October 2022 arXiv papers — page 53

Jeremy Brazas, Hanspeter Fischer

We present a 2-dimensional Peano continuum $\mathbb{T}\subseteq \mathbb{R}^3$ with the following properties: (1) There is a universal covering projection $q:\overline{\mathbb{T}}\rightarrow \mathbb{T}$ with uncountable fundamental group $\pi_1(\overline{\mathbb{T}})$; (2) For every $1\not=[\overline{\alpha}]\in \pi_1(\overline{\mathbb{T}},\ast)$, there is a

Tim Seyde, Peter Werner, Wilko Schwarting, Igor Gilitschenski

While there has been substantial success for solving continuous control with actor-critic methods, simpler critic-only methods such as Q-learning find limited application in the associated high-dimensional action spaces. However, most actor-critic methods come at the cost of added complexity: heuristics for stabilisation, compute requirements and wider hyper

Shashank Sonkar, Naiming Liu, Richard G. Baraniuk

Transformer models trained on massive text corpora have become the de facto models for a wide range of natural language processing tasks. However, learning effective word representations for function words remains challenging. Multimodal learning, which visually grounds transformer models in imagery, can overcome the challenges to some extent; however, there

Shih-Po Lee, Niraj Prakash Kini, Wen-Hsiao Peng, Ching-Wen Ma

This paper introduces a novel human pose estimation benchmark, Human Pose with Millimeter Wave Radar (HuPR), that includes synchronized vision and radio signal components. This dataset is created using cross-calibrated mmWave radar sensors and a monocular RGB camera for cross-modality training of radar-based human pose estimation. There are two advantages of

Daniel Deutsch, Rotem Dror, Dan Roth

There is significant interest in developing evaluation metrics which accurately estimate the quality of generated text without the aid of a human-written reference text, which can be time consuming and expensive to collect or entirely unavailable in online applications. However, in this work, we demonstrate that these reference-free metrics are inherently bi

Runxiang Cheng, Gargi Balasubramaniam, Yifei He, Yao-Hung Hubert Tsai

Multimodal learning considers learning from multi-modality data, aiming to fuse heterogeneous sources of information. However, it is not always feasible to leverage all available modalities due to memory constraints. Further, training on all the modalities may be inefficient when redundant information exists within data, such as different subsets of modaliti

I. F. Herrera-Gonzalez, J. A. Mendez-Bermudez

We study the localization properties of normal modes in harmonic chains with mass and spring weak disorder. Using a perturbative approach, an expression for the localization length is obtained, which is valid for arbitrary correlations of the disorder, and for practically the whole frequency band. In addition, we show how to generate effective mobility edges

Zhaoyue Sun, Jiazheng Li, Gabriele Pergola, Byron C. Wallace

The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately improve public health. Evaluating and monitoring drug safety (i.e., pharmacovigilance) involves analyzing an ever growing collection of spontaneous reports from health profession

Lahcen Oussi, Janusz Wysoczański

We present analogues of the Poisson limit distribution for the noncommutative bm-independence, which is associated with several positive symmetric cones. We construct related discrete Fock spaces with creation, annihilation and conservation operators, and prove Poisson type limit theorems for them. Properties of the positive cones, in particular the volume c

Sigurdur Orn Adalgeirsson, Cynthia Breazeal

Partially Observable Markov Decision Processes (POMDPs) offer a promising world representation for autonomous agents, as they can model both transitional and perceptual uncertainties. Calculating the optimal solution to POMDP problems can be computationally expensive as they require reasoning over the (possibly infinite) space of beliefs. Several approaches

Rakib Hassan Pran

In this research, a number of popular network measurement algorithms have been applied to several brain networks (based on applicability of algorithms) for finding out statistical correlation among these popular network measurements which will help scientists to understand these popular network measurement algorithms and their applicability to brain networks

Shikhar Jaiswal, Ravishankar Krishnaswamy, Ankit Garg, Harsha Vardhan Simhadri

State-of-the-art algorithms for Approximate Nearest Neighbor Search (ANNS) such as DiskANN, FAISS-IVF, and HNSW build data dependent indices that offer substantially better accuracy and search efficiency over data-agnostic indices by overfitting to the index data distribution. When the query data is drawn from a different distribution - e.g., when index repr

C. D. Dashwood, A. H. Walker, M. P. Kwasigroch, L. S. I. Veiga

The layered-ruthenate family of materials possess an intricate interplay of structural, electronic and magnetic degrees of freedom that yields a plethora of delicately balanced ground states. This is exemplified by Ca$_{3}$Ru$_{2}$O$_{7}$, which hosts a coupled transition in which the lattice parameters jump, the Fermi surface partially gaps and the spins un

Jatin Chowdhury

chowdsp_wdf is a C++ library for implementing real-time wave digital models of analog circuits. chowdsp_wdf differs from existing wave digital modelling libraries by providing a template meta-programming interface for modelling circuits with a fixed topology, and providing support for explicit SIMD acceleration. The motivation and design of the library are d

Zheng Zhao, Yftah Ziser, Shay B. Cohen

We investigate how different domains are encoded in modern neural network architectures. We analyze the relationship between natural language domains, model size, and the amount of training data used. The primary analysis tool we develop is based on subpopulation analysis with Singular Vector Canonical Correlation Analysis (SVCCA), which we apply to Transfor

Eric W. Aspling, John A. Marohn, Michael J. Lawler

The Unruh-DeWitt particle detector model has found success in demonstrating quantum information channels with non-zero channel capacity between qubits and quantum fields. These detector models provide the necessary framework for experimentally realizable Unruh-DeWitt Quantum Computers with near-perfect channel capacity. We propose spin qubits with gate-contr

Joshua Barnett, Irina Tezaur, Alejandro Mota

Projection-based model order reduction allows for the parsimonious representation of full order models (FOMs), typically obtained through the discretization of certain partial differential equations (PDEs) using conventional techniques where the discretization may contain a very large number of degrees of freedom. As a result of this more compact representat

Tatiana Gateva-Ivanova

Let $(X,r_X)$ and $(Y,r_Y)$ be finite nondegenerate involutive set-theoretic solutions of the Yang-Baxter equation, and let $A_X = A(\textbf{k}, X, r_X)$ and $A_Y= A(\textbf{k}, Y, r_Y)$ be their quadratic Yang-Baxter algebras over a field $\textbf{k}.$ We find an explicit presentation of the Segre product $A_X\circ A_Y$ in terms of one-generators and quadra

Nathan Canen, Anujit Chakraborty

Many experiments elicit subjects' prior and posterior beliefs about a random variable to assess how information affects one's own actions. However, beliefs are multi-dimensional objects, and experimenters often only elicit a single response from subjects. In this paper, we discuss how the incentives offered by experimenters map subjects' true belief distribu

Farid Zakaria, Thomas R. W. Scogland, Todd Gamblin, Carlos Maltzahn

High Performance Computing~(HPC) software stacks have become complex, with the dependencies of some applications numbering in the hundreds. Packaging, distributing, and administering software stacks of that scale is a complex undertaking anywhere. HPC systems deal with esoteric compilers, hardware, and a panoply of uncommon combinations. In this paper, we ex

Lin Zhao, Xiao Chen, Eric Z. Chen, Yikang Liu

Multi-contrast Magnetic Resonance Imaging (MRI) generates multiple medical images with rich and complementary information for routine clinical use; however, it suffers from a long acquisition time. Recent works for accelerating MRI, mainly designed for single contrast, may not be optimal for multi-contrast scenario since the inherent correlations among the m

Aaron Ferber, Taoan Huang, Daochen Zha, Martin Schubert

Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts. To bridge this gap, we propose $\textbf{SurCo}$ that learns linear $\underline{\text{Sur}}$rogate costs which can be used in existing $\underline{\text{Co}}

Eric Yang Yu, Zhizhen Qin, Min Kyung Lee, Sicun Gao

Long-term fairness is an important factor of consideration in designing and deploying learning-based decision systems in high-stake decision-making contexts. Recent work has proposed the use of Markov Decision Processes (MDPs) to formulate decision-making with long-term fairness requirements in dynamically changing environments, and demonstrated major challe

Vishnu Vardhan Reddy Palli

Robots are man made machines which are used to accomplish the tasks. Robots are mainly used to do complex tasks and work in hazardous environment where humans are difficult to work. They are not only designed to use in hazardous environment but also in the environment where humans are performing the same task repeatedly. These are also used for cooking purpo

Athanasios Paraskevopoulos

This paper studies the work of the French mathematician Francois Viete, known as the "father of modern algebraic notation". Along with this fundamental change in algebra, Viete adopted a radically new notation based on Greek geometric equalities. Its letters represent values rather than types, and its given values are undefined. Where algebra had previously

Ioan Baldea

We show that the conductance $G$ of molecular tunnel junctions wherein the charge transport is dominated by a single energy level can be expressed in closed analytic form which is exact and valid at arbitrary temperature $T$ and model parameter values. On this basis, we show that the single-step tunneling mechanism is compatible with a continuous thermal tra

Shuyi Yan

We study the edge-weighted online stochastic matching problem. Since Feldman, Mehta, Mirrokni, and Muthukrishnan proposed the $(1-\frac1e)$-competitive Suggested Matching algorithm, there has been no improvement for the general edge-weighted online stochastic matching problem. In this paper, we introduce the first algorithm beating the $1-\frac1e$ barrier in

Xiaoyu Wei, Andreas Klöckner, Robert C. Kirby

We present two (a decoupled and a coupled) integral-equation-based methods for the Morse-Ingard equations subject to Neumann boundary conditions on the exterior domain. Both methods are based on second-kind integral equation (SKIE) formulations. The coupled method is well-conditioned and can achieve high accuracy. The decoupled method has lower computational

Braxton Rolle, Ravi Kiran

The outbreak of COVID-19 within the last two years has resulted in much further investigation into the safety of large events that involve a gathering of people. This study aims to investigate how COVID-19 can spread through a large crowd of people shopping in a store with no safety precautions taken. The event being investigated is Black Friday, where hundr

Yuanbo Hou, Yun Wang, Wenwu Wang, Dick Botteldooren

Audio tagging aims to assign predefined tags to audio clips to indicate the class information of audio events. Sequential audio tagging (SAT) means detecting both the class information of audio events, and the order in which they occur within the audio clip. Most existing methods for SAT are based on connectionist temporal classification (CTC). However, CTC

Chenxi Whitehouse, Fenia Christopoulou, Ignacio Iacobacci

Accurate alignment between languages is fundamental for improving cross-lingual pre-trained language models (XLMs). Motivated by the natural phenomenon of code-switching (CS) in multilingual speakers, CS has been used as an effective data augmentation method that offers language alignment at the word- or phrase-level, in contrast to sentence-level via parall

Tanya Shreedhar, Sanjit K. Kaul, Roy D. Yates

We present ACP+, an age control protocol, which is a transport layer protocol that regulates the rate at which update packets from a source are sent over the Internet to a monitor. The source would like to keep the average age of sensed information at the monitor to a minimum, given the network conditions. Extensive experimentation helps us shed light on age

Langwen Huang, Torsten Hoefler

Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the data, and the resulting parameters are take

Artur M. Andermann, Luis G. C. Rego

Recombination losses contribute to reduce $J_{SC}$, $V_{OC}$ and the fill factor of organic solar cells. Recent advances in non-fullerene organic photovoltaics have shown, nonetheless, that efficient charge generation can occur under small energetic driving forces ($\Delta E_{DA}$) and low recombination losses. To shed light on this issue, we set up a coarse

F. Pizzi, G. Mamatsashvili, A. J. Barker, A. Giesecke

The properties of rotating turbulence driven by precession are studied using direct numerical simulations and analysis of the underlying dynamical processes in Fourier space. The study is carried out in the local rotating coordinate frame, where precession gives rise to a background shear flow, which becomes linearly unstable and breaks down into turbulence.

Xialin Liu, Boris Braverman, Robert W. Boyd

High-speed spatial light modulators (SLM) are crucial components for free-space communication and structured illumination imaging. Current approaches for dynamical spatial mode generation, such as liquid crystal SLMs or digital micromirror devices, are limited to a maximum pattern refresh rate of 10 kHz and have a low damage threshold. We demonstrate that ar

I. Krichever, A. Zabrodin

We introduce a new integrable hierarchy of nonlinear differential-difference equations which is a subhierarchy of the 2D Toda lattice defined by imposing a constraint to the Lax operators of the latter. The 2D Toda lattice with the constraint can be regarded as a discretization of the BKP hierarchy. We construct its algebraic-geometrical solutions in terms o

Peder J. Olesen, Azur Hodžić, Søren J. Andersen, Niels N. Sørensen

We present a formalism for dissipation-optimized decomposition of the strain rate tensor (SRT) of turbulent flow data using Proper Orthogonal Decomposition (POD). The formalism includes a novel inverse spectral SRT operator allowing the mapping of the resulting SRT modes to corresponding velocity fields, which enables a complete dissipation-optimized reconst

Kavya Ranjan Saxena, Vipul Arora

Extraction of the predominant pitch from polyphonic audio is one of the fundamental tasks in the field of music information retrieval and computational musicology. To accomplish this task using machine learning, a large amount of labeled audio data is required to train the model that predicts the pitch contour. But a classical model pre-trained on data from

Hongli Zhan, Tiberiu Sosea, Cornelia Caragea, Junyi Jessy Li

Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people's emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scatteri

Kristy Choi, Chris Cundy, Sanjari Srivastava, Stefano Ermon

Particularly in low-data regimes, an outstanding challenge in machine learning is developing principled techniques for augmenting our models with suitable priors. This is to encourage them to learn in ways that are compatible with our understanding of the world. But in contrast to generic priors such as shrinkage or sparsity, we draw inspiration from the rec

Nika Haghtalab, Michael I. Jordan, Eric Zhao

Social and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collaborative learning, group distributionally robust optimization, and fair federated learning. In each of these settings, a learner seeks to uniformly minimize its expected loss over $

Benjamin S. Baumer, Nicholas J. Horton

A substantial fraction of students who complete their college education at a public university in the United States begin their journey at one of the 935 public two-year colleges. While the number of four-year colleges offering bachelor's degrees in data science continues to increase, data science instruction at many two-year colleges lags behind. A major im

Victor Adewopo, Nelly Elsayed, Kelly Anderson

Humans are able to intuitively deduce actions that took place between two states in observations via deductive reasoning. This is because the brain operates on a bidirectional communication model, which has radically improved the accuracy of recognition and prediction based on features connected to previous experiences. During the past decade, deep learning

Graham Cormode, Igor Markov

We address two major obstacles to practical use of supervised classifiers on distributed private data. Whether a classifier was trained by a federation of cooperating clients or trained centrally out of distribution, (1) the output scores must be calibrated, and (2) performance metrics must be evaluated -- all without assembling labels in one place. In parti

Ahmad Sheykhi

We investigate the cosmological consequences of the modified Friedmann equations when the entropy associated with the apparent horizon, given by Barrow entropy, $S\sim A^{1+\delta/2}$, where $0\leq\delta\leq1$, represents the amount of the quantum-gravitational deformation of the horizon. We study implications of this model in a flat Friedmann-Robertson-Walk

Muhammed Pektas, Baris Gecer, Aybars Ugur

Despite the recent success of image generation and style transfer with Generative Adversarial Networks (GANs), hair synthesis and style transfer remain challenging due to the shape and style variability of human hair in in-the-wild conditions. The current state-of-the-art hair synthesis approaches struggle to maintain global composition of the target style a

Athiya Deviyani, Efe Sinan Hoplamaz, Alan Savio Paul

Image super-resolution (SR) is a field in computer vision that focuses on reconstructing high-resolution images from the respective low-resolution image. However, super-resolution is a well-known ill-posed problem as most methods rely on the downsampling method performed on the high-resolution image to form the low-resolution image to be known. Unfortunately

Karan Shah, Patrick Stiller, Nico Hoffmann, Attila Cangi

We demonstrate the utility of physics-informed neural networks (PINNs) as solvers for the non-relativistic, time-dependent Schr\"odinger equation. We study the performance and generalisability of PINN solvers on the time evolution of a quantum harmonic oscillator across varying system parameters, domains, and energy states.

Kei Ota, Hsiao-Yu Tung, Kevin A. Smith, Anoop Cherian

The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects with strategic trial-and-error: first pushing a door then pulling if that doesn't work. We enable these capabilities in autonomous agents by proposing "Hypothesize, Simulate, Act,

Lamberti Giuseppe, Lopez-Sintas Jordi, Pandolfo Giuseppe

Working with SEM and crosssectional data, and depending on the studied phenomenon, assuming an acyclic model may mean that we obtain only a partial view of the mechanisms that explain causal relationships between a set of theoretical constructs, treated as antecedents and consequences. Our twogiven that variables are step approach allows researchers to ident

Andrey Skripnikov

Given the importance of accurate team rankings in American college football (CFB) -- due to heavy title and playoff implications -- strides have been made to improve evaluation metrics across statistical categories, going from basic averages (e.g. points scored per game) to metrics that adjust for a team's strength of schedule, but one aspect that hasn't bee

Vladislav V. Kravchenko

Given a finite set of eigenvalues of a regular Sturm-Liouville problem for the equation -y{\prime}{\prime}+q(x)y={\lambda}y, the potential q(x) of which is unknown. We show the possibility to compute more eigenvalues without any additional information on the potential q(x). Moreover, considering the Sturm-Liouville problem with the boundary conditions y{\pri

Tyler Schmaltz, Yue Hu, Alex Lazarian

Star formation is a complex process that typically occurs in dense regions of molecular clouds mainly regulated by magnetic fields, magnetohydrodynamic (MHD) turbulence, and self-gravity. However, it remains a challenging endeavor to trace the magnetic field and determine regions of gravitational collapse where the star is forming. Based on the anisotropic p

Anas Awadalla, Mitchell Wortsman, Gabriel Ilharco, Sewon Min

We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a diverse set of architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter tuning, in-context learning, etc.). We find that, in many

Frank Rösler, Christiane Tretter

We study the computational complexity of the eigenvalue problem for the Klein-Gordon equation in the framework of the Solvability Complexity Index Hierarchy. We prove that the eigenvalue of the Klein-Gordon equation with linearly decaying potential can be computed in a single limit with guaranteed error bounds from above. The proof is constructive, i.e. we o

Cristian Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot

In this work, we tackle two widespread challenges in real applications for time-series forecasting that have been largely understudied: distribution shifts and missing data. We propose SpectraNet, a novel multivariate time-series forecasting model that dynamically infers a latent space spectral decomposition to capture current temporal dynamics and correlati

Nour Neifar, Achraf Ben-Hamadou, Afef Mdhaffar, Mohamed Jmaiel

Electrocardiogram (ECG) data collection during emergency situations is challenging, making ECG data generation an efficient solution for dealing with highly imbalanced ECG training datasets. In this paper, we propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling. Our approach leverag

Hong-lin Liao, Nan Liu, Xuan Zhao

A new discrete energy dissipation law of the variable-step fractional BDF2 (second-order backward differentiation formula) scheme is established for time-fractional Cahn-Hilliard model with the Caputo's fractional derivative of order $\alpha\in(0,1)$, under a weak step-ratio constraint $0.4753\le \tau_k/\tau_{k-1}<r^*(\alpha)$, where $\tau_k$ is the $k$-th t

Jiaming Chen, Weixin Luo, Ran Song, Xiaolin Wei

This paper presents a novel hierarchical alignment model (HAM) that learns multi-granularity visual and linguistic representations in an end-to-end manner. We extract key points and proposal points to model 3D contexts and instances, and propose point-language alignment with context modulation (PLACM) mechanism, which learns to gradually align word-level and

Amir H. Naghdi, Kamran Karimi, Axel E. Poisvert, Amin Esfandiarpour

Equiatomic NiCoCr solid solutions have been recently shown to display exceptional mechanical properties, that have been suggested to be linked to nanostructural short-range order (SRO) features that may arise from thermal treatments, such as annealing or/and aging. Here, we use hybrid Monte Carlo-Molecular Dynamics simulations to gain further insights of the

Ziqiao Ma, Ben VanDerPloeg, Cristian-Paul Bara, Huang Yidong

In the real world, autonomous driving agents navigate in highly dynamic environments full of unexpected situations where pre-trained models are unreliable. In these situations, what is immediately available to vehicles is often only human operators. Empowering autonomous driving agents with the ability to navigate in a continuous and dynamic environment and

Slaven Kožić, Marina Sertić

In this note, we consider the twisted Yangians $\text{Y}(\mathfrak{g}_N)$ associated with the orthogonal and symplectic Lie algebras $\mathfrak{g}_N=\mathfrak{o}_N,\mathfrak{sp}_N$. First, we introduce a certain subalgebra $\text{A}_c(\mathfrak{g}_N)$ of the double Yangian for $\mathfrak{gl}_N$ at the level $c\in\mathbb{C}$, which contains the centrally exte

Azimkhon Ostonov

Current methods for 3D object reconstruction from a set of planar cross-sections still struggle to capture detailed topology or require a considerable number of cross-sections. In this paper, we present, to the best of our knowledge the first 3D shape reconstruction network to solve this task which additionally uses orthographic projections of the shape. Our

Reynold Fregoli, Cheng Zheng

In this paper, we study the shrinking-target problem with target at infinity induced by the injectivity radius function under the action of a regular diagonalizable flow on $\operatorname{SL}_3(\mathbb R)/\operatorname{SL}_3(\mathbb Z)$. In particular, we establish an explicit formula for the Hausdorff dimension of the subset of points $p$ whose orbit approa

Armin Shahmardi, Salar Zamani Salimi, Outi Tammisola, Luca Brandt

We study the role of the capillary number, $Ca$ and of the surface wettability on the dynamics of the interface between an invading and a defending phase in a porous medium by means of numerical simulations. We employ a hybrid phase field-immersed boundary approach to successfully model the contact line dynamics over the solid objects. Using a phase-field me

Yang Li, Tong Chen, Peng-Fei Zhang, Zi Huang

The exponential growth of Location-based Social Networks (LBSNs) has greatly stimulated the demand for precise location-based recommendation services. Next Point-of-Interest (POI) recommendation, which aims to provide personalised POI suggestions for users based on their visiting histories, has become a prominent component in location-based e-commerce. Recen

Fucheng Zhong, Xinyu Wang

If two annihilation products of dark matter (DM) particles are non-relativistic and coupled to a light force mediator, their plane wave functions are modified due to multiple exchanges of the force mediators. This gives rise to the Sommerfeld effect (SE). We consider the attractive and repulsive force SE on the relic density in different phases of freeze-in

James Duncan, Shashank Subramanian, Peter Harrington

Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change. Making accurate high-resolution precipitation forecasts using traditional physical models remains a major challenge in operational weather forecasting as they incur substantial computational cos

Yifeng Ding, Wesley H. Holliday, Eric Pacuit

A number of rules for resolving majority cycles in elections have been proposed in the literature. Recently, Holliday and Pacuit (Journal of Theoretical Politics 33 (2021) 475-524) axiomatically characterized the class of rules refined by one such cycle-resolving rule, dubbed Split Cycle: in each majority cycle, discard the majority preferences with the smal

Arnab Sarkar, Amna Ali, K. Rajesh Nayak, A. S. Majumdar

We consider the accretion of dark energy by constituent black holes in binary formations during the present epoch of the Universe. In the context of an observationally consistent dark energy model, we evaluate the growth of black holes' masses due to accretion. We show that accretion leads to faster circularization of the binary orbits. We compute the averag

Taoufik Chtioui, Ripan Saha

In this paper, we consider compatible Hom-associative algebras as a twisted version of compatible associative algebras. Compatible Hom-associative algebras are characterized as Maurer-Cartan elements in a suitable bidifferential graded Lie algebra. We also define a cohomology theory for compatible Hom-associative algebras generalizing the classical case. As

Sergei A. Avdonin, Vladislav V. Kravchenko

A new method for solving inverse spectral problems on quantum star graphs is proposed. The method is based on Neumann series of Bessel functions representations for solutions of Sturm-Liouville equations. The representations admit estimates for the series remainders which are independent of the real part of the square root of the spectral parameter. This fea

Fenia Christopoulou, Gerasimos Lampouras, Ignacio Iacobacci

Curriculum Learning (CL) is a technique of training models via ranking examples in a typically increasing difficulty trend with the aim of accelerating convergence and improving generalisability. Current approaches for Natural Language Understanding (NLU) tasks use CL to improve in-distribution data performance often via heuristic-oriented or task-agnostic d

Hélène M. Courtois, Khaled Said, Jeremy Mould, T. H. Jarrett

The WALLABY pilot survey has been conducted using the Australian SKA Pathfinder (ASKAP). The integrated 21-cm HI line spectra are formed in a very different manner compared to usual single-dish spectra Tully-Fisher measurements. It is thus extremely important to ensure that slight differences (e.g. biases due to missing flux) are quantified and understood in

Nadav Cohen, Govind Menon, Zsolt Veraszto

The deep linear network (DLN) is a model for implicit regularization in gradient based optimization of overparametrized learning architectures. Training the DLN corresponds to a Riemannian gradient flow, where the Riemannian metric is defined by the architecture of the network and the loss function is defined by the learning task. We extend this geometric fr

Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson

Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization. Bayesian optimization selects decisions (i.e. objective function queries) with maximal expected utility with respect to the posterior distribution of a Bayesian model, which

Zhao Song, Baocheng Sun, Omri Weinstein, Ruizhe Zhang

We study the problem of interpolating a noisy Fourier-sparse signal in the time duration $[0, T]$ from noisy samples in the same range, where the ground truth signal can be any $k$-Fourier-sparse signal with band-limit $[-F, F]$. Our main result is an efficient Fourier Interpolation algorithm that improves the previous best algorithm by [Chen, Kane, Price, a

Zhangyu Wang, Ningyuan Sun

Recognition of floor plans has been a challenging and popular task. Despite that many recent approaches have been proposed for this task, they typically fail to make the room-level unified prediction. Specifically, multiple semantic categories can be assigned in a single room, which seriously limits their visual quality and applicability. In this paper, we p

Rob Burns

We provide a complete characterisation of the appearance function for paper-folding sequences for factors of any length. We make use of the software package {\tt Walnut} to establish these results.

Francesco Ardizzon, Stefano Tomasin

In physical layer authentication (PLA) mechanisms, a verifier decides whether a received message has been transmitted by a legitimate user or an intruder, according to some features of the physical channel over which the message traveled. To design the authentication check implemented at the verifier, typically either the statistics or a dataset of features

Pascal Jansen, Mark Colley, Enrico Rukzio

Automotive user interfaces constantly change due to increasing automation, novel features, additional applications, and user demands. While in-vehicle interaction can utilize numerous promising modalities, no existing overview includes an extensive set of human sensors and actuators and interaction locations throughout the vehicle interior. We conducted a sy

Zhiyan Zhou, Kevin Li, Haekyu Park, Megan Dass

We present our ongoing work NeuroMapper, an in-browser visualization tool that helps machine learning (ML) developers interpret the evolution of a model during training, providing a new way to monitor the training process and visually discover reasons for suboptimal training. While most existing deep neural networks (DNNs) interpretation tools are designed f

Alireza Roustazadeh, Behzad Ghanbarian, Mohammad B. Shadmand, Vahid Taslimitehrani

With recent advances in artificial intelligence, machine learning (ML) approaches have become an attractive tool in petroleum engineering, particularly for reservoir characterizations. A key reservoir property is hydrocarbon recovery factor (RF) whose accurate estimation would provide decisive insights to drilling and production strategies. Therefore, this s

Shunhui Yao, Yu Tian, Peng Yang, Hongbao Zhang

In the two-component Ginzburg-Landau theory of superfluidity, a pair of fractional vortices form a composite type of topological defect, usually referred to as a baby skyrmion. In this paper, we initiate the construction of such a baby skyrmion in the holographic model of two-component superfluids. As a result, two types of baby skyrmion configurations are f

Zhixin Zeng, Yuan Xiao, Jeffrey M. Wheeler, Jin-Chong Tan

Understanding of the complex mechanical behavior of metal-organic frameworks (MOF) beyond their elastic limit will allow the design of real-world applications in chemical engineering, optoelectronics, energy conversion apparatus, and sensing devices. Through in situ compression of micropillars, the uniaxial stress-strain curves of a copper paddlewheel MOF (H

Paul Scharre, Megan Lamberth

Potential advancements in artificial intelligence (AI) could have profound implications for how countries research and develop weapons systems, and how militaries deploy those systems on the battlefield. The idea of AI-enabled military systems has motivated some activists to call for restrictions or bans on some weapon systems, while others have argued that

Jean Dolbeault, Andres Zuniga

On the Euclidean space, we establish some Weighted Logarithmic Sobolev (WLS) inequalities. We characterize a symmetry range in which optimal functions are radially symmetric, and a symmetry breaking range. (WLS) inequalities are a limit case for a family of subcritical Caffarelli-Kohn-Nirenberg (CKN) inequalities with similar symmetry properties. A generaliz

Yinya Huang, Hongming Zhang, Ruixin Hong, Xiaodan Liang

In this paper, we propose a comprehensive benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Current explanation datasets often employ synthetic data with simple reasoning structures. Therefore, it cannot express more complex reasoning processes, such as the rebuttal to a reasoning step and the degree of certainty

Marco Marengon, Allison N. Miller, Arunima Ray, András I. Stipsicz

In this brief note, we investigate the $\mathbb{CP}^2$-genus of knots, i.e. the least genus of a smooth, compact, orientable surface in $\mathbb{CP}^2\setminus \mathring{B^4}$ bounded by a knot in $S^3$. We show that this quantity is unbounded, unlike its topological counterpart. We also investigate the $\mathbb{CP}^2$-genus of torus knots. We apply these re

Yichi Zhang, Jianing Yang, Jiayi Pan, Shane Storks

Recent years have seen an increasing amount of work on embodied AI agents that can perform tasks by following human language instructions. However, most of these agents are reactive, meaning that they simply learn and imitate behaviors encountered in the training data. These reactive agents are insufficient for long-horizon complex tasks. To address this lim

Yue Zhang, Bo Zhang, Zhenghua Li, Zuyi Bao

This work proposes a syntax-enhanced grammatical error correction (GEC) approach named SynGEC that effectively incorporates dependency syntactic information into the encoder part of GEC models. The key challenge for this idea is that off-the-shelf parsers are unreliable when processing ungrammatical sentences. To confront this challenge, we propose to build

Trygve Johnsen, Rakhi Pratihar, Tovohery Hajatiana Randrianarisoa

We first give two new proofs of an old result that the reduced Euler characteristic of a matroid complex is equal to the M\"obius number of the lattice of cycles of the matroid up to the sign. The purpose has been to find a model to establish an analogous result for the case of $q$-matroids and we find a relation between the Euler characteristic of the simpl

Pei Ge, Linfeng Zhang, Huan Lei

A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to construct reliable coarse-grained (CG) models, where the CG potential function needs to faithfully encode the many-body in

Dylan A. Sherman, Mario Gutiérrez, Ian Griffiths, Samraj Mollick

Luminescent metal-organic frameworks (LMOFs) are promising materials for organic light-emitting diode (OLED) alternatives to silicate-based LEDs due to their tuneable structure and programmability. Yet, the 3D nature of LMOFs creates challenges for stability, optical transparency, and device integration. Metal-organic nanosheets (MONs) potentially overcome t

Alec Owens, Alexander Mitrushchenkov, Sergei N. Yurchenko, Jonathan Tennyson

Any future detection of the calcium monohydroxide radical (CaOH) in stellar and exoplanetary atmospheres will rely on accurate molecular opacity data. Here, we present the first comprehensive molecular line list of CaOH covering the \A--\X\ rotation-vibration-electronic and \X--\X\ rotation-vibration bands. The newly computed OYT6 line list contains over 24.

F. Farshadifar

This paper deals with some results concerning finitely generated coreduced comultiplication modules over a commutative ring.

Prajjwal Bhargava, Vincent Ng

We present DiscoSense, a benchmark for commonsense reasoning via understanding a wide variety of discourse connectives. We generate compelling distractors in DiscoSense using Conditional Adversarial Filtering, an extension of Adversarial Filtering that employs conditional generation. We show that state-of-the-art pre-trained language models struggle to perfo

Mikhail Semenov, Nicholas Clark, Sergei N. Yurchenko, Gap-Sue Kim

Silicon mononitride ($^{28}$Si$^{14}$N, $^{29}$Si$^{14}$N, $^{30}$Si$^{14}$N, $^{28}$Si$^{15}$N) line lists covering infrared, visible and ultraviolet regions are presented. The \name\ line lists produced by ExoMol include rovibronic transitions between six electronic states: \XS, \AS, \BS, \DS, \asi, \bsi. The \ai\ potential energy and coupling curves, comp

Yo-Chung Lau, Kuan-Wei Tseng, I-Ju Hsieh, Hsiao-Ching Tseng

Real-time object pose estimation and tracking is challenging but essential for emerging augmented reality (AR) applications. In general, state-of-the-art methods address this problem using deep neural networks which indeed yield satisfactory results. Nevertheless, the high computational cost of these methods makes them unsuitable for mobile devices where rea