arXiv papers from September 2022

Rosario Patanè, Andrea Araldo, Tijani Chahed, Diego Kiedanski

We propose in this paper a coinvestment plan between several stakeholders of different types, namely a physical network owner, operating network nodes, e.g. a network operator or a tower company, and a set of service providers willing to use these resources to provide services as video streaming, augmented reality, autonomous driving assistance, etc. One suc

Lance Taylor, Rachel Bezanson, Arjen van der Wel, Alan Pearl

We present the first direct spectroscopic measurement of the stellar velocity dispersion function (VDF) for massive quiescent and star-forming galaxies at $0.6 < z \leq 1.0$. For this analysis we use individual measurements of stellar velocity dispersion from high-S/N spectra from the public Large Early Galaxy Astrophysics Census (LEGA-C) survey. We report a

Bridget Ngodoo Mile, Victor Ushahemba Ijirshar, Mlumun Queen Ijirshar

SMEs remain a veritable tool that generates employment opportunities. This study examined the impact of SMEs on employment creation in the Makurdi metropolis of Benue state. A sample size of 340 entrepreneurs was chosen from the population of entrepreneurs (SMEs) in the Makurdi metropolis. The study used logistic regression to analyse the impact of SME activ

Silvan G. Viëtor, Jo W. Spronck, S. Hassan HosseinNia

Actuator self-heating limits the achievable force and can cause unwanted structural deformations. This is especially apparent in quasi-static actuation systems that require the actuator to maintain a stable position over an extended period. As a solution, we use the concept of a Tunable Magnet. Tunable magnets rely on in-situ magnetization state tuning of Al

A. Siva Sesha Reddy, A. V. Kityk, J. Jedryka, N. Purnachand

In this work, nonlinear optical (NLO) studies of nickel oxide doped (ranging from 0.2 to 1.0 mol%) Li2SO4-MgO-P2O5 glasses are reported. A combination of femtosecond (fs) laser, as a pumping light source and a high-accuracy polarimeter with low power probing laser, is used to investigate the light-induced optical anisotropy (OA) in these glass materials. The

Shashank Hegde, Gaurav S. Sukhatme

Neural control of memory-constrained, agile robots requires small, yet highly performant models. We leverage graph hyper networks to learn graph hyper policies trained with off-policy reinforcement learning resulting in networks that are two orders of magnitude smaller than commonly used networks yet encode policies comparable to those encoded by much larger

Sayma Sultana, Asif Kamal Turzo, Amiangshu Bosu

Context: Contemporary software development organizations lack diversity and the ratios of women in Free and open-source software (FOSS) communities are even lower than the industry average. Although the results of recent studies hint the existence of biases against women, it is unclear to what extent such biases influence the outcomes of various software dev

Fernanda Estevan, Lucas Finamor

We investigate the impact of the Covid-19 pandemic on the transition between high school and college in Brazil. Using microdata from the universe of students that applied to a selective university, we document how the Covid-19 shock increased enrollment for students in the top 10% high-quality public and private high schools. This increase comes at the expen

Rachel Thomasson, Etienne Roberge, Mark R. Cutkosky, Jean-Philippe Roberge

Robotic manipulators navigating cluttered shelves or cabinets may find it challenging to avoid contact with obstacles. Indeed, rearranging obstacles may be necessary to access a target. Rather than planning explicit motions that place obstacles into a desired pose, we suggest allowing incidental contacts to rearrange obstacles while monitoring contacts for s

Rahul Duggal, Shengyun Peng, Hao Zhou, Duen Horng Chau

Class imbalance is a ubiquitous phenomenon occurring in real world data distributions. To overcome its detrimental effect on training accurate classifiers, existing work follows three major directions: class re-balancing, information transfer, and representation learning. In this paper, we propose a new and complementary direction for improving performance o

Hojung Choi, Dane Brouwer, Michael A. Lin, Kyle T. Yoshida

When humans socially interact with another agent (e.g., human, pet, or robot) through touch, they do so by applying varying amounts of force with different directions, locations, contact areas, and durations. While previous work on touch gesture recognition has focused on the spatio-temporal distribution of normal forces, we hypothesize that the addition of

Xiaonan Li, Olivier Marin

Most existing failure detection algorithms rely on statistical methods, and very few use machine learning (ML). This paper explores the viability of ML in the field of failure detection: is it possible to implement an ML-based detector that achieves a satisfactory quality of service? We implement a prototype that uses a basic long short-term memory neural ne

Nikola Paunkovic, Marko Vojinovic

We give a general overview of various flavors of the equivalence principle in classical and quantum physics, with special emphasis on the so-called weak equivalence principle, and contrast its validity in mechanics versus field theory. We also discuss its generalisation to a theory of quantum gravity. Our analysis suggests that only the strong equivalence pr

Yizhou Zhao, Zhenyang Li, Xun Guo, Yan Lu

Temporal modeling is crucial for various video learning tasks. Most recent approaches employ either factorized (2D+1D) or joint (3D) spatial-temporal operations to extract temporal contexts from the input frames. While the former is more efficient in computation, the latter often obtains better performance. In this paper, we attribute this to a dilemma betwe

Emily McMilin

Modern language modeling tasks are often underspecified: for a given token prediction, many words may satisfy the user's intent of producing natural language at inference time, however only one word will minimize the task's loss function at training time. We introduce a simple causal mechanism to describe the role underspecification plays in the generation o

Yunuo Chen, Minchen Li, Wenlong Lu, Chuyuan Fu

We introduce Midas, a robotics simulation framework based on the Incremental Potential Contact (IPC) model. Our simulator guarantees intersection-free, stable, and accurate resolution of frictional contact. We demonstrate the efficacy of our framework with experimental validations on high-precision tasks and through comparisons with Bullet physics. A reinfor

Jun Fang, Mingze Xu, Hao Chen, Bing Shuai

In this paper, we provide an in-depth study of Stochastic Backpropagation (SBP) when training deep neural networks for standard image classification and object detection tasks. During backward propagation, SBP calculates the gradients by only using a subset of feature maps to save the GPU memory and computational cost. We interpret SBP as an efficient way to

Amirhesam Badeanlou, Andrea Araldo, Marco Diana, Vincent Gauthier

Current transit suffers from an evident inequity: the level of service of transit in suburbs is much less satisfying than in city centers. As a consequence, private cars are still the dominant transportation mode for suburban people, which results in congestion and pollution. To achieve sustainability goals and reduce car-dependency, transit should be (re)de

Yili Ren, Jie Yang

Person re-identification (Re-ID) has become increasingly important as it supports a wide range of security applications. Traditional person Re-ID mainly relies on optical camera-based systems, which incur several limitations due to the changes in the appearance of people, occlusions, and human poses. In this work, we propose a WiFi vision-based system, 3D-ID

Song Jiang, Tomas Neuman, Remi Bretel, Alex Boeglin

A scanning tunneling microscope is used to study the fluorescence of a model charged molecule (quinacridone) adsorbed on a sodium chloride (NaCl)-covered metallic sample. Fluorescence from the neutral and positively charged species is reported and imaged using hyper-resolved fluorescence microscopy. A many-body excitation model is established based on a deta

Mario Novello, Vicente Antunes

Despite the success of the Higgs mechanism to account for the generation of the masses of Standard Model (SM) elementary particles, the ultimate nature and origin of "mass" remain open questions in contemporary physics. From a foundational perspective, mass should be fundamentally related to the gravitational interaction and, according to Mach, to the struct

Tianshu Ruan, Hao Wang, Rustam Stolkin, Manolis Chiou

This paper proposes a taxonomy of semantic information in robot-assisted disaster response. Robots are increasingly being used in hazardous environment industries and emergency response teams to perform various tasks. Operational decision-making in such applications requires a complex semantic understanding of environments that are remote from the human oper

Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng

Implicit Neural Spatial Representation (INSR) has emerged as an effective representation of spatially-dependent vector fields. This work explores solving time-dependent PDEs with INSR. Classical PDE solvers introduce both temporal and spatial discretizations. Common spatial discretizations include meshes and meshless point clouds, where each degree-of-freedo

Pankaj K. Agarwal, Tzvika Geft, Dan Halperin, Erin Taylor

We study the problem of motion planning for a collection of $n$ labeled unit disc robots in a polygonal environment. We assume that the robots have revolving areas around their start and final positions: that each start and each final is contained in a radius $2$ disc lying in the free space, not necessarily concentric with the start or final position, which

Peru Bhardwaj

Knowledge graphs represent factual knowledge about the world as relationships between concepts and are critical for intelligent decision making in enterprise applications. New knowledge is inferred from the existing facts in the knowledge graphs by encoding the concepts and relations into low-dimensional feature vector representations. The most effective rep

Yizhou Chen, Andrea Sipos, Mark Van der Merwe, Nima Fazeli

Learning representations in the joint domain of vision and touch can improve manipulation dexterity, robustness, and sample-complexity by exploiting mutual information and complementary cues. Here, we present Visuo-Tactile Transformers (VTTs), a novel multimodal representation learning approach suited for model-based reinforcement learning and planning. Our

Ruiqi Ni, Ahmed H. Qureshi

Neural Motion Planners (NMPs) have emerged as a promising tool for solving robot navigation tasks in complex environments. However, these methods often require expert data for learning, which limits their application to scenarios where data generation is time-consuming. Recent developments have also led to physics-informed deep neural models capable of repre

Adam Freese, Gerald A. Miller

We obtain the energy-momentum tensor (EMT) in the 't Hooft model of two-dimensional quantum chromodynamics. The EMT is decomposed into contributions from quark and gluon fields, with all of the (plus component of) the light front momentum being carried by the quark field. The energy is split between quark and gluon fields, with the gluon field carrying the s

Derek Hansen, Drew Yarger

The Argo project deploys thousands of floats throughout the world's oceans. Carried only by the current, these floats take measurements such as temperature and salinity at depths of up to two kilometers. These measurements are critical for scientific tasks such as modeling climate change, estimating temperature and salinity fields, and tracking the global hy

A. Magazzù, D. Bronte Ciriza, A. Musolino, A. Saidi

Cosmic dust plays a dominant role in the universe, especially in the formation of stars and planetary systems. Furthermore, the surface of cosmic dust grains is the bench-work where molecular hydrogen and simple organic compounds are formed. We manipulate individual dust particles in water solution by contactless and non-invasive techniques such as standard

Samik Sadhu, Hynek Hermansky

We present a method to remove unknown convolutive noise introduced to speech by reverberations of recording environments, utilizing some amount of training speech data from the reverberant environment, and any available non-reverberant speech data. Using Fourier transform computed over long temporal windows, which ideally cover the entire room impulse respon

Yulun Wu, Robert A. Barton, Zichen Wang, Vassilis N. Ioannidis

Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell's gene expressions under counterfactual perturbations (perturbations that this cell did not factually receive), leveraging

Sebastián Donoso, Alejandro Maass, Samuel Petite

We develop a geometric framework to address asymptoticity and nonexpansivity in topological dynamics. Our framework can be applied when the acting group is second countable and locally compact. As an application, we show extensions of Schwartzman's theorem in this context. Also, we get new results when the acting groups is ${\mathbb Z}^d$: any half-space of

Wasin Meesena, Robert Thompson

Lean manufacturing is a production method focused on reducing production times, eliminating waste, and synchronizing production with fluctuating demand. A standard lean manufacturing methodology is the product wheel, a repeating sequence of production of various items. If this product wheel sequence is short, it is easier to interrupt or alter production to

Ashley D. Ross, Ali Nejat, Virgie Greb

Adaptive planning is ideally suited for the deep uncertainties presented by climate change. While there is a robust scholarship on the theory and methods of adaptive planning, this has largely neglected how adaptive planning is affected by existing planning institutions and how to move forward within the constraints of traditional planning organizations. Thi

A. G. Sivakov, O. G. Turutanov, S. A. Kruhlov, A. V. Krevsun

Joule heat generated by resistive elements of cryogenic micro- and nanodevices often originates boiling of the cooling cryogenic liquids (helium, nitrogen). The article proposes an experimental method to explore the dynamics of the formation and development of a single vapor bubble in cryogenic liquid by sensing the temperature change of a superconducting th

HaiYing Wang

Centering is a commonly used technique in linear regression analysis. With centered data on both the responses and covariates, the ordinary least squares estimator of the slope parameter can be calculated from a model without the intercept. If a subsample is selected from a centered full data, the subsample is typically un-centered. In this case, is it still

E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles h

Talya Klinger, Michalis Agathos

The data analysis carried out by the LIGO-Virgo collaboration on gravitational-wave events utilizes nested sampling to compute Bayesian evidences and posterior distributions for inferring the source properties of compact binaries. With poor sampling from the constrained prior, nested sampling algorithms may misbehave and fail to sample the posterior distribu

Peter B. Denton, Alessio Giarnetti, Davide Meloni

Next generation neutrino oscillation experiments are expected to measure the remaining oscillation parameters with very good precision. They will have unprecedented capabilities to search for new physics that modify oscillations. DUNE, with its broad band beam, good particle identification, and relatively high energies will provide an excellent environment t

Eleanor Clifford, Ilia Shumailov, Yiren Zhao, Ross Anderson

Early backdoor attacks against machine learning set off an arms race in attack and defence development. Defences have since appeared demonstrating some ability to detect backdoors in models or even remove them. These defences work by inspecting the training data, the model, or the integrity of the training procedure. In this work, we show that backdoors can

Chris Lin, Hugh Chen, Chanwoo Kim, Su-In Lee

Despite the widespread use of unsupervised models, very few methods are designed to explain them. Most explanation methods explain a scalar model output. However, unsupervised models output representation vectors, the elements of which are not good candidates to explain because they lack semantic meaning. To bridge this gap, recent works defined a scalar exp

Mohammed A. Chamma, Fereshteh Rajabi, Aishwarya Kumar, Martin Houde

We survey the spectro-temporal properties of fast radio bursts from FRB 20121102A observed by earlier studies across a wide range of frequencies. We investigate 167 bursts from FRB 20121102A spanning frequencies 1--7.5GHz, durations of less than 1 ms to approximately 10 ms, with low and high energies, and with different wait-times. We find from this sample o

Phillip Schneider, Tim Schopf, Juraj Vladika, Mikhail Galkin

In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption with

Kun Hu, Matthew G. Baring, Alice K. Harding, Zorawar Wadiasingh

Magnetars are neutron stars characterized by strong surface magnetic fields generally exceeding the quantum critical value of 44.1 TeraGauss. High-energy photons propagating in their magnetospheres can be attenuated by QED processes like photon splitting and magnetic pair creation. In this paper, we compute the opacities due to photon splitting and pair crea

Bruce J. Hrivnak, Wenxian Lu, William C. Bakke, Peyton J. Grimm

We have carried out a new photometric V,Rc study of 12 protoplanetary nebulae, objects in the short-lived transition between the AGB and PN phases of stellar evolution. These had been the subjects of an earlier study, using data from 1994-2007, that found that all 12 varied periodically, with pulsation periods in the range of ~38 to ~150 days. They are all c

Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu

Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming. This is attributed to overheads caused by sparse matrix multiplication, which are sidestepped when training multi-layer perceptrons (MLPs) with only node features. MLPs, by ignoring graph context, are simple and faster for graph data, however they usually sacrifice

Jiaqing Kou, Laura Botero-Bolívar, Román Ballano, Oscar Marino

We present a framework for airfoil shape optimization to reduce the trailing edge noise for the design of wind turbine blades. Far-field noise is evaluated using Amiet's theory coupled with the TNO-Blake model to calculate the wall pressure spectrum and fast turn-around XFOIL simulations to evaluate the boundary layer parameters. The computational framework

Diulhio Candido de Oliveira, Bogdan Tomoyuki Nassu, Marco Aurelio Wehrmeister

In this paper, we introduce an approach for detecting modifications in assembled printed circuit boards based on photographs taken without tight control over perspective and illumination conditions. One instance of this problem is the visual inspection of gas pumps PCBs, which can be modified by fraudsters wishing to deceive costumers or evade taxes. Given t

Mengfei He, Vincent Démery, Joseph D. Paulsen

Although thin films are typically manufactured in planar sheets or rolls, they are often forced into three-dimensional shapes, producing a plethora of structures across multiple length-scales. Existing theoretical approaches have made progress by separating the behaviors at different scales and limiting their scope to one. Under large confinement, a geometri

Xiaowen Feng, Heng Li

Background: In the metagenome assembly of a microbiome community, we may think abundant species would be easier to assemble due to their deeper coverage. However, this conjucture is rarely tested. We often do not know how many abundant species we are missing and do not have an approach to recover these species. Results: Here we proposed k-mer based and 16S R

Danrui Ni, Ranuri S. Dissanayaka Mudiyanselage, Xianghan Xu, Junsik Mun

A previously unreported layered spin 1/2 triangular lattice polymorph of TiI3 is described, synthesized under 6 GPa of applied pressure at 900 C, but stable at atmospheric pressure. This air-sensitive material has a CdI2-type layered structure (P-3m1 (#164), a = 4.012 A and c = 6.641 A at 120 K, Z = 1 of Ti0.667I2) with an in-plane triangular lattice, relate

M. S. Ramkarthik, Devvrat Tiwari, Pranay Barkataki

Quantum Discord (QD) is a measure of the total quantum non-local correlations of a quantum system. The formalism of quantum discord has been applied to various two-qubit mixed states and it has been reported that there is a non-zero quantum discord even when the states are unentangled. To this end, we have calculated the Quantum Discord for higher than two q

Simon Diemert, Jens H. Weber

Modern systems are designed to operate in increasingly variable and uncertain environments. Not only are these environments complex, in the sense that they contain a tremendous number of variables, but they also change over time. Systems must be able to adjust their behaviour at run-time to manage these uncertainties. These self-adaptive systems have been st

Amin Ghiasi, Ali Shafahi, Reza Ardekani

We propose adaptive weight decay, which automatically tunes the hyper-parameter for weight decay during each training iteration. For classification problems, we propose changing the value of the weight decay hyper-parameter on the fly based on the strength of updates from the classification loss (i.e., gradient of cross-entropy), and the regularization loss

Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman

Dynamic adaptation has become an essential technique in accelerating distributed machine learning (ML) training. Recent studies have shown that dynamically adjusting model structure (e.g., lottery ticket hypothesis) or hyperparameters (e.g., batch size) can significantly accelerate training without sacrificing accuracy. However, existing ML cluster scheduler

Raviteja Vemulapalli, Warren Richard Morningstar, Philip Andrew Mansfield, Hubert Eichner

Dual encoding models that encode a pair of inputs are widely used for representation learning. Many approaches train dual encoding models by maximizing agreement between pairs of encodings on centralized training data. However, in many scenarios, datasets are inherently decentralized across many clients (user devices or organizations) due to privacy concerns

Peng Zhao, Anirban Bhattacharya, Debdeep Pati, Bani K. Mallick

Modern data science applications often involve complex relational data with dynamic structures. An abrupt change in such dynamic relational data is typically observed in systems that undergo regime changes due to interventions. In such a case, we consider a factorized fusion shrinkage model in which all decomposed factors are dynamically shrunk towards group

Lisa J. Einstein, Robert J. Moss, Mykel J. Kochenderfer

Well-executed emergency evacuations can save lives and reduce suffering. However, decision makers struggle to determine optimal evacuation policies given the chaos, uncertainty, and value judgments inherent in emergency evacuations. We propose and analyze a decision support tool for pre-crisis training exercises for teams preparing for civilian evacuations a

Oswin So, Gongjie Li, Evangelos A. Theodorou, Molei Tao

Incorporating the Hamiltonian structure of physical dynamics into deep learning models provides a powerful way to improve the interpretability and prediction accuracy. While previous works are mostly limited to the Euclidean spaces, their extension to the Lie group manifold is needed when rotations form a key component of the dynamics, such as the higher-ord

Dimitris Papatheodoulou, Pavlos Pavlou, Stelios G. Vrachimis, Kleanthis Malialis

Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies. We focus on a specific type of time series classification which we refer to as aggregated time series classification. We consider an aggregated sequence of a multi-variate time series, and propose a methodology to make predictions based solely on th

Mamadou Lamine Diop, William Kengne

We consider statistical learning question for $\psi$-weakly dependent processes, that unifies a large class of weak dependence conditions such as mixing, association,$\cdots$ The consistency of the empirical risk minimization algorithm is established. We derive the generalization bounds and provide the learning rate, which, on some H{\"o}lder class of hypoth

Junhyung Lee, Junho Koh, Youngwoo Lee, Jun Won Choi

LiDAR sensors are widely used for 3D object detection in various mobile robotics applications. LiDAR sensors continuously generate point cloud data in real-time. Conventional 3D object detectors detect objects using a set of points acquired over a fixed duration. However, recent studies have shown that the performance of object detection can be further enhan

Richard Mandel, Alexander Ushakov

Consider the equation $q_1\alpha^{x_1}+\dots+q_k\alpha^{x_k} = q$, with constants $\alpha \in \overline{\mathbb{Q}} \setminus \{0,1\}$, $q_1,\ldots,q_k,q\in\overline{\mathbb{Q}}$ and unknowns $x_1,\ldots,x_k$, referred to in this paper as an \emph{algebraic equation with exponents}. We prove that the problem to decide if a given equation has an integer solut

Ruirui Liu, Zhen Ji, Xiandong Zhao, Tianyu Zhao

Purpose: To develop a Monte Carlo (MC) therapeutic dose calculation toolkit of a recently released ring gantry linac in Geant4 (Version 10.7) for secondary dose validation of radiotherapy plan. Methods: For the Halcyon (Varian Medical Systems), the DSMLC was modeled and radiation transport in DSMLC and patient phantom was simulated using Geant4. Radiation so

Chunhui Zhang, Hongfu Liu, Jundong Li, Yanfang Ye

Prevailing deep graph learning models often suffer from label sparsity issue. Although many graph few-shot learning (GFL) methods have been developed to avoid performance degradation in face of limited annotated data, they excessively rely on labeled data, where the distribution shift in the test phase might result in impaired generalization ability. Additio

Nicholas D. Cooper, Uyen M. Ta, Melanie A. R. Reber

Here we investigate the use of passive intracavity optical filters for controlling the laser output spectrum of a polarization-mode-locked, ultrafast Ytterbium fiber laser. With strategic placement of the filter cutoff frequency, the overall lasing bandwidth can be increased or extended. Overall laser performance, including pulse compression and intensity no

Diego Dominici, Juan José Moreno Balcázar

In this paper we tackle the asymptotic behavior of a family of orthogonal polynomials with respect to a nonstandard inner product involving the forward operator {\Delta}. Concretely, we treat the generalized Charlier weights in the framework of {\Delta}--Sobolev orthogonality. We obtain an asymptotic expansion for this orthogonal polynomials where the fallin

Venkatraman Renganathan, Anders Rantzer, Olle Kjellqvist

This paper deals with a distributed implementation of minimax adaptive control algorithm for networked dynamical systems modeled by a finite set of linear models. To hedge against the uncertainty arising out of finite number of possible dynamics in each node in the network, it collects only the historical data of its neighboring nodes to decide its control a

Ivan Karpov

In this paper, we study single-bunch instabilities observed in the CERN Super Proton Synchrotron (SPS). According to the linearized Vlasov theory, radial or azimuthal mode-coupling instabilities result from a coupling of bunch-oscillation modes, which belong to either the same or adjacent azimuthal modes, respectively. We show that both instability mechanism

Larry Zamick

In an old paper [ 1] B.H. Flowers discussed calculations for odd A nuclei in the g9/2 shell. He finds when he varies the parameters of a certain interaction that the lowest energy state is always a seniority v=1 state with angular momentum J=9/2+. However experiments show about half the states have J=9/2+ lower and the other ones have J=7/2+ lower. More rece

Lin Gui, Victor Veitch

Consider the problem of estimating the causal effect of some attribute of a text document; for example: what effect does writing a polite vs. rude email have on response time? To estimate a causal effect from observational data, we need to adjust for confounding aspects of the text that affect both the treatment and outcome -- e.g., the topic or writing leve

Sina Hazratpour, Emily Riehl

Consider a locally cartesian closed category with an object I and a class of trivial fibrations, which admit sections and are stable under pushforward and retract as arrows. Define the fibrations to be those maps whose Leibniz exponential with the generic point of I defines a trivial fibration. Then the fibrations are also closed under pushforward.

Kwangyoun Kim, Felix Wu, Yifan Peng, Jing Pan

Conformer, combining convolution and self-attention sequentially to capture both local and global information, has shown remarkable performance and is currently regarded as the state-of-the-art for automatic speech recognition (ASR). Several other studies have explored integrating convolution and self-attention but they have not managed to match Conformer's

Antoine Henry, David Barral, Isabelle Zaquine, Andreas Boes

Photon-pair sources based on thin film lithium niobate on insulator technology have a great potential for integrated optical quantum information processing. We report on such a source of correlated twin-photon pairs generated by spontaneous parametric down conversion in a silicon nitride (SiN) rib loaded thin film periodically poled lithium niobate (LN) wave

Zhao Han, Tom Williams, Holly A. Yanco

As robots become increasingly complex, they must explain their behaviors to gain trust and acceptance. However, it may be difficult through verbal explanation alone to fully convey information about past behavior, especially regarding objects no longer present due to robots' or humans' actions. Humans often try to physically mimic past movements to accompany

M. Rahman, Abid Khan, Sayeed Anowar, Md Al-Imran

Industry 4.0 targets the conversion of the traditional industries into intelligent ones through technological revolution. This revolution is only possible through innovation, optimization, interconnection, and rapid decision-making capability. Numerical models are believed to be the key components of Industry 4.0, facilitating quick decision-making through s

Abid Hossain Khan, Salauddin Omar, Nadia Mushtary, Richa Verma

Surrogate modeling has brought about a revolution in computation in the branches of science and engineering. Backed by Artificial Intelligence, a surrogate model can present highly accurate results with a significant reduction in computation time than computer simulation of actual models. Surrogate modeling techniques have found their use in numerous branche

Nanyang Ye, Jingbiao Mei, Zhicheng Fang, Yuwen Zhang

To deploy deep learning algorithms on resource-limited scenarios, an emerging device-resistive random access memory (ReRAM) has been regarded as promising via analog computing. However, the practicability of ReRAM is primarily limited due to the weight drifting of ReRAM neural networks due to multi-factor reasons, including manufacturing, thermal noises, and

Anupam Ray

Dark Matter (DM) is omnipresent in our universe. Despite its abundance, the microscopic identity of DM still remains a mystery. Primordial black holes (PBHs), possibly formed via gravitational collapse of large density perturbations in the early universe, are one of the earliest proposed and viable DM candidates. Recent studies indicate that PBHs can make up

Souvik Roy, Dibyendu Nandy

To understand the global-scale physical processes behind coronal mass ejection (CME)-driven geomagnetic storms and predict their intensity as a space weather forecasting measure, we develop an interplanetary CME flux rope-magnetosphere interaction module using 3D magnetohydrodynamics. The simulations adequately describe ICME-forced dynamics of the magnetosph

B. Truc, P. Usai, F. Pennacchio, G. Berruto

Metal-insulator transitions (MIT) occurring in non-adiabatic conditions can evolve through high-energy intermediate states that are difficult to observe and control via static methods. By monitoring the out-of-equilibrium structural dynamics of a magnetite (Fe3O4) crystal via ultrafast electron diffraction, we show that MITs can evolve through different path

Julius von Rohrscheidt, Bastian Rieck

The manifold hypothesis, which assumes that data lies on or close to an unknown manifold of low intrinsic dimension, is a staple of modern machine learning research. However, recent work has shown that real-world data exhibits distinct non-manifold structures, i.e. singularities, that can lead to erroneous findings. Detecting such singularities is therefore

Naman Shah, Siddharth Srivastava

This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems. Starting with a vanilla RL formulation with a stochastic dynamics simulator and an occupancy matrix of the environment, our approach computes useful options with policies as well as high-level paths that compose the discovered

Deborah Salon, Laura Mirtich, Matthew Wigginton Bhagat-Conway, Adam Costello

This study focuses on an important transport-related long-term effect of the COVID-19 pandemic in the United States: an increase in telecommuting. Analyzing a nationally representative panel survey of adults, we find that 40-50% of workers expect to telecommute at least a few times per month post-pandemic, up from 24% pre-COVID. If given the option, 90-95% o

Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette

Recent work has shown that augmenting environments with language descriptions improves policy learning. However, for environments with complex language abstractions, learning how to ground language to observations is difficult due to sparse, delayed rewards. We propose Language Dynamics Distillation (LDD), which pretrains a model to predict environment dynam

Zheng Cao, Raymond Guo, Caesar M. Tuguinay, Mark Pock

This paper presents a methodology for combining programming and mathematics to optimize elevator wait times. Based on simulated user data generated according to the canonical three-peak model of elevator traffic, we first develop a naive model from an intuitive understanding of the logic behind elevators. We take into consideration a general array of feature

Nihal V. Nayak, Ethan R. Elenberg, Clemens Rosenbaum

Evaluating clustering quality with reliable evaluation metrics like normalized mutual information (NMI) requires labeled data that can be expensive to annotate. We focus on the underexplored problem of estimating clustering quality with limited labels. We adapt existing approaches from the few-sample model evaluation literature to actively sub-sample, with a

Donghan Yu, Sheng Zhang, Patrick Ng, Henghui Zhu

Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs. Previous methods either generate logical forms that can be executed over KBs to obtain final answers or predict answers directly. Empirical results show that the former often produces more accurate answers, b

Pouya Bashivan, Adam Ibrahim, Amirozhan Dehghani, Yifei Ren

Model ensembles have long been used in machine learning to reduce the variance in individual model predictions, making them more robust to input perturbations. Pseudo-ensemble methods like dropout have also been commonly used in deep learning models to improve generalization. However, the application of these techniques to improve neural networks' robustness

Julian Kranz, Shintaro Nishikawa

Building on Enders--Schemeitat--Tikuisis' classification, we show that a separable $C^*$-algebra $A$ with approximately inner flip in the UCT class is $K$-theoretically self-absorbing if and only if for every finite group $G$, the Bernoulli shift on $A^{\otimes G}$ is $KK^G$-equivalent to the trivial action. This in particular applies to UHF-algebras of infi

Mohammad Al-Quraan, Ahsan Khan, Anthony Centeno, Ahmed Zoha

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can

David Tempo, Ricardo Troncoso

The conformal algebra in 2D (Diff($S^{1}$)$\oplus$Diff($S^{1}$)) is shown to be preserved under a nonlinear map that mixes both chiral (holomorphic) generators $T$ and $\bar{T}$. It depends on a single real parameter and it can be regarded as a ``nonlinear $SO(1,1)$ automorphism.'' The map preserves the form of the momentum density and naturally induces a fl

Gino A. Chacon, Charles Williams, Johann Knechtel, Ozgur Sinanoglu

As industry moves toward chiplet-based designs, the insertion of hardware Trojans poses a significant threat to the security of these systems. These systems rely heavily on cache coherence for coherent data communication, making coherence an attractive target. Critically, unlike prior work, which focuses only on malicious packet modifications, a Trojan attac

Yurii Khomskii, Hrafn Valtýr Oddsson

We present a novel treatment of set theory in a four-valued paraconsistent and paracomplete logic, i.e., a logic in which propositions can be both true and false, and neither true nor false. Our approach is a significant departure from previous research in paraconsistent set theory, which has almost exclusively been motivated by a desire to avoid Russell's p

Mikael Kurula

Thirty years after the introduction of port-Hamiltonian systems, interest in this system class still remains high among systems and control researchers. Very recently, Jacob and Laasri obtained strong results on the solvability and well-posedness of time-varying linear port-Hamil\-to\-nian systems with boundary control and boundary observation. In this paper

Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani, Ali Gholami

A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features. This work proposes MaskTune, a masking strategy that prevents over-reliance on spurious (or a limited number of) features. MaskTune forces the trained mod

Sergio Brenner Miguel

We study the non-parametric estimation of an unknown stationary density fV of an unobserved strictly stationary volatility process $(\bm V_t)_{t\geq 0}$ on $\IRp^2 := (0,\infty)^2$ based on discrete-time observations in a stochastic volatility model. We identify the underlying multiplicative measurement error model and build an estimator based on the estimat

Reza Nasirigerdeh, Javad Torkzadehmahani, Daniel Rueckert, Georgios Kaissis

Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially private federated learning (DP-FL). While the unsuitability of batch normalization for these domains has already been shown, the impact of other normalization methods on the performance

Danyu Zhang

We construct non-algebraic Anosov flows in dimension $3+2n$, $n\geq 2$, by suspending the action of the fundamental group of a finite cover of the Bonatti-Langevin flow.