Yee Yong, Cheng Soon Ong

Precise measurements of the black hole mass are essential to gain insight on the black hole and host galaxy co-evolution. A direct measure of the black hole mass is often restricted to nearest galaxies and instead, an indirect method using the single-epoch virial black hole mass estimation is used for objects at high redshifts. However, this method is subjec

Diogo Bragança, Yaniv Donath, Leonardo Senatore, Henry Zheng

After the successful full-shape analyses of BOSS data using the Effective Field Theory of Large-Scale Structure, we investigate what upcoming galaxy surveys might achieve. We introduce a ``perturbativity prior" that ensures that loop terms are as large as theoretically expected, which is effective in the case of a large number of EFT parameters. After valida

Sean Gasiorek, Milena Radnović

We consider the Boltzmann system corresponding to the motion of a billiard with a linear boundary under the influence of a gravitational field. We derive analytic conditions of Cayley's type for periodicity of its trajectories and provide geometric descriptions of caustics. The topology of the phase space is discussed using Fomenko graphs.

Zhili Feng, Ezra Winston, J. Zico Kolter

Deep Boltzmann machines (DBMs), one of the first ``deep'' learning methods ever studied, are multi-layered probabilistic models governed by a pairwise energy function that describes the likelihood of all variables/nodes in the network. In practice, DBMs are often constrained, i.e., via the \emph{restricted} Boltzmann machine (RBM) architecture (which does no

Chenglong Wang, Dexuan Li, Sucheng Wang, Chengxiu Zhang

Recently, large vision model, Segment Anything Model (SAM), has revolutionized the computer vision field, especially for image segmentation. SAM presented a new promptable segmentation paradigm that exhibit its remarkable zero-shot generalization ability. An extensive researches have explore the potential and limits of SAM in various downstream tasks. In thi

Anna C. Childs, Cody Shakespeare, David R. Rice, Chao-Chin Yang

We study the formation of the TRAPPIST-1 (T1) planets starting shortly after Moon-sized bodies form just exterior to the ice line. Our model includes mass growth from pebble accretion and mergers, fragmentation, type-I migration, and eccentricity and inclination dampening from gas drag. We follow the composition evolution of the planets fed by a dust condens

Chris Chinenye Emezue, Alexandre Drouin, Tristan Deleu, Stefan Bauer

The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inference. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. To address this gap, we evaluate seven established baseline causal

Y. Li, L. Y. Zhang

In this paper, we discuss the inflationary magnetogenesis scenario, in which the coupling function is introduced to break the conformal invariance of electromagnetic action. Unlike in conventional models, we deduce the Maxwell's equations under the perturbed FRW metric. We found that, the self-consistency of the action depends on the form of the coupling fun

Ross Williams, Niyousha Hosseinichimeh, Aritra Majumdar, Navid Ghaffarzadegan

This study offers a new paradigm of individual-level modeling to address the grand challenge of incorporating human behavior in epidemic models. Using generative artificial intelligence in an agent-based epidemic model, each agent is empowered to make its own reasonings and decisions via connecting to a large language model such as ChatGPT. Through various s

Sebastian Mentemeier, Hui Xiao

We study the first passage time $\tau_u = \inf \{ n \geq 1: |V_n| > u \}$ for the multivariate perpetuity sequence $V_n = Q_1 + M_1 Q_2 + \cdots + (M_1 \ldots M_{n-1}) Q_n$, where $(M_n, Q_n)$ is a sequence of independent and identically distributed random variables with $M_1$ a $d \times d$ ($d \geq 1$) random matrix with nonnegative entries, and $Q_1$ a no

Xing Peng, Mengjie Xia

Given a graph $L$, the Tur\'an number $\textrm{ex}(n,L)$ is the maximum possible number of edges in an $n$-vertex $L$-free graph. The study of Tur\'an number of graphs is a central topic in extremal graph theory. Although the celebrated Erd\H{o}s-Stone-Simonovits theorem gives the asymptotic value of $\textrm{ex}(n,L)$ for nonbipartite $L$, it is challenging

Tianyi Miao

The precise definition of causality is currently an open problem in philosophy and statistics. We believe causality should be defined as functions (in mathematics) that map causes to effects. We propose a reductive definition of causality based on Structural Functional Model (SFM). Using delta compression and contrastive forward inference, SFM can produce ca

Jia-Yong Wu

Given a closed connected manifold smoothly immersed in a complete noncompact Riemannian manifold with nonnegative sectional curvature, we estimate the intrinsic diameter of the submanifold in terms of its mean curvature field integral. On the other hand, for a compact convex surface with boundary smoothly immersed in a complete noncompact Riemannian manifold

Wei Liu, Meng Li, Yu Niu, Ziren Luo

We investigate surface plasmons from a solid-state standpoint and highlight their ultra electron density sensitivity. When a surface plasmon is excited on a planar gold film by an evanescent wave from 625 nm incident light, only a minute fraction of the surface electron density, approximately one thousandth, participates in the process. By introducing a nois

Guoyao Deng, Ke Zou, Meng Wang, Xuedong Yuan

Frontotemporal Dementia (FTD) diagnosis has been successfully progress using deep learning techniques. However, current FTD identification methods suffer from two limitations. Firstly, they do not exploit the potential of multi-view functional magnetic resonance imaging (fMRI) for classifying FTD. Secondly, they do not consider the reliability of the multi-v

Amani Kiruga, Xi Peng

We propose a methodology to address the challenge of instance segmentation in autonomous systems, specifically targeting accessibility and inclusivity. Our approach utilizes a non-hierarchical Vision Transformer variant, EVA-02, combined with a Cascade Mask R-CNN mask head. Through fine-tuning on the AVA instance segmentation challenge dataset, we achieved a

Travis L. Scholten, Derrick Perry, Joseph Washington, Jennifer R. Glick

Quantum machine learning (QML) is a fast-growing discipline within quantum computing. One popular QML algorithm, quantum kernel estimation, uses quantum circuits to estimate a similarity measure (kernel) between two classical feature vectors. Given a set of such circuits, we give a heuristic, predictive model for the total circuit execution time required, ba

Rani Sasmita Tarmidi

In this work, we provide explicit conditions for the coefficients of a symmetric truncated cubic to give a smooth tropical curve. We also examine non-smooth cases corresponding to some specific subdivision types.

Nikhil Verma

Cross-Modal learning tasks have picked up pace in recent times. With plethora of applications in diverse areas, generation of novel content using multiple modalities of data has remained a challenging problem. To address the same, various generative modelling techniques have been proposed for specific tasks. Novel and creative image generation is one importa

Lei Xie, Hengtao He, Shenghui Song, Yonina C. Eldar

Maneuvering target tracking will be an important service of future wireless networks to assist innovative applications such as intelligent transportation. However, tracking maneuvering targets by cellular networks faces many challenges. For example, the dense network and high-speed targets make the selection of the sensing nodes (SNs) and the associated powe

Kangning Diao, Yi Mao

Large-scale numerical simulations ($\gtrsim 500\rm{Mpc}$) of cosmic reionization are required to match the large survey volume of the upcoming Square Kilometre Array (SKA). We present a multi-fidelity emulation technique for generating large-scale lightcone images of cosmic reionization. We first train generative adversarial networks (GAN) on small-scale sim

Ruixuan E. Chen, Jonathan H. Jiang, Philip E. Rosen, Kristen A. Fahy

As the search for exoplanets continues, more are being discovered orbiting Red Giant stars. We use current data from the NASA Exoplanet Archive to investigate planet distribution around Red Giant stars and their presence in the host's habitable zone. As well, we update the power law relation between planet mass and stellar radius found in previous studies an

Shingo Tagami, Tomotsugu Wakasa, Masanobu Yahiro

In our previous work, we determined matter radii $r_{\rm m}({\rm exp})$ and neutron-skin thickness $r_{\rm skin}({\rm exp})$ from reaction cross sections $\sigma_{\rm R}({\rm exp})$ of proton scattering on $^{208}$Pb, $^{58}$Ni, $^{40,48}$Ca, $^{12}$C targets, using the chiral (Kyushu) $g$-matrix folding model with the densities calculated with Gogny-D1S-HFB

Guoyao Deng, Ke Zou, Kai Ren, Meng Wang

Recently, Segmenting Anything has taken an important step towards general artificial intelligence. At the same time, its reliability and fairness have also attracted great attention, especially in the field of health care. In this study, we propose multi-box prompts triggered uncertainty estimation for SAM cues to demonstrate the reliability of segmented les

Yongjin Shin, Giulia Galli

Using first-principles calculations, we predict that tunable ferroelectricity can be realized in oxide perovskites with the Grenier structure and ordered oxygen vacancies. Specifically, we show that $R_{1/3}A_{2/3}\mathrm{FeO}_{2.67}$ solids (where $R$ is a rare-earth ion and $A$ an alkaline-earth cation) exhibit stable polar phases, with a spontaneous polar

Yi C. Huang

For absolutely convergent series we state explicitly a one-sided summation estimate that can be viewed as the discrete analogue of the change of variable formula on the half line. This estimate is implicit in Pascal Lef\`evre's recent elegant proof of the classical discrete Hardy inequality. Here we remove a superfluous irrationality condition therein and po

Sheng Luo, Wenqiang Li, Xun Li, Qingmeng Wei

In this paper, we focus on the stochastic representation of a system of coupled Hamilton-Jacobi-Bellman-Isaacs (HJB-Isaacs (HJBI), for short) equations which is in fact a system of coupled Isaacs' type integral-partial differential equation. For this, we introduce an associated zero-sum stochastic differential game, where the state process is described by a

Laurence Boxer

This paper continues a series discussing flaws in published assertions concerning fixed points in digital images.

Nikhil Verma, Deepkamal Kaur, Lydia Chau

Removing noise from images is a challenging and fundamental problem in the field of computer vision. Images captured by modern cameras are inevitably degraded by noise which limits the accuracy of any quantitative measurements on those images. In this project, we propose a novel image reconstruction framework which can be used for tasks such as image denoisi

Shuren Pan, Fenxiang Wu, Yang Zhao, Zongxin Zhang

We proposed a novel method, using an asymmetric four grating compressor (AFGC) to improve the spatial uniformity of laser beams, to suppress the small-scale self-focusing (SSSF) during the post-compression of ultrahigh peak power lasers. The spatial uniformity is an important factor in performing post-compression, due to the spatial intensity nonuniformity w

Alice Desmons, Sarah Brough, Francois Lanusse

Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers. Their properties can answer questions about the progenitor galaxies involved in the interactions. This paper presents promising results from a self-supervised machine learning model, trained on data from the U

Joudi Hajar, Taylan Kargin, Babak Hassibi

This paper presents a framework for Wasserstein distributionally robust (DR) regret-optimal (RO) control in the context of partially observable systems. DR-RO control considers the regret in LQR cost between a causal and non-causal controller and aims to minimize the worst-case regret over all disturbances whose probability distribution is within a certain W

Binpeng Shang, Nan Zhang, Pengfei Qi, Shishi Tao

The promising application of femtosecond laser filamentation in atmospheric remote sensing brings imperative demand for diagnosing the spatiotemporal dynamics of filamentation. Acoustic emission (AE) during filamentation opens a door to give the insight into the dynamic evolution of filaments in air. In particular, the frequency features of the acoustic emis

Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua

Large language models (LLMs) have formulated a blueprint for the advancement of artificial general intelligence. Its primary objective is to function as a human-centric (helpful, honest, and harmless) assistant. Alignment with humans assumes paramount significance, and reinforcement learning with human feedback (RLHF) emerges as the pivotal technological par

Simin Chen, Shiyi Wei, Cong Liu, Wei Yang

DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed host programs. However, existing DL compilers rely on a tracing mechanism, which involves feeding a runtime input to a neural network program

Shubhankar P. Patankar, Mathieu Ouellet, Juan Cervino, Alejandro Ribeiro

Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a graph, how to guide exploration best remains an open question. In this work, we propose a novel approach for exploring graph-structured data motivated by two theories of human curiosit

Chong Han, Yuanbo Li, Yinqin Wang

Large reflection and diffraction losses in the Terahertz (THz) band give rise to degraded coverage abilities in non-line-of-sight (NLoS) areas. To overcome this, a non-intelligent reflecting surface (NIRS) can be used, which is essentially a rough surface made by metal materials. NIRS is not only able to enhance received power in large NLoS areas through ric

Edward Lee, Ondřej Lhoták

Reference immutability is a type based technique for taming mutation that has long been studied in the context of object-oriented languages, like Java. Recently, though, languages like Scala have blurred the lines between functional programming languages and object oriented programming languages. We explore how reference immutability interacts with features

D. Shefer, A. Nikipelov, M. van de Kerkhof, V. Banine

Laser Light Scattering (LLS) method, combined with a long-distance microscope was utilized to detect micrometer-sized particles on a smooth substrate. LLS was capable to detect individual particle release, shrink, or fragmentation during exposure to a plasma or a gas jet. In-situ monitoring of hundreds of particles was carried out to investigate the effect o

Pawan Kumar, Jiazheng Chen, Andrew C. Meng, Wei-Chang D. Yang

Two-dimensional materials, such as transition metal dichalcogenides (TMDCs), have the potential to revolutionize the field of electronics and photonics due to their unique physical and structural properties. This research presents a novel method for synthesizing crystalline TMDCs crystals with < 10 nm size using ultra-fast migration of vacancies at elevated

Kevin Patrick Griffin, Lin Fu, Parviz Moin

In this work, a near-wall model, which couples the inverse of a recently developed compressible velocity transformation [Griffin, Fu, & Moin, PNAS, 118:34, 2021] and an algebraic temperature-velocity relation, is developed for high-speed turbulent boundary layers. As input, the model requires the mean flow state at one wall-normal height in the inner layer o

Wei Cui, Wei Yu

In reinforcement learning, the objective is almost always defined as a \emph{cumulative} function over the rewards along the process. However, there are many optimal control and reinforcement learning problems in various application fields, especially in communications and networking, where the objectives are not naturally expressed as summations of the rewa

Jian Zhang, Runwei Ding, Miaoju Ban, Ge Yang

Visual anomaly detection is essential and commonly used for many tasks in the field of computer vision. Recent anomaly detection datasets mainly focus on industrial automated inspection, medical image analysis and video surveillance. In order to broaden the application and research of anomaly detection in unmanned supermarkets and smart manufacturing, we int

Xiaofang Chen, Wenbo Xu, Yue Wang

In Internet of Things (IoT), radio frequency fingerprints (RFF) technology has been widely used for passive security authentication to identify the special emitter. However, few works took advantage of independent oscillator distortions at the receiver side, and no work has yet considered filtering receiver distortions. In this paper, we investigate the RFF

Agnimitra Sengupta, Adway Das, S. Ilgin Guler

Deep learning (DL) methods have outperformed parametric models such as historical average, ARIMA and variants in predicting traffic variables into short and near-short future, that are critical for traffic management. Specifically, recurrent neural network (RNN) and its variants (e.g. long short-term memory) are designed to retain long-term temporal correlat

Alejandro Rodriguez Dominguez, Om Hari Yadav

This paper presents a method to identify causal interactions between two time series. The largest eigenvalue follows a Tracy-Widom distribution, derived from a Coulomb gas model. This defines causal interactions as the pushing and pulling of the gas, measurable by the variability of the largest eigenvalue's explanatory power. The hypothesis that this setup a

Yachuan Li, Zongmin Li, Xavier Soria P., Chaozhi Yang

The significance of multi-scale features has been gradually recognized by the edge detection community. However, the fusion of multi-scale features increases the complexity of the model, which is not friendly to practical application. In this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate multi-scale features while maintaining the

Song-Ying Li

In this paper, the author solves the long term open problem of Kerzman on sup-norm estimate for Cauchy-Riemann equation on polydisc in $n$-dimensional complex space. The problem has been open since 1971. He also extends and solves the problem on a bounded product domain $\Omega^n$, where $\Omega$ is any bounded domain in $\mathbb{C}$ with $C^{1,\alpha}$ boun

Arul Lakshminarayan

Dual-unitary circuits are being vigorously studied as models of many-body quantum chaos that can be solved exactly for correlation functions and time evolution of states. Here we define their classical counterparts as dual-canonical transformations and associated dual-Koopman operators. Like their quantum counterparts, the correlations vanish everywhere exce

Nicolas Marie

This paper deals with a Skorokhod's integral based projection type estimator $\widehat b_m$ of the drift function $b_0$ computed from $N\in\mathbb N^*$ independent copies $X^1,\dots,X^N$ of the solution $X$ of $dX_t = b_0(X_t)dt +\sigma dB_t$, where $B$ is a fractional Brownian motion of Hurst index $H\in (1/2,1)$. Skorokhod's integral based estimators canno

Tali Khain, Michel Fruchart, Vincenzo Vitelli

Control of particle motion is generally achieved by applying an external field that acts directly on each particle. Here, we propose a global way to manipulate the motion of a particle by dynamically changing the properties of the fluid in which it is immersed. We exemplify this principle by considering a small particle sinking in an anisotropic fluid whose

Andrei Isichenko, Nitesh Chauhan, Kaikai Liu, Mark W. Harrington

Today's state of the art precision experiments in quantum, gravimetry, navigation, time keeping, and fundamental science have strict requirements on the level and spectral distribution of laser frequency noise. For example, the laser interaction with atoms and qubits requires ultra-low frequency noise at multiple offset frequencies due to hyperfine atomic tr

Kyle Luther, H. Sebastian Seung

Inverse problems generally require a regularizer or prior for a good solution. A recent trend is to train a convolutional net to denoise images, and use this net as a prior when solving the inverse problem. Several proposals depend on a singular value decomposition of the forward operator, and several others backpropagate through the denoising net at runtime

Rajdeep Mondal, Alan Tang, Ryan Beckett, Todd Millstein

We investigate whether Large Language Models (e.g., GPT-4) can synthesize correct router configurations with reduced manual effort. We find GPT-4 works very badly by itself, producing promising draft configurations but with egregious errors in topology, syntax, and semantics. Our strategy, that we call Verified Prompt Programming, is to combine GPT-4 with ve

Thomas Lumley, Xudong Huang

As complex-survey data becomes more widely used in health and social-science research, there is increasing interest in fitting a wider range of regression models. We describe an implementation of two-level linear mixed models in R using the pairwise composite likelihood approach of Rao and co-workers. We discuss the computational efficiency of pairwise compo

Yongming Li

We establish dispersive estimates and local decay estimates for the time evolution of non-self-adjoint matrix Schr\"odinger operators with threshold resonances in one space dimension. In particular, we show that the decay rates in the weighted setting are the same as in the regular case after subtracting a finite rank operator corresponding to the threshold

Ruqi Bai, Saurabh Bagchi, David I. Inouye

While prior domain generalization (DG) benchmarks consider train-test dataset heterogeneity, we evaluate Federated DG which introduces federated learning (FL) specific challenges. Additionally, we explore domain-based heterogeneity in clients' local datasets - a realistic Federated DG scenario. Prior Federated DG evaluations are limited in terms of the numbe

Zheng Wu

As the core of artificial intelligence applications, the research of convolution has become a hot topic in high performance computing. With the rapid development of the emerging SW26010 processor in artificial intelligence, there is an urgent need for high-performance convolution algorithms on the processor. However, the current support of convolution on SW2

Mohan Dantam, Richard Mayr

We consider simple stochastic games $\mathcal G$ with energy-parity objectives, a combination of quantitative rewards with a qualitative parity condition. The Maximizer tries to avoid running out of energy while simultaneously satisfying a parity condition. We present an algorithm to approximate the value of a given configuration in 2-NEXPTIME. Moreover, $\v

Il-Seung Jang, Jae-Hoon Kwon, Akito Uruno

We show that there exists a unique crystal base of a parabolic Verma module over a quantum orthosymplectic superalgebra, which is induced from a $q$-analogue of a polynomial representation of a general linear Lie superalgebra.

Elyes Manai, Mohamed Mejri, Jaouhar Fattahi

This paper investigates the impact of feature encoding techniques on the explainability of XAI (Explainable Artificial Intelligence) algorithms. Using a malware classification dataset, we trained an XGBoost model and compared the performance of two feature encoding methods: Label Encoding (LE) and One Hot Encoding (OHE). Our findings reveal a marginal perfor

X. Li, J. D. Maxwell, D. Nguyen, J. Brock

Systematic studies on metastability exchange optical pumping of $^3$He nuclei have been performed at Jefferson Lab using a 1-torr sealed cell at magnetic fields from 2 to 4 T. The effects of the discharge intensity, pump laser power, and pumping transition schemes on achievable nuclear polarization and pumping rate have been investigated. A maximum steady-st

Zhiyu Li, Xikui Ma, Amir Bahrami, Zoé-Lise Deck-Léger

Space-time modulation-based metamaterials have recently spurred considerable interest, owing to the fundamental addition of the time dimension to the medium parameters, and resulting novel properties and potential applications. However, the implementation of most related structures -- e.g., involving step, slab or gradient discontinuities -- has been hindere

Gemma S. Cairns, Brian R. Patton

Multichannel, infinite-conjugate optical systems easily allow implementation of multiple image paths and imaging modes into a single microscope. Traditional optical alignment methods which rely on additional hardware are not always simple to implement, particularly in compact open-source microscope designs. We present here an alignment algorithm and process

Emmanuel Ortiz-Pacheco, Roelof Bijker

We present a study of mass spectra and electromagnetic couplings of S- and P-wave baryons containing one, two or three heavy quarks, either charm (c) or bottom (b), in the framework of a non-relativistic harmonic oscillator quark model. A simultaneous fit to 41 known masses of heavy baryons (40 singly-heavy and 1 doubly-heavy) shows a r.m.s. deviation of 19

Arnab Singh, Junli Li, Sergio A. Montoya, Sophie Morley

Presence of multiple competing periodicities may result in a system to go through states with modulated periodicities, an example of which is the self-similar staircase-like structure called the Devil's staircase. Herein we report on a novel staircase structure of domain periodicity in an amorphous and achiral Fe/Gd magnetic thin film wherein the reciprocal

Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma

Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios. Hence, many efforts have been taken f

Marat Siddikov, Ivan Schmidt

In these proceedings we present our results on the exclusive photoproduction of $J/\psi\,\eta_{c}$ pairs in the collinear factorization framework. We argue that the process might be used as a complementary channel for studying the generalized parton distributions (GPDs) of gluons. We provide numerical estimates for the cross-section in the kinematics of the

Victoria Borish, H. J. Lewandowski

Quantum mechanics is a field often considered very mathematical, abstract, and unintuitive. One way some instructors are hoping to help familiarize their students with these complex topics is to have the students see quantum effects in experiments in undergraduate instructional labs. Here, we present results from an interview study about what it means to bot

Machiko Hatsuda, Ondřej Hulík, William D. Linch, Warren D. Siegel

In this paper, the ${\cal A}$-theory, an extension of F-theory, is described as a fully U-duality covariant brane theory. This theory has some distinguishing features not known from world-sheet models. In particular, seen as a sigma model, both world-volume and target space coordinates are specific representations of the same group (the U-duality group). The

Alessio D'Alì, Martina Juhnke-Kubitzke, Melissa Koch

Starting from any finite simple graph, one can build a reflexive polytope known as a symmetric edge polytope. The first goal of this paper is to show that symmetric edge polytopes are intrinsically matroidal objects: more precisely, we prove that two symmetric edge polytopes are unimodularly equivalent precisely when they share the same graphical matroid. Th

Bianciardi G., Ciccarelli A. M., Conzo G., D'Angelo M.

Multiband photometric observations and their evaluation to instrumental magnitudes were performed using standard Johnson-Cousins filters (B, V, Rc) as well r and g Sloan filters, and not standard ones (R, G, B, and Clear filters). These were recorded from 9 observatories and from the MicroObservatory Robotic Telescope Network. The results describe the rapid

Zoltán Füredi, Alexandr Kostochka

Let $ a,b \in {\bf Z}^+$, $r=a + b$, and let $T$ be a tree with parts $U = \{u_1,u_2,\dots,u_s\}$ and $V = \{v_1,v_2,\dots,v_t\}$. Let $U_1, \dots ,U_s$ and $V_1, \dots, V_t$ be disjoint sets, such that {$|U_i|=a$ and $|V_j|=b$ for all $i,j$}. The {\em $(a,b)$-blowup} of $T$ is the $r$-uniform hypergraph with edge set $ {\{U_i \cup V_j : u_iv_j \in E(T)\}.}$

Flavio Del Santo, Gerd Christian Krizek

David Bohm has put forward the first deterministic interpretation of quantum physics, and for this he seems to be regarded as a champion of determinism by physicists (both his contemporaries and the supporters of his interpretation, the so-called "Bohmians") as well as by historians of physics. The standard narrative is that he underwent a "conversion" from

Joost P. Wardenier, Vivien Parmentier, Michael R. Line, Elspeth K. H. Lee

Ultra-hot Jupiters are perfect targets for transmission spectroscopy. However, their atmospheres feature strong spatial variations in temperature, chemistry, dynamics, cloud coverage, and scale height. This makes transit observations at high spectral resolution challenging to interpret. In this work, we model the cross-correlation signal of five chemical spe

Cyrus Rashtchian, Charles Herrmann, Chun-Sung Ferng, Ayan Chakrabarti

Probes are small networks that predict properties of underlying data from embeddings, and they provide a targeted, effective way to illuminate the information contained in embeddings. While analysis through the use of probes has become standard in NLP, there has been much less exploration in vision. Image foundation models have primarily been evaluated for s

L. Cardenas, V. C. Orozco, P. Salgado, D. Salgado

It is shown that the compactification a la Randall Sundrum of the so called, five dimensional Einstein Chern Simons action gravity leads to an action for a four dimensional scalar tensor gravity that includes a Gauss Bonnet term, which belongs to a particular case of the action of the Horndeski theory. The five dimensional action includes new gravitational d

Muhammad Fitrah Alfian Rangga Sakti, Piyabut Burikham

In this work, we study the Kerr-Sen-de Sitter black hole~(BH) in the Nariai limit where the event and cosmological horizon coincide. We show that the near-horizon Kerr-Sen-de Sitter black hole in Nariai limit is a fiber over AdS$_2$ with an appropriate coordinate transformation, instead of fiber over dS$_2$. Hence, we can compute the associated central charg

Jiangnan Cheng, Yingjie Bi, Ao Tang

Network virtualization enables Internet service providers to run multiple heterogeneous and dedicated network architectures for different customers on a shared substrate. In existing works on virtual network embedding (VNE), each customer formulates a virtual network request (VNR) where a virtual network (VN) is required. Motivated by a concrete example wher