October 2022 arXiv papers — page 127

U. U. Jamilov

In the present paper we consider a family of non-Volterra quadratic stochastic operators depending on a parameter $\alpha$ and study their trajectory behaviors. We find all fixed points for a non-Volterra quadratic stochastic operator on a finite-dimensional simplex. We construct some Lyapunov functions. A complete description of the set of limit points is g

Shuo Lin, Ren-Jie Wang, JIan-Fei Wang, Hao-Jie Xu

We investigate the photoproduction of di-electrons in peripheral collisions of $_{44}^{96}\textrm{Ru}+_{44}^{96}\textrm{Ru}$ and $_{40}^{96}\textrm{Zr}+_{40}^{96}\textrm{Zr}$ at 200 GeV. With the charge and mass density distributions given by the calculation of the density functional theory, we calculate the spectra of transverse momentum, invariant mass and

Vivek Bharadwaj, Osman Asif Malik, Riley Murray, Aydin Buluç

Candecomp / PARAFAC (CP) decomposition, a generalization of the matrix singular value decomposition to higher-dimensional tensors, is a popular tool for analyzing multidimensional sparse data. On tensors with billions of nonzero entries, computing a CP decomposition is a computationally intensive task. We propose the first distributed-memory implementations

Lin Wang, Xiufen Ye, Donghao Zhang, Wanji He

Usually, lesions are not isolated but are associated with the surrounding tissues. For example, the growth of a tumour can depend on or infiltrate into the surrounding tissues. Due to the pathological nature of the lesions, it is challenging to distinguish their boundaries in medical imaging. However, these uncertain regions may contain diagnostic informatio

Yifan Zhang

Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary code has limited semantic information, which makes it challenging for human comprehension. At the same time, compiling source to binary code, or transpiling among different programmin

Yifan Zhang, Chen Huang, Yueke Zhang, Huajie Shao

Binary code analysis and comprehension is critical to applications in reverse engineering and computer security tasks where source code is not available. Unfortunately, unlike source code, binary code lacks semantics and is more difficult for human engineers to understand and analyze. In this paper, we present ContraBin, a contrastive learning technique that

Shantanu P. Naidu, Steven R. Chesley, Davide Farnocchia, Nick Moskovitz

We used the times of occultations and eclipses between the components of the 65803 Didymos binary system observed in its lightcurves from 2003-2021 to estimate the orbital parameters of Dimorphos relative to Didymos. We employed a weighted least-squares approach and a modified Keplerian orbit model in order to accommodate the effects from non-gravitational f

S. Ogasawara, K. Akai, T. Kobayashi, K. Nakanishi

SuperKEKB is the e-/e+ collider which targets the world highest luminosity. In recent operation, SuperKEKB achieved a new world record of $4.71\times 10^{34} cm^{-2}s^{-1}$ for luminosity with the beam current of 1.4 A. In the future, the beam current will be increased further to aim at the design value of 3.6 A and much higher luminosity. The RF system cons

Yoshiki J. Sato, Hikari Manako, Yoshiya Homma, Dexin Li

Herein, we report the discovery of a novel Gd-based magnetic compound GdPt$_2$B with a chiral crystal structure. X-ray diffraction and chemical composition analyses reveal a CePt$_2$B-type crystal structure (space group: $P6_422$) for GdPt$_2$B. Moreover, we successfully grew single crystals of GdPt$_2$B using the Czochralski method. Magnetization measuremen

Kelly Marchisio, Neha Verma, Kevin Duh, Philipp Koehn

The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces -- their degree of "isomorphism." We address the root-cause of faulty cross-lingual mapping: that word embedding training resulted in the underlying spaces being non-isomorphic. We incorporate global

Yue He, Minyue Jiang, Xiaoqing Ye, Liang Du

Current lane detection methods are struggling with the invisibility lane issue caused by heavy shadows, severe road mark degradation, and serious vehicle occlusion. As a result, discriminative lane features can be barely learned by the network despite elaborate designs due to the inherent invisibility of lanes in the wild. In this paper, we target at finding

Thamme Gowda, Mozhdeh Gheini, Jonathan May

Code-switching is a common phenomenon among multilingual speakers, where alternation between two or more languages occurs within the context of a single conversation. While multilingual humans can seamlessly switch back and forth between languages, multilingual neural machine translation (NMT) models are not robust to such sudden changes in input. This work

Qiang Du, Shreeharshini Murthy, Michael Betz, Kevin Bender

Currently ALS is undergoing an upgrade to ALSU to produce 100 times brighter soft X-ray light. The LLRF system for Accumulator Ring (AR) is composed of two identical LLRF stations, for driving RF amplifiers. The closed loop RF amplitude and phase stability is measured as $< 0.1\%$ and $< 0.1^\circ$ respectively, using the non-IQ digital down conversion toget

Takanori Kodama, Daisuke Takasuka, Sam Sherriff-Tadano, Takeshi Kuroda

Planetary climates are strongly affected by planetary orbital parameters such as obliquity, eccentricity, and precession. In exoplanetary systems, exo-terrestrial planets should have various obliquities. High-obliquity planets would have extreme seasonal cycles due to the seasonal change of the distribution of the insolation. Here, we introduce the Non-hydro

Christian Jung, Claudia Redenbach

Shortest paths play an important role in mathematical modeling and image processing. Usually, shortest path problems are formulated on planar graphs that consist of vertices and weighted arcs. In this context, one is interested in finding a path of minimum weight from a start vertex to an end vertex. The concept of minimum-weight surfaces extends shortest pa

Xiaoyi Qin, Na Li, Yuke Lin, Yiwei Ding

This paper is the system description of the DKU-Tencent System for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC22). In this challenge, we focus on track1 and track3. For track1, multiple backbone networks are adopted to extract frame-level features. Since track1 focus on the cross-age scenarios, we adopt the cross-age trials and perform QMF to cal

Girish Aradhye, George Tzougas, Deepesh Bhati

In this paper, we consider bivariate composite models for modeling jointly different types of claims and their associated costs in a flexible manner. For expository purposes, the Gumbel copula is paired with the composite Weibull-Inverse Weibull, Paralogistic-Inverse Weibull, and Inverse Burr-Inverse Weibull marginal models. The resulting bivariate copula-ba

Ahmet Orun

Selection of appropriate template matching algorithms to run effectively on real-time low-cost systems is always major issue. This is due to unpredictable changes in image scene which often necessitate more sophisticated real-time algorithms to retain image consistency. Inefficiency of low cost auxiliary hardware and time limitations are the major constraint

Luke M. Bozzetto, Miroslav D. Filipović, H. Sano, R. Z. E. Alsaberi

We present a new Australian Square Kilometre Array Pathfinder (ASKAP) sample of 14 radio Supernova Remnant (SNR) candidates in the Large Magellanic Cloud (LMC). This new sample is a significant increase to the known number of older, larger and low surface brightness LMC SNRs. We employ a multi-frequency search for each object and found possible traces of opt

Robert Raussendorf, Wang Yang, Arnab Adhikary

We present a new framework for assessing the power of measurement-based quantum computation (MBQC) on short-range entangled symmetric resource states, in spatial dimension one. It requires fewer assumptions than previously known. The formalism can handle finitely extended systems (as opposed to the thermodynamic limit), and does not require translation-invar

Yiran Li, Yunfan Huang, Xukang Lu, Moran Wang

Agglomeration, adsorption, and extraction in dispersed multiphase systems are ubiquitously encountered in biological systems, energy industry, and medical science. In this work, a novel lattice model is extended to the three-component complex systems and criterions based on the determinant \Delta_i=F_i-K_c,i are accordingly proposed to predict the aforementi

Valentin Duruisseaux, Joshua W. Burby, Qi Tang

A continuous-time dynamical system with parameter $\varepsilon$ is nearly-periodic if all its trajectories are periodic with nowhere-vanishing angular frequency as $\varepsilon$ approaches 0. Nearly-periodic maps are discrete-time analogues of nearly-periodic systems, defined as parameter-dependent diffeomorphisms that limit to rotations along a circle actio

Elizabeth A. Handorf, J. Robert Beck, Andres Correa, Chethan Ramamurthy

Purpose. Patients with advanced cancer may undergo multiple lines of treatment, switching therapies as their disease progresses. Motivated by a study of metastatic prostate cancer, we develop a microsimulation framework to study therapy sequence. Methods. We propose a discrete-time state transition model to study two lines of anti-cancer therapy. Based on di

Michael G. Richer, Anabel Arrieta, Lorena Arias, Lesly Castañeda Carlos

We study the kinematics of emission lines that arise from many physical processes in NGC 6153 based upon deep, spatially-resolved, high resolution spectra acquired with the UVES spectrograph at the ESO VLT. Our most basic finding is that the plasma in NGC 6153 is complex, especially its temperature structure. The kinematics of most emission lines defines a c

Oliver Couto, Seiji Tomita

While there is not much publications, about degree sixteen Diophantine equation we do have an identity given by Ramanujan (ref. #1). Also on the internet even though there are numerical solutions to degree sixteen for eg. (16-7-24) equation (ref. #5) there are hardly any parametric solutions. An Octic degree parameterization has been arrived at by Choudhry &

Sen Qiao, Daming Cao, Qiaosheng Zhang, Yinfei Xu

This work investigates the phase gain of intelligent reflecting surface (IRS) covert communication over complex-valued additive white Gaussian noise (AWGN) channels. The transmitter Alice intends to transmit covert messages to the legitimate receiver Bob via reflecting the broadcast signals from a radio frequency (RF) source, while rendering the adversary Wi

Vishwaraj Doshi, Shailaja Mallick, Do Young eun

We study convergence properties of competing epidemic models of the Susceptible-Infected-Susceptible (SIS) type. The SIS epidemic model has seen widespread popularity in modelling the spreading dynamics of contagions such as viruses, infectious diseases, or even rumors/opinions over contact networks (graphs).We analyze the case of two such viruses spreading

Jonathan C Balloch, Julia Kim, and Jessica L Inman, Mark O Riedl

The exploration--exploitation trade-off in reinforcement learning (RL) is a well-known and much-studied problem that balances greedy action selection with novel experience, and the study of exploration methods is usually only considered in the context of learning the optimal policy for a single learning task. However, in the context of online task transfer,

Nikita Klemyatin

The Type IIA flow is a flow of 3-forms on a 6-dimensional symplectic manifold. It was introduced by Fei, Phong, Picard, and Zhang in \cite{FPPZb}. There is little known about the singularities of this flow in general. We formulate and prove convergence theorems for this flow. We also describe the singularity models for this flow.

Fariborz Kargar, Zahra Barani, Nicholas R. Sesing, Thuc T. Mai

We report on the temperature evolution of the polarization-dependent Raman spectrum of exfoliated MoI$_3$, a van der Waals material with a "true one-dimensional" crystal structure that can be exfoliated to individual atomic chains. The temperature evolution of several Raman features reveals anomalous behavior suggesting a phase transition of a magnetic origi

Juan Carlos García-Altamirano, Mika Olsen, Jorge Cervantes-Ojeda

In 2020 Bang-Jensen et. al. generalized the Haj\'os join of two graphs to the class of digraphs and generalized several results for vertex colorings in digraphs. Although, as a consequence of these results, a digraph can be obtained by Haj\'os constructions (directed Haj\'os join and identifying non-adjacent vertices), determining the Haj\'os constructions t

Matthieu Lafrenière, Gilmer Valdes, Martina Descovich

The CyberKnife system is a robotic radiosurgery platform that allows the delivery of lung SBRT treatments using fiducial-free soft-tissue tracking. However, not all lung cancer patients are eligible for lung tumor tracking. Tumor size, density and location impact the ability to successfully detect and track a lung lesion in 2D orthogonal X-ray images. The st

Hojjat Salehinejad, Navid Hasanzadeh, Radomir Djogo, Shahrokh Valaee

WiFi sensing is an important part of the new WiFi 802.11bf standard, which can detect motion and measure distances. In recent years, some machine learning methods have been proposed for human activity recognition from WiFi signals. However, to the best of our knowledge, none of these methods have explored orientation prediction of the user using WiFi signals

E. Garcia, R. Cong, P. C. Forino, A. Tassetti

The effects of doping on the electronic evolution of the Mott insulating state have been extensively studied in efforts to understand mechanisms of emergent quantum phases of materials. The study of these effects becomes ever more intriguing in the presence of entanglement between spin and orbital degrees of freedom. Here, we present a comprehensive investig

Wanpeng Fan, Yuanzhi Su, Yuxin Huang

We present ConchShell, a multi-modal generative adversarial framework that takes pictures as input to the network and generates piano music samples that match the picture context. Inspired by I3D, we introduce a novel image feature representation method: time-convolutional neural network (TCNN), which is used to forge features for images in the temporal dime

Fan Zhou, Haoyu Dong, Qian Liu, Zhoujun Cheng

Numerical reasoning over natural language has been a long-standing goal for the research community. However, cutting-edge language models have proven difficult to reliably generalize to a broad range of numbers, although they have shown proficiency in reasoning over common and simple numbers. In this paper, we propose a novel method to elicit and exploit the

Yuya Sasaki, Yulong Wang

This paper presents two results concerning uniform confidence intervals for the tail index and the extreme quantile. First, we show that it is impossible to construct a length-optimal confidence interval satisfying the correct uniform coverage over a local non-parametric family of tail distributions. Second, in light of the impossibility result, we construct

Jiashu Xu, Sergii Stirenko

The coronavirus pandemic has been going on since the year 2019, and the trend is still not abating. Therefore, it is particularly important to classify medical CT scans to assist in medical diagnosis. At present, Supervised Deep Learning algorithms have made a great success in the classification task of medical CT scans, but medical image datasets often requ

Yin Huang, Yuecheng Shen, Georgios Veronis

We introduce non-Hermitian plasmonic waveguide-cavity structures based on the Aubry-Andre-Harper model to realize switching between right and left topological edge states (TESs) using the phase-change material Ge$_2$Sb$_2$Te$_5$ (GST). We show that switching between the crystalline and amorphous phases of GST leads to a shift of the dispersion relation of th

Yan-Hua Zhou, Xue-Feng Zhang, Tao Wang

Density shift plays one of the major roles in the uncertainty of optical lattice clock, thus has attracted lots of theoretical and experimental studies. However, most of the theoretical research considered the single-band and collective approximation, so the density shift of the system at higher temperatures can not be analyzed accurately. Here, we design a

Abhi Saxena, Arnab Manna, Rahul Trivedi, Arka Majumdar

Analog quantum simulators rely on programmable quantum devices to emulate Hamiltonians describing various physical phenomenon. Photonic coupled cavity arrays are a promising platform for realizing such devices. Using a silicon photonic coupled cavity array made up of 8 high quality-factor resonators and equipped with specially designed thermo-optic island he

Hemanth Goparaju, Kunal Kanawade, S Unnikrishnan, Datta V Gaitonde

Aspects of transition mechanisms on a $14^{\circ}$ sharp-nosed ogive-cylinder at Mach~$6$ are elucidated by considering linear and nonlinear disturbance evolution of freestream stochastic and wave packet forcing at different locations downstream of the ogive-cylinder junction. The spectral response of the stochastic forcing displays favorable agreement with

Jason Toskov, Rhys Newbury, Mustafa Mukadam, Dana Kulić

We study gravitational pivoting, a constrained version of in-hand manipulation, where we aim to control the rotation of an object around the grip point of a parallel gripper. To achieve this, instead of controlling the gripper to avoid slip, we embrace slip to allow the object to rotate in-hand. We collect two real-world datasets, a static tracking dataset a

Lorenzo Gavassino

Close to equilibrium, the kinetic coefficients of a thermodynamic system must satisfy a set of symmetry conditions, which follow from the Onsager-Casimir principle. Here, we show that, if a system of hydrodynamic equations is analysed from the perspective of the Onsager-Casimir principle, then it is possible to impose very strong symmetry conditions also on

Taoli Zheng, Peng Wang, Anthony Man-Cho So

To do dimensionality reduction on the datasets with outliers, the $\ell_1$-norm principal component analysis (L1-PCA) as a typical robust alternative of the conventional PCA has enjoyed great popularity over the past years. In this work, we consider a rotationally invariant L1-PCA, which is hardly studied in the literature. To tackle it, we propose a proxima

Zhonglin He, Kaiying Dou, Wenhui Du, Ying Dai

Topological magnetism in low-dimensional systems is of fundamental and practical importance in condensed-matter physics and material science. Here, using first-principles and Monte-Carlo simulations, we present that multiple topological magnetism (i.e., skyrmion and bimeron) can survive in van der Waals Heterostructure of MnTe2ZrS2. Arising from interlayer c

Erik Wijmans, Irfan Essa, Dhruv Batra

We present Variable Experience Rollout (VER), a technique for efficiently scaling batched on-policy reinforcement learning in heterogenous environments (where different environments take vastly different times to generate rollouts) to many GPUs residing on, potentially, many machines. VER combines the strengths of and blurs the line between synchronous and a

Berk Iskender, Zhenlin Xu, Simon Kornblith, En-Hung Chu

Many contrastive representation learning methods learn a single global representation of an entire image. However, dense contrastive representation learning methods such as DenseCL (Wang et al., 2021) can learn better representations for tasks requiring stronger spatial localization of features, such as multi-label classification, detection, and segmentation

Cameron Diao, Ricky Loynd

Transformers flexibly operate over sets of real-valued vectors representing task-specific entities and their attributes, where each vector might encode one word-piece token and its position in a sequence, or some piece of information that carries no position at all. But as set processors, transformers are at a disadvantage in reasoning over more general grap

Joseph Gallego-Mejia, Oscar Bustos-Brinez, Fabio Gonzalez

Streaming anomaly detection refers to the problem of detecting anomalous data samples in streams of data. This problem poses challenges that classical and deep anomaly detection methods are not designed to cope with, such as conceptual drift and continuous learning. State-of-the-art flow anomaly detection methods rely on fixed memory using hash functions or

Tanvir Mahmud, Diana Marculescu

An audio-visual event (AVE) is denoted by the correspondence of the visual and auditory signals in a video segment. Precise localization of the AVEs is very challenging since it demands effective multi-modal feature correspondence to ground the short and long range temporal interactions. Existing approaches struggle in capturing the different scales of multi

Benjamin Kurt Miller, Christoph Weniger, Patrick Forré

Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task. In contrast to the binary classification framework, the current formulation of the multiclass version has an intrinsic and unknown bias term, making otherwise informative diagnostics unreliable. We propose a multiclass framework fre

Cuong Hoang, Devendra Sachan, Prashant Mathur, Brian Thompson

Several recent studies have reported dramatic performance improvements in neural machine translation (NMT) by augmenting translation at inference time with fuzzy-matches retrieved from a translation memory (TM). However, these studies all operate under the assumption that the TMs available at test time are highly relevant to the testset. We demonstrate that

Daniel Tandeitnik, Thiago Guerreiro

We develop genetic algorithms for searching quantum circuits, in particular stabilizer quantum error correction codes. Quantum codes equivalent to notable examples such as the 5-qubit perfect code, Shor's code, and the 7-qubit color code are evolved out of initially random quantum circuits. We anticipate evolution as a promising tool in the NISQ era, with ap

K. C. Chan, S. Avila, A. Carnero Rosell, I. Ferrero

The three-dimensional correlation function offers an effective way to summarize the correlation of the large-scale structure even for imaging galaxy surveys. We have applied the projected three-dimensional correlation function, $\xi_{\rm p}$ to measure the Baryonic Acoustic Oscillations (BAO) scale on the first-three years Dark Energy Survey data. The sample

Christopher M. Pierce, Daniel B. Durham, Fabrizio Riminucci, Scott Dhuey

Metal photocathodes are an important source of high-brightness electron beams, ubiquitous in the operation of both large-scale accelerators and table-top microscopes. When the surface of a metal is nano-engineered with patterns on the order of the optical wavelength, it can lead to the excitation and confinement of surface plasmon polariton waves which drive

Carles Diaz-Vilor, Alexei Ashikhmin, Hong Yang

This paper investigates Cell-Free massive MIMO networks, where each access point (AP) is equipped with a hybrid transceiver, reducing the complexity and cost compared to a fully digital transceiver. Asymptotic approximations for the spectral efficiency are derived for uplink and downlink. Capitalizing on these expressions, a max-min problem is formulated to

Adam Lott

In 1997, Katok--Thouvenot and Ferenczi independently introduced a notion of ``slow entropy'' as a way to quantitatively compare measure-preserving systems with zero entropy. We develop a relative version of this theory for a measure-preserving system conditioned on a given factor. Our new definition inherits many desirable properties that make it a natural g

Yen-Tsung Chen

In the present paper, we establish an analytic continuation of Drinfeld logarithms by using the techniques introduced in [Fur20]. This result can be seen as an analogue of the analytic continuation of the elliptic integrals of the first kind for Drinfeld modules.

Giovanni Franco-Rivera, Josiah Cochran, Seiji Miyashita, Sylvain Bertaina

We report the realization of a strong coupling between a Gd$^{3+}$ spin ensemble hosted in a scheelite (CaWO$_4$) single crystal and the resonant mode of a coplanar stripline superconducting cavity leading to a large separation of spin-photon states of 146 MHz. The interaction is well described by the Dicke model and the crystal-field Hamiltonian of the mult

Joseph Gallego-Mejia, Daniela Martin-Vega, Fabio Gonzalez

Governments have to supervise and inspect social economy enterprises (SEEs). However, inspecting all SEEs is not possible due to the large number of SEEs and the low number of inspectors in general. We proposed a prediction model based on a machine learning approach. The method was trained with the random forest algorithm with historical data provided by eac

Mansi Sood, Anirudh Sridhar, Rashad Eletreby, Chai Wah Wu

A key scientific challenge during the outbreak of novel infectious diseases is to predict how the course of the epidemic changes under different countermeasures that limit interaction in the population. Most epidemiological models do not consider the role of mutations and heterogeneity in the type of contact events. However, pathogens have the capacity to mu

Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando Solar-Lezama

Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. NP techniques can interface with symbolic domain knowledge from scientists, such as prior knowledge and ex

Kouichi Hagino, Mitsuki Hayashida, Takayoshi Kohmura, Toshiki Doi

We evaluate the single event tolerance of the X-ray silicon-on-insulator (SOI) pixel sensor named XRPIX, developed for the future X-ray astronomical satellite FORCE. In this work, we measure the cross-section of single event upset (SEU) of the shift register on XRPIX by irradiating heavy ion beams with linear energy transfer (LET) ranging from 0.022 MeV/(mg/

Zeng-Zhao Li, Weijian Chen, Maryam Abbasi, Kater W. Murch

Entanglement is a key resource for quantum information technologies ranging from quantum sensing to quantum computing. Conventionally, the entanglement between two coupled qubits is established at the time scale of the inverse of the coupling strength. In this work, we study two weakly coupled non-Hermitian qubits and observe entanglement generation at a sig

Cuong Hoang, Devendra Sachan, Prashant Mathur, Brian Thompson

We explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training. We build on the idea of Retrieval Augmented Translation (RAT) where top-k in-domain fuzzy matches are found for the source sentence, and target-language translations of those fuzzy-matched sentences

Darshan Gadginmath, Vishaal Krishnan, Fabio Pasqualetti

This paper contributes a theoretical framework for data-driven feedback linearization of nonlinear control-affine systems. We unify the traditional geometric perspective on feedback linearization with an operator-theoretic perspective involving the Koopman operator. We first show that if the distribution of the control vector field and its repeated Lie brack

C. Ugwumadu, R. Thapa, Y. Al-Majali, J. Trembly

Amorphous carbon nanotubes (a-CNT) with up to four walls and sizes ranging from 200 to 3200 atoms have been simulated, starting from initial random configurations and using the Gaussian Approximation Potential [Phys. Rev. B 95, 094203 (2017)]. The important variables (like density, height, and diameter) required to successfully simulate a-CNTs, were predicte

Zubayer Islam, Mohamed Abdel-Aty, Amrita Goswamy, Amr Abdelraouf

Intersection safety often relies on the correct modelling of signal phasing and timing parameters. A slight increase in yellow time or red time can have significant impact on the rear end crashes or conflicts. This paper aims to identify the relationship between surrogate safety measures and signal phasing. Unmanned Aerial Vehicle (UAV) video data has been u

Haw-Shiuan Chang, Ruei-Yao Sun, Kathryn Ricci, Andrew McCallum

Ensembling BERT models often significantly improves accuracy, but at the cost of significantly more computation and memory footprint. In this work, we propose Multi-CLS BERT, a novel ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model. Multi-CLS BERT uses multiple CLS tokens with a parameterization and objectiv

Nisha Geng, Katerina P. Hilleke, Li Zhu, Xiaoyu Wang

Inspired by the synthesis of XB3C3 (X= Sr, La) compounds in the bipartite sodalite clathrate structure, density functional theory (DFT) calculations are performed on members of this family containing up to two different metal atoms. A DFT-chemical pressure analysis on systems with X= Mg, Ca, Sr, Ba reveals that the size of the metal cation, which can be tune

Kingshuk Mukhuti, Arnab Mandal, Basabendra Roy, Sayan Bhattacharyya

We develop techniques to study the temperature dependent localization, thermalization, and the effects of phonon scattering on the excitons in halide perovskites from the analysis of the emission spectra. The excitonic Urbach edge, when inferred from the low energy tails of the temperature dependent luminescence spectra, is shown to be sensitive to the elect

Simon Stevenson, Teagan Clarke

Gravitational waves from merging binary black holes can be used to shed light on poorly understood aspects of massive binary stellar evolution, such as the evolution of massive stars (including their mass-loss rates), the common envelope phase, and the rate at which massive stars form throughout the cosmic history of the Universe. In this paper we explore th

Zixu Wang, Yujie Zhong, Yishu Miao, Lin Ma

Despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck in the area. Most video-language models are trained via pair-level loss to predict whether a pair of video and text is aligned. However, even in paired video-text segments, only a subset of the frames are se

Pedro Rodriguez, Mahmoud Azab, Becka Silvert, Renato Sanchez

Searching troves of videos with textual descriptions is a core multimodal retrieval task. Owing to the lack of a purpose-built dataset for text-to-video retrieval, video captioning datasets have been re-purposed to evaluate models by (1) treating captions as positive matches to their respective videos and (2) assuming all other videos to be negatives. Howeve

Jianyuan Sun, Xubo Liu, Xinhao Mei, Mark D. Plumbley

Automated audio captioning (AAC) aims to describe the content of an audio clip using simple sentences. Existing AAC methods are developed based on an encoder-decoder architecture that success is attributed to the use of a pre-trained CNN10 called PANNs as the encoder to learn rich audio representations. AAC is a highly challenging task due to its high-dimens

Ryan Thomas Gibb

The development of emerging classes of hardware such as Internet of Thing devices and Augmented Reality headsets has outpaced the development of Internet infrastructure. We identify problems with latency, security and privacy in the global hierarchical distributed Domain Name System. To remedy this, we propose the Spatial Name System, an alternative network

Wenda Xu, Yilin Tuan, Yujie Lu, Michael Saxon

Is it possible to build a general and automatic natural language generation (NLG) evaluation metric? Existing learned metrics either perform unsatisfactorily or are restricted to tasks where large human rating data is already available. We introduce SESCORE, a model-based metric that is highly correlated with human judgements without requiring human annotati

Qiang Liu, Tao Han, Jiang, Xie

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying net

Weiting Tan, Kevin Heffernan, Holger Schwenk, Philipp Koehn

Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find

Evaldo M. F. Curado, Carlos E. Cedeño, Ivano Damião Soares, Constantino Tsallis

In 1911, J\"uttner proposed the generalization, for a relativistic gas, of the Maxwell-Boltzmann distribution of velocities. Here we want to discuss, among others, J\"uttner probability density function (PDF). Both the velocity space and, consequently, the momentum space are not flat in special relativity. The velocity space corresponds to the Lobachevsky on

D. Ahmad, M. Donatelli, M. Mazza, S. Serra-Capizzano

We consider the numerical solution of time-dependent space tempered fractional diffusion equations. The use of Crank-Nicolson in time and of second-order accurate tempered weighted and shifted Gr\"unwald difference in space leads to dense (multilevel) Toeplitz-like linear systems. By exploiting the related structure, we design an ad-hoc multigrid solver and

Ang Li, Judea Pearl

The unit selection problem defined by Li and Pearl identifies individuals who have desired counterfactual behavior patterns, for example, individuals who would respond positively if encouraged and would not otherwise. Li and Pearl showed by example that their unit selection model is beyond the A/B test heuristics. In this paper, we reveal the essence of the

Hokuto Konno, Anubhav Mukherjee, Masaki Taniguchi

In a small simply-connected closed 4-manifold, we construct infinitely many pairs of exotic codimension-$1$ submanifolds with diffeomorphic complements that remain exotic after any number of stabilizations by $ S^2 \times S^2$. We also give new constructions of exotic embeddings of 3-spheres in 4-manifolds with diffeomorphic complements.

Sébastien Roux, Clarisse Fournier, Kenji Watanabe, Takashi Taniguchi

The ability to locally activate or generate quantum emitters in two-dimensional materials is of major interest for the realization of integrated quantum photonic devices. In particular, hexagonal boron nitride (hBN) has recently been shown to allow a variety of techniques for obtaining quantum emitters at desired locations. Here, we use cathodoluminescence (

Ang Li, Ruirui Mao, Judea Pearl

The probabilities of causation are commonly used to solve decision-making problems. Tian and Pearl derived sharp bounds for the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN) using experimental and observational data. The assumption is that one is in possession of a large enough samp

Matias D. Cattaneo, Yingjie Feng, Filippo Palomba, Rocio Titiunik

We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees. From a methodological perspective, we provide a detailed discussion of different causal quantities to be predic

Rongqian Ma

This ongoing study revisits the connotations of "digital humanists" and explores the reasons why a researcher does or does not self-identify as a digital humanist. Building on semi-structured interview data collected from fourteen researchers and practitioners engaging in digital humanities (DH) projects, this poster illustrates researchers' various understa

Frederick del Pozo, Loïc Herviou, Karyn Le Hur

We investigate the topological phases of two one-dimensional (1D) interacting superconducting wires and propose topological markers directly measurable from ground state correlation functions. These quantities remain powerful tools in the presence of couplings and interactions. We show with the density matrix renormalization group that the double critical Is

Kyler Larsen

As advancements in technology and medicine are being made, many countries are still unable to access quality medical care due to cost and lack of qualified medical personnel. This discrepancy in healthcare has caused many preventable deaths, either due to lack of detection or lack of care. One of the most prevalent diseases in the world is pneumonia, an infe

Max Asselmeier, Dhruv Ahuja, Abdel Zaro, Ahmad Abuaish

This paper extends the family of gap-based local planners to unknown dynamic environments through generating provable collision-free properties for hierarchical navigation systems. Existing perception-informed local planners that operate in dynamic environments rely on emergent or empirical robustness for collision avoidance as opposed to performing formal a

Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar

Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (artificial) data from the same distribution. However, this traditional viewpoint does not explain the success of p

Yulun Tian, Jonathan P. How

We propose fast and communication-efficient optimization algorithms for multi-robot rotation averaging and translation estimation problems that arise from collaborative simultaneous localization and mapping (SLAM), structure-from-motion (SfM), and camera network localization applications. Our methods are based on theoretical relations between the Hessians of

Giuseppe Castiglione, Ga Wu, Christopher Srinivasa, Simon Prince

Machine learning models are vulnerable to biases that result in unfair treatment of individuals from different populations. Recent work that aims to test a model's fairness at the individual level either relies on domain knowledge to choose metrics, or on input transformations that risk generating out-of-domain samples. We describe a new approach for testing

Walter Didimo, Siddharth Gupta, Philipp Kindermann, Giuseppe Liotta

Orthogonal graph drawings are used in applications such as UML diagrams, VLSI layout, cable plans, and metro maps. We focus on drawing planar graphs and assume that we are given an \emph{orthogonal representation} that describes the desired shape, but not the exact coordinates of a drawing. Our aim is to compute an orthogonal drawing on the grid that has min

Chenxi Liu, Zhaoqi Leng, Pei Sun, Shuyang Cheng

Developing neural models that accurately understand objects in 3D point clouds is essential for the success of robotics and autonomous driving. However, arguably due to the higher-dimensional nature of the data (as compared to images), existing neural architectures exhibit a large variety in their designs, including but not limited to the views considered, t

Jeff Achter, Sebastian Casalaina-Martin, Charles Vial

Albanese varieties provide a standard tool in algebraic geometry for converting questions about varieties in general, to questions about Abelian varieties. A result of Serre provides the existence of an Albanese variety for any geometrically connected and geometrically reduced scheme of finite type over a field, and a result of Grothendieck--Conrad establish

Mario Midence-Ordóñez

We give a combinatorial proof of a recent result of B\'ona by constructing a bijection from the set of all neighbors of leaves of increasing trees of size $n$ to the set of derangements of length $n$.

Michael H. Lim, Tyler J. Becker, Mykel J. Kochenderfer, Claire J. Tomlin

Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms

Kimiya Saadat, Richard Zhao

In recent years, Monte Carlo tree search (MCTS) has achieved widespread adoption within the game community. Its use in conjunction with deep reinforcement learning has produced success stories in many applications. While these approaches have been implemented in various games, from simple board games to more complicated video games such as StarCraft, the use