November 2022 arXiv papers — page 96

Haoran Zhang, Junhui Wang

Longitudinal network consists of a sequence of temporal edges among multiple nodes, where the temporal edges are observed in real time. It has become ubiquitous with the rise of online social platform and e-commerce, but largely under-investigated in literature. In this paper, we propose an efficient estimation framework for longitudinal network, leveraging

Zilong Wang, Yichao Zhou, Wei Wei, Chen-Yu Lee

Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry. Although recent multi-modal language models have achieved impressive results, we find that existing benchmarks do not reflect the complexity of real documents seen in industry. In this work, we

Caitlin M. Casey, Jeyhan S. Kartaltepe, Nicole E. Drakos, Maximilien Franco

We present the survey design, implementation, and outlook for COSMOS-Web, a 255 hour treasury program conducted by the James Webb Space Telescope in its first cycle of observations. COSMOS-Web is a contiguous 0.54 deg$^2$ NIRCam imaging survey in four filters (F115W, F150W, F277W, and F444W) that will reach 5$\sigma$ point source depths ranging $\sim$27.5-28

Shangchao Su, Mingzhao Yang, Bin Li, Xiangyang Xue

Federated learning (FL) enables multiple clients to collaboratively train a global model without disclosing their data. Previous researches often require training the complete model parameters. However, the emergence of powerful pre-trained models makes it possible to achieve higher performance with fewer learnable parameters in FL. In this paper, we propose

Yichao Liu, Kun Chao, Fei Sun, Shaojie Chen

Remote temperature control can be obtained by a long-focus thermal lens that can focus heat fluxes into a spot far away from the back surface of the lens and create a virtual thermal source/sink in the background material, around which the temperature field distribution can be remotely controlled by changing the parameters of the thermal lens. However, due t

Yuka Hashizume, Li Li, Tomoki Toda

The criteria for measuring music similarity are important for developing a flexible music recommendation system. Some data-driven methods have been proposed to calculate music similarity from only music signals, such as metric learning based on a triplet loss using tag information on each musical piece. However, the resulting music similarity metric usually

Jun Xian, Xiaoda Xu

We study the expected $ L_2-$discrepancy under two classes of partitions, explicit and exact formulas are derived respectively. These results attain better expected $L_2-$discrepancy formulas than jittered sampling.

Ye He, Krishnakumar Balasubramanian, Bharath K. Sriperumbudur, Jianfeng Lu

The Stein Variational Gradient Descent (SVGD) algorithm is a deterministic particle method for sampling. However, a mean-field analysis reveals that the gradient flow corresponding to the SVGD algorithm (i.e., the Stein Variational Gradient Flow) only provides a constant-order approximation to the Wasserstein Gradient Flow corresponding to the KL-divergence

Jinhyuk Choi, Seong-Lyun Kim, Seung-Woo Ko

With the rise of edge computing, various AI services are expected to be available at a mobile side through the inference based on deep neural network (DNN) operated at the network edge, called edge inference (EI). On the other hand, the resulting AI quality (e.g., mean average precision in objective detection) has been regarded as a given factor, and AI qual

Kun He, Chang Liu, Stephen Lin, John E. Hopcroft

In recent years, many data augmentation techniques have been proposed to increase the diversity of input data and reduce the risk of overfitting on deep neural networks. In this work, we propose an easy-to-implement and model-free data augmentation method called Local Magnification (LOMA). Different from other geometric data augmentation methods that perform

Vukan Levajac, Grzegorz P. Mazur, Nick van Loo, Francesco Borsoi

Semiconducting nanowire Josephson junctions represent an attractive platform to investigate the anomalous Josephson effect and detect topological superconductivity by studying Josephson supercurrent. However, an external magnetic field generally suppresses the supercurrent through hybrid nanowire junctions and significantly limits the field range in which th

Thomas Haettel

We motivate the study of metric spaces with a unique convex geodesic bicombing, which we call CUB spaces. These encompass many classical notions of nonpositive curvature, such as CAT(0) spaces and Busemann-convex spaces. Groups having a geometric action on a CUB space enjoy numerous properties. We want to know when a simplicial complex, endowed with a natura

Jiaxing Huang, Tuen Wai Ng

We apply Rossi's half-plane version of Borel's Theorem to study the zero distribution of linear combinations of $\mathcal{A}$-entire functions (Theorem 1.2). This provides a unified way to study linear $q$-difference, difference and differential operators (with entire coefficients) preserving subsets of $\mathcal{A}$-entire functions, and hence obtain severa

Cao Xinxin, Lei Xiaolan, Murtadha Ahmed

Language difference is one of the factors that hinder the acquisition of second language skills. In this article, we introduce a novel solution that leverages the strength of deep neural networks to measure the semantic dissimilarity between languages based on their word distributions in the embedding space of the multilingual pre-trained language model (e.g

Hasan Mustafa, Sai Nandan Morapakula, Prateek Jain, Srinjoy Ganguly

Quantum computing has gained a lot of attention recently, and scientists have seen potential applications in this field using quantum computing for Cryptography and Communication to Machine Learning and Healthcare. Protein folding has been one of the most interesting areas to study, and it is also one of the biggest problems of biochemistry. Each protein fol

Bofeng Zhu, Li-Jun Lang, Qiang Wang, Qi Jie Wang

We study one-dimensional lattices with imaginary-valued Aubry-Andre-Harper (AAH) potentials. Such lattices can host edge states with purely imaginary eigenenergies, which differ from the edge states of the Hermitian AAH model and are stabilized by a non-Hermitian particle-hole symmetry. The edge states arise when the period of the imaginary potential is a mu

Aaron Charous, Pierre F. J. Lermusiaux

We develop two new sets of stable, rank-adaptive Dynamically Orthogonal Runge-Kutta (DORK) schemes that capture the high-order curvature of the nonlinear low-rank manifold. The DORK schemes asymptotically approximate the truncated singular value decomposition at a greatly reduced cost while preserving mode continuity using newly derived retractions. We show

Makoto Sakuda, Toshio Suzuki, Mandeep Singh Reen, Ken'ichiro Nakazato

We first discuss and determine the isospin mixing of the two $2^-$ states (12.53 MeV and 12.97 MeV) of $^{16}$O nucleus using the inelastic electron scattering data. We then evaluate the cross section of 4.4-MeV $\gamma$ rays produced in the neutrino neutral-current (NC) reaction $^{16}$O($\nu, \nu^{\prime}$)$^{16}$O$(12.97 {\rm MeV}, 2^-$) in a water Cheren

Jorge C. Castellanos, Jo Conroy, Valey Kamalov, Mattia Cantono

Coronal mass ejections (CMEs) can trigger geomagnetic storms and induce geoelectric currents that degrade the performance of terrestrial power grid operations; in particular, CMEs are known for causing large-scale outages in electrical grids. Submarine internet cables are powered through copper conductors spanning thousands of kilometers and are vulnerable t

Xiaomeng Chen, Yuchi Wu, Xinlei Yi, Minyi Huang

Information compression techniques are majorly employed to address the concern of reducing communication cost over peer-to-peer links. In this paper, we investigate distributed Nash equilibrium (NE) seeking problems in a class of non-cooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distrib

Anthony B. Davis

There are a handful of spectral windows in the near-IR through which we can see down to Venus' surface on the night side of the planet. The surface of our sister planet has thus been imaged by sensors on Venus-orbiting platforms (Venus Express, Akatsuki) and during fly-by with missions to other planets (Galileo, Cassini). The most tantalizing finding, so far

Yoonchang Sung, Zizhao Wang, Peter Stone

As robots become increasingly capable of manipulation and long-term autonomy, long-horizon task and motion planning problems are becoming increasingly important. A key challenge in such problems is that early actions in the plan may make future actions infeasible. When reaching a dead-end in the search, most existing planners use backtracking, which exhausti

Tao Pu, Qianru Lao, Hefeng Wu, Tianshui Chen

As a promising solution of reducing annotation cost, training multi-label models with partial positive labels (MLR-PPL), in which merely few positive labels are known while other are missing, attracts increasing attention. Due to the absence of any negative labels, previous works regard unknown labels as negative and adopt traditional MLR algorithms. To reje

Jinsong Chen, Boyu Li, Kun He

The decoupled Graph Convolutional Network (GCN), a recent development of GCN that decouples the neighborhood aggregation and feature transformation in each convolutional layer, has shown promising performance for graph representation learning. Existing decoupled GCNs first utilize a simple neural network (e.g., MLP) to learn the hidden features of the nodes,

Michael Murray, Hui Jin, Benjamin Bowman, Guido Montufar

Under mild conditions on the network initialization we derive a power series expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward networks in the infinite width limit. We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activation function as well as the depth of the n

Xunjian Yin, Xinyu Hu, Jin Jiang, Xiaojun Wan

Chinese Spelling Check (CSC) aims to detect and correct error tokens in Chinese contexts, which has a wide range of applications. However, it is confronted with the challenges of insufficient annotated data and the issue that previous methods may actually not fully leverage the existing datasets. In this paper, we introduce our plug-and-play retrieval method

Gabriel Orlanski, Seonhye Yang, Michael Healy

Despite the increase in popularity of language models for code generation, it is still unknown how training on bimodal coding forums affects a model's code generation performance and reliability. We, therefore, collect a dataset of over 2.2M StackOverflow questions with answers for finetuning. These fine-tuned models have average $pass@k$ improvements of 54.

Aoi Hayashi, Akitada Sakurai, Shin Nishio, William J. Munro

The quantum extreme reservoir computation (QERC) is a versatile quantum neural network model that combines the concepts of extreme machine learning with quantum reservoir computation. Key to QERC is the generation of a complex quantum reservoir (feature space) that does not need to be optimized for different problem instances. Originally, a periodically-driv

Philippe Brax, Anne-Christine Davis, Benjamin Elder

We compute bounds and forecasts on screened modified gravity theories, specialising to the chameleon model in Casimir force experiments. In particular, we investigate the classical interaction between a plate and sphere subject to a screened interaction of the chameleon type. We compare numerical simulations of the field profile and the classical pressure ex

Jiaan Cao, Lyuzhou Ye, Dawei He, Xiao Zheng

Magnetic circular dichroism (MCD) is a widely used spectroscopic technique which reveals valuable information about molecular geometry and electronic structure. However, the weak signal and the necessary strong magnets impose major limitations on its application. We propose a novel protocol to overcome these limitations by using pulsed vector beams (VBs), wh

Ann Merchant Boesgaard

This is a report on some highlights of some research on the rare light elements, lithium (Li), beryllium (Be), and boron (B), that I presented in my Henry Norris Russell Lecture in January, 2020. It is not a comprehensive review of work on these light elements, but contains sections on Big Bang nucleosynthesis of Li and the rarity of these light elements. It

Navya Martin Kollapally, Yan Chen, Julia Xu, James Geller

Social determinants of health are societal factors, such as where a person was born, grew up, works, lives, etc, along with socioeconomic and community factors that affect individual health. Social Determinants of Health are correlated with many clinical outcomes, hence it is desirable to record SDOH data in Electronic Health Records (EHRs). Besides storing

Nick Scoville, Andreas Faisst, John Weaver, Sune Toft

ALMA observations of the long wavelength dust continuum are used to estimate the gas masses in a sample of 708 star-forming (SF) galaxies at z = 0.3 to 4.5. We determine the dependence of gas masses and star formation efficiencies (SFE=SFR per unit gass mass). We find that 70 percent of the increase in SFRs of the MS is due to the increased gas masses at ear

Matt Visser

Finding affine parameters for null geodesics is often of considerable physical importance, especially when studying the paths swept out by null geodesics or when dealing with conservation laws and/or averaged energy conditions. But explicitly finding null affine parameters is also often quite tedious and can sometimes even be somewhat tricky. Herein we shall

Zhuojun Yu, Jonathan E. Rubin, Peter J. Thomas

Similar activity patterns may arise from model neural networks with distinct coupling properties and individual unit dynamics. These similar patterns may, however, respond differently to parameter variations and, specifically, to tuning of inputs that represent control signals. In this work, we analyze the responses resulting from modulation of a localized i

Robin K. S. Hankin

Here I present the freegroup package for working with the free group on a finite set of symbols. The package is vectorised; internally it uses an efficient matrix-based representation for free group objects but uses a configurable print method. A range of R-centric functionality is provided. It is available on CRAN at https://CRAN.R-project.org/package=freeg

Son Tay Le, Tuan Ngoc Nguyen, Dac-Khuong Bui, Tuan Duc Ngo

Renewable energy storage (RES) is essential to address the intermittence issues of renewable energy systems, thereby enhancing the system stability and reliability. This study presents an optimisation study of sizing and operational strategy parameters of a grid-connected photovoltaic (PV)-hydrogen/battery systems using a Multi-Objective Modified Firefly Alg

Tica Lin, Zhutian Chen, Johanna Beyer, Yincai Wu

Most sports visualizations rely on a combination of spatial, highly temporal, and user-centric data, making sports a challenging target for visualization. Emerging technologies, such as augmented and mixed reality (AR/XR), have brought exciting opportunities along with new challenges for sports visualization. We share our experience working with sports domai

Constantine E. Theodosiou

We revisit the photoabsorption from the ground state of K, ending below and above the ionization threshold, with special emphasis on the shape of the photoionization cross section around the Cooper minimum. The present treatment, including core polarization effects and dynamic core polarizability matches very well the most accurate measurements resolving an

Terra Blevins, Hila Gonen, Luke Zettlemoyer

Although pretrained language models (PLMs) can be prompted to perform a wide range of language tasks, it remains an open question how much this ability comes from generalizable linguistic understanding versus surface-level lexical patterns. To test this, we present a structured prompting approach for linguistic structured prediction tasks, allowing us to per

Shaddin Dughmi, Yusuf Hakan Kalayci, Neel Patel

Motivated by recent progress on stochastic matching with few queries, we embark on a systematic study of the sparsification of stochastic packing problems (SPP) more generally. Specifically, we consider SPPs where elements are independently active with a probability p, and ask whether one can (non-adaptively) compute a sparse set of elements guaranteed to co

Rishabh Bhardwaj, George Polovets, Monica Sunkara

Semi-parametric Nearest Neighbor Language Models ($k$NN-LMs) have produced impressive gains over purely parametric LMs, by leveraging large-scale neighborhood retrieval over external memory datastores. However, there has been little investigation into adapting such models for new domains. This work attempts to fill that gap and suggests the following approac

Jinghua Liu, Jingqing Li, Binyuan Liu, Ian W. Hamley

The Mpemba effect and its inverse can be understood as a result of nonequilibrium thermodynamics. In polymers, changes of state are generally non-equilibrium processes. However, the Mpemba effect has been rarely reported in the crystallization of polymers. In the melt, polybutene-1 (PB-1) has the lowest critical cooling rate in polyolefins and tends to maint

Carter M. Gustin, Gary Goldstein

Quantum simulation of quantum field theories offers a new way to investigate properties of the fundamental constituents of matter. We develop quantum simulation algorithms based on the light-front formulation of relativistic field theories. The process of quantizing the system in light-cone coordinates will be explained for a Hamiltonian formulation, which b

Adham Elarabawy, Harish Kamath, Samuel Denton

With the rise of large, publicly-available text-to-image diffusion models, text-guided real image editing has garnered much research attention recently. Existing methods tend to either rely on some form of per-instance or per-task fine-tuning and optimization, require multiple novel views, or they inherently entangle preservation of real image identity, sema

Ian Lizarraga, Robert Marangell

We determine the nonlinear stability of shock-fronted travelling waves arising in a reaction-nonlinear diffusion PDE, subject to a fourth-order spatial derivative term multiplied by a small parameter $\varepsilon$ that models {\it nonlocal regularization}. Motivated by the authors' recent stability analysis of shock-fronted travelling waves under viscous rel

Michael P. Leung, Pantelis Loupos

This paper studies causal inference with observational data from a single large network. We consider a nonparametric model with interference in both potential outcomes and selection into treatment. Specifically, both stages may be the outcomes of simultaneous equations models, allowing for endogenous peer effects. This results in high-dimensional network con

Yisai Xue, Yichong Liu, Liying Kang

Given a graph $T$ and a family of graphs $\mathcal{F}$, the generalized Tur\'an number of $\mathcal{F}$ is the maximum number of copies of $T$ in an $\mathcal{F}$-free graph on $n$ vertices, denoted by $ex(n,T,\mathcal{F})$. When $T = K_r$, $ex(n, K_r, \mathcal{F})$ is a function specifying the maximum possible number of $r$-cliques in an $\mathcal{F}$-free

Manh Duc Le, Tatiana Guidi, Robert I. Bewley, J. Ross Stewart

The MARI direct geometry time-of-flight neutron spectrometer at ISIS has been upgraded with an $m=3$ supermirror guide and new detector electronics. This has resulted in a flux gain of ${\approx}6{\times}$ at $\lambda=1.8$ {\AA}, and improvements on discriminating electrical noise, allowing MARI to continue to deliver a high quality science program well into

Anjun Hu, Jean-Pierre R. Falet, Brennan S. Nichyporuk, Changjian Shui

We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and

Minqi Jiang, Tim Rocktäschel, Edward Grefenstette

We are at the cusp of a transition from "learning from data" to "learning what data to learn from" as a central focus of artificial intelligence (AI) research. While the first-order learning problem is not completely solved, large models under unified architectures, such as transformers, have shifted the learning bottleneck from how to effectively train our

Shen Sang, Tiancheng Zhi, Guoxian Song, Minghao Liu

Stylized 3D avatars have become increasingly prominent in our modern life. Creating these avatars manually usually involves laborious selection and adjustment of continuous and discrete parameters and is time-consuming for average users. Self-supervised approaches to automatically create 3D avatars from user selfies promise high quality with little annotatio

Shivakumar Mahesh, Anshuka Rangi, Haifeng Xu, Long Tran-Thanh

Motivated by cognitive radios, stochastic Multi-Player Multi-Armed Bandits has been extensively studied in recent years. In this setting, each player pulls an arm, and receives a reward corresponding to the arm if there is no collision, namely the arm was selected by one single player. Otherwise, the player receives no reward if collision occurs. In this pap

Arnav Gupta, Timothy J. Healey

We study the nucleation and development of crack patterns in thin composite fibers under tension in this work. A fiber comprises an elastic core and an outer layer of a weaker brittle material. In recent tensile experiments on such composites, multiple cracks were observed to develop simultaneously on the outer layer. We propose here a simple one-dimensional

Shuqi Ke, Chao Huang, Xin Liu

Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing studies focus on FL algorithm development to tackle data heterogeneity across clients, the important issue of data quality (e.g., label noise) in FL is overlooked. This paper aims to fill

L Alberto Campos, Jennifer Andreoli-Fang, Vivek Ganti

An approach to enable advanced troubleshooting, granular analysis and service quality of experience assessment is presented. The use of topology information in the identification of each cable network element along with granular information of the element configuration and health is proposed. This technique covers multiple layers including the service layer.

Matthew Peroni, Marharyta Kurban, Sun Young Yang, Young Sun Kim

With increasing interest in applying machine learning to develop healthcare solutions, there is a desire to create interpretable deep learning models for survival analysis. In this paper, we extend the Neural Additive Model (NAM) by incorporating pairwise feature interaction networks and equip these models with loss functions that fit both proportional and n

Shota Kikuchi, Tatsuo Kobayashi, Kaito Nasu, Shohei Takada

We study fermion zero-mode wavefunctions on $T^4/Z_N$ orbifold with background magnetic fluxes. The number of zero-modes is analyzed by use of $Sp(4,\mathbb{Z})$ modular transformation. Conditions needed to realize three generation models are clarified. We also study parity transformation in the compact space which leads to better understanding of relationsh

An-yi Huang, Heng-nian Li

Trajectory optimization of low-thrust perturbed orbit rendezvous is a crucial technology for space missions in low Earth orbits, which is difficult to solve due to its initial value sensitivity, especially when the transfer trajectory has many revolutions. This paper investigated the time-fixed perturbed orbit rendezvous between low-eccentricity orbits and p

Nathan Kaplan, Deepesh Singhal

We study statistical properties of numerical semigroups of genus $g$ as $g$ goes to infinity. More specifically, we answer a question of Eliahou by showing that as $g$ goes to infinity, the proportion of numerical semigroups of genus $g$ with embedding dimension close to $g/\sqrt{5}$ approaches $1$. We prove similar results for the type and weight of a numer

Andrea Cavaglià, Simon Ekhammar, Nikolay Gromov, Paul Ryan

Despite the rich and fruitful history of the integrability approach to string theory on the $AdS_3\times S^3\times T^4$ background, it has not been possible to extract many concrete predictions from integrability, except in a strict asymptotic regime of large quantum numbers, due to the severity of wrapping effects. The situation changed radically with two i

Vincent Lahoche, Dine Ousmane Samary

This paper aims to address the low-temperature dynamics issue for the $p=2$ spin dynamics with confining potential, focusing especially on quartic and sextic cases. The dynamics are described by a Langevin equation for a real vector $q_i$ of size $N$, where disorder is materialized by a Wigner matrix and we especially investigate the self consistent evolutio

Razvan-Octavian Radu

In "Global regularity for vortex patches" (Commun. Math. Phys. 1993), Bertozzi and Constantin formulate the vortex patch problem in the level-set framework and prove a priori estimates for this active scalar equation. By extending the tools used to prove these estimates, we construct solutions and show propagation of higher H\"older regularity. This constitu

Ji Won Park, Simon Birrer, Madison Ueland, Miles Cranmer

We present a Bayesian graph neural network (BGNN) that can estimate the weak lensing convergence ($\kappa$) from photometric measurements of galaxies along a given line of sight. The method is of particular interest in strong gravitational time delay cosmography (TDC), where characterizing the "external convergence" ($\kappa_{\rm ext}$) from the lens environ

J. -Q. Yan, M. A. McGuire

Transition metal halides can host a large variety of novel phenomena, such as magnetism in the monolayer limit, quantum spin liquid and spiral spin liquid states, and topological magnons and phonons. Sizeable high quality single crystals are necessary for investigations of magnetic and lattice excitations by, for example, inelastic neutron scattering. In thi

Ruo Yu Tao, Adam White, Marlos C. Machado

In many, if not every realistic sequential decision-making task, the decision-making agent is not able to model the full complexity of the world. The environment is often much larger and more complex than the agent, a setting also known as partial observability. In such settings, the agent must leverage more than just the current sensory inputs; it must cons

Michael Witbrock, Patrick Haffner

We present SVCnet, a system for modelling speaker variability. Encoder Neural Networks specialized for each speech sound produce low dimensionality models of acoustical variation, and these models are further combined into an overall model of voice variability. A training procedure is described which minimizes the dependence of this model on which sounds hav

Benjamin Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs, Rich Caruana

Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed. We add to the conversation regarding inflammation, hemostasis and vascular function in COVID-19 by performing a time-varying observational analysis of

Premchand V Chandra, Pratikash P Panda, Pradip Dutta

A geometrical mechanism that generates augmented swirling and round jets is being proposed. The proposed geometry has an axial inlet port and three tangential inlet ports, each of diameter 10mm. A parameter called Split ratio, defined as the percentage of airflow split through these inlet ports, is introduced for the augmented jet. Flow at four different spl

Xiang Huo, Jin Dong, Borui Cui, Boming Liu

The flexible, efficient, and reliable operation of grid-interactive efficient buildings (GEBs) is increasingly impacted by the growing penetration of distributed energy resources (DERs). Besides, the optimization and control of DERs, buildings, and distribution networks are further complicated by their interconnections. In this paper, we exploit load-side fl

Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Moein Heidari, Reza Azad

Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. A diffusion probabilistic model defines a forward diffusion stage where the input data is gradually perturbed over several steps by adding Gaussian noise and then learns to reverse the diffusion process to retrieve the desired no

Lin-Ding Yuan, Alex Zunger

Energy bands in antiferromagnets are generally spin degenerate in the absence of spin-orbit coupling (SOC). Recent studies [Physical Review B 102, 014422 (2020)] identified formal symmetry conditions for crystals for which this degeneracy can be lifted even in the zero SOC limit. Such materials could enable "spin-split" antiferromagnetic spintronics without

Cailan Li

We compute Ext groups between Soergel Bimodules associated to the infinite/finite dihedral group for a realization in characteristic 0 and show that they are free right $R-$modules. In particular, we obtain an explicit diagrammatic basis for the Hochschild cohomology of indecomposable Soergel Bimodules. We then give a diagrammatic presentation for the corres

Hanwu Li, Frank Riedel, Shuzhen Yang

We characterize optimal consumption policies in a recursive intertemporal utility framework with local substitution. We establish existence and uniqueness and a version of the Kuhn-Tucker theorem characterizing the optimal consumption plan. An explicit solution is provided for the case when the felicity function is of the Epstein-Zin's type.

Erik Gengel, Zafrir Kuplik, Dror Angel, Eyal Heifetz

We propose a model for the structure formation of jellyfish swimming based on active Brownian particles. We address the phenomena of counter-current swimming, avoidance of turbulent flow regions and foraging. We motivate corresponding mechanisms from observations of jellyfish swarming reported in the literature and incorporate them into the generic modelling

Mitsuru Tohyama

The number conservation law in the odd-particle number random-phase approximation (oRPA) and its extension (EoRPA) is studied by applying them to a pairing model and $^{16}$O. It is found in the application to $^{16}$O that the number conservation law is not fulfilled in oRPA and EoRPA and that it is drastically improved in EoRPA due to the inclusion of grou

Rajan Shankar, Jonathan Spreer

We present a procedure to sample uniformly from the set of combinatorial isomorphism types of balanced triangulations of surfaces - also known as graph-encoded surfaces. For a given number $n$, the sample is a weighted set of graph-encoded surfaces with $2n$ triangles. The sampling procedure relies on connections between graph-encoded surfaces and permutatio

Ningkun Zheng, Xiaoxiang Liu, Bolun Xu, Yuanyuan Shi

This paper proposes a novel energy storage price arbitrage algorithm combining supervised learning with dynamic programming. The proposed approach uses a neural network to directly predicts the opportunity cost at different energy storage state-of-charge levels, and then input the predicted opportunity cost into a model-based arbitrage control algorithm for

Mohsen Ghaffari, Christoph Grunau, Slobodan Mitrović

This paper presents an $O(\log\log \bar{d})$ round massively parallel algorithm for $1+\epsilon$ approximation of maximum weighted $b$-matchings, using near-linear memory per machine. Here $\bar{d}$ denotes the average degree in the graph and $\epsilon$ is an arbitrarily small positive constant. Recall that $b$-matching is the natural and well-studied genera

Yuhang Li, Ruokai Yin, Hyoungseob Park, Youngeun Kim

We study the Human Activity Recognition (HAR) task, which predicts user daily activity based on time series data from wearable sensors. Recently, researchers use end-to-end Artificial Neural Networks (ANNs) to extract the features and perform classification in HAR. However, ANNs pose a huge computation burden on wearable devices and lack temporal feature ext

Nauman Dawalatabad, Sameer Khurana, Antoine Laurent, James Glass

Pseudo-label (PL) filtering forms a crucial part of Self-Training (ST) methods for unsupervised domain adaptation. Dropout-based Uncertainty-driven Self-Training (DUST) proceeds by first training a teacher model on source domain labeled data. Then, the teacher model is used to provide PLs for the unlabeled target domain data. Finally, we train a student on a

César Martínez-Guardiola, Nathaniel K. Brown, Fernando Silva-Coira, Dominik Köppl

MONI (Rossi et al., 2022) can store a pangenomic dataset T in small space and later, given a pattern P, quickly find the maximal exact matches (MEMs) of P with respect to T. In this paper we consider its one-pass version (Boucher et al., 2021), whose query times are dominated in our experiments by longest common extension (LCE) queries. We show how a small m

Zhihong Pan, Xin Zhou, Hao Tian

Transferring large amount of high resolution images over limited bandwidth is an important but very challenging task. Compressing images using extremely low bitrates (<0.1 bpp) has been studied but it often results in low quality images of heavy artifacts due to the strong constraint in the number of bits available for the compressed data. It is often said t

H. P. Saldaño, M. Rubio, A. D. Bolatto, C. Verdugo

The Small Magellanic Cloud (SMC) is the closest low-metallicity galaxy to the Milky Way where the dynamical state of molecular clouds can be analyzed. We present a CO(2-1) survey at 9 pc resolution obtained with the APEX telescope in an extensive region of the SMC and characterize the properties of the molecular clouds. We study the dynamical state and stabi

Yongqiang Wang

We propose a new dynamic average consensus algorithm that is robust to information-sharing noise arising from differential-privacy design. Not only is dynamic average consensus widely used in cooperative control and distributed tracking, it is also a fundamental building block in numerous distributed computation algorithms such as multi-agent optimization an

Thomas J. Hardin, Michael Chandross, Rahul Meena, Spencer Fajardo

Durable interest in developing a framework for the detailed structure of glassy materials has produced numerous structural descriptors that trade off between general applicability and interpretability. However, none approach the combination of simplicity and wide-ranging predictive power of the lattice-grain-defect framework for crystalline materials. Workin

Qi Liu, Kunlin Ran, Yanping Huang, Gang Li

A search for the dimuon decay of the Standard Model Higgs boson is performed using the Monte Carlo simulated events to mimic data corresponding to an integrated luminosity of 5.6 ab$^{-1}$ collected with the Circular Electron-Positron Collider detector in $e^{+}e^{-}$ collisions at $\sqrt{s}=240$ GeV. The paper studies $e^{+}e^{-}\to ZH,\,Z\to q\bar{q},\,H\t

Alberto Nardin, Eddy Ardonne, Leonardo Mazza

We prove a generic spin-statistics relation for the fractional quasiparticles that appear in abelian quantum Hall states on the disk. The proof is based on an efficient way for computing the Berry phase acquired by a generic quasiparticle translated in the plane along a circular path, and on the crucial fact that once the gauge-invariant generator of rotatio

Richard Friedberg

I provide for the first time in English a line-by-line translation of the entire text of the monumental 1840 memoir of Olinde Rodrigues, "On the geometrical laws governing the motions of a solid system..." published in French in the Journal de Math\'{e}matiques Pures et Appliqu\'{e}es. I accompany the translation with copious notes in italics, in which I exp

Carol E. Cleland, Paul B. Rimmer

We are of the opinion that several anomalies in the atmosphere of Venus provide evidence of yet-unknown processes and systems that are out of equilibrium. The investigation of these anomalies on Venus should be open to the wide range of explanations, including unknown biological activity. We provide an overview of two anomalies, the tentative detection of am

Carlos Alvarado, Janelly Bautista, Alexander J. Stuart

By assuming there exist three massive non-degenerate Majorana neutrinos, it is possible to describe neutrino mixing with a residual, unbroken discrete Klein subgroup of a larger spontaneously broken flavor symmetry group. Motivated by forthcoming measurements of leptonic CP violation, we revisit this framework by applying group presentation rules to it. We d

O. A. Acevedo, B. M. Pimentel

We develop a complete formulation of quantum gauge invariance in light-front dynamics for interacting theories with massless vector gauge fields in the framework of null-plane causal perturbation theory. We apply the general results to quantum electrodynamics, showing that the so-called "gauge terms" present in the photon commutation distribution when quanti

Kai Zhang, Chen Fang, Zhesen Yang

In this paper, we introduce the concept of dynamical degeneracy splitting to describe the anisotropic decay behaviors in non-Hermitian systems. We demonstrate that systems with dynamical degeneracy splitting exhibit two distinctive features: (i) the system shows frequency-resolved non-Hermitian skin effect; (ii) Green's function exhibits anomalous at given f

Muzammil Shahbaz

Test Impact Analysis is an approach to obtain a subset of tests impacted by code changes. This approach is mainly applied to unit testing where the link between the code and its associated tests is easy to obtain. On the integration level, however, it is not straightforward to find such a link programmatically, especially when the integration tests are held

Yi-Hsuan Lin, Jesse Railo, Philipp Zimmermann

We investigate global uniqueness for an inverse problem for a nonlocal diffusion equation on domains that are bounded in one direction. The coefficients are assumed to be unknown and isotropic on the entire space. We first show that the partial exterior Dirichlet-to-Neumann map locally determines the diffusion coefficient in the exterior domain. In addition,

C. Maggi, B. Nath, F. Saglimbeni, V. Carmona Sosa

Sperm swimming is crucial to fertilise the egg, in nature and in assisted reproductive technologies. Modelling the sperm dynamics involves elasticity, hydrodynamics, internal active forces, and out-of-equilibrium noise. Here we demonstrate experimentally the relevance of energy dissipation for sperm beating fluctuations. For each motile cell, we reconstruct

Ismail Abouamal

We construct a quantum satisitical mechanical system which generalizes the Connes-Marcolli $GL_2$ system. In particular we introduce the Connes-Marcolli system associated to the Siegel modular variety of degree $2$. We classify its $\text{KMS}_\beta$-states for inverse temperatures $\beta >0$ and show that a spontaneous phase transition occurs at $\beta=3$.

Pierre Balty, Philippe Chatelain, Thomas Gillis

Massively parallel Fourier transforms are widely used in computational sciences, and specifically in computational fluid dynamics which involves unbounded Poisson problems. In practice the latter is usually the most time-consuming operation due to its inescapable all-to-all communication pattern. The original flups library tackles that issue with an implemen

Rafid Umayer Murshed, Md. Abrar Istiak, Md. Toufiqur Rahman, Zulqarnain B Ashraf

The recent pandemic has refocused the medical world's attention on the diagnostic techniques associated with cardiovascular disease. Heart rate provides a real-time snapshot of cardiovascular health. A more precise heart rate reading provides a better understanding of cardiac muscle activity. Although many existing diagnostic techniques are approaching the l

Mahmoud AlHallak, Khalil Kalid Al Said, Nidal Chamoun, Moustafa Sayem El-Daher

We investigate Natural Inflation with non-minimal coupling to gravity, characterized either by a quadratic or a periodic term, within the Warm Inflation paradigm during the slow roll stage, in both strong and weak dissipation limits, and show that, in the case of $T$-linearly dependent dissipative term, it can accommodate the spectral index $n_s$ and tensor-