November 2022 arXiv papers — page 95

Haoqin Tu, Yitong Li

Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language. The employment of deep neural architectures has been largely explored in a multitude of context and tasks to fulfill various user needs. On one hand, producing textual content that

Franco Dassi, David Mora, Carlos Reales, Ivàn Velàsquez

In this work we analyze a virtual element method on polyhedral meshes for solving the sixth-order elliptic problem with simply supported boundary conditions. We apply the Ciarlet-Raviart arguments to introduce an auxiliary unknown $\sigma:=-\Delta^2 u$ and to search the main uknown $u$ in the $H^2\cap H_0^1$ Sobolev space. The virtual element discretization

Yu Guo, Lizhong Huang

Quantum mutual information (QMI) not only displays the mutual information in the system but also demonstrates some quantum correlation beyond entanglement. We explore here the two alternatives of multipartite quantum mutual information (MQMI) based on the von Neumann entropy according to the framework of the complete measure of multi-particle quantum system.

Kyungsu Kim, Minju Park, Haesun Joung, Yunkee Chae

As digital music production has become mainstream, the selection of appropriate virtual instruments plays a crucial role in determining the quality of music. To search the musical instrument samples or virtual instruments that make one's desired sound, music producers use their ears to listen and compare each instrument sample in their collection, which is t

Kyle Richardson, Ronen Tamari, Oren Sultan, Reut Tsarfaty

Can we teach natural language understanding models to track their beliefs through intermediate points in text? We propose a representation learning framework called breakpoint modeling that allows for learning of this type. Given any text encoder and data marked with intermediate states (breakpoints) along with corresponding textual queries viewed as true/fa

Kévin Colin, Håkan Hjalmarsson, Xavier Bombois

In this work, we consider the problem of regret minimization in adaptive minimum variance and linear quadratic control problems. Regret minimization has been extensively studied in the literature for both types of adaptive control problems. Most of these works give results of the optimal rate of the regret in the asymptotic regime. In the minimum variance ca

Ce Wang

In this paper, we present a quantum Bernoulli noises approach to quantum walks on hypercubes. We first obtain an alternative description of a general hypercube and then, based on the alternative description, we find that the operators $\partial_k^* + \partial_k$ behave actually as the shift operators, where $\partial_k$ and $\partial_k^*$ are the annihilatio

Arpita Sanyal, Amit Saha, Banani Saha, Amlan Chakrabarti

Clique problem has a wide range of applications due to its pattern matching ability. There are various formulation of clique problem like $k$-clique problem, maximum clique problem, etc. The $k$-Clique problem, determines whether an arbitrary network has a clique or not whereas maximum clique problem finds the largest clique in a graph. It is already exhibit

Swastik Bhattacharya, S. Shankaranarayanan

Black hole horizons interact with external fields when matter-energy falls through them. Such non-stationary black hole horizons can be described using viscous fluid equations. This work attempts to describe this process using effective field theory methods. Such a description can provide important insights beyond classical black hole physics. In this work,

Daeseong Kim, Haanju Yoo, Sewon Kim

International Classification of Diseases (ICD) is a global medical classification system which provides unique codes for diagnoses and procedures appropriate to a patient's clinical record. However, manual coding by human coders is expensive and error-prone. Automatic ICD coding has the potential to solve this problem. With the advancement of deep learning t

Joohwan Seo, Nikhil Potu Surya Prakash, Alexander Rose, Jongeun Choi

After its introduction, impedance control has been utilized as a primary control scheme for robotic manipulation tasks that involve interaction with unknown environments. While impedance control has been extensively studied, the geometric structure of SE(3) for the robotic manipulator itself and its use in formulating a robotic task has not been adequately a

Bibhash Mondal, Ripan Saha

Rota-Baxter operators have been paid much attention in the last few decades as they have many applications in mathematics and physics. In this paper, our object of study is modified Rota-Baxter operators on Leibniz algebras. We investigate modified Rota-Baxter Leibniz algebras from the cohomological point of view. We study a one-parameter formal deformation

Cao H. Nam

Based on the swampland program establishing the constraints that an effective field theory must satisfy in order to come from quantum gravity, we point to that if this program is true the brane-world scenario with the branon dark matter (DM) would be ruled out without needing the experimental observations. In other words, the constraints of quantum gravity i

Geunyeong Byeon, Minseok Ryu, Kibaek Kim

This paper proposes a linear approximation of the alternating current optimal power flow problem for multiphase distribution networks with voltage-dependent loads connected in both wye and delta configurations. We establish a set of linear equations that exactly describes the relationship between power changes at a bus and the corresponding values from a del

Pranav Agarwal, Marek Teichmann, Sheldon Andrews, Samira Ebrahimi Kahou

Training novice users to operate an excavator for learning different skills requires the presence of expert teachers. Considering the complexity of the problem, it is comparatively expensive to find skilled experts as the process is time-consuming and requires precise focus. Moreover, since humans tend to be biased, the evaluation process is noisy and will l

Dickson Odhiambo Owuor, Thomas Runkler, Anne Laurent

Swarm intelligence is a discipline that studies the collective behavior that is produced by local interactions of a group of individuals with each other and with their environment. In Computer Science domain, numerous swarm intelligence techniques are applied to optimization problems that seek to efficiently find best solutions within a search space. Gradual

Lorenzo Bellone, Boris Galkin, Emiliano Traversi, Enrico Natalizio

Network slicing is a well assessed approach enabling virtualization of the mobile core and radio access network (RAN) in the emerging 5th Generation New Radio. Slicing is of paramount importance when dealing with the emerging and diverse vertical applications entailing heterogeneous sets of requirements. 5G is also envisioning Unmanned Aerial Vehicles (UAVs)

M. R. Azimi, Z. Naghdi

A conditional weighted composition operator $T_u: L^p(\Sigma)\rightarrow L^p(\mathcal{A})$ ($1\leq p<\infty$), is defined by $T_u(f):= E^{\mathcal{A}}(u f\circ \varphi)$, where $\varphi: X\rightarrow X$ is a measurable transformation, $u$ is a weight function on $X$ and $E^{\mathcal{A}}$ is the conditional expectation operator with respect to $\mathcal{A}$.

Ángel Chávez, Stephan Ramon Garcia, Jackson Hurley

We introduce a family of norms on the $n \times n$ complex matrices. These norms arise from a probabilistic framework, and their construction and validation involve probability theory, partition combinatorics, and trace polynomials in noncommuting variables. As a consequence, we obtain a generalization of Hunter's positivity theorem for the complete homogene

Yanli Liu, Kaiqing Zhang, Tamer Başar, Wotao Yin

In this paper, we revisit and improve the convergence of policy gradient (PG), natural PG (NPG) methods, and their variance-reduced variants, under general smooth policy parametrizations. More specifically, with the Fisher information matrix of the policy being positive definite: i) we show that a state-of-the-art variance-reduced PG method, which has only b

Ronald J. Riegert

We show that a proposal by Vishwakarma to realize conformal-covariance for the Weyl-Lanczos equation is nonviable.

Kallal Pal, Sumit Chandok

In this article, we generalize the notion of orthogonality as a linear combination of norm derivatives in order to give a novel concept that we refer to as $\rho_{\alpha,\beta}$-orthogonality. Also, we discuss some of its geometric properties in a real normed linear space and present some sufficient criteria for the smoothness of a normed space by using $\rh

Mohsen Khodadi, Gaetano Lambiase, Ahmad Sheykhi

By keeping the cosmological principle i.e., an isotropic and homogeneous universe, we consider the cosmology of a vector-tensor theory of gravitation known as the \textit{bumblebee} model. In this model a single Lorentz-violating timelike vector field with a nonzero vacuum expectation value (VEV) couples to the Ricci tensor and scalar, as well. Taking the an

Kangheun Kim, Jaewook Ahn

Tomographic reconstruction of the many-body quantum state of a scalable qubit system is of paramount importance in quantum computing technologies. However, conventional approaches which use tomographically orthogonal base measurements require precise and individual qubit controls which are often experimentally daunting. Here, we propose, as a quantum-mechani

Carlos Aguirre, Mark Dredze, Philip Resnik

Stressors are related to depression, but this relationship is complex. We investigate the relationship between open-ended text responses about stressors and depressive symptoms across gender and racial/ethnic groups. First, we use topic models and other NLP tools to find thematic and vocabulary differences when reporting stressors across demographic groups.

Junki Mori, Tomoyuki Yoshiyama, Furukawa Ryo, Isamu Teranishi

Federated learning (FL) is a decentralized machine learning technique that enables multiple clients to collaboratively train models without requiring clients to reveal their raw data to each other. Although traditional FL trains a single global model with average performance among clients, statistical data heterogeneity across clients has resulted in the dev

Tianling Jin, Jingang Xiong, Xuzhou Yang

In this paper, we study a nonlinear boundary diffusion equation of porous medium type arising from a boundary control problem. We give a complete and sharp characterization of the asymptotic behavior of its solutions, and prove the stability of its separable solutions.

Jiaojiao Pan

In this paper, we study the instability of highly-oscillating solutions to semi-linear hyperbolic systems. A instability criterion was given in \cite{Lu} under rather strong separation conditions of resonance sets: coupled resonance sets are pairwise disjoint. Here we show that such separation conditions in \cite{Lu} can be relaxed: one of the coupled non-tr

Zhaoyang Zhang, Xuying Wang, Xiaoming Mei, Chao Tao

The existing SSCL of RSI is built based on constructing positive and negative sample pairs. However, due to the richness of RSI ground objects and the complexity of the RSI contextual semantics, the same RSI patches have the coexistence and imbalance of positive and negative samples, which causing the SSCL pushing negative samples far away while pushing posi

Watse Sybesma

We consider a charged Lifshitz black hole in the large transverse dimension limit. In this setup, the dynamics near the black hole horizon are shown to be effectively governed by a family of two-dimensional models of dilaton gravity depending on the ratio of the dynamical parameter characterizing the black hole and the dimension of spacetime. This family inc

Takahiro Matsuoka, Rahul Rao, Michael A. Susner, Benjamin S. Conner

We have studied the insulator-to-metal transition and crystal structure evolution under high pressure in the van der Waals compound CoPS$_3$ through $\textit{in-situ}$ electrical resistance, Hall resistance, magnetoresistance, X-ray diffraction, and Raman scattering measurements. CoPS$_3$ exhibits a $C2/m$ $\rightarrow$ $P\overline{3}$ structural transformat

Takahiro Kashihara, Yoshihiro Michishita, Robert Peters

Geometric aspects of physics play a crucial role in modern condensed matter physics. The quantum metric is one of these geometric quantities which defines the distance on a parameter space and contributes to various physical phenomena, such as superconductivity and nonlinear conductivity. Despite its importance, the quantum metric in interacting systems is p

Mitsunori Ogihara, Kei Uchizawa

Dynamical Systems is a field that studies the collective behavior of objects that update their states according to some rules. Discrete-time Boolean Finite Dynamical System (DT-BFDS) is a subfield where the systems have some finite number of objects whose states are Boolean values, and the state updates occur in discrete time. In the subfield of DT-BFDS, res

Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron Courville

Large language models (LLMs) have shown increasing in-context learning capabilities through scaling up model and data size. Despite this progress, LLMs are still unable to solve algorithmic reasoning problems. While providing a rationale with the final answer has led to further improvements in multi-step reasoning problems, Anil et al. 2022 showed that even

Vaibhav Pandey, Yevgeniya Tarasova

In this paper, we prove that the generic link of a generic determinantal ring defined by maximal minors is strongly $F$-regular. In the process, we strengthen a result of Chardin and Ulrich in the graded setting. They showed that the generic residual intersections of a complete intersection ring with rational singularities again have rational singularities.

Stephen Colegate, Changrui Liu

Based on previous work done in this field, we build a dynamical system that describes changes in drug addiction in an isolated population when two addictive substances are available simultaneously. We then use our model to investigate whether the system captures the process of users switching drug habits. One of the motivations for this project is to mathema

Y. B. Shi, Z. Song

The dynamic balance between pair creation and annihilation processes takes a crucial role to the topological superconductivity in Kitaev model. Here we study the effect of spatial separation of creation and annihilation terms, i.e., sources and drains of pair are arranged alternatively. In this regard, a non-Hermitian Hamiltonian is naturally considered, whi

Shijia Huang, Feng Li, Hao Zhang, Shilong Liu

Reference Expression Segmentation (RES) and Reference Expression Generation (REG) are mutually inverse tasks that can be naturally jointly trained. Though recent work has explored such joint training, the mechanism of how RES and REG can benefit each other is still unclear. In this paper, we propose a unified mutual supervision framework that enables two tas

Turbasu Chatterjee, Arnav Das, Subhayu Kumar Bala, Amit Saha

In the recent years, numerous research advancements have extended the limit of classical simulation of quantum algorithms. Although, most of the state-of-the-art classical simulators are only limited to binary quantum systems, which restrict the classical simulation of higher-dimensional quantum computing systems. Through recent developments in higher-dimens

Xiao-Qing Liu, Jia Liu, Zheng-Yuan Xue

Quantum computation attaches importance to high-precision quantum manipulation, where the quantum state transfer with high fidelity is necessary. Here, we propose a new scheme to implement the quantum state transfer of high fidelity and long distance, by adding on-site potential into the qubit chain and enlarging the proportion of the coupling strength betwe

Siddhi Brahmbhatt

Roads in medium-sized Indian towns often have lots of traffic but no (or disregarded) traffic stops. This makes it hard for the blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-based safety classifiers thus have the potential to help the blind to cross Indian roads. Yet, we currently lack

Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani, James Bailey

Backdoor attacks have emerged as one of the major security threats to deep learning models as they can easily control the model's test-time predictions by pre-injecting a backdoor trigger into the model at training time. While backdoor attacks have been extensively studied on images, few works have investigated the threat of backdoor attacks on time series d

Yuli B. Rudyak

Here I discuss ideas that makes a synthesis of topology and probability theory. The idea is the following: given a set $X$, assign a number $p(A)\in [0,1]$ for any subset $A$ of $X$. We can interpret $p(A)$ as the probability of openness of $A$.

S. K. Guang, N. Li, R. L. Luo, Q. Huang

We report an in-plane thermal transport study of the honeycomb Kitaev material Na$_2$Co$_2$TeO$_6$ at subKelvin temperatures. In zero field, the $\kappa(T)$ displays a rather weak $T$-dependence but has a non-zero residual term $\kappa_0/T$, indicating strong phonon scattering by magnetic excitation and the possibility of itinerant spinon-like excitations co

Xiaojing Weng, Altai Perry, Michael Maroun, Luat T. Vuong

We analyze the singular value decomposition (SVD) and SVD entropy of Cantor fractals produced by the Kronecker product. Our primary results show that SVD entropy is a measure of image ``complexity dimension" that is invariant under the number of Kronecker-product self-iterations (i.e., fractal order). SVD entropy is therefore similar to the fractal Hausdorff

Jianfeng Hou, Heng Li, Qinghou Zeng

The Tur\'{a}n number of a graph $H$, $\text{ex}(n,H)$, is the maximum number of edges in an $n$-vertex graph that does not contain $H$ as a subgraph. For a vertex $v$ and a multi-set $\mathcal{F}$ of graphs, the suspension $\mathcal{F}+v$ of $\mathcal{F}$ is the graph obtained by connecting the vertex $v$ to all vertices of $F$ for each $F\in \mathcal{F}$. F

Chih-Hui Ho, Srikar Appalaraju, Bhavan Jasani, R. Manmatha

We present YORO - a multi-modal transformer encoder-only architecture for the Visual Grounding (VG) task. This task involves localizing, in an image, an object referred via natural language. Unlike the recent trend in the literature of using multi-stage approaches that sacrifice speed for accuracy, YORO seeks a better trade-off between speed an accuracy by e

Haoran Lin, Yajin Zhou, Lei Wu

Despite the success in various scenarios, blockchain systems, especially EVM-compatible ones that serially execute transactions, still face the significant challenge of limited throughput. Concurrent transaction execution is a promising technique to accelerate transaction processing and increase the overall throughput. Existing concurrency control algorithms

Zihan Yang, Peng Chen, Ziyu Guo, Dahai Ni

In this work, we consider the Direction-of-Arrival (DOA) estimation problem in a low-cost architecture where only one antenna as the receiver is aided by a reconfigurable intelligent surface (RIS). We introduce the one-bit RIS as a signal reflector to enhance signal transmission in non-line-of-sight (NLOS) situations and substantially simplify the physical h

Yiming Fei, Jiangang Li, Yanan Li

In radial basis function neural network (RBFNN) based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data. However, with forgetting mechanisms, some useful knowledge will get lost simply because they are learned a long time ago, which we refer to as the passive knowledge forgetting

Ruiyuan Chen, Grigory Terlov, Anush Tserunyan

We prove the a.e. nonamenability of locally finite quasi-pmp Borel graphs whose every component admits at least three nonvanishing ends with respect to the underlying Radon--Nikodym cocycle. We witness their nonamenability by constructing Borel subforests with at least three nonvanishing ends per component, and then applying Tserunyan and Tucker-Drob&#39;s r

Namrata Deka, Danica J. Sutherland

We introduce a method, MMD-B-Fair, to learn fair representations of data via kernel two-sample testing. We find neural features of our data where a maximum mean discrepancy (MMD) test cannot distinguish between representations of different sensitive groups, while preserving information about the target attributes. Minimizing the power of an MMD test is more

Bailin Wang, Ivan Titov, Jacob Andreas, Yoon Kim

We describe a neural transducer that maintains the flexibility of standard sequence-to-sequence (seq2seq) models while incorporating hierarchical phrases as a source of inductive bias during training and as explicit constraints during inference. Our approach trains two models: a discriminative parser based on a bracketing transduction grammar whose derivatio

Ziqi Yu, Suhua Wang, Enhui Shi

We show that a continuous map $f$ from a quasi-graph $G$ to itself is pointwise recurrent if and only if one of the following two statements holds: (1) $X$ is a simple closed curve and $f$ is topologically conjugate to an irrational rotation on the unit circle $\mathbb S^1$; (2) $f$ is a perodic homeomorphism.

Siddharth Jain, Denis Boyer, Arnab Pal, Leonardo Dagdug

Transport of particles through channels is of paramount importance in physics, chemistry and surface science due to its broad real world applications. Much insights can be gained by observing the transition paths of a particle through a channel and collecting statistics on the lifetimes in the channel or the escape probabilities from the channel. In this pap

Zequn Jin, Lihua Lin, Zhengyu Zhang

This paper proposes a new class of heterogeneous causal quantities, named \textit{outcome conditioned} average structural derivatives (OASD) in a general nonseparable model. OASD is the average partial effect of a marginal change in a continuous treatment on the individuals located at different parts of the outcome distribution, irrespective of individuals'

Arnhav Datar, Arun Rajkumar, John Augustine

We study the problem of rank aggregation where the goal is to obtain a global ranking by aggregating pair-wise comparisons of voters over a set of items. We consider an adversarial setting where the voters are partitioned into two sets. The first set votes in a stochastic manner according to the popular score-based Bradley-Terry-Luce (BTL) model for pairwise

Qihua Zhou, Ruibin Li, Song Guo, Peiran Dong

Recent years have witnessed the dramatic growth of Internet video traffic, where the video bitstreams are often compressed and delivered in low quality to fit the streamer's uplink bandwidth. To alleviate the quality degradation, it comes the rise of Neural-enhanced Video Streaming (NVS), which shows great prospects for recovering low-quality videos by mostl

Souvik Das, Ahmed Atteya, Pralay Kumar Karmakar

The omnipresence of dust particulates in space and astrophysical plasmas has been attracting numerous researchers to study the collective excitation and propagation dynamics of different eigen-mode structures in diversifed astrocosmic circumstances for years. It includes planetary rings, interplanetary space, cometary tails, asteroid zones, planetary atmosph

Huck Bennett, Mahdi Cheraghchi, Venkatesan Guruswami, João Ribeiro

We prove that the Minimum Distance Problem (MDP) on linear codes over any fixed finite field and parameterized by the input distance bound is W[1]-hard to approximate within any constant factor. We also prove analogous results for the parameterized Shortest Vector Problem (SVP) on integer lattices. Specifically, we prove that SVP in the $\ell_p$ norm is W[1]

Keivan G. Stassun, Guillermo Torres, Marina Kounkel, Dax L. Feliz

We report the discovery of 2M1222-57 as a low-mass, pre-main-sequence (PMS) eclipsing binary (EB) in the Lower Centaurus Crux (LCC) association for which, using Gaia parallaxes and proper motions with a neural-net age estimator, we determine an age of 16.2$\pm$2.2 Myr. The broadband spectral energy distribution (SED) shows clear excess at ~10 um indicative o

Ankit Pal

This paper introduces DeepParliament, a legal domain Benchmark Dataset that gathers bill documents and metadata and performs various bill status classification tasks. The proposed dataset text covers a broad range of bills from 1986 to the present and contains richer information on parliament bill content. Data collection, detailed statistics and analyses ar

Yimeng Li, Arnab Debnath, Gregory Stein, Jana Kosecka

We consider the problem of time-limited robotic exploration in previously unseen environments where exploration is limited by a predefined amount of time. We propose a novel exploration approach using learning-augmented model-based planning. We generate a set of subgoals associated with frontiers on the current map and derive a Bellman Equation for explorati

Katie Clinch, Jackson Goerner, Tony Huynh, Freddie Illingworth

In 2017, Ron Aharoni made the following conjecture about rainbow cycles in edge-coloured graphs: If $G$ is an $n$-vertex graph whose edges are coloured with $n$ colours and each colour class has size at least $r$, then $G$ contains a rainbow cycle of length at most $\lceil \frac{n}{r} \rceil$. One motivation for studying Aharoni's conjecture is that it is a

Ben Morris, Hans Oberschelp

We provide a new proof of Maurer, Renard, and Pietzak's result that the sum of the nCPA advantages of random permutations $P$ and $Q$ bound the CCA advantage of $P^{-1} \circ Q$. Our proof uses probability directly, as opposed to information theory, and has the advantage of providing an alternate sufficient condition of low CCA advantage. Namely, the CCA adv

Darshan Chalise, David G. Cahill

While several 2D thermometry techniques exist, there is a lack of 3D thermometry techniques that work for wide range of materials and offer good resolution in time, space and temperature. X-ray diffraction (XRD) and nuclear magnetic resonance (NMR) imaging can provide 3D temperature information. However, XRD is typically limited to crystalline materials whil

Sambit Das, Vikram Gavini

We present an efficient preconditioning technique for accelerating the fixed point iteration in real-space Kohn-Sham density functional theory (DFT) calculations. The preconditioner uses a low rank approximation of the dielectric matrix (LRDM) based on G\^ateaux derivatives of the residual of fixed point iteration along appropriately chosen direction functio

Madhura Joshi, Ankit Pal, Malaikannan Sankarasubbu

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our s

K. Y. Jung, B. Y. Han, E. J. Jeon, Y. Jeong

A convolutional neural network (CNN) architecture is developed to improve the pulse shape discrimination (PSD) power of the gadolinium-loaded organic liquid scintillation detector to reduce the fast neutron background in the inverse beta decay candidate events of the NEOS-II data. A power spectrum of an event is constructed using a fast Fourier transform of

Heyu Huang, Runmin Cong, Lianhe Yang, Ling Du

The hippocampus plays a vital role in the diagnosis and treatment of many neurological disorders. Recent years, deep learning technology has made great progress in the field of medical image segmentation, and the performance of related tasks has been constantly refreshed. In this paper, we focus on the hippocampus segmentation task and propose a novel hierar

Peter Melchior, Yan Liang, ChangHoon Hahn, Andy Goulding

We introduce the neural network architecture SPENDER as a core differentiable building block for analyzing, representing, and creating galaxy spectra. It combines a convolutional encoder, which pays attention to up to 256 spectral features and compresses them into a low-dimensional latent space, with a decoder that generates a restframe representation, whose

Jessica Y. Bo, Hen-Wei Huang, Alvin Chan, Giovanni Traverso

Medical datasets often face the problem of data scarcity, as ground truth labels must be generated by medical professionals. One mitigation strategy is to pretrain deep learning models on large, unlabelled datasets with self-supervised learning (SSL). Data augmentations are essential for improving the generalizability of SSL-trained models, but they are typi

Cedric Xia, Joseph Zhang, Allan Zhou

Let $G$ be an $n$-vertex connected graph. A cyclic base ordering of $G$ is a cyclic ordering of all edges such that every cyclically consecutive $n-1$ edges induce a spanning tree of $G$. In this project, we study cyclic base ordering of triangular grid graphs, series composition of graphs, generalized theta graphs, and circulant graphs.

Arnab Bhabak, Subhamay saha

In this paper, we investigate a partially observable zero sum games where the state process is a discrete time Markov chain. We consider a general utility function in the optimization criterion. We show the existence of value for both finite and infinite horizon games and also establish the existence of optimal polices. The main step involves converting the

Jin Li, Gui Zhou, Tantao Gong, Nan Liu

Integrated sensing and communication (ISAC) unifies sensing and communication, and improves the efficiency of the spectrum, energy, and hardware. In this work, we investigate the ISAC beamforming design to maximize the mutual information between the target response matrix of a point radar target and the echo signals, while ensuring the data rate requirements

Anthony Mallama, Richard E. Cole, Scott Harrington, Paul D. Maley

Observations have been carried out in order to assess the optical characteristics of the BlueWalker 3 spacecraft which is the prototype for a new satellite constellation. The illumination phase function has been determined and evaluated. The average visual magnitude when seen overhead at the beginning or ending of astronomical twilight is found to be +1.4.

Qin Zhang, Shangsi Chen, Dongkuan Xu, Qingqing Cao

Open domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language processing (NLP). Recent works have predominantly focused on improving the answering accuracy and achieved promising progress. However, higher accuracy often comes with more memory c

Justin Dulay, Walter J. Scheirer

Recent trends in the machine learning community show that models with fidelity toward human perceptual measurements perform strongly on vision tasks. Likewise, human behavioral measurements have been used to regularize model performance. But can we transfer latent knowledge gained from this across different learning objectives? In this work, we introduce PER

Changrong Liu, S. Suvorova, R. J. Evans, B. Moran

The problem of detecting a sinusoidal signal with randomly varying frequency has a long history. It is one of the core problems in signal processing, arising in many applications including, for example, underwater acoustic frequency line tracking, demodulation of FM radio communications, laser phase drift in optical communications and, recently, continuous g

Xuan Ji, Nora Bailey, Daniel Fabrycky, Edwin S. Kite

The climate of a planet can be strongly affected by its eccentricity due to variations in the stellar flux. There are two limits for the dependence of the inner habitable zone boundary (IHZ) on eccentricity: (1) the mean-stellar flux approximation ($S_{\mbox{IHZ}} \propto \sqrt{1-e^2}$), in which the temperature is approximately constant throughout the orbit

Yue Guo, Joseph Campbell, Simon Stepputtis, Ruiyu Li

Action advising is a knowledge transfer technique for reinforcement learning based on the teacher-student paradigm. An expert teacher provides advice to a student during training in order to improve the student's sample efficiency and policy performance. Such advice is commonly given in the form of state-action pairs. However, it makes it difficult for the s

Hengrui Zhang, Wei Wayne Chen, James M. Rondinelli, Wei Chen

Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in exis

Kaitlin N. Smith, Joshua Viszlai, Lennart Maximilian Seifert, Jonathan M. Baker

Cloud-based quantum computers have become a reality with a number of companies allowing for cloud-based access to their machines with tens to more than 100 qubits. With easy access to quantum computers, quantum information processing will potentially revolutionize computation, and superconducting transmon-based quantum computers are among some of the more pr

Adrián Lozano-Durán, H. Jane Bae

A wall model for large-eddy simulation (LES) is proposed by devising the flow as a combination of building blocks. The core assumption of the model is that a finite set of simple canonical flows contains the essential physics to predict the wall-shear stress in more complex scenarios. The model is constructed to predict zero/favourable/adverse mean pressure

Zidu Lin, Abhinav Rijal, Cecilia Lunardini, Manuel D. Morales

We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neutrino and gravitational wave (GW) signals. In the neutrino channel, this method performs a likelihood ratio test for the presence of SASI in the frequency domain. For gravitational

Geraint F. Lewis, Brendon J. Brewer, Dougal Mackey, Annette M. N. Ferguson

Based upon a metallicity selection, we identify a significant sub-population of the inner halo globular clusters in the Andromeda Galaxy which we name the Dulais Structure. It is distinguished as a co-rotating group of 10-20 globular clusters which appear to be kinematically distinct from, and on average more metal-poor than, the majority of the inner halo p

Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim

In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challenge (BraTS-Reg 2022). Brain tumor registration is a fundamental requirement in brain image analysis for quantifying tumor changes. This is a

Ye Tao, Yuze Jiang, Pengfei Lin, Manabu Tsukada

Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed

Shrijan Roy, Teruaki Suyama

It has been suggested in the literature that the sound velocity of the nuclear matter $v_s$ violates the so-called sound velocity bound $v_s \le c/\sqrt{3}$ at high density, where $c$ is the speed of light. In this paper, we revisit this issue and confront the current measurements of mass, radius, and tidal deformability of neutron stars with $10^5$ differen

Siddhant Bhambri, Amrita Bhattacharjee, Dimitri Bertsekas

In this paper we address the solution of the popular Wordle puzzle, using new reinforcement learning methods, which apply more generally to adaptive control of dynamic systems and to classes of Partially Observable Markov Decision Process (POMDP) problems. These methods are based on approximation in value space and the rollout approach, admit a straightforwa

Giuseppe De Nittis, Kiyonori Gomi

"Quaternionic" vector bundles are the objects which describe the topological phases of quantum systems subjected to an odd time-reversal symmetry (class AII). In this work we prove that the FKMM invariant provides the correct fundamental characteristic class for the classification of "Quaternionic" vector bundles in dimension less than, or equal to three (lo

Michael Yuhas, Arvind Easwaran

In a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle's operation. However, ML models are unlikely to make accurate decisions when presented with data outside their training distribution. Out-of-distribution (OOD) detection can act as a safe

Mohamed Hozayen, Tasneem Darwish, Gunes Karabulut, Halim Yanikomeroglu

Future satellite networks are expected to have thousands of low Earth orbit (LEO) satellites orbiting Earth at very high speeds. User equipment (UE) communicating directly with LEO satellites will experience frequent handovers. Managing the handover process is complicated due to the high frequency of handovers and the availability of multiple LEO satellites

Shaowen Xie, Hao Zhu, Zhen Liu, Qi Zhang

Implicit neural representation (INR) characterizes the attributes of a signal as a function of corresponding coordinates which emerges as a sharp weapon for solving inverse problems. However, the capacity of INR is limited by the spectral bias in the network training. In this paper, we find that such a frequency-related problem could be largely solved by re-

Xian-Hu Zha, Xiufang Ma, Jing-Ting Luo, Chen Fu

Aluminum nitride (AlN) is an important piezoelectric material for a wide range of applications, many efforts are devoted to improving its piezoelectric response by alloying with transition metals (TMs). In this paper, the influence of the type and distribution of TM on the piezoelectric response is discussed for the first time. TM0.0625Al0.9375N with twenty-

Dharm Veer Singh, Aradhya Shukla, Sudhaker Upadhyay

We construct an exact black hole solution for the Einstein gravity coupled with the nonlinear electrodynamics (which corresponds to the Maxwell electrodynamics in the weak field limit) in the presence of a cloud of strings as the source. We study the thermodynamical properties of the black hole solutions and derive the corrected first-law of thermodynamics.

Wensi Wu, Mitchell Daneker, Matthew A. Jolley, Kevin T. Turner

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material pr

Tom Osika, Ebrahim Ebrahim, Martin Styner, Marc Niethammer

A major data pre-processing step for large, multi-site studies is to handle site effects by harmonizing data, generating a dataset that enables more powerful analyses and more robust algorithms. There is a wide variety of data harmonization techniques, but there are few tools that streamline the process of harmonizing data, comparing across techniques, and b

Badal Joshi, Gheorghe Craciun

In a reaction network, the concentration of a species with the property of dynamic absolute concentration robustness (dynamic ACR) converges to the same value independent of the overall initial values. This property endows a biochemical network with output robustness and therefore is essential for its functioning in a highly variable environment. It is impor

Ian G. Malone, Kaleb E. Smith, Morgan E. Urdaneta, Tyler S. Davis

Epilepsy affects millions of people, reducing quality of life and increasing risk of premature death. One-third of epilepsy cases are drug-resistant and require surgery for treatment, which necessitates localizing the seizure onset zone (SOZ) in the brain. Attempts have been made to use cortico-cortical evoked potentials (CCEPs) to improve SOZ localization b