January 2022 arXiv papers — page 12

Amaç Herdağdelen, Lada Adamic, Bogdan State

We use exploratory factor analysis to investigate the online persistence of known community-level patterns of social capital variance in the U.S. context. Our analysis focuses on Facebook groups, specifically those that tend to connect users in the same local area. We investigate the relationship between established, localized measures of social capital at t

Sherry Wang, Carlisle Adams, Anne Broadbent

In a post-quantum world, where attackers may have access to full-scale quantum computers, all classical password-based authentication schemes will be compromised. Quantum copy-protection prevents adversaries from making copies of existing quantum software; we suggest this as a possible approach for designing post-quantum-secure password authentication system

Georgia Himona, Vassilios Kovanis, Yannis Kominis

The global structure of the Isochrons and the corresponding Phase Response Curves are, for the first time, investigated and numerically computed for the fundamental photonic oscillator consisted of an Optically Injected Laser. Their crucial role in the synchronization dynamics under a periodic modulation of the injection beam is shown, along with their capab

Yanhao Tang, Jie Gu, Song Liu, Kenji Watanabe

The metal-insulator transition (MIT) driven by electronic correlations is a fundamental and challenging problem in condensed-matter physics. Particularly, whether such a transition can be continuous remains open. The emergence of semiconducting moir\'e materials with continuously tunable bandwidth provides an ideal platform to study interaction-driven MITs.

V. K. B. Kota, Manan Vyas

Statistical nuclear spectroscopy (also called spectral distribution method), introduced by J.B. French in late 60's and developed in detail in the later years by his group and many other groups, is based on the Gaussian forms for the state (eigenvalue) and transition strength densities in shell model spaces with their extension to partial densities defined o

Jürgen Herzog, Shinya Kumashiro

We study the upper bound of the colength of trace of the canonical module in one-dimensional Cohen-Macaulay rings. We answer the two questions posed by Herzog-Hibi-Stamate and Kobayashi.

Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu, Debadeepta Dey

Knowledge distillation (KD) methods compress large models into smaller students with manually-designed student architectures given pre-specified computational cost. This requires several trials to find a viable student, and further repeating the process for each student or computational budget change. We use Neural Architecture Search (NAS) to automatically

Yunfang Fu, Qiuqi Ruan, Ziyan Luo, Gaoyun An

In this paper, a novel approach via embedded tensor manifold regularization for 2D+3D facial expression recognition (FERETMR) is proposed. Firstly, 3D tensors are constructed from 2D face images and 3D face shape models to keep the structural information and correlations. To maintain the local structure (geometric information) of 3D tensor samples in the low

Qifan Shen

This article mainly aims to give combinatorial characterizations and topological descriptions of quasitoric manifolds with string property. We provide a necessary and sufficient condition for a simple polytope in dimension 2 and 3 to be realizable as the orbit polytope of a string quasitoric manifold. In particular, a complete description of string quasitori

Qihong Huang, He Huang, Jun Chen, Lu Zhang

Using the generalized Tsallis entropy, the Tsallis holographic dark energy(THDE) was proposed recently. In this paper we analyze the cosmological consequences of the THDE model with an interaction between dark energy and dark matter $Q=H(\alpha\rho_{m}+\beta\rho_{D})$. We find that the THDE model can explain the current accelerated cosmic expansion, and it i

Anton Izosimov

Recutting is an operation on planar polygons defined by cutting a polygon along a diagonal to remove a triangle, and then reattaching the triangle along the same diagonal but with opposite orientation. Recuttings along different diagonals generate an action of the affine symmetric group on the space of polygons. We show that this action is given by cluster t

Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao

Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, granularity is used to measure the semantic coverage between the summary and source document. However, developing systems that can generate summaries with customizable semantic cover

Ibraheem Muhammad Moosa, Mahmud Elahi Akhter, Ashfia Binte Habib

Script diversity presents a challenge to Multilingual Language Models (MLLM) by reducing lexical overlap among closely related languages. Therefore, transliterating closely related languages that use different writing scripts to a common script may improve the downstream task performance of MLLMs. We empirically measure the effect of transliteration on MLLMs

Qian Qin

The theory of two projections is utilized to study two-component Gibbs samplers. Through this theory, previously intractable problems regarding the asymptotic variances of two-component Gibbs samplers are reduced to elementary matrix algebra exercises. It is found that in terms of asymptotic variance, the two-component random-scan Gibbs sampler is never much

Alexander Gribov, Khalid Duri

This paper proposes a novel solution for constructing line features modeling each catenary curve present within a series of points representing multiple catenary curves. The solution can be applied to extract power lines from lidar point clouds, which can then be used in downstream applications like creating digital twin geospatial models and evaluating the

Soumil Rathi

This paper is an analysis of the different methods proposed to achieve AGI, including Human Brain Emulation, AIXI and Integrated Cognitive Architecture. First, the definition of AGI as used in this paper has been defined, and its requirements have been stated. For each proposed method mentioned, the method in question was summarized and its key processes wer

Yihao Xue, Kyle Whitecross, Baharan Mirzasoleiman

Self-supervised Contrastive Learning (CL) has been recently shown to be very effective in preventing deep networks from overfitting noisy labels. Despite its empirical success, the theoretical understanding of the effect of contrastive learning on boosting robustness is very limited. In this work, we rigorously prove that the representation matrix learned by

Matthew Nicoletti, Leonid Petrov

We introduce a family of Markov growth processes on discrete height functions defined on the 2-dimensional square lattice. Each height function corresponds to a configuration of the six vertex model on the infinite square lattice. We focus on the stochastic six vertex model corresponding to a particular two-parameter family of weights within the ferroelectri

Xiaxia Guan, Xian'an Jin, Tianlong Ma

T. K\'{a}lm\'{a}n (A version of Tutte's polynomial for hypergraphs, Adv. Math. 244 (2013) 823-873.) introduced the interior and exterior polynomials which are generalizations of the Tutte polynomial $T(x,y)$ on plane points $(1/x,1)$ and $(1,1/y)$ to hypergraphs. The two polynomials are defined under a fixed ordering of hyperedges, and are proved to be indep

Ge Li, Xiheng Shi, Qiguo Tian, Luming Sun

We present here a detailed analysis of an unusual absorption line system in the quasar SDSS J122826.79+100532.2. The absorption lines in the system have a common redshifted velocity structure starting from $v\sim0$ and extending to $\sim1,000\ \mathrm{km~s}^{-1}$, and are clearly detected in hydrogen Balmer series up to H$\iota$, in metastable neutral helium

Xu'an Dou, Zhennan Zhou

We study a two-speed model with variant drift fields, which generalizes the Goldstein-Taylor model but the two advection fields are not necessarily in proportion. Due to the lack of a local equilibrium structure of the steady state, prevailing hypocoercivity methods could not be directly applied to such a system. To prove the exponential convergence to equil

Ahmad Mozaffer Karim

One of the most important properties of deep auto-encoders (DAEs) is their capability to extract high level features from row data. Hence, especially recently, the autoencoders are preferred to be used in various classification problems such as image and voice recognition, computer security, medical data analysis, etc. Despite, its popularity and high perfor

S. A. Miskovich, F. Montes, G. P. A. Berg, J. Blackmon

The SEparator for CApture Reactions (SECAR) is a next-generation recoil separator system at the Facility for Rare Isotope Beams (FRIB) designed for the direct measurement of capture reactions on unstable nuclei in inverse kinematics. To maximize the performance of this system, stringent requirements on the beam alignment to the central beam axis and on the i

Xiaoping Fang, Youjun Deng

In this paper, we consider the plasmon resonance in multi-layer structures. The conductivity problem associated with uniformly distributed background field is considered. We show that the plasmon mode is equivalent to the eigenvalue problem of a matrix, whose order is the same to the number of layers. For any number of layers, the exact characteristic polyno

Carlo R. Laing

Chimeras occur in networks of two coupled populations of oscillators when the oscillators in one population synchronise while those in the other are asynchronous. We consider chimeras of this form in networks of planar oscillators for which one parameter associated with the dynamics of an oscillator is randomly chosen from a uniform distribution. A generalis

Xizixiang Wei, Cong Shen, Jing Yang, H. Vincent Poor

We propose a novel uplink communication method, coined random orthogonalization, for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. The key novelty of random orthogonalization comes from the tight coupling of FL model aggregation and two unique characteristics of massive MIMO - channel hardening and favorable

Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng

One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have encountered bottlenecks in multi-item auctions, recently, there has been much progress on finding the optimal mechanism through deep learning. However, these works either focus on a

Haizhou Du, Ryan Yang, Yijian Chen, Qiao Xiang

Distributed machine learning (DML) over time-varying networks can be an enabler for emerging decentralized ML applications such as autonomous driving and drone fleeting. However, the commonly used weighted arithmetic mean model aggregation function in existing DML systems can result in high model loss, low model accuracy, and slow convergence speed over time

Ngoc Bui, Duy Nguyen, Viet Anh Nguyen

Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains. A counterfactual plan consists of multiple possibilities to modify a given instance so that the model's prediction will be altered. As the predictive model can be updated subject to the future arrival of ne

Xiaohan Yu, Shaochen Mao

Attentive Neural Process (ANP) improves the fitting ability of Neural Process (NP) and improves its prediction accuracy, but the higher time complexity of the model imposes a limitation on the length of the input sequence. Inspired by models such as Vision Transformer (ViT) and Masked Auto-Encoder (MAE), we propose Patch Attentive Neural Process (PANP) using

Ke-Ji Chen, Fan Wu, Lianyi He, Wei Yi

We show that pairing in an ultracold Fermi gas under spin-orbital-angular-momentum coupling (SOAMC) can acquire topological characters encoded in the quantized angular degrees of freedom. The resulting topological superfluid is the angular analog of its counterpart in a one-dimensional Fermi gas with spin-orbit coupling, but characterized by a Zak phase defi

Liande Li, Jirong Mao

We select 52 long gamma-ray bursts(GRBs) having the precursor activity in the third Swift-BAT catalog. Each episode shown in both the precursors and the main bursts is fitted by the Norris function. We systematically analyze the temporal properties for both the precursors and the main bursts. We do not find any significant difference between the temporal pro

Angelina Brilliantova, Hadi Hosseini

The stable matching problem sets the economic foundation of several practical applications ranging from school choice and medical residency to ridesharing and refugee placement. It is concerned with finding a matching between two disjoint sets of agents wherein no pair of agents prefer each other to their matched partners. The Deferred Acceptance (DA) algori

Dominick Cichon, Guillaume Eurin, Florian Jörg, Teresa Marrodán Undagoitia

Understanding liquid xenon scintillation and ionization processes is of great interest to improve analysis methods in current and future detectors. In this paper, we investigate the dynamics of the scintillation process for excitation by $\mathcal{O}$(10 keV) electrons from a $^{83m}$Kr source and $\mathcal{O}$(6 MeV) $\alpha$-particles from a $^{222}$Rn sou

Feng Li, Xuyang Yuan, Lina Wang, Huan Yang

We consider a K-armed bandit problem in general graphs where agents are arbitrarily connected and each of them has limited memorizing capabilities and communication bandwidth. The goal is to let each of the agents eventually learn the best arm. It is assumed in these studies that the communication graph should be complete or well-structured, whereas such an

Bingrong Huang

In this paper, we prove effective quantitative decorrelation of values of two Hecke eigenforms as the weight goes to infinity. As consequences, we get an effective version of equidistribution of mass and zeros of certain linear combinations of Hecke eigenforms.

Md Hasibul Amin, Mohammed Elbtity, Ramtin Zand

Fully-analog in-memory computing (IMC) architectures that implement both matrix-vector multiplication and non-linear vector operations within the same memory array have shown promising performance benefits over conventional IMC systems due to the removal of energy-hungry signal conversion units. However, maintaining the computation in the analog domain for t

Xuhua He, George Lusztig

In this paper, we study the interaction between the totally positive monoid $G_{\ge 0}$ attached to a connected reductive group $G$ with a pinning and the conjugacy classes in $G$. In particular, we study how a conjugacy class meets the various cells of $G_{\ge0}$. We also state a conjectural Jordan decomposition for $G_{\ge0}$ and prove it in some special c

Neal Bez, Shohei Nakamura, Hiroshi Tsuji

We provide deficit estimates for Nelson's hypercontractivity inequality, the logarithmic Sobolev inequality, and Talagrand's transportation cost inequality under the restriction that the inputs are semi-log-subharmonic, semi-log-convex, or semi-log-concave. In particular, our result on the logarithmic Sobolev inequality complements a recently obtained result

Jiakun Liu, Shuo Shao, Wenyi Zhang, H. Vincent Poor

A new source model, which consists of an intrinsic state part and an extrinsic observation part, is proposed and its information-theoretic characterization, namely its rate-distortion function, is defined and analyzed. Such a source model is motivated by the recent surge of interest in the semantic aspect of information: the intrinsic state corresponds to th

Marwan Gebran, Kathleen Connick, Hikmat Farhat, Frédéric Paletou

Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep Learning techniques in the context of stellar parameters determination. Utilizing the Convolutional Neural Network archit

Yue Wan, Benben Liao, Chang-Yu Hsieh, Shengyu Zhang

Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The task is to predict the reactants given a core product. With the advancement of machine learning, computer-aided synthesis planning has gained increasing interest. Numerous methods were proposed to solve this problem with different levels of dependency on additional chemi

Shunlin Huang, Peng Wang, Xiong Shen, Jun Liu

Spatiotemporal optical vortex (STOV) is a unique optical vortex with phase singularity in the space-time domain and the photons in a STOV can carry transverse orbital angular momentum (OAM). The STOV shows many fantastic properties which are worth exploring. Here, we theoretically and experimentally study the diffraction property of STOV, which is a fundamen

Zeeshan Ahmad, Rebecca A. Scheidt, Matthew P. Hautzinger, Kai Zhu

The presence of unreacted lead iodide in organic-inorganic lead halide perovskite solar cells is widely correlated with an increase in power conversion efficiency. We investigate the mechanism for this increase by identifying the role of surfaces and interfaces present between methylammonium lead iodide perovskite films and excess lead iodide. We show how ty

Takayuki Kihara

Kechris and Martin showed that the Wadge rank of the $\omega$-th level of the decreasing difference hierarchy of coanalytic sets is $\omega_2$ under the axiom of determinacy. In this article, we give an alternative proof of the Kechris-Martin theorem, by understanding the $\omega$-th level of the decreasing difference hierarchy of coanalytic sets as the (rel

Tony Albers, Jaime Cisternas, Günter Radons

In previous publications, we showed that the incremental process of the chaotic diffusion of dissipative solitons in a prototypical complex Ginzburg-Landau equation, known, e.g., from nonlinear optics, is governed by a simple Markov process leading to an Anti-Persistent Random Walk of motion or by a more complex Hidden Markov Model with continuous output den

Fred Wiffen, Woon Hau Chin, Angela Doufexi

The use of a large excess of service antennas brings a variety of performance benefits to distributed MIMO C-RAN, but the corresponding high fronthaul data loads can be problematic in practical systems with limited fronthaul capacity. In this work we propose the use of lossy dimension reduction, applied locally at each remote radio head (RRH), to reduce this

Minjia Zhang, Niranjan Uma Naresh, Yuxiong He

In recent years, large pre-trained Transformer-based language models have led to dramatic improvements in many natural language understanding tasks. To train these models with increasing sizes, many neural network practitioners attempt to increase the batch sizes in order to leverage multiple GPUs to improve training speed. However, increasing the batch size

Shahriar Iravanian, Carl Julius Martensen, Alessandro Cheli, Shashi Gowda

Most computer algebra systems (CAS) support symbolic integration as core functionality. The majority of the integration packages use a combination of heuristic algebraic and rule-based (integration table) methods. In this paper, we present a hybrid (symbolic-numeric) methodology to calculate the indefinite integrals of univariate expressions. The primary mot

Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng

The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm. However, conventional FL methods perform poorly due to the uniqueness of the task: broadcasting class centers among clients is crucial for recognition performances but leads to privacy leakage. To resolve the privacy-utility parad

C. Y. Tan, B. Schulz

We introduce a Fourier method (Fm) for the determination of best focus for telescopes with stars. Our method fits a power function, that we will derive in this paper, to a set of images taken as a function of focuser position. The best focus position is where the power is maximum. Fm was first tested with small refractor and Schmidt-Cassegrain (SCT) telescop

Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu

As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become challenging. While computational needs have driven recent compiler, networking, and hardware advancements, utilization of those advancements by machine learning tools is occurring at a

Rui Chen, Zhenyang Tian, Wen-Xuan Long, Xiaodong Wang

Radio frequency-orbital angular momentum (RF-OAM) is a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. Since OAM requires precise alignment of the transmit and the receive antennas, the electronic beam steering approach has been proposed for the uniform circular array (UCA)-based O

Deborah S. Katz, Christopher S. Timperley, Claire Le Goues

Autonomous and Robotics Systems (ARSs) are widespread, complex, and increasingly coming into contact with the public. Many of these systems are safety-critical, and it is vital to detect software errors to protect against harm. We propose a family of novel techniques to detect unusual program executions and incorrect program behavior. We model execution beha

Subrata Ghosh, S. R. Polaki, Andrea Macrelli, Carlo S. Casari

As renewable energy is becoming a critical energy source to meet the global demand, electrochemical energy storage devices become indispensable for the efficient energy storage and reliable supply. The electrode material is the key factor determining the energy storage capacity and the power delivery of the devices. Carbon-based materials are emerging as a v

Julius Frost, Olivia Watkins, Eric Weiner, Pieter Abbeel

In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time. Some policy interpretability methods facilitate this by capturing the policy's decision making in a set of agent rollouts. However, even the most informative trajectories of training time beh

Katarzyna Krzyzanowska, Jorge Ferreras, Changhyun Ryu, Edward Carlo Samson

Confining the propagating wavepackets of an atom interferometer inside a waveguide can substantially reduce the size of the device while preserving high sensitivity. We have realized a two-dimensional Sagnac atom interferometer in which Bose-condensed $^{87}$Rb atoms propagate within a tight waveguide formed by a collimated laser beam, a matter wave analog o

Hui Huang, Jinniao Qiu, Konstantin Riedl

In this paper we provide a rigorous convergence analysis for the renowned particle swarm optimization method by using tools from stochastic calculus and the analysis of partial differential equations. Based on a time-continuous formulation of the particle dynamics as a system of stochastic differential equations, we establish convergence to a global minimize

Mohammad Hassan Khatami, Udson C. Mendes, Nathan Wiebe, Philip M. Kim

Protein design is a technique to engineer proteins by modifying their sequence to obtain novel functionalities. In this method, amino acids in the sequence are permutated to find the low energy states satisfying the configuration. However, exploring all possible combinations of amino acids is generally impossible to achieve on conventional computers due to t

E. Kyriakopoulos

The line element of a class of solutions which match to the solution of Kerr on an oblate spheroid if the two functions $ F(r)$ and $H(r)$ on which it depends satisfy certain matching conditions is presented. The non vanishing components of the Ricci tensor $R_{\mu\nu}$, the Ricci scalar $ R$, the second order curvature invariant $K$, the eigenvalues of the

Jonathan A. Hillman

We show that torsion-free elementary amenable groups of Hirsch length $\leq3$ are solvable, of derived length $\leq3$. This class includes all solvable groups of cohomological dimension 3. We show also that groups in the latter subclass are either polycyclic, semidirect products $BS(1,n)\rtimes\mathbb{Z}$ or properly ascending HNN extensions with base $\math

James P. Edwards, C. Moctezuma Mata, Christian Schubert

We summarize recent progress in applying the worldline formalism to the analytic calculation of one-loop N-point amplitudes. This string-inspired approach is well-adapted to avoiding some of the calculational inefficiencies of the standard Feynman diagram approach, most notably by providing master formulas that sum over diagrams differing only by the positio

Qinda Guo, Maciej Dendzik, Antonija Grubišić-Čabo, Magnus H. Berntsen

Here we present a high repetition rate, narrow band-width, extreme ultraviolet (XUV) photon source for time- and angle-resolved photoemission spectroscopy (tr-ARPES). The narrow band width pulses $\Delta E=9, 14, 18$ meV for photon energies $h\nu=10.8, 18.1, 25.3$ eV are generated through High Harmonic Generation (HHG) using ultra-violet (UV) drive pulses wi

Gabriel I. López-Morales, Alexander Hampel, Gustavo E. López, Vinod M. Menon

We use density functional theory (DFT) to explore the physical properties of an $Er_{ W}$ point defect in monolayer $WS_{ 2}$. Our calculations indicate that electrons localize at the dangling bonds associated with a tungsten vacancy ($V_{W}$) and at the $Er^{ 3+}$ ion site, even in the presence of a net negative charge in the supercell. The system features

Daniel Gibney, Sharma V. Thankachan, Srinivas Aluru

Aligning a sequence to a walk in a labeled graph is a problem of fundamental importance to Computational Biology. For finding a walk in an arbitrary graph with $|E|$ edges that exactly matches a pattern of length $m$, a lower bound based on the Strong Exponential Time Hypothesis (SETH) implies an algorithm significantly faster than $O(|E|m)$ time is unlikely

Brad Emmons, Xiao Xiao

The arithmetic partial derivative (with respect to a prime $p$) is a function from the set of integers that sends $p$ to 1 and satisfies the Leibniz rule. In this paper, we prove that the $p$-adic valuation of the sequence of higher order partial derivatives is eventually periodic. We also prove a criterion to determine when an integer has integral anti-part

Erik D. Demaine, Kritkorn Karntikoon

An orthotube consists of orthogonal boxes (e.g., unit cubes) glued face-to-face to form a path. In 1998, Biedl et al. showed that every orthotube has a grid unfolding: a cutting along edges of the boxes so that the surface unfolds into a connected planar shape without overlap. We give a new algorithmic grid unfolding of orthotubes with the additional propert

William Merrill, Nikolaos Tsilivis

One way to interpret the behavior of a blackbox recurrent neural network (RNN) is to extract from it a more interpretable discrete computational model, like a finite state machine, that captures its behavior. In this work, we propose a new method for extracting finite automata from RNNs inspired by the state merging paradigm from grammatical inference. We de

Noah Shutty, Christopher Chamberland

Universal fault-tolerant quantum computers will require the use of efficient protocols to implement encoded operations necessary in the execution of algorithms. In this work, we show how solvers for satisfiability modulo theories (SMT solvers) can be used to automate the construction of Clifford circuits with certain fault-tolerance properties and we apply o

Ana M. Bianco, Graciela Boente, Gonzalo Chebi

Penalized $M-$estimators for logistic regression models have been previously study for fixed dimension in order to obtain sparse statistical models and automatic variable selection. In this paper, we derive asymptotic results for penalized $M-$estimators when the dimension $p$ grows to infinity with the sample size $n$. Specifically, we obtain consistency an

José Nicasio, Christian Schubert, Naser Ahmadiniaz, James P. Edwards

Within the worldline approach to quantum electrodynamics (QED), a change of the photon's covariant gauge parameter $\xi$ is investigated to analyse the non-perturbative gauge dependence of the configuration space fermion correlation functions, deriving a generalization of the Landau-Kalatnikov-Fradkin transformations (LKFt). These transformations reveal how

Oscar Telschow, Miguel Albaladejo-Siguan, Lena Merten, Alexander D. Taylor

The use of antisolvents during the fabrication of solution-processed lead halide perovskite layers is increasingly common. Usually, the antisolvent is applied by pipetting during the spin-coating process, which often irreversibly alters the composition of the perovskite layer, resulting in the formation of PbI2 at the surface and bulk of the perovskite layer

Mark Dong, David Heim, Alex Witte, Genevieve Clark

Visible-wavelength very large-scale integration (VLSI) photonic circuits have potential to play important roles in quantum information and sensing technologies. The realization of scalable, high-speed, and low-loss photonic mesh circuits depends on reliable and well-engineered visible photonic components. Here we report a low-voltage optical phase shifter ba

Timothy A. Sipkens, Joel C. Corbin, Triantafillos Koukoulas, Andrew Oldershaw

The particle filtration efficiency (PFE) of a respirator or face mask is one of its key properties. While the physics of particle filtration results in the PFE being size-dependent, measurement standards are specified using a single, integrated PFE, for simplicity. This integrated PFE is commonly defined with respect to either the number (NBFE) or mass (MBFE

László Lempert

Consider a compact K\"ahler manifold $(X,\omega)$ and the space $\cal E(X,\omega)=\cal E$ of $\omega$--plurisubharmonic functions of full Monge--Amp\`ere mass on it. We introduce a quantity $\rho[u,v]$ to measure the distance between $u, v\in\cal E$; $\rho[u,v]$ is not a number but rather a decreasing function on a certain interval $(0,V)\subset\mathbb R$. W

Zahra Ghahreman, Mehdi Dehghani, Majid Monemzadeh

We construct a quantum mechanics based on the hypothesis of existing compact extra dimensions for a particle that wants to detect it. By introducing a probability function, we express the transition of particle to the extra 2d window. The general properties of this function has been examined and a length scale for occurrence of particle to extra window is gi

Deepesh Giri, Haitham El Kadiri, Christopher Barrett

Capturing twin nucleation in full-field crystal plasticity is a long-standing problem in materials science. The challenge resides mainly in the biased regional lattice transformation associated with twin formation in defiance of its obedience to a threshold stress law which could be fulfilled in regions where twinning is deferred. Hence, determining a favora

Michael Ben-Zvi, Jiayi Lou, Genevieve S. Walsh

The aim of these notes is to connect the theory of hyperbolic and relatively hyperbolic groups to the theory of manifolds and Kleinian groups. We also give definitions and many examples of relatively hyperbolic groups and their boundaries. We survey some of the extensive work that has been done in the field. These notes are based on lectures given by the thi

Derek Hanely, Jeremy L. Martin, Daniel McGinnis, Dane Miyata

We show that the base polytope $P_M$ of any paving matroid $M$ can be systematically obtained from a hypersimplex by slicing off certain subpolytopes, namely base polytopes of lattice path matroids corresponding to panhandle-shaped Ferrers diagrams. We calculate the Ehrhart polynomials of these matroids and consequently write down the Ehrhart polynomial of $

Chau Tran, Pedro Cisneros-Velarde, Sang-Yun Oh, Alexander Petersen

Recently, a special case of precision matrix estimation based on a distributionally robust optimization (DRO) framework has been shown to be equivalent to the graphical lasso. From this formulation, a method for choosing the regularization term, i.e., for graphical model selection, was proposed. In this work, we establish a theoretical connection between the

Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi

Certified robustness in machine learning has primarily focused on adversarial perturbations of the input with a fixed attack budget for each point in the data distribution. In this work, we present provable robustness guarantees on the accuracy of a model under bounded Wasserstein shifts of the data distribution. We show that a simple procedure that randomiz

Naftaly H. Minsky

A recent paper (circa 2020) by Osterwile et al., entitled "21 Years of Distributed Denial of Service: A Call to Action", states: "We are falling behind in the war against distributed denial-of-service attacks. Unless we act now, the future of the Internet could be at stake." And an earlier (circa 2007) paper by Peng et al. states: "a key challenge for the de

Prajjwal Bhargava, Vincent Ng

While commonsense knowledge acquisition and reasoning has traditionally been a core research topic in the knowledge representation and reasoning community, recent years have seen a surge of interest in the natural language processing community in developing pre-trained models and testing their ability to address a variety of newly designed commonsense knowle

Brent Griffin

This paper addresses the problem of mobile robot manipulation using object detection. Our approach uses detection and control as complimentary functions that learn from real-world interactions. We develop an end-to-end manipulation method based solely on detection and introduce Task-focused Few-shot Object Detection (TFOD) to learn new objects and settings.

Keane Lucas, Ross E. Allen

Cooperative artificial intelligence with human or superhuman proficiency in collaborative tasks stands at the frontier of machine learning research. Prior work has tended to evaluate cooperative AI performance under the restrictive paradigms of self-play (teams composed of agents trained together) and cross-play (teams of agents trained independently but usi

Andrea Collevecchio, Robert C. Griffiths

We study a large class of long-range random walks which take values on the vertices of an N dimensional hypercube. These processes are connected with multivariate Bernoulli autoregression.

Haonan Yu, Haichao Zhang, Wei Xu

Maximum entropy (MaxEnt) RL maximizes a combination of the original task reward and an entropy reward. It is believed that the regularization imposed by entropy, on both policy improvement and policy evaluation, together contributes to good exploration, training convergence, and robustness of learned policies. This paper takes a closer look at entropy as an

Yuhang Yao, Weizhao Jin, Srivatsan Ravi, Carlee Joe-Wong

Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated. However, the cross-client edges naturally exist among clients. Thus, distributed methods for training a model on a single graph incur either significant comm

Elena S. Hafner

Recent work of Pechenik, Speyer, and Weigandt proved a formula for the degree of any Grothendieck polynomial. A distinct formula for the degree of vexillary Grothendieck polynomials was proven by Rajchgot, Robichaux, and Weigandt. We give a new proof of Pechenik, Speyer, and Weigandt's formula in the special case of vexillary permutations and characterize th

Uri Alon, Frank F. Xu, Junxian He, Sudipta Sengupta

Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time. While effective, a major bottleneck of using these models in practice is the computationally costly datastore search, which can be performed as frequently as every t

Se Yoon Lee

Nonlinear mixed effects models have become a standard platform for analysis when data is in the form of continuous and repeated measurements of subjects from a population of interest, while temporal profiles of subjects commonly follow a nonlinear tendency. While frequentist analysis of nonlinear mixed effects models has a long history, Bayesian analysis of

Omid Zabeti

Suppose $X$ is a locally solid vector lattice. It is known that there are several non-equivalent notions for compact operators on $X$. Furthermore, notion of the $AM$-property in $X$ as an extension for the $AM$-spaces in Banach lattices has been considered, recently. In this paper, we establish a variant of the known Krengel's theorem for different types of

Tyler Cody, Erin Lanus, Daniel D. Doyle, Laura Freeman

This paper demonstrates the systematic use of combinatorial coverage for selecting and characterizing test and training sets for machine learning models. The presented work adapts combinatorial interaction testing, which has been successfully leveraged in identifying faults in software testing, to characterize data used in machine learning. The MNIST hand-wr

Haonan Yu, Wei Xu, Haichao Zhang

We consider the safe reinforcement learning (RL) problem of maximizing utility with extremely low constraint violation rates. Assuming no prior knowledge or pre-training of the environment safety model given a task, an agent has to learn, via exploration, which states and actions are safe. A popular approach in this line of research is to combine a model-fre

Isabeau Prémont-Schwarz, Jaroslav Vítků, Jan Feyereisl

If the trend of learned components eventually outperforming their hand-crafted version continues, learned optimizers will eventually outperform hand-crafted optimizers like SGD or Adam. Even if learned optimizers (L2Os) eventually outpace hand-crafted ones in practice however, they are still not provably convergent and might fail out of distribution. These a

Ruofan Liang, Hongyi Sun, Nandita Vijaykumar

Implicit neural representations with multi-layer perceptrons (MLPs) have recently gained prominence for a wide variety of tasks such as novel view synthesis and 3D object representation and rendering. However, a significant challenge with these representations is that both training and inference with an MLP over a large number of input coordinates to learn a

Tanya Khovanova, Atharva Pathak

This paper studies a single-suit version of the card game War on a finite deck of cards. There are varying methods of how players put the cards that they win back into their hands, but we primarily consider randomly putting the cards back and deterministically always putting the winning card before the losing card. The concept of a \emph{passthrough} is defi

Ross DeMott, Sam Major, Alex Flournoy

We consider the stability of the maximally-extended Reissner-Nordstr\"om solution in a Minkowski, de Sitter, or anti-de Sitter background. In a broad class of situations, prior work has shown that spherically symmetric perturbations from a massless scalar field cause the inner horizon of an RN black hole to become singular and collapse. Even if this is the c

Nathan C. Frey, Baolin Li, Joseph McDonald, Dan Zhao

Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains. Neural architecture searches, hyperparameter sweeps, and rapid prototyping consume immense resources that can prevent resource-constrained researchers from experimenting with large models and carry considerable environmental impact. As such

Fanze Kong, Juncheng Wei

The strong Allee effect plays an important role on the evolution of population in ecological systems. One important concept is the Allee threshold that determines the persistence or extinction of the population in a long time. In general, a small initial population size is harmful to the survival of a species since when the initial data is below the Allee th