October 2022 arXiv papers — page 112

Yash Vekaria, Rishab Nithyanand, Zubair Shafiq

Ad-tech enables publishers to programmatically sell their ad inventory to millions of demand partners through a complex supply chain. Bogus or low quality publishers can exploit the opaque nature of the ad-tech to deceptively monetize their ad inventory. In this paper, we investigate for the first time how misinformation sites subvert the ad-tech transparenc

Xiaokun Teng, Ji Seop Oh, Hengxin Tan, Lebing Chen

Electron correlations often lead to emergent orders in quantum materials. Kagome lattice materials are emerging as an exciting platform for realizing quantum topology in the presence of electron correlations. This proposal stems from the key signatures of electronic structures associated with its lattice geometry: flat band induced by destructive interferenc

Takao Fuji, Tatsuo Kaneyasu Masaki Fujimoto, Yasuaki Okano, Elham Salehi

Ultraviolet and extreme ultraviolet electric-fields produced by relativistic electrons in an undulator of a synchrotron light source are characterized by using spectral phase interferometry for direct electric-field reconstruction (SPIDER). A tandem undulator with a phase shifter produces a pair of wavelength shifted wave packets with some delay. The interfe

Dmitrii Chaikovskii, Aleksei Liubavin, Ye Zhang

In this paper, we develop an asymptotic expansion-regularization (AER) method for inverse source problems in two-dimensional nonlinear and nonstationary singularly perturbed partial differential equations (PDEs). The key idea of this approach is the use of the asymptotic-expansion theory, which allows us to determine the conditions for the existence and uniq

Tsun-Hsuan Wang, Wei Xiao, Tim Seyde, Ramin Hasani

The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how learning-based controllers make decisions is crucial since robots are often safety-critical systems. This urges a formal and quantitative understanding of the explanatory factors i

Md. Shirajum Munir, Ki Tae Kim, Apurba Adhikary, Walid Saad

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous m

Rafael C. R. de Lima, Jonas P. Pereira, Jaziel G. Coelho, Rafael C. Nunes

3XMM J185246.6+003317 is a transient magnetar located in the vicinity of the supernova remnant Kes\,79. So far, observations have only set upper limits to its surface magnetic field and spindown, and there is no estimate for its mass and radius. Using ray-tracing modelling and Bayesian inference for the analysis of several light curves spanning a period of a

Alex Kapiamba

Invariant classes under parabolic and near-parabolic renormalization have proved extremely useful for studying the dynamics of polynomials. The first such class was introduced by Inou-Shishikura to study quadratic polynomials; their argument has been extended to the unicritical cubic case by Yang and the general unicritical case by Ch\'eritat. However, all o

Yuuki Tachioka

We developed a dialogue system for Dialogue Robot Competition 2022. Our system is composed of three parts. First part investigates participants' demographic information by rule-based interview. Second part recommends a point of interest (POI) based on the collected demographic information. Third part answers participants' question based on the combination of

Jacob Mayle, Rakvi

We consider elliptic curves $E / \mathbb{Q}$ for which the image of the adelic Galois representation $\rho_E$ is as large as possible given a constraint on the image modulo 2. For such curves, we give a characterization in terms of their $\ell$-adic images, compute all examples of conductor at most 500,000, precisely describe the image of $\rho_E$, and offer

Sil Hamilton, Andrew Piper

In this paper, we explore the use of large language models to assess human interpretations of real world events. To do so, we use a language model trained prior to 2020 to artificially generate news articles concerning COVID-19 given the headlines of actual articles written during the pandemic. We then compare stylistic qualities of our artificially generate

Christopher Kuo, Wenyuan Li

For a subanalytic Legendrian $\Lambda \subseteq S^{*}M$, we prove that when $\Lambda$ is either swappable or a full Legendrian stop, the microlocalization at infinity $m_\Lambda: \operatorname{Sh}_\Lambda(M) \rightarrow \operatorname{\mu sh}_\Lambda(\Lambda)$ is a spherical functor, and the spherical cotwist is the Serre functor on the subcategory $\operator

Eric Ming Chen, Jin Sun, Apoorv Khandelwal, Dani Lischinski

How can one visually characterize people in a decade? In this work, we assemble the Faces Through Time dataset, which contains over a thousand portrait images from each decade, spanning the 1880s to the present day. Using our new dataset, we present a framework for resynthesizing portrait images across time, imagining how a portrait taken during a particular

Xuzhe Ying, A. A. Burkov, Chong Wang

We discuss the modified quantum electrodynamics from a time-reversal-breaking Weyl semimetal coupled with a $U(1)$ gauge (electromagnetic) field. A key role is played by the soft dispersion of the photons in a particular direction, say $\hat{z}$, due to the Hall conductivity of the Weyl semimetal. Due to the soft photon, the fermion velocity in $\hat{z}$ is

Brian R. Bartoldson, Bhavya Kailkhura, Davis Blalock

Although deep learning has made great progress in recent years, the exploding economic and environmental costs of training neural networks are becoming unsustainable. To address this problem, there has been a great deal of research on *algorithmically-efficient deep learning*, which seeks to reduce training costs not at the hardware or implementation level,

Timothy B. Armstrong, Martin Weidner, Andrei Zeleneev

We consider estimation and inference for a regression coefficient in panels with interactive fixed effects (i.e., with a factor structure). We demonstrate that existing estimators and confidence intervals (CIs) can be heavily biased and size-distorted when some of the factors are weak. We propose estimators with improved rates of convergence and bias-aware C

Alan Bu, Joseph Vulakh, Alex Zhao

An atomic monoid $M$ is called length-factorial if for every non-invertible element $x \in M$, no two distinct factorizations of $x$ into irreducibles have the same length (i.e., number of irreducible factors, counting repetitions). The notion of length-factoriality was introduced by J. Coykendall and W. Smith in 2011 under the term 'other-half-factoriality'

Tochukwu Elijah Ogri, S. M. Nahid Mahmud, Zachary I. Bell, Rushikesh Kamalapurkar

Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances. This paper presents an online, output-feedback, critic-only, model-based reinforcement learning architecture that simultaneously learns and implements an optimal controller while maintaining stability d

Hongyu Sun, Yan Yang, Kamyar Azizzadenesheli, Robert W. Clayton

Earthquake hypocenters form the basis for a wide array of seismological analyses. Pick-based earthquake location workflows rely on the accuracy of phase pickers and may be biased when dealing with complex earthquake sequences in heterogeneous media. Time-reversal imaging of passive seismic sources with the cross-correlation imaging condition has potential fo

Yongsheng Mei, Tian Lan, Mahdi Imani, Suresh Subramaniam

Bayesian optimization (BO) is a popular global optimization scheme for sample-efficient optimization in domains with expensive function evaluations. The existing BO techniques are capable of finding a single global optimum solution. However, finding a set of global and local optimum solutions is crucial in a wide range of real-world problems, as implementing

A. Suresh, R. D. Soares, P. Mondal, J. P. Santos Pires

Using the nascent concept of quantum spin-transfer torque [A. Zholud et al., Phys. Rev. Lett. {\bf 119}, 257201 (2017); M. D. Petrovi\'{c} {\em et al.}, Phys. Rev. X {\bf 11}, 021062 (2021)], we demonstrate that a current pulse can be harnessed to entangle quantum localized spins of two spatially separated ferromagnets (FMs) which are initially unentangled.

Xiyang Hu, Xinchi Chen, Peng Qi, Deguang Kong

Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages. We present an effective method to train multilingual IR systems when only English IR training data and some parallel corpora between English and other languages are available. We leverage parallel and non-parallel corpora to improve the

Sri Krishna Vadlamani, Dirk Englund, Ryan Hamerly

While analog neural network (NN) accelerators promise massive energy and time savings, an important challenge is to make them robust to static fabrication error. Present-day training methods for programmable photonic interferometer circuits, a leading analog NN platform, do not produce networks that perform well in the presence of static hardware errors. Mor

A. Shimony, C. M. Huntington, K. A. Flippo, Y. Elbaz

We report a novel measurement of the late time self-similar growth constant $\alpha_B$ of the Rayleigh-Taylor instability (RTI) from controlled initial perturbations. To this end, we have developed a laser-driven experiment, fielded on the National Ignition Facility (NIF), to explore deeply non-linear, multimode hydrodynamic growth of a planar interface. The

Qi Qi, Shervin Ardeshir, Yi Xu, Tianbao Yang

Improving fairness between privileged and less-privileged sensitive attribute groups (e.g, {race, gender}) has attracted lots of attention. To enhance the model performs uniformly well in different sensitive attributes, we propose a principled \underline{R}obust \underline{A}dversarial \underline{A}ttribute \underline{N}eighbourhood (RAAN) loss to debias the

Siddharth Varia, Shuai Wang, Kishaloy Halder, Robert Vacareanu

Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity. Most computational approaches focus on some of the ABSA sub-tasks such as tuple (aspect term, sentiment polarity) or triplet (aspect term, opinion term, se

Shankar Kantharaj, Xuan Long Do, Rixie Tiffany Ko Leong, Jia Qing Tan

Charts are very popular to analyze data and convey important insights. People often analyze visualizations to answer open-ended questions that require explanatory answers. Answering such questions are often difficult and time-consuming as it requires a lot of cognitive and perceptual efforts. To address this challenge, we introduce a new task called OpenCQA,

Rirong Yuan

In this paper, we bend a closed Riemannian manifold in the conformal class, through solving a fully nonlinear equation. As a result, we prove that each metric of quasi-negative Ricci curvature is conformal to a metric with negative Ricci curvature.

Sally Zhu, Zhenhao Li

The Poincar\'e problem is a model of two-dimensional internal waves in stable-stratified fluid. The chess billiard flow, a variation of a typical billiard flow, drives the formation behind and describes the evolution of these internal waves, and its trajectories can be represented as rotations around the boundary of a given domain. We find that for sufficien

Aldo Figallo-Orellano

In this paper, we present full models for some Paraconsistent Set Theories (PSTs). These models are built over Fidel semantics where they are specific first-order structures in the sense of Model Theory. These structures are known as F-structures in the literature and they are not algebras in the universal algebra sense. We demonstrate how is possible to pre

Takeshi Torii

There is a natural action of a kind of Hecke algebra $\mathcal{H}_n$ on the $n$th Morava $E$-theory of spaces. We construct Hecke operators in an amalgamated cohomology theory of the $n$th and the $(n+1)$st Morava $E$-theories. These operations are natural extensions of the Hecke operators in the $(n+1)$st Morava $E$-theory, and they induce an action of the

Lampros Gavalakis

It is proven that a conjecture of Tao (2010) holds true for log-concave random variables on the integers: For every $n \geq 1$, if $X_1,\ldots,X_n$ are i.i.d. integer-valued, log-concave random variables, then $$ H(X_1+\cdots+X_{n+1}) \geq H(X_1+\cdots+X_{n}) + \frac{1}{2}\log{\Bigl(\frac{n+1}{n}\Bigr)} - o(1) $$ as $H(X_1) \to \infty$, where $H$ denotes the

Shalin Patil, Ruikun Sun, Shinian Cheng, Shiwang Cheng

Rheodielectric spectroscopy is employed, for the first time, to investigate the effect of external shear on the Debyelike relaxation of a model monohydroxy alcohol, i.e., the 2-ethyl-1-hexanol (2E1H). Shear deformation leads to strong acceleration in the structural relaxation, the Debye relaxation, and the terminal relaxation of 2E1H. Moreover, the shear-ind

Himanshu, Tarak K Patra

Deep learning models are gaining popularity and potency in predicting polymer properties. These models can be built using pre-existing data and are useful for the rapid prediction of polymer properties. However, the performance of a deep learning model is intricately connected to its topology and the volume of training data. There is no facile protocol avail

Yue Bi, Michèle Wigger, Yue Wu

We consider a full-duplex wireless Distributed Computing (DC) system under the MapReduce framework. New upper and lower bounds on the optimal tradeoff between Normalized Delivery Time (NDT) and computation load are presented. The upper bound strictly improves over the previous reported upper bounds and is based on a novel interference alignment (IA) scheme t

Marc Box

This article presents new methodology for sample-based Bayesian inference when data are partitioned and communication between the parts is expensive, as arises by necessity in the context of "big data" or by choice in order to take advantage of computational parallelism. The method, which we call the Laplace enriched multiple importance estimator, uses new m

Alastair N. Fletcher, Daniel Stoertz, Vyron Vellis

The primary aim of this paper is to give topological obstructions to Cantor sets in $\mathbb{R}^3$ being Julia sets of uniformly quasiregular mappings. Our main tool is the genus of a Cantor set. We give a new construction of a genus $g$ Cantor set, the first for which the local genus is $g$ at every point, and then show that this Cantor set can be realized

David Berga, Pau Gallés, Katalin Takáts, Eva Mohedano

Latest advances in Super-Resolution (SR) have been tested with general purpose images such as faces, landscapes and objects, mainly unused for the task of super-resolving Earth Observation (EO) images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both Full-Reference and No-Reference Image Quality Assessme

Luciano Gottardi, Stephen Smith

Large arrays of superconducting transition-edge sensor (TES) microcalorimeters are becoming the key technology for future space-based X-ray observatories and ground-based experiments in the fields of astrophysics, laboratory astrophysics, plasma physics, particle physics and material analysis. Thanks to their sharp superconducting-to-normal transition, TESs

Lizhong Zhang, Omer Blaes, Yan-Fei Jiang

We perform 2D axisymmetric radiative relativistic MHD simulations of radiation pressure supported neutron star accretion columns in split-monopole magnetic fields. The accretion columns exhibit quasi-periodic oscillations, which manifest in the luminosity power spectrum as 2-10 kHz peaks, together with broader extensions to somewhat higher frequencies. The p

Jason Toy

Since their Nobel Prize winning discovery in 2005, grid cells have been studied extensively by neuroscientists. Their multi-scale periodic firing rates tiling the environment as the animal moves around has been shown as critical for path integration. Multiple experiments have shown that grid cells also fire for other representations such as olfactory, attent

Mikhail Shifman

I give a general non-technical review of supersymmetry and its modern applications in phenomenology and quantum field theories at strong coupling. Invited Talk at the conference {\sl Frontiers of Fundamental Physics, FFP16, May 23-26, 2022, Istanbul, T\"urkiye}

Marc Vucovich, Amogh Tarcar, Penjo Rebelo, Narendra Gade

Machine learning has helped advance the field of anomaly detection by incorporating classifiers and autoencoders to decipher between normal and anomalous behavior. Additionally, federated learning has provided a way for a global model to be trained with multiple clients' data without requiring the client to directly share their data. This paper proposes a no

Jixian Li, Danielle Van Boxel, Joshua A. Levine

Though analyzing a single scalar field using Morse complexes is well studied, there are few techniques for visualizing a collection of Morse complexes. We focus on analyses that are enabled by looking at a Morse complex as an embedded domain decomposition. Specifically, we target 2D scalar fields, and we encode the Morse complex through binary images of the

M. Abdullah Al-Mashad, A. van Hameren, H. Kakkad, P. Kotko

We present a state-of-the-art computation for the production of forward dijets in proton-proton and proton-lead collisions at the LHC, in rapidity domains covered by the ATLAS calorimeter and the planned FoCal extension of the ALICE detector. We use the small-x improved TMD (ITMD) formalism, together with collinearly improved TMD gluon distributions and full

Paula Truöl

We show that every non-trivial strongly quasipositive link is smoothly concordant to infinitely many pairwise non-isotopic strongly quasipositive links. In contrast to our result, Baker conjectured that smoothly concordant strongly quasipositive fibered knots are isotopic. Our construction uses a satellite operation whose companion is a slice knot with maxim

A. Arda Gitmez, Román Andrés Zárate

Can proximity make friendships more diverse? To address this question, we propose a learning-driven friendship formation model to study how proximity and similarity influence the likelihood of forming social connections. The model predicts that proximity affects more friendships between dissimilar than similar individuals, in opposition to a preference-drive

Liyuan Xu, Arthur Gretton

We consider the estimation of average and counterfactual treatment effects, under two settings: back-door adjustment and front-door adjustment. The goal in both cases is to recover the treatment effect without having an access to a hidden confounder. This objective is attained by first estimating the conditional mean of the desired outcome variable given rel

Lan Feng, Quanyi Li, Zhenghao Peng, Shuhan Tan

Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called TrafficGen for traffic scenario generation. It learns from the fragmented human driving data collected in the real world and then can generate realistic traffic scenarios. TrafficGen is

J. Furtado, G. Alencar

In this paper, we study the charged and uncharged BTZ counterpart of the Black-Bounce proposed by Simpson and Visser recently. For the uncharged case, we find that the temperature is not modified by the bounce parameter. We also find that the wormhole side of the solution must always be supported by exotic matter over the throat. For the charged case we find

Paolo Aceto, Aliakbar Daemi, Jennifer Hom, Tye Lidman

We prove that there are homology three-spheres that bound definite four-manifolds, but any such bounding four-manifold must be built out of many handles. The argument uses the homology cobordism invariant $\Gamma$ from instanton Floer homology.

Jinsong Xu, Jiaming He, J. -S. Zhou, Danru Qu

The observation of the magnon Hall effect (MHE) has relied solely on the challenging measurement of the thermal Hall conductivity. Here, we report a highly sensitive electrical Seebeck-contrast method for the observation of MHE in Lu$_2$V$_2$O$_7$/heavy metal heterostructures, that is highly desirable for the exploration of new MHE materials and their applic

Lakshay Sharma, Michael Everett, Donggun Lee, Xiaoyi Cai

A key challenge in fast ground robot navigation in 3D terrain is balancing robot speed and safety. Recent work has shown that 2.5D maps (2D representations with additional 3D information) are ideal for real-time safe and fast planning. However, the prevalent approach of generating 2D occupancy grids through raytracing makes the generated map unsafe to plan i

Yuxi Lu, Jason L. Curtis, Ruth Angus, Trevor J. David

The intermediate period gap, discovered by Kepler, is an observed dearth of stellar rotation periods in the temperature-period diagram at $\sim$ 20 days for G dwarfs and up to $\sim$ 30 days for early-M dwarfs. However, because Kepler mainly targeted solar-like stars, there is a lack of measured periods for M dwarfs, especially those at the fully convective

Anton Dereventsov, Anton Bibin

We propose a method for generating simulated contextual bandit environments for personalization tasks from recommendation datasets like MovieLens, Netflix, Last.fm, Million Song, etc. This allows for personalization environments to be developed based on real-life data to reflect the nuanced nature of real-world user interactions. The obtained environments ca

Nikolay M. Babayan, Mamikon S. Ginovyan

One of the main problem in prediction theory of discrete-time second-order stationary processes $X(t)$ is to describe the asymptotic behavior of the best linear mean squared prediction error in predicting $X(0)$ given $ X(t),$ $-n\le t\le-1$, as $n$ goes to infinity. This behavior depends on the regularity (deterministic or nondeterministic) and on the depen

Jinsong Xu, Jiaming He, J. -S. Zhou, Danru Qu

Spintronic phenomena to date have been established in magnets with collinear moments, where the spin injection through the spin Seebeck effect (SSE) is always along the out-of-plane direction. Here, we report the observation of a vector SSE in a noncollinear antiferromagnet (AF) LuFeO$_3$, where temperature gradient along the out-of-plane and also the in-pla

Kuan Fang, Patrick Yin, Ashvin Nair, Homer Walke

The utilization of broad datasets has proven to be crucial for generalization for a wide range of fields. However, how to effectively make use of diverse multi-task data for novel downstream tasks still remains a grand challenge in robotics. To tackle this challenge, we introduce a framework that acquires goal-conditioned policies for unseen temporally exten

Yunmo Chen, William Gantt, Weiwei Gu, Tongfei Chen

We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i.e., N-tuples representing a mapping from named slots to spans of text) within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and

Saptarashmi Bandyopadhyay, Shraman Pal, Hao Zou, Abhranil Chandra

Question Answering (QA) systems require a large amount of annotated data which is costly and time-consuming to gather. Converting datasets of existing QA benchmarks are challenging due to different formats and complexities. To address these issues, we propose an algorithm to automatically generate shorter questions resembling day-to-day human communication i

Giovanni Otalora, Emmanuel N. Saridakis

We investigate cosmological scenarios with spin-gravity coupling. In particular, due to the spin of the baryonic and dark matter particles and its coupling to gravity, they probe an effective spin-dependent metric, which can be calculated semi-classically in the Mathisson-Papapetrou-Tulczyjew-Dixon formalism. Hence, the usual field equations give rise to mod

Yi Sui, Junfeng Wen, Yenson Lau, Brendan Leigh Ross

In the traditional federated learning setting, a central server coordinates a network of clients to train one global model. However, the global model may serve many clients poorly due to data heterogeneity. Moreover, there may not exist a trusted central party that can coordinate the clients to ensure that each of them can benefit from others. To address the

Muhammet Balcilar, Bharath Bhushan Damodaran, Pierre Hellier

During the last four years, we have witnessed the success of end-to-end trainable models for image compression. Compared to decades of incremental work, these machine learning (ML) techniques learn all the components of the compression technique, which explains their actual superiority. However, end-to-end ML models have not yet reached the performance of tr

Salem Selim

We establish a global uniqueness result for an inverse boundary problem with partial data for the magnetic Schr\"odinger operator with a magnetic potential of class $W^{1,n}\cap L^\infty$, and an electric potential of class $L^n$. Our result is an extension, in terms of the regularity of the potentials, of the results [16] and [25]. As a consequence, we also

Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco

Treatment effect estimation is a fundamental problem in causal inference. We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some treatment on a population of $n$ individuals. In particular, we study sample-constrained treatment effect estimation, where we must select a subset of $s \ll n$ individuals from the

Qinzi Zhang, Hoang Tran, Ashok Cutkosky

We develop a new reduction that converts any online convex optimization algorithm suffering $O(\sqrt{T})$ regret into an $\epsilon$-differentially private stochastic convex optimization algorithm with the optimal convergence rate $\tilde O(1/\sqrt{T} + \sqrt{d}/\epsilon T)$ on smooth losses in linear time, forming a direct analogy to the classical non-privat

Ganesh Tata, Gautham Krishna Gudur, Gopinath Chennupati, Mohammad Emtiyaz Khan

Calibration can reduce overconfident predictions of deep neural networks, but can calibration also accelerate training? In this paper, we show that it can when used to prioritize some examples for performing subset selection. We study the effect of popular calibration techniques in selecting better subsets of samples during training (also called sample prior

Cedric Gerbelot, Emanuele Troiani, Francesca Mignacco, Florent Krzakala

We prove closed-form equations for the exact high-dimensional asymptotics of a family of first order gradient-based methods, learning an estimator (e.g. M-estimator, shallow neural network, ...) from observations on Gaussian data with empirical risk minimization. This includes widely used algorithms such as stochastic gradient descent (SGD) or Nesterov accel

Dimitris Bertsimas, Driss Lahlou Kitane

We consider the problem of maximizing the variance explained from a data matrix using orthogonal sparse principal components that have a support of fixed cardinality. While most existing methods focus on building principal components (PCs) iteratively through deflation, we propose GeoSPCA, a novel algorithm to build all PCs at once while satisfying the ortho

Adam Dorian Wong

StyleGAN is the open-sourced TensorFlow implementation made by NVIDIA. It has revolutionized high quality facial image generation. However, this democratization of Artificial Intelligence / Machine Learning (AI/ML) algorithms has enabled hostile threat actors to establish cyber personas or sock-puppet accounts in social media platforms. These ultra-realistic

Mehdi Sadeghi

In this paper, we study the holographic dual to an asymptotically anti-de Sitter black brane in an Einstein-Yang-Mills model with a non-minimal coupling between the Riemann and Yang-Mills fields. First, we construct a planar black hole solution of this model up to the first order of the non-minimal coupling of the Yang-Mills field with the Riemann-Christoffe

Eric Nguyen, Karan Goel, Albert Gu, Gordon W. Downs

Visual data such as images and videos are typically modeled as discretizations of inherently continuous, multidimensional signals. Existing continuous-signal models attempt to exploit this fact by modeling the underlying signals of visual (e.g., image) data directly. However, these models have not yet been able to achieve competitive performance on practical

Michael Müger, Lars Tuset

We reduce the Mathieu conjecture for $SU(2)$ to a conjecture about moments of Laurent polynomials in two variables with single variable polynomial coefficients.

Horatiu Nastase, Jacob Sonnenschein

In this paper, we construct a charged soliton with a finite energy and no delta function source in a pure Abelian gauge theory. Specifically, we first consider the 3-dimensional Abelian gauge theory, with a Maxwell term and a l evel $N$ CS term. We find a static solution that carries charge $N$, angular momentum $\frac{N}{2}$ and whose radius is $N$ independ

Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui

Recently, substantial progress has been made in text ranking based on pretrained language models such as BERT. However, there are limited studies on how to leverage more powerful sequence-to-sequence models such as T5. Existing attempts usually formulate text ranking as classification and rely on postprocessing to obtain a ranked list. In this paper, we prop

John R. Klein

The object of this paper is to show that non-homotopy finite Poincar\'e duality spaces are plentiful. Let $\pi$ be finitely presented group. Assuming that the reduced Grothendieck group $\tilde K_0(\Bbb Z[\pi])$ has a non-trivial 2-divisible element, we construct a finitely dominated Poincar\'e space $X$ with fundamental group $\pi$ such that $X$ is not homo

Fei Ye, Adrian G. Bors

Learning from non-stationary data streams, also called Task-Free Continual Learning (TFCL) remains challenging due to the absence of explicit task information. Although recently some methods have been proposed for TFCL, they lack theoretical guarantees. Moreover, forgetting analysis during TFCL was not studied theoretically before. This paper develops a new

Shubham Sharma, Alan H. Gee, Jette Henderson, Joydeep Ghosh

Counterfactual explanations have substantially increased in popularity in the past few years as a useful human-centric way of understanding individual black-box model predictions. While several properties desired of high-quality counterfactuals have been identified in the literature, three crucial concerns: the speed of explanation generation, robustness/sen

Huanzhi Hu, Jennifer Lin, Mikolaj D. Uryszek, Frank Krüger

We investigate the stability of the N\'eel quantum critical point of two-dimensional quantum antiferromagnets, described by a non-linear $\sigma$ model (NL$\sigma$M), in the presence of a Kondo coupling to $N_f$ flavours of two-component Dirac fermion fields. The long-wavelength order parameter fluctuations are subject to Landau damping by electronic particl

Moussa Kamal Eddine, Guokan Shang, Michalis Vazirgiannis

The rapid development of large pretrained language models has revolutionized not only the field of Natural Language Generation (NLG) but also its evaluation. Inspired by the recent work of BARTScore: a metric leveraging the BART language model to evaluate the quality of generated text from various aspects, we introduce DATScore. DATScore uses data augmentati

Qiyu Dai, Yan Zhu, Yiran Geng, Ciyu Ruan

In this work, we tackle 6-DoF grasp detection for transparent and specular objects, which is an important yet challenging problem in vision-based robotic systems, due to the failure of depth cameras in sensing their geometry. We, for the first time, propose a multiview RGB-based 6-DoF grasp detection network, GraspNeRF, that leverages the generalizable neura

François Bachoc, Louis Béthune, Alberto Gonzalez-Sanz, Jean-Michel Loubes

We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport. We propose an Hilbertian embedding of the space of probabilities using their Sinkhorn potentials, which are solutions of the dual entropic relaxed optimal transport between the probabilities and a reference measure $\mathcal{U}$. W

Samuel Muñoz-Echániz

We prove that the mapping class group is not an $h$-cobordism invariant of high-dimensional manifolds by exhibiting $h$-cobordant manifolds whose mapping class groups have different cardinalities. In order to do so, we introduce a moduli space of "$h$-block" bundles and understand its difference with the moduli space of ordinary block bundles.

Jinpu Lin

This dissertation explores the interaction between high-intensity lasers and plasmas to accelerate electrons and produce radiation via experimental and computational efforts. The laser pulses used in this dissertation have ultrashort duration (< 100 fs), near-infrared to mid-infrared wavelength (0.8 $\mu$m, 2 $\mu$m, or 3.9 $\mu$m), millijoules of energy, an

Viktória Kecskeméthy, Csaba Kiss, Róbert Szakáts, András Pál

The K2 mission of the Kepler Space Telescope allowed the observations of light curves of small solar system bodies throughout the whole Solar system. In this paper we present the results of a collection of K2 transneptunian object observations, between Campaigns C03 (November 2014 -- February 2015) to C19 (August -- September, 2018), which includes 66 target

Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng

Artificial lights commonly leave strong lens flare artifacts on images captured at night. Nighttime flare not only affects the visual quality but also degrades the performance of vision algorithms. Existing flare removal methods mainly focus on removing daytime flares and fail in nighttime. Nighttime flare removal is challenging because of the unique luminan

Jinliang Ning, Christopher Lane, Yubo Zhang, Matthew Matzelle

The role of lattice dynamics in unconventional high-temperature superconductivity is still vigorously debated. Theoretical insights into this problem have long been prevented by the absence of an accurate first-principles description of the combined electronic, magnetic, and lattice degrees of freedom. Utilizing the recently constructed r$^2$SCAN density fun

Kunal P. Mooley, Jay Anderson, Wenbin Lu

The afterglow of the binary neutron star merger GW170817 gave evidence for a structured relativistic jet and a link between such mergers and short gamma-ray bursts. Superluminal motion, found using radio very long baseline interferometry (VLBI), together with the afterglow light curve provided constraints on the viewing angle (14-28 degrees), the opening ang

Elaine Crooks, Yini Du

In this paper, we present an approach to characterising self-similar fast-reaction limits of systems with nonlinear diffusion. For appropriate initial data, in the fast-reaction limit as k tends to infinithy,spatial segregation results in the two components of the original systems converging to the positive and negative parts of a self-similar limit profile

Madhumita Sushil, Dana Ludwig, Atul J. Butte, Vivek A. Rudrapatna

Several biomedical language models have already been developed for clinical language inference. However, these models typically utilize general vocabularies and are trained on relatively small clinical corpora. We sought to evaluate the impact of using a domain-specific vocabulary and a large clinical training corpus on the performance of these language mode

Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace

Pretraining multimodal models on Electronic Health Records (EHRs) provides a means of learning representations that can transfer to downstream tasks with minimal supervision. Recent multimodal models induce soft local alignments between image regions and sentences. This is of particular interest in the medical domain, where alignments might highlight regions

Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo

Computer simulations have proven a valuable tool for understanding complex phenomena across the sciences. However, the utility of simulators for modelling and forecasting purposes is often restricted by low data quality, as well as practical limits to model fidelity. In order to circumvent these difficulties, we argue that modellers must treat simulators as

Andreas Seifahrt, Jacob L. Bean, David Kasper, Julian Stürmer

MAROON-X is a fiber-fed, optical EPRV spectrograph at the 8-m Gemini North Telescope on Mauna Kea, Hawai'i. MAROON-X was commissioned as a visiting instrument in December 2019 and is in regular use since May 2020. Originally designed for RV observations of M-dwarfs, the instrument is used for a broad range of exoplanet and stellar science cases and has trans

Gaëlle Richer, Alexis Pister, Moataz Abdelaal, Jean-Daniel Fekete

We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990-2020 to characterize the different notions of scalability in these works. While many papers have addressed scalability issues, our survey identifies a lack of consistency in the use of t

Ethan Dlugie

We use a geometric approach to show that the reduced Burau representation specialized at roots of unity has another incarnation as the monodromy representation of a moduli space of Euclidean cone metrics on the sphere, as described by Thurston. Using the theory of orbifolds, we leverage this connection to identify the kernels of these specializations in some

Paola Pinilla

Brown dwarfs and very low mass stars are a significant fraction of stars in our galaxy, and they are interesting laboratories to investigate planet formation in extreme conditions of low temperature and densities. In addition, the dust radial drift of particles is expected to be a more difficult barrier to overcome during the first steps of planet formation

Konstanty Junosza-Szaniawski, Dariusz Nogalski, Paweł Rzążewski

In a DDoS attack (Distributed Denial of Service), an attacker gains control of many network users through a virus. Then the controlled users send many requests to a victim, leading to its resources being depleted. DDoS attacks are hard to defend because of their distributed nature, large scale and various attack techniques. One possible mode of defense is to

S. Mahmoudi, Kh. Jafarzade, S. H. Hendi

Inspired by the Lifshitz gravity as a theory with anisotropic scaling behavior, we suggest a new $(n+1)-$dimensional metric in which the time and spatial coordinates scale anisotropically as $(t,r,\theta_{i})\,\to (\lambda^{z}t,\lambda^{-1}r,\lambda^{x_i}\,\theta_{i})$. Due to the anisotropic scaling dimension of the spatial coordinates, this spacetime does

Purba Chatterjee, Sean Fancher, Eleni Katifori

Existing models of adaptation in biological flow networks consider their constituent vessels (e.g. veins and arteries) to be rigid, thus predicting a non physiological response when the drive (e.g. the heart) is dynamic. Here we show that incorporating pulsatile driving and properties such as fluid inertia and vessel compliance into a general adaptation fram

Yinuo Han, Peter G. Tuthill, Ryan M. Lau, Anthony Soulain

The Wolf-Rayet (WR) binary system WR140 is a close (0.9-16.7 mas) binary star consisting of an O5 primary and WC7 companion and is known as the archetype of episodic dust-producing WRs. Dust in WR binaries is known to form in a confined stream originating from the collision of the two stellar winds, with orbital motion of the binary sculpting the large-scale