November 2022 arXiv papers — page 109

Lingji Chen, Alok Sharma, Chinmay Shirore, Chengjie Zhang

The DEtection TRansformer (DETR) approach, which uses a transformer encoder-decoder architecture and a set-based global loss, has become a building block in many transformer based applications. However, as originally presented, the assignment cost and the global loss are not aligned, i.e., reducing the former is likely but not guaranteed to reduce the latter

Zhiyi Zhang, Bin Cheng, Jeremy Lim, Anyuan Gao

Realizing both ultra-low breakdown voltage and ultra-high gain has been one of the major challenges in the development of high-performance avalanche photodetector. Here, we report that an ultra-high avalanche gain of 3*10^5 can be realized in the graphite/InSe Schottky photodetector at a breakdown voltage down to 5.5 V. Remarkably, the threshold breakdown vo

Bat-Od Battseren

We adapt the notion of quasi-Hermition group to the pairs $(G,H)$ of discrete group $G$ and its subgroup $H$. We show that a quasi-Hermitian pair is amenable in the sense of Eymard.

Xingyi Zhao, Lu Zhang, Depeng Xu, Shuhan Yuan

Many word-level adversarial attack approaches for textual data have been proposed in recent studies. However, due to the massive search space consisting of combinations of candidate words, the existing approaches face the problem of preserving the semantics of texts when crafting adversarial counterparts. In this paper, we develop a novel attack strategy to

Subhash Nerella, Kia Khezeli, Andrea Davidson, Patrick Tighe

Pain is a common occurrence among patients admitted to Intensive Care Units. Pain assessment in ICU patients still remains a challenge for clinicians and ICU staff, specifically in cases of non-verbal sedated, mechanically ventilated, and intubated patients. Current manual observation-based pain assessment tools are limited by the frequency of pain observati

Xiaoqing Tan, Zhengling Qi, Christopher W. Seymour, Lu Tang

This paper introduces RISE, a robust individualized decision learning framework with sensitive variables, where sensitive variables are collectible data and important to the intervention decision, but their inclusion in decision making is prohibited due to reasons such as delayed availability or fairness concerns. A naive baseline is to ignore these sensitiv

Sebastian Calcetero-Vanegas, Andrei L. Badescu, X. Sheldon Lin

Experience rating in insurance uses a Bayesian credibility model to upgrade the current premiums of a contract by taking into account policyholders' attributes and their claim history. Most data-driven models used for this task are mathematically intractable, and premiums must be obtained through numerical methods such as simulation via MCMC. However, these

M. Namdar Baboli, Y. Estaremi

In this paper we deal with unbounded composition operators defined in Orlicz spaces. Indeed, we provide some necessary and sufficient condition for densely definedness of composition operators on Orlicz spaces. Also, we will investigate the adjoint of densely defined composition operators and we give some equivalent conditions for it to be densely defined. I

Russell Lee, Bo Sun, Mohammad Hajiesmaili, John C. S. Lui

This paper develops learning-augmented algorithms for energy trading in volatile electricity markets. The basic problem is to sell (or buy) $k$ units of energy for the highest revenue (lowest cost) over uncertain time-varying prices, which can framed as a classic online search problem in the literature of competitive analysis. State-of-the-art algorithms ass

Haotian Zhang, Linxiaoyi Wan

To design a drug given a biological molecule by using deep learning methods, there are many successful models published recently. People commonly used generative models to design new molecules given certain protein. LiGAN was regarded as the baseline of deep learning model which was developed on convolutional neural networks. Recently, GraphBP showed its abi

Guangtao Lyu

Scene text removal aims to remove the text and fill the regions with perceptually plausible background information in natural images. It has attracted increasing attention due to its various applications in privacy protection, scene text retrieval, and text editing. With the development of deep learning, the previous methods have achieved significant improve

Ngai Pok Kwan, Chirag Modi, Yin Li, Shirley Ho

In the light of GPU accelerations, sequential operations such as solving ordinary differential equations can be bottlenecks for gradient evaluations and hinder potential speed gains. In this work, we focus on growth functions and their time derivatives in cosmological particle mesh simulations and show that these are the majority time cost when using gradien

Jason P. Bell, Be'eri Greenfeld

We give a combinatorial characterization of amenability of monomial algebras and prove the existence of monomial Folner sequences, answering a question due to Ceccherini-Silberstein and Samet-Vaillant. We then use our characterization to prove that over projectively simple monomial algebras, every module is exhaustively amenable; we conclude that convolution

Heitor R. Guimarães, Arthur Pimentel, Anderson R. Avila, Mehdi Rezagholizadeh

Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech and/or emotion recognition. Existing models, such as HuBERT, however, can be fairly large thus may not be suitable for edge speech applications. Moreover, realistic applications typi

Peter ED Love, Jane Matthews, Weili Fang, Stuart Porter

Explainable artificial intelligence is an emerging and evolving concept. Its impact on construction, though yet to be realised, will be profound in the foreseeable future. Still, XAI has received limited attention in construction. As a result, no evaluation frameworks have been propagated to enable construction organisations to understand the what, why, how,

Savinay Nagendra, Chaopeng Shen, Daniel Kifer

The purpose of binary segmentation models is to determine which pixels belong to an object of interest (e.g., which pixels in an image are part of roads). The models assign a logit score (i.e., probability) to each pixel and these are converted into predictions by thresholding (i.e., each pixel with logit score $\geq \tau$ is predicted to be part of a road).

Sang Wanchao, Gao Hui, Xu Wenjun

It is becoming a key feature of UAV communication network to integrate sensing function into communication equipment to realize sensing communication integration.The existing perceptual communication fusion schemes focus on the design under a single waveform and single frequency band, and lack of discussion on the adaptation of scene and algorithm complexity

Pinchen Xie, Roberto Car, Weinan E

We introduce a machine learning-based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed

Zhihao Wang, Haoyao Chen, Shiwu Zhang, Yunjiang Lou

The visual simultaneous localization and mapping(vSLAM) is widely used in GPS-denied and open field environments for ground and surface robots. However, due to the frequent perception failures derived from lacking visual texture or the {swing} of robot view direction on rough terrains, the accuracy and robustness of vSLAM are still to be enhanced. The study

Nian-Ci Wu, Cheng-Zhi Liu

For large-scale data fitting, the least-squares progressive-iterative approximation (LSPIA) methods were proposed by Lin et al. (SIAM Journal on Scientific Computing, 2013, 35(6):A3052-A3068) and Deng et al. (Computer-Aided Design, 2014, 47:32-44), where the constant step sizes were used. In this work, we further accelerate the LSPIA method in the sense of a

Xuehui Dong, Rujing Xiong, Tiebin Mi, Yuan Xie

This paper investigates the problem of maximizing the signal-to-noise ratio (SNR) in reconfigurable intelligent surface (RIS)-assisted MISO communication systems. The problem will be reformulated as a complex quadratic form problem with unit circle constraints. We proved that the SNR maximizing problem has a closed-form global optimal solution when it is a r

S. J. Spencer, C. G. Deacon, G. T. Andrews

A study of wetting was carried out on porous silicon films with pore diameters spanning three orders of magnitude. Water contact angle measurements on adjoining porous and nonporous regions yielded Wenzel roughness ratios that were either unphysical (less than unity) or unrealistically low when compared to those expected from specific surface area considerat

Sahisnu Mazumder, Bing Liu

Dialogue systems, commonly known as chatbots, have gained escalating popularity in recent times due to their wide-spread applications in carrying out chit-chat conversations with users and task-oriented dialogues to accomplish various user tasks. Existing chatbots are usually trained from pre-collected and manually-labeled data and/or written with handcrafte

Shrestha Mohanty, Negar Arabzadeh, Milagro Teruel, Yuxuan Sun

Human intelligence can remarkably adapt quickly to new tasks and environments. Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions. To facilitate research which can enable similar capabilities in machines, we made the follo

David Nualart, Bhargobjyoti Saikia

We consider a system of $d$ non-linear stochastic heat equations driven by an $m$-dimensional space-time white noise on $\mathbb{R}_+\times \mathbb{R}$. In this paper we study the asymptotic behavior of spatial averages over large intervals $[-R,R]$. We establish a rate of convergence to a multivariate normal distribution in the Wasserstein distance and a fu

Florimond Houssiau, James Jordon, Samuel N. Cohen, Owen Daniel

Personal data collected at scale promises to improve decision-making and accelerate innovation. However, sharing and using such data raises serious privacy concerns. A promising solution is to produce synthetic data, artificial records to share instead of real data. Since synthetic records are not linked to real persons, this intuitively prevents classical r

Janosch Döcker, Simone Linz, Charles Semple

In the context of reconstructing phylogenetic networks from a collection of phylogenetic trees, several characterisations and subsequently algorithms have been established to reconstruct a phylogenetic network that collectively embeds all trees in the input in some minimum way. For many instances however, the resulting network also embeds additional phylogen

Nishanth Rao, Suresh Sundaram, Varun Raghavendra

This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A model learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The sp

Sandeep Kothinti, Dimitra Emmanouilidou

The analysis, processing, and extraction of meaningful information from sounds all around us is the subject of the broader area of audio analytics. Audio captioning is a recent addition to the domain of audio analytics, a cross-modal translation task that focuses on generating natural descriptions from sound events occurring in an audio stream. In this work,

Yikang Wang, Xingming Wang, Hiromitsu Nishizaki, Ming Li

A reliable voice anti-spoofing countermeasure system needs to robustly protect automatic speaker verification (ASV) systems in various kinds of spoofing scenarios. However, the performance of countermeasure systems could be degraded by channel effects and codecs. In this paper, we show that using the low-frequency subbands of signals as input can mitigate th

Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link pre

Soojung Hong, Kwanghee Choi

As maintaining road networks is labor-intensive, many automatic road extraction approaches have been introduced to solve this real-world problem, fueled by the abundance of large-scale high-resolution satellite imagery and advances in computer vision. However, their performance is limited for fully automating the road map extraction in real-world services. H

Yuki Yada, Jiaying Feng, Tsuneo Matsumoto, Nao Fukushima

Dark patterns, which are user interface designs in online services, induce users to take unintended actions. Recently, dark patterns have been raised as an issue of privacy and fairness. Thus, a wide range of research on detecting dark patterns is eagerly awaited. In this work, we constructed a dataset for dark pattern detection and prepared its baseline det

Hanchen David Wang, Meiyi Ma

Physical therapy (PT) is crucial for patients to restore and maintain mobility, function, and well-being. Many on-site activities and body exercises are performed under the supervision of therapists or clinicians. However, the postures of some exercises at home cannot be performed accurately due to the lack of supervision, quality assessment, and self-correc

Nader Haghighipour, Morris Podolak, Esther Podolak

As part of our ongoing initiative on accurately calculating the accretion rate of planetesimals in the core-accretion model, we demonstrated in a recent article that when the calculations include the gravitational force of the Sun (the original core-accretion model did not include solar gravity), results change considerably [ApJ, 899:45]. In this paper, we h

Richard Anantua, Joaquín Dúran, Nathan Ngata, Lani Oramas

This work proposes a methodology to test phenomenologically-motivated emission processes that account for the flux and polarization distribution and global structure of the 230 GHz sources imaged by the Event Horizon Telescope (EHT): Messier (M)87* and Sagittarius (Sgr) A*. We introduce to general relativistic magnetohydrodynamic (GRMHD) simulations some nov

Fábio Botler, Phablo F. S. Moura, Tássio Naia

Seymour's Second Neighborhood Conjecture (SNC) states that every oriented graph contains a vertex whose second neighborhood is as large as its first neighborhood. We investigate the SNC for orientations of both binomial and pseudo random graphs, verifying the SNC asymptotically almost surely (a.a.s.) (i) for all orientations of $G(n,p)$ if $\limsup_{n\to\inf

Maximilien Barbier, Arseni Goussev, Shashi C. L. Srivastava

Quantum backflow refers to the counterintuitive fact that the probability can flow in the direction opposite to the momentum of a quantum particle. This phenomenon has been seen to be small and fragile for one-dimensional systems, in which the maximal amount of backflow has been found to be bounded. Quantum backflow exhibits dramatically different features i

Reza Javadinezhad, Massimo Porrati

The angular momentum radiated in gravitational scattering can be changed by performing a supertranslation of the asymptotic metric, i.e. by adding radiation with infinite wavelenght to the metric. This puzzling property can be avoided by adopting a supertranslation-invariant definition of the angular momentum flux in general relativity. Definitions currently

Zengjing Chen, Huaijin Liang, Wei Wang, Xiaodong Yan

No matter how much some gamblers occasionally win, as long as they continue to gamble, sooner or later they will lose more to the casino, which is the so-called long bet will lose. Our results demonstrate the counter-intuitive phenomenon, that gamblers involved in long bets will lose but casinos always advertise their unprofitable circumstances. Here we expo

Florian Streibelt, Martina Lindorfer, Seda Gürses, Carlos H. Gañán

Researchers and practitioners often face the issue of having to attribute an IP address to an organization. For current data this is comparably easy, using services like whois or other databases. Similarly, for historic data, several entities like the RIPE NCC provide websites that provide access to historic records. For large-scale network measurement work,

Erica Cai, Andrew McGregor, David Jensen

Learning causal structure is useful in many areas of artificial intelligence, including planning, robotics, and explanation. Constraint-based structure learning algorithms such as PC use conditional independence (CI) tests to infer causal structure. Traditionally, constraint-based algorithms perform CI tests with a preference for smaller-sized conditioning s

Li-Wei Chen, Shinji Watanabe, Alexander Rudnicky

We present a unified system to realize one-shot voice conversion (VC) on the pitch, rhythm, and speaker attributes. Existing works generally ignore the correlation between prosody and language content, leading to the degradation of naturalness in converted speech. Additionally, the lack of proper language features prevents these systems from accurately prese

Joseph Ryan, Brandon Stevenson, Cynthia Trendafilova, Joel Meyers

The planning and design of future experiments rely heavily on forecasting to assess the potential scientific value provided by a hypothetical set of measurements. The Fisher information matrix, due to its convenient properties and low computational cost, provides an especially useful forecasting tool. However, the Fisher matrix only provides a reasonable app

Yi Yan, Tian Xie, Ercan E. Kuruoglu

The adaptive estimation of coexisting temporal vertex (node) and edge signals on graphs is a critical task when a change in edge signals influences the temporal dynamics of the vertex signals. However, the current Graph Signal Processing algorithms mostly consider only the signals existing on the graph vertices and have neglected the fact that signals can re

Y. Bonder, J. E. Herrera, A. M. Rubiol

Energy conservation has the status of a fundamental physical principle. However, measurements in quantum mechanics do not comply with energy conservation. Therefore, it is expected that a more fundamental theory of gravity -- one that is less incompatible with quantum mechanics -- should admit energy nonconservations. This paper begins by identifying the con

L. A. Martinez, Z. Peng, D. Appelö, D. M. Tennant

In the higher levels of superconducting transmon devices, and more generally charge sensitive devices, $T_2^*$ measurements made in the presence of low-frequency time-correlated $1/f$ charge noise and quasiparticle-induced parity flips can give an underestimation of the total dephasing time. The charge variations manifest as beating patterns observed in the

Christopher A. Choquette-Choo, H. Brendan McMahan, Keith Rush, Abhradeep Thakurta

We introduce new differentially private (DP) mechanisms for gradient-based machine learning (ML) with multiple passes (epochs) over a dataset, substantially improving the achievable privacy-utility-computation tradeoffs. We formalize the problem of DP mechanisms for adaptive streams with multiple participations and introduce a non-trivial extension of online

Navdeep K. Panesar, Viggo H. Hansteen, Sanjiv K. Tiwari, Mark C. M. Cheung

We report high-resolution, high-cadence observations of five small-scale coronal jets in an on-disk quiet Sun region observed with Solar Orbiter's EUI/\hri\ in 174 \AA. We combine the \hri\ images with the EUV images of SDO/AIA and investigate magnetic setting of the jets using co-aligned line-of-sight magnetograms from SDO/HMI. The \hri\ jets are miniature

Lijun Thayyil Raju, Christian Diddens, Javier Rodríguez-Rodríguez, Marjolein N. van der Linden

Evaporation of multi-component liquid mixtures in confined geometries, such as capillaries, is crucial in applications such as microfluidics, two-phase cooling devices, and inkjet printing. Predicting the behaviour of such systems becomes challenging because evaporation triggers complex spatio-temporal changes in the composition of the mixture. These changes

Katherine Metcalf, Miguel Sarabia, Barry-John Theobald

Preference-based reinforcement learning (RL) algorithms help avoid the pitfalls of hand-crafted reward functions by distilling them from human preference feedback, but they remain impractical due to the burdensome number of labels required from the human, even for relatively simple tasks. In this work, we demonstrate that encoding environment dynamics in the

Luigi De Giovanni, Guglielmo Lulli, Carlo Lancia

In this paper, we present a novel data-driven optimization approach for trajectory based air traffic flow management (ATFM). A key aspect of the proposed approach is the inclusion of airspace users' trajectory preferences, which are computed from traffic data by combining clustering and classification techniques. Machine learning is also used to extract cons

Jennifer Chien, Anna Guitart, Ana Fernandez del Rio, Africa Perianez

Mobile health apps provide a unique means of collecting data that can be used to deliver adaptive interventions.The predicted outcomes considerably influence the selection of such interventions. Recourse via counterfactuals provides tangible mechanisms to modify user predictions. By identifying plausible actions that increase the likelihood of a desired pred

Won Joon Yun, Hankyul Baek, Joongheon Kim

In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing. Among these emerging areas, quantum federated learning (QFL) has gained particular attention due to the integration of quantum neural networks (QNNs) with traditional fe

Tanay Roy, Ziqian Li, Eliot Kapit, David I. Schuster

Processing quantum information using quantum three-level systems or qutrits as the fundamental unit is an alternative to contemporary qubit-based architectures with the potential to provide significant computational advantages. We demonstrate a fully programmable two-qutrit quantum processor by utilizing the third energy eigenstates of two transmons. We deve

James M. Dolezal, Rachelle Wolk, Hanna M. Hieromnimon, Frederick M. Howard

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have l

Loukas Georgiadis, Giuseppe F. Italiano, Evangelos Kosinas, Debasish Pattanayak

We show how to find and efficiently maintain maximal k-edge-connected subgraphs in undirected graphs. In particular, we provide the following results. (1) A general framework for maintaining the maximal k-edge-connected subgraphs upon insertions of edges or vertices, by successively partitioning the graph into its k-edge-connected components. This defines a

Lucas Affonso, Rodrigo Bissacot, Marcelo Laca

We use a groupoid model for the spin algebra to introduce boundary conditions on quantum spin systems via a Poisson point process representation. We can describe KMS states of quantum systems by means of a set of equations resembling the standard DLR equations of classical statistical mechanics. We introduce a notion of quantum specification which recovers t

Oliver Daniels-Koch, Rachel Freedman

Reinforcement learning from human feedback (RLHF) is a powerful technique for training agents to perform difficult-to-specify tasks. However, human feedback can be noisy, particularly when human teachers lack relevant knowledge or experience. Levels of expertise vary across teachers, and a given teacher may have differing levels of expertise for different co

Li Zeng, Stein B. Jacobsen, Dimitar D. Sasselov, Michail I. Petaev

The exoplanet field now abounds with new discoveries of planets and planetary systems. It is of great interest for the scientific community to understand the compositions and internal structures of these new planets outside our own solar system, and then infer their formation scenarios. In particular, the proper implementation of the equation-of-states of va

Masato Nozawa, Takashi Torii

We present a new family of charged C-metrics in ${\cal N}=2$ gauged supergravity in four dimensions. The double Wick rotation of the C-metric allows us to bring our solution into a different family of the C-metrics previously found by L\"u and V\'azquez-Poritz. In the case of zero acceleration limit, our solution with vanishing charges reduces to the scalar

Vashist Avadhanula, Omar Abdul Baki, Hamsa Bastani, Osbert Bastani

We describe the current content moderation strategy employed by Meta to remove policy-violating content from its platforms. Meta relies on both handcrafted and learned risk models to flag potentially violating content for human review. Our approach aggregates these risk models into a single ranking score, calibrating them to prioritize more reliable risk mod

Colin Ponce, Ruipeng Li, Christina Mao, Panayot Vassilevski

A common challenge in regression is that for many problems, the degrees of freedom required for a high-quality solution also allows for overfitting. Regularization is a class of strategies that seek to restrict the range of possible solutions so as to discourage overfitting while still enabling good solutions, and different regularization strategies impose d

Antonios Zitridis

In this paper, we study the well-posedness (existence and uniqueness) of the Master Equation of Mean Field Games under invariance-type conditions, otherwise known as viability conditions for the controlled dynamics. The interior regularity of the solutions of the associated Mean Field Game system and its linearized version, which plays a crucial role in the

Mikhail Hayhoe, Hans Riess, Victor M. Preciado, Alejandro Ribeiro

We introduce an architecture for processing signals supported on hypergraphs via graph neural networks (GNNs), which we call a Hyper-graph Expansion Neural Network (HENN), and provide the first bounds on the stability and transferability error of a hypergraph signal processing model. To do so, we provide a framework for bounding the stability and transferabi

Yuhan Zhao, Quanyan Zhu

Guided cooperation allows intelligent agents with heterogeneous capabilities to work together by following a leader-follower type of interaction. However, the associated control problem becomes challenging when the leader agent does not have complete information about follower agents. There is a need for learning and adaptation of cooperation plans. To this

Esha Saha, Lam Si Tung Ho, Giang Tran

Predicting the evolution of diseases is challenging, especially when the data availability is scarce and incomplete. The most popular tools for modelling and predicting infectious disease epidemics are compartmental models. They stratify the population into compartments according to health status and model the dynamics of these compartments using dynamical s

Stephanie M. Ribet, Colin Ophus, Roberto dos Reis, Vinayak P. Dravid

Material properties strongly depend on the nature and concentration of defects. Characterizing these features may require nano- to atomic-scale resolution to establish structure-property relationships. 4D-STEM, a technique where diffraction patterns are acquired at a grid of points on the sample, provides a versatile method for highlighting defects. Computat

Sam Wilken, Aria Chaderjian, Omar A. Saleh

Many recent studies of liquid-liquid phase separation in biology focus on phase separation as a dynamic control mechanism for cellular function, but it can also result in complex mesoscopic structures. We primarily investigate a model system consisting of DNA nanostars: finite-valence, self-assembled particles that form micron-scale liquid droplets via a bin

SofÍa Molfese, Diego Rossit, Mariano Frutos, Antonella Cavallin

Municipal solid waste management is a paramount activity in modern cities due to the environmental, social and economic problems that can arise when mishandled. In this work, the sequencing of micro-routes in the Argentine city of Bah\'ia Blanca is addressed, which is modeled as a vehicle routing problem with travel time limit and the vehicle's capacity. Par

Hsin-Yi Lin, Huan-Hsin Tseng, Yu Tsao

It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives. Although network models have potential to be a surrogate for complex human hearing perception, they may contain instabilities in predictions. This work shows that deep speech quality predictor

Amin Nayebi, Sindhu Tipirneni, Chandan K Reddy, Brandon Foreman

Unpacking and comprehending how black-box machine learning algorithms make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models. However, existing approaches to explain such models are frequently un

Zhichao Wang, Andrew Engel, Anand Sarwate, Ioana Dumitriu

We investigate the spectral properties of linear-width feed-forward neural networks, where the sample size is asymptotically proportional to network width. Empirically, we show that the spectra of weight in this high dimensional regime are invariant when trained by gradient descent for small constant learning rates; we provide a theoretical justification for

Dongjie Jia, Mahdi Esmaily

Several finite element methods for simulating incompressible flows rely on the streamline upwind Petrov-Galerkin stabilization (SUPG) term, which is weighted by tau_SUPG. The conventional formulation of tau_SUPG includes a constant that depends on the time step size, producing an overall method that becomes exceedingly less accurate as the time step size app

Bruce C. Berndt, Likun Xie, Alexandru Zaharescu

We provide a proof of a conjecture made by Richard McIntosh in 1996 on the values of the Franel integrals, $$\int_0^1((ax))((bx))((cx))((ex))\,dx,$$ where $((x))$ is the first periodic Bernoulli function. Secondly, we extend our ideas to prove a similar theorem for $$\int_0^1((a_1x))((a_2x))\cdots ((a_{n}x))\,dx.$$ Lastly, we prove a further generalization i

Antoine Tordeux, Claudia Totzeck

Port-Hamiltonian systems (PHS) theory is a recent but already well-established modelling approach for non-linear physical systems. Some studies have shown lately that PHS frameworks are relevant for modelling and control of swarm and multi-agent systems. We identify in this contribution a general class of microscopic force-based pedestrian models that can be

Kay Lächler, Hélène Lajous, Michael Unser, Meritxell Bach Cuadra

Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI) traditionally relies on the availability of several orthogonal low-resolution series of 2-dimensional thick slices (volumes). In practice, only a few low-resolution volumes are acquired. Thus, optimization-based image-reconstruction methods require strong regularization using hand-cra

Josh Paik, Christopher Griffin

The replicator equations are a family of ordinary differential equations that arise in evolutionary game theory, and are closely related to Lotka-Volterra. We produce an infinite family of replicator equations which are Liouville-Arnold integrable. We show this by explicitly providing conserved quantities and a Poisson structure. As a corollary, we classify

Md Rayhanur Rahman, Laurie Williams

Attackers utilize a plethora of adversarial techniques in cyberattacks to compromise the confidentiality, integrity, and availability of the target organizations and systems. Information security standards such as NIST, ISO/IEC specify hundreds of security controls that organizations can enforce to protect and defend the information systems from adversarial

Upol Ehsan, Mark O. Riedl

There is a growing frustration amongst researchers and developers in Explainable AI (XAI) around the lack of consensus around what is meant by 'explainability'. Do we need one definition of explainability to rule them all? In this paper, we argue why a singular definition of XAI is neither feasible nor desirable at this stage of XAI's development. We view XA

G. D. Adebanjo, J. P. Hague, P. E. Kornilovitch

We systematically examine how long-range hopping and its synergy with extended interactions leads to light bound pairs. Pair properties are determined for a dilute extended Hubbard model with large on-site repulsion ($U$) and both near- and next-nearest neighbour hopping ($t$ and $t'$) and attraction ($V$ and $V'$), for cubic and tetragonal lattices. The pre

Sergey Bravyi, Yash Sharma, Mario Szegedy, Ronald de Wolf

Motivated by quantum network applications over classical channels, we initiate the study of $n$-party resource states from which LOCC protocols can create EPR-pairs between any $k$ disjoint pairs of parties. We give constructions of such states where $k$ is not too far from the optimal $n/2$ while the individual parties need to hold only a constant number of

Benjamin L. Badger

Deep learning models develop successive representations of their input in sequential layers, the last of which maps the final representation to the output. Here we investigate the informational content of these representations by observing the ability of convolutional image classification models to autoencode the model's input using embeddings existing in va

Md Rayhanur Rahman, Laurie Williams

Cyberattacks use adversarial techniques to bypass system defenses, persist, and eventually breach systems. The MITRE ATT\&CK framework catalogs a set of adversarial techniques and maps between adversaries and their used techniques and tactics. Understanding how adversaries deploy techniques in conjunction is pivotal for learning adversary behavior, hunting p

Franck Le, Mudhakar Srivatsa, Raghu Ganti, Vyas Sekar

Foundational models have caused a paradigm shift in the way artificial intelligence (AI) systems are built. They have had a major impact in natural language processing (NLP), and several other domains, not only reducing the amount of required labeled data or even eliminating the need for it, but also significantly improving performance on a wide range of tas

Xiaofei Wang, Zhuo Chen, Yu Shi, Jian Wu

Employing a monaural speech separation (SS) model as a front-end for automatic speech recognition (ASR) involves balancing two kinds of trade-offs. First, while a larger model improves the SS performance, it also requires a higher computational cost. Second, an SS model that is more optimized for handling overlapped speech is likely to introduce more process

Yonghoon Lee, Doga Murat Kurkcuoglu, Gabriel Nathan Perdue

We formally study the effects of a restricted single-qubit noise model inspired by real quantum hardware, and corruption in quantum training data, on the performance of binary classification using quantum circuits. We find that, under the assumptions made in our noise model, that the measurement of a qubit is affected only by the noises on that qubit even in

Rahnuma Rahman, Supriyo Bandyopadhyay

We propose and analyze a compact and non-volatile nanomagnetic (all-spin) non-binary matrix multiplier performing the multiply-and-accumulate (MAC) operation using two magnetic tunnel junctions - one activated by strain to act as the multiplier, and the other activated by spin-orbit torque pulses to act as a domain wall synapse that performs the operation of

Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang, Yoshua Bengio

Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations. In this paper, we propose an alternative that avoids this architectural constraint by learning to produce canonical representations of the data. These canonicalization functions can readily be plugged into non-equivari

Chirag R. Skolar, Kolter Bradshaw, James Juno, Bhuvana Srinivasan

In this work, we examine sheath formation in the presence of bias potentials in the current saturation regime for pulsed power fusion experiments. It is important to understand how the particle and heat fluxes at the wall may impact the wall material and affect electrode degradation. Simulations are performed using the 1X-1V Boltzmann-Poisson system for a pr

Vivek Khatana, Murti V. Salapaka

Motivated by the needs of resiliency, scalability, and plug-and-play operation, distributed decision-making is becoming increasingly prevalent. The problem of achieving consensus in a multi-agent system is at the core of distributed decision-making. In this article, we study the problem of achieving average consensus over a directed multi-agent network when

G. A. P. Ribeiro

We study the partition function per site of the integrable $Sp(2n)$ vertex model on the square lattice. We establish a set of transfer matrix fusion relations for this model. The solution of these functional relations in the thermodynamic limit allows us to compute the partition function per site of the fundamental $Sp(2n)$ representation of the vertex model

A. González-Juárez, M. Momennia, A. Villalobos-Ramírez, A. Herrera-Aguilar

Context. Motivated by the recent achievements of a full general relativistic method in determining black hole (BH) parameters, we continue to estimate the mass-to-distance ratio of the supermassive BHs hosted at the core of the active galactic nuclei (AGNs) of the megamaser galaxies NGC 1320, NGC 1194, NGC 5495, and Mrk 1029. Aims. Our aim is to study the pr

Vladimir Burichenko

Studying algorithms admitting nontrivial symmetries is a prospective way of constructing new short algorithms of matrix multiplication. The main result of the article is that if there exists an algorithm of multiplicative length $l\leq22$ for multuplication of $3\times3$ matrices then its automorphism group is isomorphic to a subgroup of $S_l\times S_3$.

Adam D. Cobb, Anirban Roy, Daniel Elenius, Susmit Jha

Computer-aided design (CAD) is a promising new area for the application of artificial intelligence (AI) and machine learning (ML). The current practice of design of cyber-physical systems uses the digital twin methodology, wherein the actual physical design is preceded by building detailed models that can be evaluated by physics simulation models. These phys

Kyle Fridberg

We construct a polygonal spiral by arranging a sequence of regular $n$-gons such that each $n$-gon shares a specified side and vertex with the $(n+1)$-gon in the construction. By offering flexibility for determining the size of each $n$-gon in the spiral, we show that a number of different analytical and asymptotic behaviors can be achieved.

Riley Weinmann, Eduardo Cotilla-Sanchez, Ted K. A. Brekken

A Cascadia Subduction Zone (CSZ) earthquake will cause widespread damage to numerous lifelines and infrastructure along the northern US west coast. The goal of the presented research is to provide a bottom up estimate of the impact on and subsequent recovery of a Cascadia Subduction Zone earthquake on the US western grid to supplement and enhance the expert

Guanlong Zhao, Quan Wang, Han Lu, Yiling Huang

In this work we propose a novel token-based training strategy that improves Transformer-Transducer (T-T) based speaker change detection (SCD) performance. The conventional T-T based SCD model loss optimizes all output tokens equally. Due to the sparsity of the speaker changes in the training data, the conventional T-T based SCD model loss leads to sub-optima

Salvatore Capozziello, Nisha Godani

We consider Non-local Gravity in view to obtain stable and traversable wormhole solutions. In particular, the class of Non-local Integral Kernel Theories of Gravity, with the inverse d'Alembert operator in the gravitational action, is taken into account. We obtain constraints for the null energy condition and derive the field equations. Two special cases for

Trevor Gunn

In a recent paper, Matthew Baker and Oliver Lorscheid showed that Descartes's Rule of Signs and Newton's Polygon Rule can both be interpreted as multiplicities of polynomials over hyperfields. Hyperfields are a generalization of fields which encode things like the arithmetic of signs or of absolute values. By looking at multiplicities of polynomials over suc