November 2022 arXiv papers — page 106

Arka Banerjee, Dootika Vats

Strong invariance principles in Markov chain Monte Carlo are crucial to theoretically grounded output analysis. Using the wide-sense regenerative nature of the process, we obtain explicit bounds in the strong invariance converging rates for partial sums of multivariate ergodic Markov chains. Consequently, we present results on the existence of strong invaria

Nils Margenberg, Franz X. Kärtner, Markus Bause

We investigate the accurate full broadband simulation of complex nonlinear optical processes. A mathematical model and numerical simulation techniques in the time domain are developed to simulate complex nonlinear optical processes without the usual used slowly varying envelope approximation. We illustrate the accuracy by numerical simulations. Furthermore,

D. S. Agafontsev, A. A. Gelash, R. I. Mullyadzhanov, V. E. Zakharov

We study numerically the integrable turbulence in the framework of the one-dimensional nonlinear Schrodinger equation (1D-NLSE) of the focusing type using a new approach called the "growing of turbulence". In this approach, we add a small linear pumping term to the equation and start evolution from statistically homogeneous Gaussian noise. After reaching a c

Yang Liu, Z. Y. Xie, Hong-Gang Luo, Jize Zhao

By using the tensor-network state algorithm, we study a spin-orbital model with SU(2)$\times$SU(2)$\times$U(1) symmetry on the triangular lattice. This model was proposed to describe some triangular $d^1$ materials and was argued to host a spin-orbital liquid ground state. In our work the trial wavefunction of its ground state is approximated by an infinite

Yasmine Fittouhi, Anthony Joseph

A "Composition map" is constructed, leaning heavily on earlier work [Y. Fittouhi and A. Joseph, Parabolic adjoint action, Weierstrass sections and components of the nilfibre in type $A$, Indag Math. and Y. Fittouhi and A. Joseph, The canonical component of the nilfibre for parabolic adjoint action, Weierstrass sections in type $A$, preprint, Weizmann, 2021].

Tal Alon, Paul Dütting, Yingkai Li, Inbal Talgam-Cohen

We consider a hidden-action principal-agent model, in which actions require different amounts of effort, and the agent privately knows his ability that determines his cost of effort. We show that linear contracts admit approximation guarantees that improve with a natural metric that captures the degree of uncertainty in the contracting setting. We thus show

Yuan Xiao, Yuchen Chen, Shiqing Ma, Chunrong Fang

The robustness of neural network classifiers is important in the safety-critical domain and can be quantified by robustness verification. At present, efficient and scalable verification techniques are always sound but incomplete, and thus, the improvement of verified robustness results is the key criterion to evaluate the performance of incomplete verificati

Diego Gonzalez, Naoya Kitajima, Fuminobu Takahashi, Wen Yin

We study the formation and evolution of domain walls with initial inflationary fluctuations by numerical lattice calculations that, for the first time, correctly take into account correlations on superhorizon scales. We find that, contrary to the widely-held claim {over the past few tens of years}, the domain wall network exhibits remarkable stability even w

Colin D. Reid

Let $\Omega$ be a set equipped with an equivalence relation $\sim$; we refer to the equivalence classes as blocks of $\Omega$. A permutation group $G \le \mathrm{Sym}(\Omega)$ is $k$-by-block-transitive if $\sim$ is $G$-invariant, with at least $k$ blocks, and $G$ is transitive on the set of $k$-tuples of points such that no two entries lie in the same block

Tong Li, Rui-Jia Zhang, Chang-Jie Dai

The Witten effect implies the electromagnetic interactions between axions and magnetic monopoles. Based on the quantum electromagnetodynamics, a generic low-energy axion-photon effective field theory was built by introducing two four-potentials ($A^\mu$ and $B^\mu$) to describe a photon. More anomalous axion-photon interactions and couplings ($g_{aAA}$, $g_{

Nicolle Garber, Vukosi Marivate

The subject of conversational mining has become of great interest recently due to the explosion of social and other online media. Supplementing this explosion of text is the advancement in pre-trained language models which have helped us to leverage these sources of information. An interesting domain to analyse is conversations in terms of complexity and val

Yasmine Fittouhi, Anthony Joseph

This work is a continuation of [Fittouhi and Joseph, Parabolic adjoint action, Weierstrass Sections and components of the nilfibre in type $A$]. Let $P$ be a parabolic subgroup of an irreducible simple algebraic group $G$, $P'$ its derived group and $\mathfrak m$ be the nilradical to its Lie algebra. A theorem of Richardson implies that the subalgebra $\math

S. Y. Lou, Xia-zhi Hao, Man Jia

Utilizing some conservation laws of (1+1)-dimensional integrable local evolution systems, it is conjectured that higher dimensional integrable equations may be regularly constructed by a deformation algorithm. The algorithm can be applied to Lax pairs and higher order flows. In other words, if the original lower dimensional model is Lax integrable (possesses

Yibing Liu, Chris Xing Tian, Haoliang Li, Shiqi Wang

Learning invariant representations via contrastive learning has seen state-of-the-art performance in domain generalization (DG). Despite such success, in this paper, we find that its core learning strategy -- feature alignment -- could heavily hinder model generalization. Drawing insights in neuron interpretability, we characterize this problem from a neuron

Si Duc Quang, Do Thi Thuy Hang

In this article, we study the uniqueness problem for the generalized gauss maps of minimal surfaces (with the same base) immersed in $\mathbb R^{n+1}$ which have the same inverse image of some hypersurfaces in a projective subvariety $V\subset\mathbb P^n(\mathbb C)$. As we know, this is the first time the unicity of generalized gauss maps on minimal surfaces

Yabin Zhang, Jiehong Lin, Ruihuang Li, Kui Jia

Masked autoencoder has demonstrated its effectiveness in self-supervised point cloud learning. Considering that masking is a kind of corruption, in this work we explore a more general denoising autoencoder for point cloud learning (Point-DAE) by investigating more types of corruptions beyond masking. Specifically, we degrade the point cloud with certain corr

Yufei Huang, Yujia Qin, Huadong Wang, Yichun Yin

Recently, prompt tuning (PT) has gained increasing attention as a parameter-efficient way of tuning pre-trained language models (PLMs). Despite extensively reducing the number of tunable parameters and achieving satisfying performance, PT is training-inefficient due to its slow convergence. To improve PT's training efficiency, we first make some novel observ

Yiwen Qiu, Jialong Wu, Zhangjie Cao, Mingsheng Long

Existing imitation learning works mainly assume that the demonstrator who collects demonstrations shares the same dynamics as the imitator. However, the assumption limits the usage of imitation learning, especially when collecting demonstrations for the imitator is difficult. In this paper, we study out-of-dynamics imitation learning (OOD-IL), which relaxes

Minrui Xu, Dusit Niyato, Benjamin Wright, Hongliang Zhang

Metaverse can obscure the boundary between the physical and virtual worlds. Specifically, for the Metaverse in vehicular networks, i.e., the vehicular Metaverse, vehicles are no longer isolated physical spaces but interfaces that extend the virtual worlds to the physical world. Accessing the Metaverse via autonomous vehicles (AVs), drivers and passengers can

Xuetong Wang, Kanhao Zhao, Rong Zhou, Alex Leow

Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer's Disease (AD). Specifically, we first train

Jun Jin, Hongming Zhang, Jun Luo

This paper tackles the problem of how to pre-train a model and make it generally reusable backbones for downstream task learning. In pre-training, we propose a method that builds an agent-environment interaction model by learning domain invariant successor features from the agent's vast experiences covering various tasks, then discretize them into behavior p

Kazuki Yamanoi, Satoru Oishi, Kenji Kawaike

Prediction of the extent and probability of debris flow under rainfall conditions can contribute to precautionary activities through risk quantification. To this end, quantifying the debris-flow risk against rainfall involves three components: predicting the debris-flow initiation locations under rainfall conditions, setting appropriate physical parameters r

Hui Ma, Chao Qian, Jing Wu, Yongsheng Zhang

Let $G$ be a compact connected subgroup of $SO(n+1)$. In $\mathbb{R}^{n+1}$, we gain interior $G$-symmetry for minimal hypersurfaces and hypersurfaces of constant mean curvature (CMC) which have $G$-invariant boundaries and $G$-invariant contact angles along boundaries. The main ingredients of the proof are to build an associated Cauchy problem based on infi

Yi-Kuan Hsieh, Jun-Wei Hsieh, Yu-Chee Tseng, Ming-Ching Chang

We develop a Synthetic Fusion Pyramid Network (SPF-Net) with a scale-aware loss function design for accurate crowd counting. Existing crowd-counting methods assume that the training annotation points were accurate and thus ignore the fact that noisy annotations can lead to large model-learning bias and counting error, especially for counting highly dense cro

Allen W. Shafter, Kenta Taguchi, Jingyuan Zhao, Kamil Hornoch

We report the discovery of a previously unknown eruption of the recurrent nova M31N 2017-01e that took place on 11 January 2012. The earlier eruption was detected by Pan-STARRS and occurred 1847 days (5.06 yr) prior to the eruption on 31 January 2017 (M31N 2017-01e). The nova has now been seen to have had a total of four recorded eruptions (M31N 2012-01c, 20

Peng Zhao, Ruixia Wang, Mengjun Hu, Teng Ma

Accurate control of qubits is the central requirement for building functional quantum processors. For the current superconducting quantum processor, high-fidelity control of qubits is mainly based on independently calibrated microwave pulses, which could differ from each other in frequencies, amplitudes, and phases. With this control strategy, the needed phy

Kyuhyoun Cho, Paola Testa, Bart De Pontieu, Vanessa Polito

Nanoflares are regarded as one of the major mechanisms of magnetic energy release and coronal heating in the solar outer atmosphere. We conduct a statistical study on the response of the chromosphere and transition region to nanoflares, as observed by the Interface Region Imaging Spectrograph (IRIS), by using an algorithm for the automatic detection of these

Maged Shoman, Armstrong Aboah, Abdulateef Daud, Yaw Adu-Gyamfi

Because traffic characteristics display stochastic nonlinear spatiotemporal dependencies, traffic prediction is a challenging task. In this paper develop a graph convolution gated recurrent unit (GC GRU N) network to extract the essential Spatio temporal features. we use Seattle loop detector data aggregated over 15 minutes and reframe the problem through sp

Dekui Peng

The topological group version of the celebrated Banach-Mazur problem asks wether every infinite topological group has a non-trivial separable quotient group. It is known that compact groups have infinite separable metrizable quotient groups. However, as dense subgroups of compact groups, precompact groups may admit no non-trivial metrizable quotient groups,

Eric van Damme, Xu Lang

We consider two-person bargaining problems in which (only) the disagreement outcome is private (and possibly correlated) information and it is common knowledge that disagreement is inefficient. We show that if the Pareto frontier is linear, the outcome of an ex post efficient mechanism cannot depend on the disagreement payoffs. If the frontier is non-linear,

Harsh Shrivastava, Urszula Chajewska

Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different methods and study the advances in techn

Quang-Huy Nguyen, Cuong Q. Nguyen, Dung D. Le, Hieu H. Pham

This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehens

Shravan Mohan

The problems of determining the optimal power allocation, within maximum power bounds, to (i) maximize the minimum Shannon capacity, and (ii) minimize the weighted latency are considered. In the first case, the global optima can be achieved in polynomial time by solving a sequence of linear programs (LP). In the second case, the original non-convex problem i

Sven Brüggemann, Robert H. Moroto, Robert R. Bitmead

We study a gas network flow regulation control problem showing the closed-loop consequences of using interconnected component models, which have been designed to preserve a variant of mass flow conservation without the inclusion of algebraic constraints into the dynamics. These are candidate \textit{control-oriented} models because they are linear state-spac

Guang-Lei Wu, Yun-Wei Yu, Shao-Ze Li

A rapidly rotating and highly magnetized remnant neutron star (NS; magnetar) could survive from a merger of double NSs and drive a powerful relativistic wind. The early interaction of this wind with the previous merger ejecta can lead to shock breakout (SBO) emission mainly in ultraviolet and soft X-ray bands, which provides an observational signature for th

Jia-Zhen Li, Cong-Jun Zou, Yan-Xiong Du, Qing-Xian Lv

Topological vacua are a family of degenerate ground states of Yang-Mills fields with zero field strength but nontrivial topological structures. They play a fundamental role in particle physics and quantum field theory, but have not yet been experimentally observed. Here we report the first theoretical proposal and experimental realization of synthetic topolo

Hongryol Jeon, Mahdi Sadjadi, Varda F. Hagh

Critically elastic materials - those that are rigid with a single state of self-stress - can be generated from parent systems with two states of self-stress by the removal of one of many constraints. We show that the elastic moduli of the resulting homogeneous and isotropic daughter systems are interrelated by a universal functional form parametrized by prop

Bijie Bai, Xilin Yang, Yuzhu Li, Yijie Zhang

Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specializ

Katherine E. Stange

We demonstrate that a modification of the classical index calculus algorithm can be used to factor integers. More generally, we reduce the factoring problem to finding an overdetermined system of multiplicative relations in any factor base modulo $n$, where $n$ is the integer whose factorization is sought. The algorithm has subexponential runtime $\exp(O(\sq

Ruikai Cui, Shi Qiu, Saeed Anwar, Jing Zhang

Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence. Differing from existing deterministic approaches, we advocate generative modeling based unsupervised point cloud completion to explore the missing correspondence. Specifically, we propose a novel framework th

Daniel Kongsgaard

This paper can be seen as an update to part of the author's dissertation. We study the mod $p$ cohomology of the pro-$p$ Iwahori subgroups $I$ of $\operatorname{SL}_{n}(\mathbb Q_{p})$ (and $\operatorname{GL}_{n}(\mathbb{Q}_{p})$) for $n=2$ and $n=3$. Here we use the spectral sequence $E_{1}^{s,t} = H^{s,t}(\mathfrak{g},\mathbb{F}_{p}) \Longrightarrow H^{s+t

Meghana Sistla, Swarat Chaudhuri, Thomas Reps

This paper presents a new compressed representation of Boolean functions, called CFLOBDDs (for Context-Free-Language Ordered Binary Decision Diagrams). They are essentially a plug-compatible alternative to BDDs (Binary Decision Diagrams), and hence useful for representing certain classes of functions, matrices, graphs, relations, etc. in a highly compressed

Akimitsu Kirikoshi, Satoru Hayami

We theoretically investigate an intrinsic nonlinear anomalous Hall effect (INAHE) in space-time ($\mathcal{PT}$) symmetric antiferromagnetic metals. The INAHE is characterized by an asymmetric and non-dissipative part of the second-order electric conductivity tensor in the clean limit in contrast to the Drude-type symmetric conductivity tensor with dissipati

Yan Luo, Yangcheng Gao, Zhao Zhang, Haijun Zhang

Quantization approximates a deep network model with floating-point numbers by the one with low bit width numbers, in order to accelerate inference and reduce computation. Quantizing a model without access to the original data, zero-shot quantization can be accomplished by fitting the real data distribution by data synthesis. However, zero-shot quantization a

N. K. Raut, J. Miller, H. Hart, R. Chiao

Levitation of a magnet by superconductor has been an active area of research to explore the quantum mechanical phenomenon. One of the techniques used is to measure the levitation of a magnet placed inside the superconducting microwave cavity. The levitation height can be probed by measuring the change in microwave frequency. Here, we report measurements of t

Nethra Venkatayogi, Qin Hu, Ozdemir Can Kara, Tarunraj G. Mohanraj

In this study, with the goal of reducing the early detection miss rate of colorectal cancer (CRC) polyps, we propose utilizing a novel hyper-sensitive vision-based tactile sensor called HySenSe and a complementary and novel machine learning (ML) architecture that explores the potentials of utilizing dilated convolutions, the beneficial features of the ResNet

Sven Brüggemann, Robert H. Moroto, Robert R. Bitmead

This work provides linear control-oriented state space models of gas flow through standard equipment, such as valves, compressors, manifolds and non-trivial pipe geometries. After presenting the catalog of components, we show how to interconnect them: we briefly recall the matrix methodology derived elsewhere, accompanied by a Matlab example for a gas loop;

Yuhang Yao, Mohammad Mahdi Kamani, Zhongwei Cheng, Lin Chen

Much of the value that IoT (Internet-of-Things) devices bring to ``smart'' homes lies in their ability to automatically trigger other devices' actions: for example, a smart camera triggering a smart lock to unlock a door. Manually setting up these rules for smart devices or applications, however, is time-consuming and inefficient. Rule recommendation systems

C. Domínguez, J. Fowlie, A. B. Georgescu, B. Mundet

In this work we present a model system built out of artificially layered materials, allowing us to understand the interrelation of magnetic phases with that of the metallic-insulating phase at long length-scales, and enabling new strategies for the design and control of materials in devices. The artificial model system consists of superlattices made of SmNiO

Warut Thawinrak

The stretched Littlewood-Richardson coefficient $c^{t\nu}_{t\lambda,t\mu}$ was conjectured by King, Tollu, and Toumazet to be a polynomial function in $t.$ It was shown to be true by Derksen and Weyman using semi-invariants of quivers. Later, Rassart used Steinberg's formula, the hive conditions, and the Kostant partition function to show a stronger result t

Bruno Hideki Fukushima-Kimura, Yoshinori Kamijima, Kazushi Kawamura, Akira Sakai

The topic we address in this paper concerns the minimization of a Hamiltonian function for an Ising model through the application of simulated annealing algorithms based on (single-site) Glauber dynamics and stochastic cellular automata (SCA). Some rigorous results are presented in order to justify the application of simulated annealing for a particular kind

Remy MacDonald, Benjamin Seiyon Lee

Nonstationary and non-Gaussian spatial data are common in various fields, including ecology (e.g., counts of animal species), epidemiology (e.g., disease incidence counts in susceptible regions), and environmental science (e.g., remotely-sensed satellite imagery). Due to modern data collection methods, the size of these datasets have grown considerably. Spat

Wanyun Cui, Xingran Chen

In this paper, we propose a new method for knowledge base completion (KBC): instance-based learning (IBL). For example, to answer (Jill Biden, lived city,? ), instead of going directly to Washington D.C., our goal is to find Joe Biden, who has the same lived city as Jill Biden. Through prototype entities, IBL provides interpretability. We develop theories fo

Pengcheng Zheng, Zhaoxiang Zhu, Xiangcan Pei, Qinfei Wu

The super-oscillation lens (SOL) can achieve super-resolution focusing but have to trade-off with weaker hotspots and higher sidebands. We propose a single compound SOL to achieve reflective confocal imaging in principle without additional lenses. The designed SOL consists of an outer lens and an inner lens which play the role of focusing lens and collective

Chenyang Zhong

In this paper, we introduce a novel and general method for computing partition functions of solvable lattice models with free fermionic Boltzmann weights. The method is based on the ``permutation graph'' and the ``$F$-matrix'': the permutation graph is a generalization of the $R$-matrix, and the $F$-matrix is constructed based on the permutation graph. The m

Jia-Yu Zhao

Platforms for all-optical computing possess photonic circuits made of silicon-based channels, metal wires or optical fibers, etc. However, such solid waveguides suffer from the lack of reversibility and reconfigurability if facing the next generation of light-guiding-light (LGL) computing scheme, which envisions circuitry-free and rapidly reconfigurable syst

Christoforos Neofytidis

Following Thurston's geometrisation picture in dimension three, we study geometric manifolds in a more general setting in arbitrary dimensions, with respect to the following problems: (i) The existence of maps of non-zero degree (domination relation or Gromov's order); (ii) The Gromov-Thurston monotonicity problem for numerical homotopy invariants with respe

Neil J. Y. Fan, Peter L. Guo, Rui Xiong

We develop Pieri type as well as Murnaghan--Nakayama type formulas for equivariant Chern--Schwartz--MacPherson classes of Schubert cells in the classical flag variety. These formulas include as special cases many previously known multiplication formulas for Chern--Schwartz--MacPherson classes or Schubert classes. We apply the equivariant Murnaghan--Nakayama

Zhaoliang Zheng, Thomas R. Bewley, Falko Kuester, Jiaqi Ma

This paper explores a rapid, optimal smooth path-planning algorithm for robots (e.g., autonomous vehicles) in point cloud environments. Derivative maps such as dense point clouds, mesh maps, Octomaps, etc. are frequently used for path planning purposes. A bi-directional target-oriented point planning algorithm, directly using point clouds to compute the opti

Stephanie Schoch, Haifeng Xu, Yangfeng Ji

Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label detection. In particular, due to the desirable axiomatic properties, several Shapley value approximation methods have been proposed. In these methods, the value function is typically

Punnag Padhy, Mohammad Asif Zaman, Michael Anthony Jensen, Yao-Te Cheng

Solid-phase synthesis techniques underpin the synthesis of DNA, oligopeptides, oligosaccharides, and combinatorial libraries for drug discovery. State-of-the-art solid-phase synthesizers can produce oligonucleotides up to 200-300 nucleotides while using excess reagents. Accumulated errors over multiple reaction cycles prevent the synthesis of longer oligonuc

Vasileios A. Letsios

In 1964, Lipkin discovered the zilches, a set of conserved quantities in free electromagnetism. Among the zilches, optical chirality was identified by Tang and Cohen in 2010, serving as a measure of the handedness of light and leading to investigations into light's interactions with chiral matter. While the symmetries underlying the conservation of the zilch

X. Li, X. Zhang, X. Yi, D. Liu

Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study me

Qi Zhang, Shanshe Wang, Xinfeng Zhang, Chuanmin Jia

Video Coding for Machines (VCM) aims to compress visual signals for machine analysis. However, existing methods only consider a few machines, neglecting the majority. Moreover, the machine's perceptual characteristics are not leveraged effectively, resulting in suboptimal compression efficiency. To overcome these limitations, this paper introduces Satisfied

Jiayu Zhao

In the world of epsilon-near-zero (ENZ) materials, the plasma is unique for its natural ENZ properties at the plasma frequency (wp). However, for the air plasma during femtosecond laser filamentation with wp in terahertz (THz) band, which is also known as a broadband THz emitter, the possible ENZ effect has long been neglected. In this work, interactions bet

Pete Rigas

Motivated by recent developments over the past few years in the study of the correlation length of the random-field Ising model due to Ding and Wirth in a paper first available in 2020, we pursue one natural direction of research that the authors propose is of interest, namely in confirming that the same scaling for the correlation length for the random-fiel

Pavel Svetlichnyy, Shivan Mittal, T. A. B. Kennedy

A uniform matrix product state defined on a tripartite system of spins, denoted by $ABC,$ is shown to be an approximate quantum Markov chain when the size of subsystem $B,$ denoted $|B|,$ is large enough. The quantum conditional mutual information (QCMI) is investigated and proved to be bounded by a function proportional to $\exp(-q(|B|-K)+2K\ln|B|)$, with $

Kaixin Wang, Cheng Long, Da Yan, Jie Zhang

As the popularity of graph data increases, there is a growing need to count the occurrences of subgraph patterns of interest, for a variety of applications. Many graphs are massive in scale and also fully dynamic (with insertions and deletions of edges), rendering exact computation of these counts to be infeasible. Common practice is, instead, to use a small

Boyuan Liu

The first step in drug discovery is finding drug molecule moieties with medicinal activity against specific targets. Therefore, it is crucial to investigate the interaction between drug-target proteins and small chemical molecules. However, traditional experimental methods for discovering potential small drug molecules are labor-intensive and time-consuming.

Zhigang Xu, Tianyou Wang, Zhizhao Che

The impact of droplets on a liquid pool is ubiquitous in nature and important in many industrial applications. A droplet impacting on a liquid pool can result in the pinch-off of a regular bubble or entrap a large bubble under certain impact conditions. In this study, the cavity deformation and the bubble entrapment during the impact of droplets on a liquid

Raphael A. Meyer, Cameron Musco, Christopher Musco, David P. Woodruff

We study $L_p$ polynomial regression. Given query access to a function $f:[-1,1] \rightarrow \mathbb{R}$, the goal is to find a degree $d$ polynomial $\hat{q}$ such that, for a given parameter $\varepsilon > 0$, $$ \|\hat{q}-f\|_p\le (1+\varepsilon) \cdot \min_{q:\text{deg}(q)\le d}\|q-f\|_p. $$ Here $\|\cdot\|_p$ is the $L_p$ norm, $\|g\|_p = (\int_{-1}^1 |

John Toner

I show that non-equilibrium two-dimensional interfaces between three dimensional phase separated fluids exhibit a peculiar "sub-logarithmic" roughness. Specifically, an interface of lateral extent $L$ will fluctuate vertically (i.e., normal to the mean surface orientation) a typical RMS distance $w\equiv\sqrt{\langle |h(\br,t)|^2\rangle} \propto [\ln{(L/a)}]

Liang Xiao, Jiaolong Xu, Dawei Zhao, Erke Shang

Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentation strategies can significantly improve the accuracy and robustness. However, due to the non-differentiable properties of image transformations, searching algorithms such as reinfo

Jiayun Liang, Zakaria Y. Al Balushi

Achieving control over the motion of dissolved particles in liquid metals is of importance for the meticulous realization of hierarchical particle assemblies in a variety of nanofabrication processes. Brownian forces can impede the motion of such particles, impacting the degree of perfection that can be realized in assembled structures. Here we show that lig

Paolo Conti, Giorgio Gobat, Stefania Fresca, Andrea Manzoni

Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, which might become prohibitive when approximating steady-state solutions of PDEs for multiple combinations of control parameters and initial conditions. Therefore, constructing effici

Shana Li, Xiang Xu, Canjun Li, Ziyan Xu

Mammalian brain is a complex organ that contains billions of neurons. These neurons form various neural circuits that control the perception, cognition, emotion and behavior. Developing in vivo neuronal labeling and imaging techniques is crucial for studying the structure and function of neural circuits. In vivo techniques can provide true physiological info

Atra Akandeh

We present two solutions to sentence-level SLR. Sentence-level SLR required mapping videos of sign language sentences to sequences of gloss labels. Connectionist Temporal Classification (CTC) has been used as the classifier level of both models. CTC is used to avoid pre-segmenting the sentences into individual words. The first model is an LRCN-based model, a

Hiromichi Takagi

Abstract. In our previous paper arXiv:2210.16008, we show that any prime $\mathbb{Q}$-Fano 3-folds $X$ with only $1/2(1,1,1)$-singularities in certain 5 classes can be embedded as linear sections into bigger dimensional $\mathbb{Q}$-Fano varieties called key varieties, where each of the key varieties is constructed from certain data of the Sarkisov link star

Abhijit Suprem, Sanjyot Vaidya, Avinash Venugopal, Joao Eduardo Ferreira

Machine Learning has become the bedrock of recent advances in text, image, video, and audio processing and generation. Most production systems deal with several models during deployment and training, each with a variety of tuned hyperparameters. Furthermore, data collection and processing aspects of ML pipelines are receiving increasing interest due to their

Thilina Ranbaduge, Ming Ding

A successful machine learning (ML) algorithm often relies on a large amount of high-quality data to train well-performed models. Supervised learning approaches, such as deep learning techniques, generate high-quality ML functions for real-life applications, however with large costs and human efforts to label training data. Recent advancements in federated le

Jun Li, Yanzeng Zhang, Xian-Zhu Tang

In tokamak disruptions where the magnetic connection length becomes comparable to or even shorter than the plasma mean-free-path, parallel transport can dominate the energy loss and the thermal quench of the core plasma goes through four phases (stages) that have distinct temperature ranges and durations. The main temperature drop occurs while the core plasm

Ashutosh Singh, Ashish Singh, Aria Masoomi, Tales Imbiriba

Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well. These methods are extensively used for data-exploration tasks in various areas of Natural Sciences. However, most of these methods fail to handle unwanted biases in datasets. For datasets where a data sample represents multiple attributes, naively

Christopher Janjigian, Firas Rassoul-Agha, Timo Seppäläinen

The Kardar-Parisi-Zhang (KPZ) equation on the real line is well-known to admit Brownian motion with a linear drift as a stationary distribution (modulo additive constants). We show that these solutions are attractive, a result known as a one force--one solution (1F1S) principle or synchronization: the solution to the KPZ equation started in the distant past

Qiuhao Lu, Dejing Dou, Thien Huu Nguyen

Deep learning models have demonstrated superior performance in various healthcare applications. However, the major limitation of these deep models is usually the lack of high-quality training data due to the private and sensitive nature of this field. In this study, we propose a novel textual data augmentation method to generate artificial clinical notes in

Fengyue Guo, Zhongding Cheng, Xiaoming Kong, Yatao Zhang

Barium stars are chemically peculiar stars that exhibit enhancement of s-process elements. Chemical abundance analysis of barium stars can provide crucial clues for the study of the chemical evolution of the Galaxy. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has released more than 6 million low-resolution spectra of FGK-type stars

Nat Gopalswamy

The explosion of space weather research since the early 1990s has been partly fueled by the unprecedented, uniform, and extended observations of solar disturbances from space and ground based instruments. Coronal mass ejections (CMEs) from closed magnetic field regions and high speed streams (HSS) from open field regions on the Sun account for most of the di

Taehoon Kim, Mark Marsden, Pyunghwan Ahn, Sangyun Kim

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enable

Razieh Emami, Paul Tiede, Sheperd S. Doeleman, Freek Roelofs

We propose to trace the dynamical motion of a shearing hot spot near the SgrA* source through a dynamical image reconstruction algorithm, StarWarps. Such a hot spot may form as the exhaust of magnetic reconnection in a current sheet near the black hole horizon. A hot spot that is ejected from the current sheet into an orbit in the accretion disk may shear an

M. G. Naber

The two-dimensional hydrogen-like atom in a constant magnetic field is considered. It is found that this is actually two separate problems. One for which the magnetic field causes an effective attraction between the nucleus and the electron and one for which it causes an effective repulsion. Each of the two problems has three separate cases depending on the

K. Pruett, W. Dawson, M. S. Medford, C. Lam

Primordial black holes (PBHs), theorized to have originated in the early universe, are speculated to be a viable form of dark matter. If they exist, they should be detectable through photometric and astrometric signals resulting from gravitational microlensing of stars in the Milky Way. Population Synthesis for Compact-object Lensing Events, or PopSyCLE, is

Donato Scarcella

We consider the planar three-body problem perturbed by a celestial body modeled as a time-dependent perturbation that decays in time. We assume that the motion of the celestial body is given and is unbounded with a non-zero asymptotic velocity. We prove the existence of orbits converging in time to some motions that are ``close'' to the quasiperiodic solutio

Tixian Wang, Udit Halder, Ekaterina Gribkova, Mattia Gazzola

The octopus arm is a neuromechanical system that involves a complex interplay between peripheral nervous system (PNS) and arm musculature. This makes the arm capable of carrying out rich maneuvers. In this paper, we build a model for the PNS and integrate it with a muscular soft octopus arm. The proposed neuromuscular architecture is used to qualitatively re

Shengjie Xu, Kevin Mok

This paper uses a simple state machine to develop a control algorithm for controlling an infant humanoid in the context of a simple model system. The algorithm is inspired by a baby who starts learning to stand and walk at 7 to 12 months of age: he or she initially learns to crawl and then, once the lower limb muscles are strong enough, can learn to walk by

M. B. Alves, O. M. Del Cima, D. H. T. Franco, E. A. Pereira

This paper is devoted to the study of the spectral properties of the Weyl-Dirac or massless Dirac operators, describing the behavior of quantum quasi-particles in dimension 2 in a homogeneous magnetic field, $B^{\rm ext}$, perturbed by a chiral-magnetic field, $b^{\rm ind}$, with decay at infinity and a short-range scalar electric potential, $V$, of the Bess

Alexander Hustinx, Fabio Hellmann, Ömer Sümer, Behnam Javanmardi

Rare genetic disorders affect more than 6% of the global population. Reaching a diagnosis is challenging because rare disorders are very diverse. Many disorders have recognizable facial features that are hints for clinicians to diagnose patients. Previous work, such as GestaltMatcher, utilized representation vectors produced by a DCNN similar to AlexNet to m

Georgios Papas

Following our work in \cite{papas2022height}, we extend the height bounds established by Y. Andr\'e in his seminal research monograph \cite{andre1989g} for $1$-parameter families of abelian varieties defined over number fields. In our exposition we no longer assume that the family acquires completely multiplicative reduction at some point, as in Andr\'e's or

Yueqian Liu, Fengyu Quan, Haoyao Chen

Omnidirectional micro aerial vehicles (OMAVs) are more capable of doing environmentally interactive tasks due to their ability to exert full wrenches while maintaining stable poses. However, OMAVs often incorporate additional actuators and complex mechanical structures to achieve omnidirectionality. Obtaining precise mathematical models is difficult, and the

Saad Bin Ahmed, Umaid M. Zaffar, Marium Aslam, Muhammad Imran Malik

In machine learning applications, it is common practice to feed as much information as possible. In most cases, the model can handle large data sets that allow to predict more accurately. In the presence of data scarcity, a Few-Shot learning (FSL) approach aims to build more accurate algorithms with limited training data. We propose a novel end-to-end lightw

Sayak Sengupta

For a polynomial $u(x)$ in $\mathbb{Z}[x]$ and $r\in\mathbb{Z}$, we consider the orbit of $u(x)$ at $r$, $\mathcal{O}_u(r):=\{u(r),u(u(r)),\ldots\}$. We ask two questions here: (i) what are the polynomials $u$ for which $0\in \mathcal{O}_u(r)$ and (ii) what are the polynomials for which $0\not\in \mathcal{O}_u(r)$ but, modulo every prime $p$, $0\in \mathcal{

Nicole Hayes, Ekaterina Merkurjev, Guo-Wei Wei

In molecular and biological sciences, experiments are expensive, time-consuming, and often subject to ethical constraints. Consequently, one often faces the challenging task of predicting desirable properties from small data sets or scarcely-labeled data sets. Although transfer learning can be advantageous, it requires the existence of a related large data s