arXiv papers from April 2022

Venkat Margapuri, Trevor Rife, Chaney Courtney, Brandon Schlautman

A common requirement of plant breeding programs across the country is companion planting -- growing different species of plants in close proximity so they can mutually benefit each other. However, the determination of companion plants requires meticulous monitoring of plant growth. The technique of ocular monitoring is often laborious and error prone. The av

Yi Huang, B. I. Shklovskii, M. A. Zudov

Motivated by recent breakthrough in molecular beam epitaxy of GaAs/AlGaAs quantum wells [Y. J. Chung \textit{et al.}, Nature Materials \textbf{20}, 632 (2021)], we examine contributions to mobility and quantum mobility from various scattering mechanisms and their dependencies on the electron density. We find that at lower electron densities, $n_e \lesssim 1

Burhan A. Mudassar, Sho Ko, Maojingjing Li, Priyabrata Saha

Interactive autonomous applications require robustness of the perception engine to artifacts in unconstrained videos. In this paper, we examine the effect of camera motion on the task of action detection. We develop a novel ranking method to rank videos based on the degree of global camera motion. For the high ranking camera videos we show that the accuracy

Fangyu Liu, Guy Emerson, Nigel Collier

Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image

Ayaz Ur Rehman, Anas Nadeem, Muhammad Zubair Malik

The feature subset selection problem aims at selecting the relevant subset of features to improve the performance of a Machine Learning (ML) algorithm on training data. Some features in data can be inherently noisy, costly to compute, improperly scaled, or correlated to other features, and they can adversely affect the accuracy, cost, and complexity of the i

Luhao Zhang, Jincheng Yang, Rui Gao

We present a general duality result for Wasserstein distributionally robust optimization that holds for any Kantorovich transport cost, measurable loss function, and nominal probability distribution. Assuming an interchangeability principle inherent in existing duality results, our proof only uses one-dimensional convex analysis. Furthermore, we demonstrate

Aline R. Ioste, Alan M. Durham, Marcelo Finger

We introduce CoLN, Combined Learning of Neural network weights, a novel method to securely combine Machine Learning models over sensitive data with no sharing of data. With CoLN, local hosts use the same Neural Network architecture and base parameters to train a model using only locally available data. Locally trained models are then submitted to a combining

Zhuocheng Zhang, Zehao Lin, Pai-Ying Liao, Vahid Askarpour

In this work, we demonstrate atomic-layer-deposited (ALD) single-channel indium oxide (In2O3) gate-all-around (GAA) nanoribbon FETs in a back-end-of-line (BEOL) compatible process. A maximum on-state current (ION) of 19.3 mA/{\mu}m (near 20 mA/{\mu}m) is achieved in an In2O3 GAA nanoribbon FET with a channel thickness (TIO) of 3.1 nm, channel length (Lch) of

Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd

Influence estimation analyzes how changes to the training data can lead to different model predictions; this analysis can help us better understand these predictions, the models making those predictions, and the data sets they're trained on. However, most influence-estimation techniques are designed for deep learning models with continuous parameters. Gradie

Zhilei Liang, Apala Majumdar, Dehua Wang, Yixuan Wang

The equations of stationary compressible flows of active liquid crystals are considered in a bounded three-dimensional domain. The system consists of the stationary Navier-Stokes equations coupled with the equation of Q-tensors and the equation of the active particles. The existence of weak solutions to the stationary problem is established through a two-lev

Zehao Lin, Mengwei Si, Vahid Askarpour, Chang Niu

High drive current is a critical performance parameter in semiconductor devices for high-speed, low-power logic applications or high-efficiency, high-power, high-speed radio frequency (RF) analog applications. In this work, we demonstrate an In2O3 transistor grown by atomic layer deposition (ALD) at back-end-of-line (BEOL) compatible temperatures with a reco

Mohammad Javad Feizollahi

In this paper, we propose two exact distributed algorithms to solve mixed integer linear programming (MILP) problems with multiple agents where data privacy is important for the agents. A key challenge is that, because of the non-convex nature of MILPs, classical distributed and decentralized optimization approaches cannot be applied directly to find their o

Xu Wang, Simin Fan, Jessica Houghton, Lu Wang

NLP-powered automatic question generation (QG) techniques carry great pedagogical potential of saving educators' time and benefiting student learning. Yet, QG systems have not been widely adopted in classrooms to date. In this work, we aim to pinpoint key impediments and investigate how to improve the usability of automatic QG techniques for educational purp

Ahmed A. A. Elhag, Gabriele Corso, Hannes Stärk, Michael M. Bronstein

Traditional Graph Neural Networks (GNNs) rely on message passing, which amounts to permutation-invariant local aggregation of neighbour features. Such a process is isotropic and there is no notion of `direction' on the graph. We present a new GNN architecture called Graph Anisotropic Diffusion. Our model alternates between linear diffusion, for which a close

Sucheol Shin, Guang Shi, Hyun Woo Cho, D. Thirumalai

The organization of interphase chromosomes in a number of species is starting to emerge thanks to advances in a variety of experimental techniques. However, much less is known about the dynamics, especially in the functional states of chromatin. Some experiments have shown that the motility of individual loci in human interphase chromosome decreases during t

Jagdeep Singh, Jahrul Alam

In large eddy simulation of atmospheric boundary layer flows over wind farms, wall-layer models are generally imposed for the surface fluxes without considering the spatial variability of the surface roughness. In this study, we consider the near-surface model in conjunction with square of the velocity gradient tensor to model the adaptive dissipation of tur

Nicholas Bender, Arthur Goetschy, Chia Wei Hsu, Hasan Yilmaz

From the earth's crust to the human brain, remitted waves are used for sensing and imaging in a diverse range of diffusive media. Separating the source and detector increases the penetration depth of remitted light, yet rapidly decreases the signal strength, leading to a poor signal-to-noise ratio. Here, we experimentally and numerically show that wavefront

Lang Liu, Carlos Cinelli, Zaid Harchaoui

Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We establish non-asymptotic bounds on the excess risk of orthogonal statistical learning methods with a loss function satisfying a self-concordance property. Our bounds improve upon exist

Gaurav Chaudhary, Anton A. Burkov, Olle G. Heinonen

Twisted bilayer graphene (TBG) near "magic angles" has emerged as a rich platform for strongly correlated states of two-dimensional Dirac semimetals. Here we show that twisted bilayers of thin-film magnetic topological insulators (MTI) with large in-plane magnetization can realize flat bands near 2D Dirac nodes. Using a simple model for thin films of MTIs, w

Abu Hasnat Mohammad Rubaiyat, Shiying Li, Xuwang Yin, Mohammad Shifat E Rabbi

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport-based generative model to define the classification problem. We then make use of mathematical properties of the SCDT to render the problem easier in transform domain, and solve for the class of an unknown sample us

Kerem Turgutlu, Sanat Sharma, Jayant Kumar

Image compositing is one of the most fundamental steps in creative workflows. It involves taking objects/parts of several images to create a new image, called a composite. Currently, this process is done manually by creating accurate masks of objects to be inserted and carefully blending them with the target scene or images, usually with the help of tools su

Hazim Al Gharrawi, Majid Bani Yaghoub

In this paper we demonstrate how one of the most powerful methods in mathematics and statistics can be used to optimally control and reduce traffics in smart cities. The main goal of the present work is to use the least squares method [5] to identify the best location of a portable drone station in a metropolitan city. By the best location we mean a location

A. A. Zheltukhin

Scalar fields in curved backgrounds are assumed to be composite objects. As an example realizing such a possibility we consider a model of the massless tensor field $l_{\mu\nu}(x)$ in a 4-dim. background $g_{\mu\nu}(x)$ with spontaneously broken Weyl and scale symmetries. It is shown that the potential of $l_{\mu\nu}$, represented by a scalar quartic polynom

Kushal Chawla, Gale M. Lucas, Jonathan May, Jonathan Gratch

Opponent modeling is the task of inferring another party's mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorities

Liviu Aolaritei, Nicolas Lanzetti, Hongruyu Chen, Florian Dörfler

This paper addresses the limitations of standard uncertainty models, e.g., robust (norm-bounded) and stochastic (one fixed distribution, e.g., Gaussian), and proposes to model uncertainty via Optimal Transport (OT) ambiguity sets. These constitute a very rich uncertainty model, which enjoys many desirable geometrical, statistical, and computational propertie

Vishwas Bhargava, Sumanta Ghosh, Zeyu Guo, Mrinal Kumar

Multivariate multipoint evaluation is the problem of evaluating a multivariate polynomial, given as a coefficient vector, simultaneously at multiple evaluation points. In this work, we show that there exists a deterministic algorithm for multivariate multipoint evaluation over any finite field $\mathbb{F}$ that outputs the evaluations of an $m$-variate polyn

Daniel Neamati, Sriramya Bhamidipati, Grace Gao

Risk-aware urban localization with the Global Navigation Satellite System (GNSS) remains an unsolved problem with frequent misdetection of the user's street or side of the street. Significant advances in 3D map-aided GNSS use grid-based GNSS shadow matching alongside AI-driven line-of-sight (LOS) classifiers and server-based processing to improve localizatio

Peter Leviant, Qian Xu, Liang Jiang, Serge Rosenblum

Bosonic qubits encoded in continuous-variable systems provide a promising alternative to two-level qubits for quantum computation and communication. So far, photon loss has been the dominant source of errors in bosonic qubits, but the significant reduction of photon loss in recent bosonic qubit experiments suggests that dephasing errors should also be consid

Yuxi Lu, Tobias Buck, Ivan Minchev, Melissa K. Ness

Recovering the birth radii of observed stars in the Milky Way is one of the ultimate goals of Galactic Archaeology. One method to infer the birth radius and the evolution of the ISM metallicity assumes a linear relation between the ISM metallicity with radius at any given look-back time. Here we test the reliability of this assumption by using 4 zoom-in cosm

Nikos Barakitis

In this thesis, the numerical solution of three different classes of problems have been studied. Specifically, new techniques have been proposed and their theoretical analysis has been performed, accompanied by a wide set of numerical experiments, for investigating further and comparing the effectiveness and performance of the presented approach. The first t

Debashree Sen, Gargi Chaudhuri

In the present work we have achieved phase transition from $\beta$ stable hadronic matter to color-flavor locked (CFL) quark matter with Maxwell construction. The hybrid equation of state (EoS), obtained for different values of bag pressure $B$ and gap parameter $\Delta$, have been used to compute the speed of sound in hybrid star (HS) matter. The structural

Mohamed Ouerfelli, Vincent Rivasseau, Mohamed Tamaazousti

Assuming some familiarity with quantum field theory and with the tensor track approach that one of us presented in the previous series Tensor Track I to VI, we provide, as usual, the developments in quantum gravity of the last two years. Next we present in some detail two algorithms inspired by Random Tensor Theory which has been developed in the quantum gra

Steven Amelotte, Benjamin Briggs

A simple polytope $P$ is called $B$-rigid if its combinatorial type is determined by the cohomology ring of the moment-angle manifold $\mathcal{Z}_P$ over $P$. We show that any tensor product decomposition of this cohomology ring is geometrically realized by a product decomposition of the moment-angle manifold up to equivariant diffeomorphism. As an applicat

Alton Barbehenn, Sihai Dave Zhao

The simultaneous estimation of multiple unknown parameters lies at heart of a broad class of important problems across science and technology. Currently, the state-of-the-art performance in the such problems is achieved by nonparametric empirical Bayes methods. However, these approaches still suffer from two major issues. First, they solve a frequentist prob

Mohammadreza Mahmoudi

This paper aims to analyze the effect of Bitcoin on portfolio optimization using mean-variance, conditional value-at-risk (CVaR), and Markov regime switching approaches. I assessed each approach and developed the next based on the prior approach's weaknesses until I ended with a high level of confidence in the final approach. Though the results of mean-varia

Guruprasad Raghavan, Bahey Tharwat, Surya Narayanan Hari, Dhruvil Satani

Transformers have emerged as the state of the art neural network architecture for natural language processing and computer vision. In the foundation model paradigm, large transformer models (BERT, GPT3/4, Bloom, ViT) are pre-trained on self-supervised tasks such as word or image masking, and then, adapted through fine-tuning for downstream user applications

Wei Jiang, Hans D. Schotten

Exploiting the degree of freedom in the frequency domain and the near-far effect among different access points (APs), this paper proposes an opportunistic transmission scheme in cell-free massive MIMO-OFDM systems. The key idea is to orthogonally assign subcarriers among different users, so that there is only one user on each subcarrier. Then, a user is only

Sergey Stepanov, Irina Tsyganok

In this paper, we prove several Liouville-type theorems on the non-existence of Killing-Yano tensors, Killing tensors, and harmonic symmetric tensors on Hadamard manifolds and, in particular, on Riemannian symmetric spaces of non-compact type. These theorems supplement the well-known vanishing theorems for the above tensors, obtained using the Bochner techni

Wei Jiang, Hans Dieter Schotten

Intelligent reflecting surface (IRS) is a cost-efficient technique to improve power efficiency and spectral efficiency. However, IRS-aided multi-antenna transmission needs to jointly optimize the passive and active beamforming, imposing a high computational burden and high latency due to its iterative optimization process. Making use of hybrid analog-digital

Daniela Bertacchi, Juri Lember, Fabio Zucca

We study a class of evolution models, where the breeding process involves an arbitrary exchangeable process, allowing for mutations to appear. The population size $n$ is fixed, hence after breeding, selection is applied. Individuals are characterized by their genome, picked inside a set $X$ (which may be uncountable), and there is a fitness associated to eac

Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin

Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios. Our goal is twofold. First, we want to understand the

Mithun Das, Punyajoy Saha, Binny Mathew, Animesh Mukherjee

Due to the sheer volume of online hate, the AI and NLP communities have started building models to detect such hateful content. Recently, multilingual hate is a major emerging challenge for automated detection where code-mixing or more than one language have been used for conversation in social media. Typically, hate speech detection models are evaluated by

Weng-Tai Su, Yi-Chun Hung, Po-Jen Yu, Chia-Wen Lin

Visualizing information inside objects is an ever-lasting need to bridge the world from physics, chemistry, biology to computation. Among all tomographic techniques, terahertz (THz) computational imaging has demonstrated its unique sensing features to digitalize multi-dimensional object information in a non-destructive, non-ionizing, and non-invasive way. Ap

Yuri Bakhtin, Hong-Bin Chen, Zsolt Pajor-Gyulai

We study small white noise perturbations of planar dynamical systems with heteroclinic networks in the limit of vanishing noise. We show that the probabilities of transitions between various cells that the network tessellates the plane into decay as powers of the noise magnitude. We show that the most likely scenario for the realization of these rare transit

Mohammad Javad Pour Razzaghi, Seyed Mojtaba Rezaei Sani, Yasin Masoumi, Guoping Huan

The present work studied various models for predicting turbulence in the problem of injecting a fluid microjet into the boundary layer of a turbulent flow. For this purpose, the one-equation Spalart-Allmaras (SA), two-equation k-$\epsilon$ and k-$\omega$, multi-equation transition k-kL-$\omega$, transition shear stress transport (SST), and Reynolds stress mo

Yi-Chun Hung, Ta-Hsuan Chao, Pojen Yu, Shang-Hua Yang

Terahertz computed tomography (THz CT) has drawn significant attention because of its unique capability to bring multi-dimensional object information from invisible to visible. However, current physics-model-based THz CT modalities present low data use efficiency on time-resolved THz signals and low model fusion extensibility, limiting their application fiel

Blair Subbaraman, Nadya Peek

Machine settings and tuning are critical for digital fabrication outcomes. However, exploring these parameters is non-trivial. We seek to enable exploration of the full design space of digital fabrication. To identify where we might intervene, we studied how practitioners approach 3D printing. We found that beyond using CAD/CAM, they create bespoke routines

Chottiwatt Jittprasong

In practically every industry today, artificial intelligence is one of the most effective ways for machines to assist humans. Since its inception, a large number of researchers throughout the globe have been pioneering the application of artificial intelligence in medicine. Although artificial intelligence may seem to be a 21st-century concept, Alan Turing p

Werner Porod

Composite Higgs models with a fermionic UV completion can contain additional colored states beside the usual top-partners. We focus here on a model which contains in addition SU(3) color octet top partners as well as color singlet ones. The latter can in principle serve as a dark matter candidate. We consider a particular composite Higgs model which contains

Yoon A Park, Frank Rudzicz

Existing studies have investigated the tendency of autoregressive language models to generate contexts that exhibit undesired biases and toxicity. Various debiasing approaches have been proposed, which are primarily categorized into data-based and decoding-based. In our study, we investigate the ensemble of the two debiasing paradigms, proposing to use toxic

A. de A. Coelho

There is a consensus that Urban Heat Island phenomenon - UHI occurs in every large city. This effect is characterized by higher air temperatures in cities than in the neighboring countryside at night. However, to date, there has been no systematic study on the Fortaleza case, the Brazil's 5th largest city. By the comparison between screen-level air temperatu

Leiwen Gao, Sudipto Banerjee, Beate Ritz

Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To capture health disparities over regions, we seek "difference boundaries" that separate neighboring regions with significant

Salman Ahamad Khan, Binoy Krishna Patra

We have studied the transport coefficients as a tool to probe the collision integral appeared in the Boltzmann equation. For this purpose, we have estimated the transport coefficients (momentum: \{$\eta$,$\zeta$\}, heat: \{$\kappa$\}, and charge: \{$\sigma_{\rm el}$\}) in the kinetic theory with Bhatnagar-Gross-Krook (BGK) and the collision integral in RTA a

Alexandros Stathas, Ioannis Stefanou

In Stathas and Stefanou (2022) we presented the frictional response of a bounded fault gouge under largecoseismic slip. We did so by taking into account the evolution of the Principal Slip Zone (PSZ) thickness using a Cosserat micromorphic continuum model for the description of the fault's mechanical response. The numerical results obtained differ significan

Leonard Susskind

I want to call attention to a simple previously noted fact about the double-scaled version of the SYK model which suggests that it may be holographically dual to de Sitter space.

Alexandros Stathas, Ioannis Stefanou

In this paper, we study the role of thermal pressurization in the frictional response of a fault under large coseismic slip. We investigate the role of the seismic slip velocity, mixture compressibility, characteristic grain size and viscosity parameter in the frictional response of the coupled thermo-hydro-mechanical problem, taking into account the fault's

Cheng-Te Li, Cheng Hsu, Yang Zhang

Sequential recommendation (SR) learns from the temporal dynamics of user-item interactions to predict the next ones. Fairness-aware recommendation mitigates a variety of algorithmic biases in the learning of user preferences. This paper aims at bringing a marriage between SR and algorithmic fairness. We propose a novel fairness-aware sequential recommendatio

Daehan Kim, Minseok Seo, Jinsun Park, Dong-Geol Choi

In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation. We propose domain adaptation by sampling and exploiting only a meaningful subset from source data for training. Our key assumption is that the entire source domain data may contain samples that are unhelpful for the adaptation. Therefor

J. Peña-Rodríguez, P. Salgado-Meza, L. Flórez-Villegas, L. A. Núñez

During thunderstorms, the atmospheric electric field can increase above hundreds of kV/m, causing an acceleration in the charged particles of secondary cosmic rays. Such an acceleration causes avalanche processes in the atmosphere, enhancing/reducing the particle flux at ground level depending on the strength/polarity of the electric field. We present the de

Santanu Mandal, Ranjit Mehatari, Kinkar Chandra Das

We study various spectral properties of the Seidel matrix $S$ of a connected chain graph. We prove that $-1$ is always an eigenvalue of $S$ and all other eigenvalues of $S$ can have multiplicity at most two. We obtain the multiplicity of the Seidel eigenvalue $-1$, minimum number of distinct eigenvalues, eigenvalue bounds, characteristic polynomial, lower an

Adrian M. P. Braşoveanu, Răzvan Andonie

During the last decade, Natural Language Processing has become, after Computer Vision, the second field of Artificial Intelligence that was massively changed by the advent of Deep Learning. Regardless of the architecture, the language models of the day need to be able to process or generate text, as well as predict missing words, sentences or relations depen

Sasidev Mahendran, Claudia M. Costa, Julie A. Wernert, Craig A. Stewart

The Nobel Prize is awarded each year to individuals who have conferred the greatest benefit to humankind in Physics, Chemistry, Medicine, Economics, Literature, and Peace, and is considered by many to be the most prestigious recognition for one's body of work. Receiving a Nobel prize confers a sense of financial independence and significant prestige, vaultin

Santiago Capriotti, Viviana Alejandra Díaz, Eduardo García-Toraño Andrés, Tom Mestdag

We discuss Lagrangian and Hamiltonian field theories that are invariant under a symmetry group. We apply the polysymplectic reduction theorem for both types of field equations and we investigate aspects of the corresponding reconstruction process. We identify the polysymplectic structures that lie at the basis of cotangent bundle reduction and Routh reductio

Yelena Mejova, Jisun An, Gianmarco De Francisci Morales, Haewoon Kwak

The United States have some of the highest rates of gun violence among developed countries. Yet, there is a disagreement about the extent to which firearms should be regulated. In this study, we employ social media signals to examine the predictors of offline political activism, at both population and individual level. We show that it is possible to classify

Tianxin Tao, Matthew Wilson, Ruiyu Gou, Michiel van de Panne

Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not resemble human get-up strategies, or are based on tracking recorded human get-up motions. In this paper, we present a staged approach using reinforcement learning, without recourse t

Frido Rolloos

Two novel closed-form formulas for the price of barrier options in stochastic volatility models with zero interest rate and dividend yield but nonzero correlation between the asset and its instantaneous volatility are derived. The first is a Hull and White type formula, and the second is a decomposition formula similar in form to the Al\`os decomposition for

Praneeth Chityala, Claudia M. Costa, Julie A. Wernert, Craig A. Stewart

The research landscape in science and engineering is heavily reliant on computation and data storage. The intensity of computation required for many research projects illustrates the importance of the availability of high performance computing (HPC) resources and services. This paper summarizes the results of a recent study among principal investigators that

Chin-Lun Fu, Zih-Ching Chen, Yun-Ru Lee, Hung-yi Lee

Transformer-based pre-trained models with millions of parameters require large storage. Recent approaches tackle this shortcoming by training adapters, but these approaches still require a relatively large number of parameters. In this study, AdapterBias, a surprisingly simple yet effective adapter architecture, is proposed. AdapterBias adds a token-dependen

Cheuk Hin Cheng, Kin Wai Chan

We propose a general framework to construct self-normalized multiple-change-point tests with time series data. The only building block is a user-specified one-change-point detecting statistic, which covers a wide class of popular methods, including cumulative sum process, outlier-robust rank statistics and order statistics. Neither robust and consistent esti

Min Zhou, Chenchen Xu, Ye Ma, Tiezheng Ge

In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic

Pengbo Hu, Xingyu Li, Yi Zhou

As deep learning advances, there is an ever-growing demand for models capable of synthesizing information from multi-modal resources to address the complex tasks raised from real-life applications. Recently, many large multi-modal datasets have been collected, on which researchers actively explore different methods of fusing multi-modal information. However,

Fan Yan, Ming Nie, Xinyue Cai, Jianhua Han

We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performance of following planning and control tasks in autonomous driving due to the case of uneven road. Predicting the 3D lane layout is thus necessary and enables effective and safe dri

Kayahan Saritas, Sohrab Ismail-Beigi

We predict that monolayer FeCl$_2$ is a two-dimensional piezoelectric ferromagnet (PFM) with easy-axis magnetism and a Curie temperature of 260 K. Our ab-initio calculations combined with data mining reveal 2H-FeCl$_2$ as the only easy-axis 2D monolayer PFM, and that its magnetic anisotropy increases many-fold with moderate hole doping. We develop an analysi

Yisi Sang, Xiangyang Mou, Jing Li, Jeffrey Stanton

As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks. Based on narrative theories, reading comprehension theories, as well as existing machine narrative reading comprehension tasks and datasets, we propose a

Abhay Ashtekar, Adrián del Río, Marc Schneider

The big bang and the Schwarzschild singularities are space-like. They are generally regarded as the "final frontiers" at which space-time ends and general relativity breaks down. We review the status of such space-like singularities from three increasingly more general perspectives. They are provided by (i) A reformulation of classical general relativity mot

Tiberiu Harko, Eniko J. Madarassy

We consider the effects of an uncorrelated random potential on the properties of Bose-Einstein Condensate (BEC) dark matter halos, which acts as a source of disorder, and which is added as a new term in the Gross-Pitaevskii equation, describing the properties of the halo. By using the hydrodynamic representation we derive the basic equation describing the de

Carlo Iazeolla, Per Sundell

We review and extend some recent results concerning the analysis of spacetime singularities in four-dimensional higher spin gravity, summarizing how the coupling of the gravitational field to massless higher spins may provide resolution mechanisms. We elucidate such mechanisms at the level of curvature singularities and degenerate metrics in exact as well as

I. Jianu, S. M. Jeloaica, M. D. Tudorache

This paper assesses the effects of greenhouse gas emissions drivers in EU-27 over the period 2010-2019, using a Panel EGLS model with period fixed effects. In particular, we focused our research on studying the effects of GDP, renewable energy, households energy consumption and waste on the greenhouse gas emissions. In this regard, we found a positive relati

Daniel Maria Busiello, Carlos Fiore

Generalized empirical currents represent a vast class of thermodynamic observables of mesoscopic systems. Their fluctuations satisfy the thermodynamic uncertainty relations (TURs), as they can be bounded by the average entropy production. Here, we derive a general closed expression for the hyperaccurate current in discrete-state Markovian systems, i.e., the

Konstantin Sozykin, Andrei Chertkov, Roman Schutski, Anh-Huy Phan

We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle. We demonstrate the applicability of the new Tensor Train Optimizer (TTOpt) method for various tasks, ranging from minimization of multidimensional functions to reinforcement learning. Our

Wenkui Ding, Yanxia Liu, Zhenyu Zheng, Shu Chen

We propose a dynamic quantum sensing scheme by using a quantum many-spin system composed of a central spin interacting with many surrounding spins. Starting from a generalized Ising ring model, we investigate the error propagation formula of the central spin and it indicates that Heisenberg scaling can be reached while the probe state only needs to be a prod

Lasse Peters, David Fridovich-Keil, Laura Ferranti, Cyrill Stachniss

In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional "predict then plan" approaches. However, curr

Francis A. Cucinotta, Sungmin Pak

Proton interactions with $^{12}$C nuclei are a frequent nuclear interaction leading to secondary radiation in tissues for space radiation and cancer therapy with protons or $^{12}$C beams. The fragmentation of $^{12}$C by protons produces a large number of heavy ion (A>4) target or projectile fragments often with high ionization density. Here we develop an a

D. Blas

I summarize our recent results to use the orbits of globular clusters (GCs) in the Fornax dwarf spheroidal (dSph) galaxy to learn more about dark matter (DM) properties. Our focus is on clarifying how dynamical friction (DF) from the DM halo is modified from the different microscopic properties of DM, which may alter $both$ the scattering processes responsib

Alexey Sholokhov, Xuechen Liu, Md Sahidullah, Tomi Kinnunen

Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short utterances, (iii) possibly absent speaker labels of the enrollment data (passive enrollment), and (iv) presence of unknown persons (guests). While many commercial products exist,

Ashish Jaiswal, Mohammad Zaki Zadeh, Aref Hebri, Fillia Makedon

Fatigue is a loss in cognitive or physical performance due to physiological factors such as insufficient sleep, long work hours, stress, and physical exertion. It adversely affects the human body and can slow reaction times, reduce attention, and limit short-term memory. Hence, there is a need to monitor a person's state to avoid extreme fatigue conditions t

Nikolaos Evangelou, Felix Dietrich, Juan M. Bello-Rivas, Alex Yeh

We construct a reduced, data-driven, parameter dependent effective Stochastic Differential Equation (eSDE) for electric-field mediated colloidal crystallization using data obtained from Brownian Dynamics Simulations. We use Diffusion Maps (a manifold learning algorithm) to identify a set of useful latent observables. In this latent space we identify an eSDE

Carlo Bellacoscia

Ranking (or top-k) and skyline queries are the most popular approaches used to extract interesting data from large datasets. The first one is based on a scoring function to evaluate and rank tuples. Its computation is fast, but it is sensitive to the choice of the evaluating function. Skyline queries are based on the idea of dominance and the result is the s

Rafael Gonzalez-Hernandez, Carlos Pinilla, Bernardo Uribe

Band inversion is a known feature in a wide range of topological insulators characterized by a change of orbital type around a high-symmetry point close to the Fermi level. In some cases of band inversion in topological insulators, the existence of quasinodal spheres has been detected, and the change of orbital type is shown to be concentrated along these sp

Chao-Lin Liu

Wordle is a very popular word game that is owned by the New York Times. We can design parameterized strategies for solving Wordle, based on probabilistic, statistical, and information-theoretical information about the games. The strategies can handle a reasonably large family of Wordle-like games both systematically and dynamically, meaning that we do not re

Shaily Desai, Atharva Kshirsagar, Aditi Sidnerlikar, Nikhil Khodake

This paper describes the approach to the Emotion Classification shared task held at WASSA 2022 by team PVGs AI Club. This Track 2 sub-task focuses on building models which can predict a multi-class emotion label based on essays from news articles where a person, group or another entity is affected. Baseline transformer models have been demonstrating good res

Ghazaleh Kia, Laura Ruotsalainen, Jukka Talvitie

The advent of Artificial Intelligence (AI) has impacted all aspects of human life. One of the concrete examples of AI impact is visible in radio positioning. In this article, for the first time we utilize the power of AI by training a Convolutional Neural Network (CNN) using 5G New Radio (NR) fingerprints consisting of beamformed Channel State Information (C

Shaojie Li, Sheng Ouyang, Yong Liu

The theoretical analysis of spectral clustering mainly focuses on consistency, while there is relatively little research on its generalization performance. In this paper, we study the excess risk bounds of the popular spectral clustering algorithms: \emph{relaxed} RatioCut and \emph{relaxed} NCut. Firstly, we show that their excess risk bounds between the em

Qian Ma, Wei Gao, Qiang Xiao, Lingsong Ding

Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm

Toshiyuki Nakayama, Stefan Tappe

The goal of this paper is to clarify when the solutions to stochastic partial differential equations stay close to a given subset of the state space for starting points which are close as well. This includes results for deterministic partial differential equations. As an example, we will consider the situation where the subset is a finite dimensional submani

Srinivas Arigapudi, Omer Edhan, Yuval Heller, Ziv Hellman

We study games in which the set of strategies is multi-dimensional, and new agents might learn various strategic dimensions from different mentors. We introduce a new family of dynamics, the recombinator dynamics, which is characterised by a single parameter, the recombination rate r in [0,1]. The case of r = 0 coincides with the standard replicator dynamics

Matheus Hansen, Paulo R. Protachevicz, Kelly C. Iarosz, Ibere L. Caldas

We study the time delay in the synaptic conductance for suppression of spike synchronisation in a random network of Hodgkin Huxley neurons coupled by means of chemical synapses. In the first part, we examine in detail how the time delay acts over the network during the synchronised and desynchronised neuronal activities. We observe a relation between the neu

Thijs van der Horst, Maarten Löffler, Frank Staals

Let $P$ be a set of $n$ colored points. We develop efficient data structures that store $P$ and can answer chromatic $k$-nearest neighbor ($k$-NN) queries. Such a query consists of a query point $q$ and a number $k$, and asks for the color that appears most frequently among the $k$ points in $P$ closest to $q$. Answering such queries efficiently is the key t

Xinyang Wang, Igor A. Shovkovy

Using the Landau-level representation for the imaginary part of the photon polarization tensor, we derive an explicit expression for the dilepton production rate from a hot quark-gluon plasma in a quantizing background magnetic field. We study in detail the dependence of the production rate on the dilepton invariant mass and the transverse momentum at mid-ra

Xinyu Yang, Tilo Burghardt, Majid Mirmehdi

We propose a novel end-to-end curriculum learning approach for sparsely labelled animal datasets leveraging large volumes of unlabelled data to improve supervised species detectors. We exemplify the method in detail on the task of finding great apes in camera trap footage taken in challenging real-world jungle environments. In contrast to previous semi-super