October 2022 arXiv papers — page 97

Ling An, Liming Ling, Xiaoen Zhang

In this paper, based on the nonlinear fractional equations proposed by Ablowitz, Been, and Carr in the sense of Riesz fractional derivative, we explore the fractional coupled Hirota equation and give its explicit form. Unlike the previous nonlinear fractional equations, this type of nonlinear fractional equation is integrable. Therefore, we obtain the fracti

Tianxiao Li, Hongyu Guo, Filippo Grazioli, Mark Gerstein

In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally non-trivial, and requires

Lingkun Luo, Liming Chen, Shiqiang Hu

In leveraging manifold learning in domain adaptation (DA), graph embedding-based DA methods have shown their effectiveness in preserving data manifold through the Laplace graph. However, current graph embedding DA methods suffer from two issues: 1). they are only concerned with preservation of the underlying data structures in the embedding and ignore sub-do

Shanshan Wang, Zhen Zeng, Xun Yang, Xingyi Zhang

Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts. Despite the effectiveness of existing efforts, previous methods always considered the mastery level on the whole students, so they still suffer from the Long

Tariq M. Khan, Muhammad Arsalan, Antonio Robles-Kelly, Erik Meijering

Image segmentation is an important task in medical imaging. It constitutes the backbone of a wide variety of clinical diagnostic methods, treatments, and computer-aided surgeries. In this paper, we propose a multi-kernel image segmentation net (MKIS-Net), which uses multiple kernels to create an efficient receptive field and enhance segmentation performance.

Peng-Fei Zhou, Ying Lu, Jia-Hao Wang, Shi-Ju Ran

Efficient methods to access the entanglement of a quantum many-body state, where the complexity generally scales exponentially with the system size $N$, have long a concern. Here we propose the Schmidt tensor network state (Schmidt TNS) that efficiently represents the Schmidt decomposition of finite- and even infinite-size quantum states with nontrivial bipa

Zixian Li, Wenjie Liu

In this paper, we present a secure multiparty computation (SMC) protocol for least common multiple (LCM) based on Shor's quantum period-finding algorithm (QPA). Our protocol is based on the following principle: the connection of multiple periodic functions is also a periodic function whose period is exactly the least common multiple of all small periods. Sin

Kaiyue Lu, Zexiang Liu, Jianyuan Wang, Weixuan Sun

Vision Transformers have achieved impressive performance in video classification, while suffering from the quadratic complexity caused by the Softmax attention mechanism. Some studies alleviate the computational costs by reducing the number of tokens in attention calculation, but the complexity is still quadratic. Another promising way is to replace Softmax

Cheng Cheng, Jia-Sheng Huang, Ian Smail, Haojing Yan

We report the results of James Webb Space Telescope/NIRCam observations of 19 (sub)millimeter (submm/mm) sources detected by the Atacama Large Millimeter Array (ALMA). The accurate ALMA positions allowed unambiguous identifications of their NIRCam counterparts. Taking gravitational lensing into account, these represent 16 distinct galaxies in three fields an

Ziqing Wang, Zhirong Ye, Yuyang Du, Yi Mao

DBSCAN has been widely used in density-based clustering algorithms. However, with the increasing demand for Multi-density clustering, previous traditional DSBCAN can not have good clustering results on Multi-density datasets. In order to address this problem, an adaptive Multi-density DBSCAN algorithm (AMD-DBSCAN) is proposed in this paper. An improved param

Hao Feng, Wengang Zhou, Jiajun Deng, Yuechen Wang

In document image rectification, there exist rich geometric constraints between the distorted image and the ground truth one. However, such geometric constraints are largely ignored in existing advanced solutions, which limits the rectification performance. To this end, we present DocGeoNet for document image rectification by introducing explicit geometric r

Xiaoming Li, Shiguang Zhang, Shangchen Zhou, Lei Zhang

To improve the performance of blind face restoration, recent works mainly treat the two aspects, i.e., generic and specific restoration, separately. In particular, generic restoration attempts to restore the results through general facial structure prior, while on the one hand, cannot generalize to real-world degraded observations due to the limited capabili

An Tao, Yueqi Duan, Yingqi Wang, Jiwen Lu

In this paper, we investigate the dynamics-aware adversarial attack problem of adaptive neural networks. Most existing adversarial attack algorithms are designed under a basic assumption -- the network architecture is fixed throughout the attack process. However, this assumption does not hold for many recently proposed adaptive neural networks, which adaptiv

Ruchen Wen, Tom Williams

Language-capable robots hold unique persuasive power over humans, and thus can help regulate people's behavior and preserve a better moral ecosystem, by rejecting unethical commands and calling out norm violations. However, miscalibrated norm violation responses (when the harshness of a response does not match the actual norm violation severity) may not only

Chunmao Huang, Yukun Ren, Runze Li

We consider a branching random walk on $d$-dimensional real space with immigration in a time-dependent random environment. Let $Z_n(\mathbf t)$ be the so-called partition function of the process, namely, the moment generating function of the counting measure describing the dispersion of individuals at time $n$. For $\mathbf t$ fixed, the logarithm $\log Z_n(

Dun Zhou

The current paper is devoted to the investigation of the influence of nested invariant cone structure on the dynamics, in the context of non-autonomous (time almost periodic)cases. We first prove that the nested invariant cone structure can persistent under C1 perturbations; and the dynamics of the omega-limit set of any precompact orbit can be reduced to th

Guillem Cobos, Brendan Guilfoyle

Asgeirsson's theorem establishes a mean value property for solutions of the ultrahyperbolic equation. In the case of four variables, it states that the integrals of a solution over certain pairs of conjugate circles are the same. In this paper, the invariance of the four dimensional ultrahyperbolic equation under conformal maps of the pseudo-Euclidean space

Jong-Wan Lee, Ed Bennett, Deog Ki Hong, Ho Hsiao

We perform numerical calculations of masses and decay constants of the lightest (flavoured) pseudoscalar, vector and axial vector mesons in the $Sp(4)$ lattice gauge theory with three Dirac fermions in the antisymmetric representation. The corresponding continuum theory plays an important role in certain ultra-violet complete realisations of composite Higgs,

Jin Zhang, Siyuan Li, Chongjie Zhang

The ability to reuse previous policies is an important aspect of human intelligence. To achieve efficient policy reuse, a Deep Reinforcement Learning (DRL) agent needs to decide when to reuse and which source policies to reuse. Previous methods solve this problem by introducing extra components to the underlying algorithm, such as hierarchical high-level pol

Alexis Coissard, Adolfo G. Grushin, Cécile Repellin, Louis Veyrat

Electronic edge states in topological insulators have become a major paradigm in physics. The oldest and primary example is that of quantum Hall (QH) edge channels that propagate along the periphery of two-dimensional electron gases (2DEGs) under perpendicular magnetic field. Yet, despite 40 years of intensive studies using a variety of transport and scannin

Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Amina Salahuddin

The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the predictive performance of the model, or produce explanations that are not transparent, trustworthy or do not capture the diversity of the data. To address these shortcomings, we p

Sophie MacDonald

We prove a generalization of Krieger's embedding theorem, in the spirit of zero-error information theory. Specifically, given a mixing shift of finite type $X$, a mixing sofic shift $Y$, and a surjective sliding block code $\pi: X \to Y$, we give necessary and sufficient conditions for a subshift $Z$ of topological entropy strictly lower than that of $Y$ to

Jian Yan, Zhuoxi Li, Xianyang Zhang

Testing the equality of two conditional distributions is crucial in various modern applications, including transfer learning and causal inference. Despite its importance, this fundamental problem has received surprisingly little attention in the literature, with existing works focusing exclusively on global two-sample conditional distribution testing. Based

Ilya Archakov, Peter Reinhard Hansen, Yiyao Luo

We propose a new method for generating random correlation matrices that makes it simple to control both location and dispersion. The method is based on a vector parameterization, gamma = g(C), which maps any distribution on R^d, d = n(n-1)/2 to a distribution on the space of non-singular nxn correlation matrices. Correlation matrices with certain properties,

Xiao-Xiao Man, Pei-Han Sun, Jian-Feng Zhang, Zhong-Yi Lu

Recent experiments have reported that inserting metal atoms or small molecules in between the FeSe layers of $\beta$-FeSe can significantly enhance the superconducting transition temperature. Here, based on first-principles electronic structure calculations, we propose a stable compound SnOFeSe by alternatively stacking the SnO and $\beta$-FeSe layers. The p

Nikita Salkar, Thomas Trikalinos, Byron C. Wallace, Ani Nenkova

We provide a quantitative and qualitative analysis of self-repetition in the output of neural summarizers. We measure self-repetition as the number of n-grams of length four or longer that appear in multiple outputs of the same system. We analyze the behavior of three popular architectures (BART, T5, and Pegasus), fine-tuned on five datasets. In a regression

Jacob Haqq-Misra, Thomas J. Fauchez

An expanding civilization could rapidly spread through the galaxy, so the absence of extraterrestrial settlement in the solar system implies that such expansionist civilizations do not exist. This argument, often referred to as the Fermi paradox, typically assumes that expansion would proceed uniformly through the galaxy, but not all stellar types may be equ

L. C. Vestal, Z. E. Musielak

Among different Lagrangians, null Lagrangians are known for having identically zero the Euler-Lagrange equation and, therefore, they have no effects on the resulting equations of motion. However, there is a special family of null Lagrangians that can be used to convert linear and undriven equations of motion into nonlinear and driven ones. To identify this s

Eddy Keming Chen

Physical laws are strikingly simple, yet there is no a priori reason for them to be so. I propose that nomic realists -- Humeans and non-Humeans -- should recognize simplicity as a fundamental epistemic guide for discovering and evaluating candidate physical laws. This proposal helps resolve several longstanding problems of nomic realism and simplicity. A ke

Vivswan Shah, Nathan Youngblood

AnalogVNN, a simulation framework built on PyTorch which can simulate the effects of optoelectronic noise, limited precision, and signal normalization present in photonic neural network accelerators. We use this framework to train and optimize linear and convolutional neural networks with up to 9 layers and ~1.7 million parameters, while gaining insights int

Nicholas A. Nobile, John R. Erickson, Carlos Ríos, Yifei Zhang

Optical phase-change materials are highly promising for emerging applications such as tunable metasurfaces, reconfigurable photonic circuits, and non-von Neumann computing. However, these materials typically require both high melting temperatures and fast quenching rates to reversibly switch between their crystalline and amorphous phases, a significant chall

Xiaoming Zhai, Joseph Krajcik

Pseudo Artificial Intelligence bias (PAIB) is broadly disseminated in the literature, which can result in unnecessary AI fear in society, exacerbate the enduring inequities and disparities in access to and sharing the benefits of AI applications, and waste social capital invested in AI research. This study systematically reviews publications in the literatur

Da Long, Nicole Mrvaljevic, Shandian Zhe, Bamdad Hosseini

This article presents a three-step framework for learning and solving partial differential equations (PDEs) using kernel methods. Given a training set consisting of pairs of noisy PDE solutions and source/boundary terms on a mesh, kernel smoothing is utilized to denoise the data and approximate derivatives of the solution. This information is then used in a

Jack C. Straton

The central impediment to reducing multidimensional integrals of transition amplitudes to analytic form, or at least to a fewer number of integral dimensions, is the presence of magnitudes of coordinate vector differences (square roots of polynomials) $|{\bf x}_{1}-{\bf x}_{2}|^{2}=\sqrt{x_{1}^{2}-2x_{1}x_{2}\cos\theta+x_{2}^{2}}$ in disjoint products of fun

Vahid Balazadeh, Vasilis Syrgkanis, Rahul G. Krishnan

We consider the problem of partial identification, the estimation of bounds on the treatment effects from observational data. Although studied using discrete treatment variables or in specific causal graphs (e.g., instrumental variables), partial identification has been recently explored using tools from deep generative modeling. We propose a new method for

Felipe Hernández

In this paper we prove an inequality inspired by a conjecture of Brezis, which asks for a bound for the topological degree of a map from the circle to itself in terms of a nonlocal integral.

Ahalya Prabhakar, Aude Billard

As assistive and collaborative robots become more ubiquitous in the real-world, we need to develop interfaces and controllers that are safe for users to build trust and encourage adoption. In this Blue Sky paper, we discuss the need for co-evolving task and user-specific safety controllers that can accommodate people's safety preferences. We argue that while

Jiang Zhang, Hadi Askari, Konstantinos Psounis, Zubair Shafiq

Online content platforms optimize engagement by providing personalized recommendations to their users. These recommendation systems track and profile users to predict relevant content a user is likely interested in. While the personalized recommendations provide utility to users, the tracking and profiling that enables them poses a privacy issue because the

Maurizio Parton, Marco Angelone, Carlo Metta, Stefania D'Ovidio

This paper aims to investigate the possibility of using artificial intelligence (AI) to resolve the legal issues raised by the Covid-19 emergency about the fate of continuing execution contracts, or those with deferred or periodic execution, as well as, more generally, to deal with exceptional events and contingencies. We first study whether the Italian lega

Gayane Vardoyan, Stephanie Wehner

Network Utility Maximization (NUM) is a mathematical framework that has endowed researchers with powerful methods for designing and analyzing classical communication protocols. NUM has also enabled the development of distributed algorithms for solving the resource allocation problem, while at the same time providing certain guarantees, e.g., that of fair tre

Björn J. R. Davidsson, F. Peter Schloerb, Sonia Fornasier, Nilda Oklay

Between 2014 December 31 and 2015 March 17, the OSIRIS cameras on Rosetta documented the growth of a 140m wide and 0.5m deep depression in the Hapi region on Comet 67P/Churyumov-Gerasimenko. This shallow pit is one of several that later formed elsewhere on the comet, all in smooth terrain that primarily is the result of airfall of coma particles. We have com

Youssef Aserrar, Elhoucien Elqorachi

Our main result is that we describe the solutions $g,f:S\rightarrow\mathbb{C}$ of the functional equation \[g(x\sigma(y))=g(x)g(y)-f(x)f(y)+\alpha f(x\sigma(y)),\quad x,y\in S,\] where $S$ is a semigroup, $\alpha \in \mathbb{C}$ is a fixed constant and $\sigma :S\rightarrow S$ an involutive automorphism.

Weili Wang, Omid Abbasi, Halim Yanikomeroglu, Chengchao Liang

Vertical heterogenous networks (VHetNets) and artificial intelligence (AI) play critical roles in 6G and beyond networks. This article presents an AI-native VHetNets architecture to enable the synergy of VHetNets and AI, thereby supporting varieties of AI services while facilitating automatic and intelligent network management. Anomaly detection in Internet

Fan Lu, Joel Mathias, Sean Meyn, Karanjit Kalsi

Convex Q-learning is a recent approach to reinforcement learning, motivated by the possibility of a firmer theory for convergence, and the possibility of making use of greater a priori knowledge regarding policy or value function structure. This paper explores algorithm design in the continuous time domain, with finite-horizon optimal control objective. The

Jenya Sapir

We study the geometry of the space of projectivized filling geodesic currents $\mathbb P \mathcal C_{fill}(S)$. Bonahon showed that Teichm\"uller space, $\mathcal T(S)$ embeds into $\mathbb P \mathcal C_{fill}(S)$. We extend the symmetrized Thurston metric from $\mathcal T(S)$ to the entire (projectivized) space of filling currents, and we show that $\mathca

Shubhanshu Mishra, Aman Saini, Raheleh Makki, Sneha Mehta

Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications. We introduce TweetNERD, a dataset of 340K+ Tweets across 2010-2021, for benchmarking NERD systems on Tweets. This is the largest and most temporally diverse open

Santiago Quintero, Carlos Pinzón, Sergio Ramírez, Frank Valencia

Let $L$ be a finite lattice and $\mathcal{E}(L)$ be the set of join endomorphisms of $L$. We consider the problem of given $L$ and $f,g \in \mathcal{E}(L)$, finding the greatest lower bound $f \sqcap_{{\scriptsize \mathcal{E}(L)}} g$ in the lattice $\mathcal{E}(L)$. (1) We show that if $L$ is distributive, the problem can be solved in time $O(n)$ where $n=|

Emma Dauterman, Vivian Fang, Natacha Crooks, Raluca Ada Popa

Many systems today distribute trust across multiple parties such that the system provides certain security properties if a subset of the parties are honest. In the past few years, we have seen an explosion of academic and industrial cryptographic systems built on distributed trust, including secure multi-party computation applications (e.g., private analytic

Naseem Alhousani, Matteo Saveriano, Ibrahim Sevinc, Talha Abdulkuddus

Reinforcement learning (RL) is a popular technique that allows an agent to learn by trial and error while interacting with a dynamic environment. The traditional Reinforcement Learning (RL) approach has been successful in learning and predicting Euclidean robotic manipulation skills such as positions, velocities, and forces. However, in robotics, it is commo

Ali Taani

Recently, extra motivation has been given to the investigations of an unresolved problem of millisecond pulsars (MSPs) produced by the recycling process, as an apparent role of the accretion-induced collapse (AIC) in white dwarfs (WDs) was suggested to this concern. I have found that the distribution of the orbital periods of binary MSPs in the Galactic disk

Tobias Serwatka, Roger G. Melko, Anton Burkov, Pierre-Nicholas Roy

The concept of quantum phase transitions (QPT) plays a central role in the description of condensed matter systems. In this contribution, we perform high-quality wavefunction-based simulations to demonstrate the existence of a quantum phase transition in a crucially relevant molecular system, namely water, forming linear chains of rotating molecules. We dete

Yangzheng Wu, Alireza Javaheri, Mohsen Zand, Michael Greenspan

We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure unordered point cloud geometry as input without RGB information. The proposed cascaded keypoint voting method, called RCVPose3D, is based upon a novel architecture which separates the task of semantic segmentation from that of keypoint regression, thereby increasing the e

Ajay Jaiswal, Peihao Wang, Tianlong Chen, Justin F. Rousseau

Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-structured data, most of the current GCNs are shallow due to the notoriously challenging problems of over-smoothening and information squashing along with conventional difficulty caused by vanishing gradients and over-fitting. Previous works have been primarily focused on t

Saad Yalouz, Vincent Pouthier

We study the quantum dynamics of a photo-excitation uniformly distributed at the periphery of an extended star network (with $N_B$ branches of length $L_B$). More specifically, we address here the question of the energy absorption at the core of the network and how this process can be improved (or not) by the inclusion of peripheral defects with a tunable en

Alex Zihao Zhu, Vincent Casser, Reza Mahjourian, Henrik Kretzschmar

Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance segmentation that jointly leverages measurements from multiple sensor modalities, such as cameras and LiDAR. Our method learns to predict embeddings for each pixel or point that give

Siavash Monfared, Guruswami Ravichandran, Jose E. Andrade, Amin Doostmohammadi

Using three-dimensional representation of confluent cell layers, we map the amorphous solid to fluid phase transition in active cell layers onto the two-dimensional (2D) site percolation universality class. Importantly, we unify two distinct, predominant, pathways associated with this transition; namely (i) cell-cell adhesion and (ii) active traction forces.

Yu-Ming Chen, Gabriel Nelson, Robert Griffin, Michael Posa

Complex multibody legged robots can have complex rotational control challenges. In this paper, we propose a concise way to understand and formulate a \emph{whole-body orientation} that (i) depends on system configuration only and not a history of motion, (ii) can be representative of the orientation of the entire system while not being attached to any specif

Zhaoyue Chen, Yifan Sun

The Frank-Wolfe algorithm has regained much interest in its use in structurally constrained machine learning applications. However, one major limitation of the Frank-Wolfe algorithm is the slow local convergence property due to the zig-zagging behavior. We observe the zig-zagging phenomenon in the Frank-Wolfe method as an artifact of discretization, and prop

Carlo Cannarozzo, Alexie Leauthaud, Grecco A. Oyarzún, Carlo Nipoti

We compare stellar mass surface density, metallicity, age, and line-of-sight velocity dispersion profiles in massive ($M_*\geq10^{10.5}\,\mathrm{M_\odot}$) present-day early-type galaxies (ETGs) from the MaNGA survey with simulated galaxies from the TNG100 simulation of the IllustrisTNG suite. We find an excellent agreement between the stellar mass surface d

M. E. Carrington, A. R. Frey, B. A. Meggison

We study the semi-metal/insulator phase transition in graphene using a Schwinger-Dyson approach. We consider various forms of vertex ansaetze to truncate the hierarchy of Schwinger-Dyson equations. We define a Ball-Chiu type vertex that truncates the equations without violating gauge invariance. We show that there is a family of these vertices, parametrized

Shamak Dutta, Nils Wilde, Pratap Tokekar, Stephen L. Smith

We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the resources used (samples or tour length) while guaranteeing estimation accuracy. We give approximation algorithms for both problems in convex environments. These improve previously

Tom Overman, Garrett Blum, Diego Klabjan

Very few methods for hybrid federated learning, where clients only hold subsets of both features and samples, exist. Yet, this scenario is extremely important in practical settings. We provide a fast, robust algorithm for hybrid federated learning that hinges on Fenchel Duality. We prove the convergence of the algorithm to the same solution as if the model i

Gilberto Medeiros Kremer

In this work the influence of the post-Newtonian corrections to the equations of stellar structure is analysed. The post-Newtonian Lane-Emden equation follows from the corresponding momentum density balance equation. From a polytropic equation of state the solutions of the Lane-Endem equations in the Newtonian and post-Newtonian theories are determined and t

Arsalan Motamedi, Pooya Ronagh

Gibbs sampling from continuous real-valued functions is a challenging problem of interest in machine learning. Here we leverage quantum Fourier transforms to build a quantum algorithm for this task when the function is periodic. We use the quantum algorithms for solving linear ordinary differential equations to solve the Fokker--Planck equation and prepare a

Azzam Habib

Jaberi [7] presented approximation algorithms for the problem of computing a minimum size 2-vertex strongly biconnected subgraph in directed graphs. We have implemented approximation algorithms presented in [7] and we have tested the implementation on some graphs. The experimental results show that these algorithms work well in practice.

Swapna Thorve, Young Yun Baek, Samarth Swarup, Henning Mortveit

Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze the impacts of weather, electricity prices, electric vehicles, solar, and occupancy

Alex Szorkovszky, Frank Veenstra, Kyrre Glette

For a robot to be both autonomous and collaborative requires the ability to adapt its movement to a variety of external stimuli, whether these come from humans or other robots. Typically, legged robots have oscillation periods explicitly defined as a control parameter, limiting the adaptability of walking gaits. Here we demonstrate a virtual quadruped robot

Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos, Nicu Sebe

Deep learning has achieved state-of-the-art performance on several computer vision tasks and domains. Nevertheless, it still has a high computational cost and demands a significant amount of parameters. Such requirements hinder the use in resource-limited environments and demand both software and hardware optimization. Another limitation is that deep models

Anne Fabricant, Irina Novikova, Georg Bison

This article is designed as a step-by-step guide to optically pumped magnetometers based on alkali atomic vapor cells. We begin with a general introduction to atomic magneto-optical response, as well as expected magnetometer performance merits and how they are affected by main sources of noise. This is followed by a brief comparison of different magnetometer

Martin G. Gonzalez, Matias Vera, Leonardo Rey Vega

In this paper we consider the problem of image reconstruction in optoacoustic tomography. In particular, we devise a deep neural architecture that can explicitly take into account the band-frequency information contained in the sinogram. This is accomplished by two means. First, we jointly use a linear filtered back-projection method and a fully dense UNet f

Sarah M. Harrison, Alexander Maloney, Tokiro Numasawa

Liouville theory describes the dynamics of surfaces with constant negative curvature and can be used to study the Weil-Petersson geometry of the moduli space of Riemann surfaces. This leads to an efficient algorithm to compute the Weil--Petersson metric to arbitrary accuracy using Zamolodchikov's recursion relation for conformal blocks. For example, we compu

Guanqun Yang, Mirazul Haque, Qiaochu Song, Wei Yang

The recently proposed capability-based NLP testing allows model developers to test the functional capabilities of NLP models, revealing functional failures that cannot be detected by the traditional heldout mechanism. However, existing work on capability-based testing requires extensive manual efforts and domain expertise in creating the test cases. In this

Ksheera Sagar, Yang Ni, Veerabhadran Baladandayuthapani, Anindya Bhadra

We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in the sense that they depend on individual-specific covariates. The individualized DAG structure of the proposed approach can be uniquely identified at any given quantile, based on purely observational data wi

Luning Sun, Daniel Zhengyu Huang, Hao Sun, Jian-Xun Wang

Nonlinear dynamics are ubiquitous in science and engineering applications, but the physics of most complex systems is far from being fully understood. Discovering interpretable governing equations from measurement data can help us understand and predict the behavior of complex dynamic systems. Although extensive work has recently been done in this field, rob

Ian P. Roberts, Himal A. Suraweera

Wireless communication systems can be enhanced at the link level, in medium access, and at the network level when transceivers are equipped with full-duplex capability: the transformative ability to simultaneously transmit and receive over the same frequency spectrum. Effective methods to cancel self-interference are required to facilitate full-duplex operat

Ian P. Roberts, Aditya Chopra, Thomas Novlan, Sriram Vishwanath

Characterizing self-interference is essential to the design and evaluation of in-band full-duplex communication systems. Until now, little has been understood about this coupling in full-duplex systems operating at millimeter wave (mmWave) frequencies, and it has been shown that the highly-idealized models proposed for such do not align with practice. This w

Adam Polevoy, Marin Kobilarov, Joseph Moore

Approaches for stochastic nonlinear model predictive control (SNMPC) typically make restrictive assumptions about the system dynamics and rely on approximations to characterize the evolution of the underlying uncertainty distributions. For this reason, they are often unable to capture more complex distributions (e.g., non-Gaussian or multi-modal) and cannot

William T. Ross

This survey paper explores various aspects of the Cesaro operator and how it relates to various areas of functional analysis and linear algebra.

John Nguyen, Jianyu Wang, Kshitiz Malik, Maziar Sanjabi

An oft-cited challenge of federated learning is the presence of heterogeneity. \emph{Data heterogeneity} refers to the fact that data from different clients may follow very different distributions. \emph{System heterogeneity} refers to the fact that client devices have different system capabilities. A considerable number of federated optimization methods add

Mathias Garny, Dominik Laxhuber, Roman Scoccimarro

We present non-linear solutions of Vlasov Perturbation Theory (VPT), describing gravitational clustering of collisionless dark matter with dispersion and higher cumulants induced by orbit crossing. We show that VPT can be cast into a form that is formally analogous to standard perturbation theory (SPT), but including additional perturbation variables, non-li

Mathias Garny, Dominik Laxhuber, Roman Scoccimarro

The standard perturbation theory (SPT) approach to gravitational clustering is based on a fluid approximation of the underlying Vlasov-Poisson dynamics, taking only the zeroth and first cumulant of the phase-space distribution function into account (density and velocity fields). This assumption breaks down when dark matter particle orbits cross and leads to

Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Andreas Krause, Ilija Bogunovic

Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-making given side information, with important applications, e.g., in wind energy systems. In this setting, the learner receives context (e.g., weather conditions) at each round, and has to choose an action (e.g., turbine parameters). Standard algorithms assume no cost for

Md. Shakib Khan, Kazi Nabiul Alam, Abdur Rab Dhruba, Hasib Zunair

Melanoma is regarded as the most threatening among all skin cancers. There is a pressing need to build systems which can aid in the early detection of melanoma and enable timely treatment to patients. Recent methods are geared towards machine learning based systems where the task is posed as image recognition, tag dermoscopic images of skin lesions as melano

Nathan J. Wispinski, Andrew Butcher, Kory W. Mathewson, Craig S. Chapman

Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimization problem is understudied in artificial intelligence research. Patch foraging is especially amenable to study given that it has a known optimal solution, which may be difficult

Maged Iskandar, Christiaan van Ommeren, Xuwei Wu, Alin Albu-Schaffer

Modern Lightweight robots are constructed to be collaborative, which often results in a low structural stiffness compared to conventional rigid robots. Therefore, the controller must be able to handle the dynamic oscillatory effect mainly due to the intrinsic joint elasticity. Singular perturbation theory makes it possible to decompose the flexible joint dyn

Sudarshan Devkota, Summanta Pattanaik

The benefits of medical imaging are enormous. Medical images provide considerable amounts of anatomical information and this facilitates medical practitioners in performing effective disease diagnosis and deciding upon the best course of medical treatment. A transition from traditional monochromatic medical images like CT scans, X-Rays or MRI images to a col

Cary Malkiewich

We solve the higher version of Hilbert's Third Problem for one-dimensional geometries, and in higher dimensions we reduce the problem to a computation in group homology. Our central result concerns the scissors congruence $K$-theory spectrum of Zakharevich, whose homotopy groups are the correct higher version of the classical scissors congruence groups. We p

Aldo Figallo-Orellano, Juan Sebastian Slagter

Symmetric and k-cyclic structure of modal pseudocomplemented De Morgan algebras algebras was introduced previously. In this paper, we first present the construction of epimorphims between finite symmetric (or 2-cyclic) modal pseudocomplemented De Morgan algebras. Furthermore, we compute the cardinality of the set of all epimorphism between finite structures.

Sudarshan Devkota, Sumanta Pattanaik

Modern-day display systems demand high-quality rendering. However, rendering at higher resolution requires a large number of data samples and is computationally expensive. Recent advances in deep learning-based image and video super-resolution techniques motivate us to investigate such networks for high-fidelity upscaling of frames rendered at a lower resolu

Kashti S. Umare, Weimao Ke

The Discounted Least Information Theory of Entropy (DLITE) is a new information measure that quantifies the amount of entropic difference between two probability distributions. It manifests multiple critical properties both as an information-theoretic quantity and as metric distance. In the report, we provide a proof of the triangular inequality of DLITE's c

Michael Radica, Étienne Artigau, David Lafrenière, Charles Cadieux

The cross-correlation function and template matching techniques have dominated the world of precision radial velocities for many years. Recently, a new technique, named line-by-line, has been developed as an outlier resistant way to efficiently extract radial velocity content from high resolution spectra. We apply this new method to archival HARPS and CARMEN

Zengjing Chen, Larry G. Epstein, Guodong Zhang

This paper studies a sequential decision problem where payoff distributions are known and where the riskiness of payoffs matters. Equivalently, it studies sequential choice from a repeated set of independent lotteries. The decision-maker is assumed to pursue strategies that are approximately optimal for large horizons. By exploiting the tractability afforded

Ian E. Ochs, Elijah J. Kolmes, Mikhail E. Mlodik, Tal Rubin

The proton-Boron 11 (p-B11) fusion reaction is much harder to harness for commercial power than the easiest fusion reaction, namely the deuterium and tritium (DT) reaction. The p-B11 reaction requires much higher temperatures, and, even at those higher temperatures, the cross section is much smaller. However, as opposed to tritium, the reactants are both abu

Giuseppe Arnone, Florinda Capone, Roberta De Luca, Giuliana Massa

Perfectly incompressible materials do not exist in nature but are a useful approximation of several media which can be deformed in non-isothermal processes but undergo very small volume variation. In this paper the linear analysis of the Darcy-B\'enard problem is performed in the class of extended-quasi-thermal-incompressible fluids, introducing a factor $\b

Elias J. P. Biral, Natália S. Móller, Axel Pelster, F. Ednilson A. dos Santos

Within the many different models that appeared with the use of cold atoms to design BECs the bubble trap shaped potential has been of great interest. For the anisotropic bubble trap physics in the thin-shell limit the relationship between the physical parameters and the resulting manifold geometry is yet to be fully understood. In this paper, we work towards

Kanishk Gandhi, Siddharth Karamcheti, Madeline Liao, Dorsa Sadigh

Imitation learning from human-provided demonstrations is a strong approach for learning policies for robot manipulation. While the ideal dataset for imitation learning is homogenous and low-variance -- reflecting a single, optimal method for performing a task -- natural human behavior has a great deal of heterogeneity, with several optimal ways to demonstrat

Georgios Zacharegkas, Damiano Caprioli, Colby Haggerty, Siddhartha Gupta

The nonresonant streaming instability (Bell instability) plays a pivotal role in the acceleration and confinement of cosmic rays (CRs); yet, the exact mechanism responsible for its saturation and the magnitude of the final amplified magnetic field have not been assessed from first-principles. Using a survey of hybrid simulations (with kinetic ions and fluid

Charlie Oncebay, Juliana M. P. Almeida, Gustavo F. B. Almeida, Sergio R. Muniz

Among hundreds of impurities and defects in diamond, the nitrogen-vacancy (NV) center is one of the most interesting to be used as a platform for quantum technologies and nanosensing. Traditionally, synthetic diamond is irradiated with high-energy electrons or nitrogen ions to generate these color-centers. For precise positioning of the NV centers, fs-laser

Aldo Figallo-Orellano, Juan Sebastian Slagter

In this paper, we build Fidel-structures valued models following the methodology developed for Heyting-valued models; recall that Fidel structures are not algebras in the universal algebra sense. Taking models that verify Leibniz law, we are able to prove that all set-theoretic axioms of ZF are valid over these models. The proof is strongly based on the exis

Matt Jordan, Jonathan Hayase, Alexandros G. Dimakis, Sewoong Oh

Neural network verification aims to provide provable bounds for the output of a neural network for a given input range. Notable prior works in this domain have either generated bounds using abstract domains, which preserve some dependency between intermediate neurons in the network; or framed verification as an optimization problem and solved a relaxation us