May 2022 arXiv papers — page 37

Amartansh Dubey, Ross Murch

In this work we present a novel linear iterative solution to an electromagnetic inverse scattering problem with phaseless data for strongly scattering, lossy media. It is based on an extended Rytov approximation that significantly widens the validity range of the conventional Rytov approximation. By modifying this extension and including it in a distorted wa

Shupeng Zhang, Yibin Zhang, Xixi Zhang, Jinlong Sun

Radio Frequency Fingerprint (RFF) identification on account of deep learning has the potential to enhance the security performance of wireless networks. Recently, several RFF datasets were proposed to satisfy requirements of large-scale datasets. However, most of these datasets are collected from 2.4G WiFi devices and through similar channel environments. Me

Anton Galajinsky

Equations of fluid dynamics are formulated, which hold invariant under the action of the l-conformal Galilei group. They include the conventional continuity equation, a higher order material derivative analogue of the Euler equation, and a suitable modification of the conventional equation of state. Conserved charges associated with the l-conformal Galilei s

Joel Davidsson, Rohit Babar, Danial Shafizadeh, Ivan G. Ivanov

The negatively charged silicon vacancy ($\mathrm{V_{Si}^-}$) in silicon carbide is a well-studied point defect for quantum applications. At the same time, a closer inspection of ensemble photoluminescence and electron paramagnetic resonance measurements reveals an abundance of related but so far unidentified signals. In this study, we search for defects in 4

Etienne Jaupart, Gilles Chabrier

In the first article of this series, we have used the ergodic theory to assess the validity of a statistical approach to characterize various properties of star-forming molecular clouds (MCs) from a limited number of observations or simulations. This allows the proper determination of confidence intervals for various volumetric averages of statistical quanti

Vladimir Egorov, Aleksei Shpilman

Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on Centralized Training with Decentralized Execution (CTDE) paradigm. CTDE has been a dominant approach for both cooperative and mixed environments due to its capability to efficiently train decentralized policies. While in mixed environments full autonomy of the agents can

Pin-Tian Lyu, Qing-Yue Li, Pei Wu, Chao Sun

Energy carrier evolution is crucial for material performance. Ultrafast microscopy has been widely applied to visualize the spatiotemporal evolution of energy carriers. However, direct imaging of small amounts of energy carriers on nanoscale remains difficult due to extremely weak transient signals. Here we present a method for ultrasensitive and high-throug

Eduardo Gallo

Classical mathematical techniques such as discrete integration, gradient descent optimization, and state estimation (exemplified by the Runge-Kutta method, Gauss-Newton minimization, and extended Kalman filter or EKF, respectively), rely on linear algebra and hence are only applicable to state vectors belonging to Euclidean spaces when implemented as describ

Etienne Jaupart, Gilles Chabrier

The proper characterization of the general statistical behavior of these fluctuations, from a limited sample of observations or simulations, is of prime importance to understand the process of star formation. In this article, we use the ergodic theory for any random field of fluctuations, as commonly used in statistical physics, to derive rigorous statistica

Nora Kassner, Fabio Petroni, Mikhail Plekhanov, Sebastian Riedel

Existing work on Entity Linking mostly assumes that the reference knowledge base is complete, and therefore all mentions can be linked. In practice this is hardly ever the case, as knowledge bases are incomplete and because novel concepts arise constantly. This paper created the Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities, t

Borja Molina-Coronado, Usue Mori, Alexander Mendiburu, Jose Miguel-Alonso

As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been presented to date, often reporting impresive detection performances. However, the lack of reproducibility and the absence of a standard eva

Daria Dziubałtowska

The goal of the thesis is to study and perform an analysis of the possible attack vectors on the Iota network 2.0 version of the protocol. In this work, existing attack vectors on Distributed Ledger Technologies are studied and their applicability to the Iota 2.0 protocol is discussed. A specific attack that targets the capability of honest participants to w

Hongting Song, Areeya Chantasri, Behnam Tonekaboni, Howard M. Wiseman

Spectator qubits (SQs) are a tool to mitigate noise in hard-to-access data qubits. The SQ, designed to be much more sensitive to the noise, is measured frequently, and the accumulated results used rarely to correct the data qubits. For the hardware-relevant example of dephasing from random telegraph noise, we introduce a Bayesian method employing complex lin

Behnam Tonekaboni, Areeya Chantasri, Hongting Song, Yanan Liu

In a scenario where data-storage qubits are kept in isolation as far as possible, with minimal measurements and controls, noise mitigation can still be done using additional noise probes, with corrections applied only when needed. Motivated by the case of solid-state qubits, we consider dephasing noise arising from a two-state fluctuator, described by random

Andrea Meilán-Vila, Rosa M. Crujeiras, Mario Francisco-Fernández

Changes on temperature patterns, on a local scale, are perceived by individuals as the most direct indicators of global warming and climate change. As a specific example, for an Atlantic climate location, spring and fall seasons should present a mild transition between winter and summer, and summer and winter, respectively. By observing daily temperature cur

Pooya Elahi, Jude A. Horsley, Taylor D. Sparks

The recent emergence of multi-species multi-phase materials provides intriguing opportunities to maximize electrochemical performance in various electrochemical devices. This work summarizes the current understanding of the coupled transport reactions in multi-phase multi-species ionic conductors. We also provide experimental results of the fabrication of mu

Jiaxin Wei, Lige Liu, Ran Cheng, Wenqing Jiang

Recent years have witnessed the surge of learned representations that directly build upon point clouds. Though becoming increasingly expressive, most existing representations still struggle to generate ordered point sets. Inspired by spherical multi-view scanners, we propose a novel sampling model called Spotlights to represent a 3D shape as a compact 1D arr

Anna Vesely, Jelle J. Goeman, Livio Finos

We propose a novel resampling-based method to construct an asymptotically exact test for any subset of hypotheses on coefficients in high-dimensional linear regression. It can be embedded into any multiple testing procedure to make confidence statements on relevant predictor variables. The method constructs permutation test statistics for any individual hypo

Eugenio Cuniato, Nicholas Lawrance, Marco Tognon, Roland Siegwart

As the performance of autonomous systems increases, safety concerns arise, especially when operating in non-structured environments. To deal with these concerns, this work presents a safety layer for mechanical systems that detects and responds to unstable dynamics caused by external disturbances. The safety layer is implemented independently and on top of a

Haruyoshi Tanaka

We study higher-order asymptotic expansions of eigenvalues in perturbed transfer operators, of the corresponding eigenfunctions and of the corresponding eigenvectors of the dual operators. In our main result, we give explicit expressions of these coefficients and these remainders under mild conditions of linear operators and of (there is even no norm) linear

En-Bo Huo, Ting-Ting Sun, Ke-Ran Li, Xiao-Ying Qu

The possible exotic nuclear properties in the neutron-rich Ca, Ni, Zr, and Sn isotopes are explored with the continuum Skyrme Hartree-Fock-Bogoliubov theory formulated with the Green's function method. The available experimental two-neutron separation energies $S_{\rm 2n}$ and one-neutron separation energies $S_{\rm n}$ are well reproduced. Much shorter drip

Ganesh Ghalme, Xin Huang, Nidhi Rathi

We study the classic problem of fairly dividing a heterogeneous and divisible resource -- represented by a cake, $[0,1]$ -- among $n$ agents. This work considers an interesting variant of the problem where agents are embedded on a graph. The graphical constraint entails that each agent evaluates her allocated share only against her neighbor's share. Given a

Sachin Kumar, Biswajit Paria, Yulia Tsvetkov

Large pretrained language models generate fluent text but are notoriously hard to controllably sample from. In this work, we study constrained sampling from such language models: generating text that satisfies user-defined constraints, while maintaining fluency and the model's performance in a downstream task. We propose MuCoLa -- a sampling procedure that c

Raimund Bürger, Julio Careaga, Stefan Diehl, Romel Pineda

A non-negligible part of the biological reactions in the activated sludge process for treatment of wastewater takes place in secondary settling tanks that follow biological reactors. It is therefore of interest to develop models of so-called reactive settling that describe the spatial variability of reaction rates caused by the variation of local concentrati

Harald Upmeier

We analyze the "eigenbundle" (localization bundle) of certain Hilbert modules over bounded symmetric domains of rank $r,$ giving rise to complex-analytic fibre spaces which are stratified of length $r+1.$ The fibres are described in terms of K\"ahler geometry as line bundle sections over flag manifolds, and the metric embedding is determined by taking deriva

Dong Zhang, Ran Li, Xin Lou, Jianwen Luo

Optical-resolution photoacoustic microscopy has been validated as a high-resolution and high-sensitivity imaging modality for angiographic studies in the past decades. Quantitative vascular analysis reveals critical information of physiological changes, where vessel segmentation is the key step. In this work, we developed a Hessian filter-assisted, adaptive

Jiao Sun, Yu Hou, Jiin Kim, Nanyun Peng

Information-seeking AI assistant systems aim to answer users' queries about knowledge in a timely manner. However, both the human-perceived helpfulness of information-seeking assistant systems and its fairness implication are under-explored. In this paper, we study computational measurements of helpfulness. We collect human annotations on the helpfulness of

Gunter Malle, Geoffrey R. Robinson

We investigate finite non-Abelian simple groups $G$ for which the projective cover of the trivial module coincides with the permutation module on a subgroup and classify all cases unless $G$ is of Lie type in defining characteristic.

Krzysztof Redlich, Natasha Sharma

We investigate the increase in strangeness production with charged particle multiplicity ($dN_{ch}/dy$) seen by the ALICE collaboration at CERN in p-p, p-Pb and Pb-Pb collisions using the hadron resonance gas model. The strangeness canonical ensemble is used taking into account the interactions among hadrons using S-matrix corrections based on known phase sh

Bin Ren, Yahui Liu, Yue Song, Wei Bi

Position Embeddings (PEs), an arguably indispensable component in Vision Transformers (ViTs), have been shown to improve the performance of ViTs on many vision tasks. However, PEs have a potentially high risk of privacy leakage since the spatial information of the input patches is exposed. This caveat naturally raises a series of interesting questions about

Mona Buisson-Fenet, Valery Morgenthaler, Sebastian Trimpe, Florent Di Meglio

Identifying dynamical systems from experimental data is a notably difficult task. Prior knowledge generally helps, but the extent of this knowledge varies with the application, and customized models are often needed. Neural ordinary differential equations can be written as a flexible framework for system identification and can incorporate a broad spectrum of

Antoine Godichon-Baggioni, Nicklas Werge, Olivier Wintenberger

This paper addresses stochastic optimization in a streaming setting with time-dependent and biased gradient estimates. We analyze several first-order methods, including Stochastic Gradient Descent (SGD), mini-batch SGD, and time-varying mini-batch SGD, along with their Polyak-Ruppert averages. Our non-asymptotic analysis establishes novel heuristics that lin

Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang

Prompting has shown impressive success in enabling large pretrained language models (LMs) to perform diverse NLP tasks, especially when only few downstream data are available. Automatically finding the optimal prompt for each task, however, is challenging. Most existing work resorts to tuning soft prompt (e.g., embeddings) which falls short of interpretabili

Nico Gneist, Dominik Kiese, Ravn Henkel, Ronny Thomale

We study competing orders of spinless fermions in the triangular-lattice Hubbard model with nearest-neighbor interaction. We calculate the effective, momentum-resolved two-particle vertex in an unbiased way in terms of the functional renormalization group method and compare two different schemes for the momentum discretization, one based on dividing the Ferm

Nicolas Boutry, Laurent Najman, Thierry Géraud

In Mathematical Morphology (MM), connected filters based on dynamics are used to filter the extrema of an image. Similarly, persistence is a concept coming from Persistent Homology (PH) and Morse Theory (MT) that represents the stability of the extrema of a Morse function. Since these two concepts seem to be closely related, in this paper we examine their re

Miguel A. S. Pinto, Tiberiu Harko, Francisco S. N. Lobo

We explore the possibility of gravitationally generated particle production in the scalar-tensor representation of $f(R,T)$ gravity. Due to the explicit nonminimal curvature-matter coupling in the theory, the divergence of the matter energy-momentum tensor does not vanish. We explore the physical and cosmological implications of this property by using the fo

Kyung Ho Park

Visual localization is an essential component of intelligent transportation systems, enabling broad applications that require understanding one's self location when other sensors are not available. It is mostly tackled by image retrieval such that the location of a query image is determined by its closest match in the previously collected images. Existing ap

Vera Wesselkamp, Konrad Rieck, Daniel Arp, Erwin Quiring

Generative adversarial networks (GANs) have made remarkable progress in synthesizing realistic-looking images that effectively outsmart even humans. Although several detection methods can recognize these deep fakes by checking for image artifacts from the generation process, multiple counterattacks have demonstrated their limitations. These attacks, however,

Brihi Joshi, Aaron Chan, Ziyi Liu, Shaoliang Nie

By explaining how humans would solve a given task, human rationales can provide strong learning signal for neural language models (LMs). Explanation regularization (ER) aims to improve LM generalization by pushing the LM's machine rationales (Which input tokens did the LM focus on?) to align with human rationales (Which input tokens would humans focus on?).

Xiangyu Jiang, Wei Sun, Feiyu Chen, Ying Chen

We perform the first lattice study on the mixing of the isoscalar pseudoscalar meson $\eta$ and the pseudoscalar glueball $G$ in the $N_f=2$ QCD at the pion mass $m_\pi\approx 350$ MeV. The $\eta$ mass is determined to be $m_\eta=714(6)(16)$ MeV. Through the Witten-Veneziano relation, this value can be matched to a mass value of $\sim 981$ MeV for the $\math

Young-Kwon Han, Jeong-Won Seo, Taewon Yuk, Sang-Jin Sin

We first point out that the Laia-Tong model realizes the Lieb lattice in the holographic setup. It generates a flat band of sharp particle spectrum together with a Dirac band of unparticle spectrum. We then construct a model which opens a gap to the Dirac band so that one can realize a well-separated flat band, which can play the role of the hydrogen atom of

Le Ngoc Luyen, Marie-Hélène Abel, Philippe Gouspillou

Measurement of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data such as knowledge acquisition, recommender system, and natural language processing. Over the past few years, many ontologies have been developed and used as a form of structured representation of k

Pruthvi Patel, Swaroop Mishra, Mihir Parmar, Chitta Baral

Large Language Models (LMs) have achieved state-of-the-art performance on many Natural Language Processing (NLP) benchmarks. With the growing number of new benchmarks, we build bigger and more complex LMs. However, building new LMs may not be an ideal option owing to the cost, time and environmental impact associated with it. We explore an alternative route:

Yasong Sun, Yang Hu, Kezhang Shi, Jihong Zhang

In this work, the near-field radiative heat transfer (NFRHT) between two Weyl semimetal (WSM) nanoparticles (NPs) is investigated. The numerical results show that negative differential thermal conductance (NDTC) effect can be obtained in this system, i.e., when the temperature of the emitter is fixed, the heat flux does not decrease monotonically with the in

Seunghyuk Cho, Juyong Lee, Jaesik Park, Dongwoo Kim

We present a rotated hyperbolic wrapped normal distribution (RoWN), a simple yet effective alteration of a hyperbolic wrapped normal distribution (HWN). The HWN expands the domain of probabilistic modeling from Euclidean to hyperbolic space, where a tree can be embedded with arbitrary low distortion in theory. In this work, we analyze the geometric propertie

M. Haj Tahar, D. Kiselev, A. Knecht, D. Laube

The High Intensity Proton Accelerator (HIPA) cyclotron at the Paul Scherrer Institut (PSI) delivers 590 MeV CW proton beam with a maximum power of 1.42 MW. After extraction, the beam is transferred in a 120 m long channel towards two target stations (TgM and TgE) for surface muon production before depositing its remaining power at the spallation target SINQ

Changyang He, Yue Deng, Wenjie Yang, Bo Li

Posting help-seeking requests on social media has been broadly adopted by victims during natural disasters to look for urgent rescue and supplies. The help-seeking requests need to get sufficient public attention and be promptly routed to the intended target(s) for timely responses. However, the huge volume and diverse types of crisis-related posts on social

M. Chadeeva, S. Korpachev

The paper describes a novel neural-network-based approach to study the distributions of secondaries produced in hadronic showers using observables provided by highly granular calorimeters. The response is analysed of the highly granular scintillator-steel hadron calorimeter to negative pions with momenta from 10 to 80 GeV simulated with two physics lists fro

James Langley, Miguel Monteiro, Charles Jones, Nick Pawlowski

Variational autoencoders (VAEs) are a popular class of deep generative models with many variants and a wide range of applications. Improvements upon the standard VAE mostly focus on the modelling of the posterior distribution over the latent space and the properties of the neural network decoder. In contrast, improving the model for the observational distrib

Geraud Nangue Tasse, Devon Jarvis, Steven James, Benjamin Rosman

It is desirable for an agent to be able to solve a rich variety of problems that can be specified through language in the same environment. A popular approach towards obtaining such agents is to reuse skills learned in prior tasks to generalise compositionally to new ones. However, this is a challenging problem due to the curse of dimensionality induced by t

A. D. Kerin, A. Melatos

The elastic crust of a neutron star fractures repeatedly as it spins down electromagnetically. An idealised, macroscopic model of inhomogeneous crustal failure is presented based on a cellular automaton with nearest-neighbour tectonic interactions involving strain redistribution and thermal dissipation. Predictions are made of the size and waiting-time distr

Hee-Suk Cho

We study the impact of eccentricity on gravitational-wave parameter estimation for binary neutron star systems. For signals with small eccentricity injected into the advanced LIGO sensitivity, we perform Bayesian parameter estimation using the circular waveform model and show how the recovered parameters can be biased from their true values, focusing on the

Manoj Mathews

This paper presents a study on quantum radar technology developments, design Consideration for its integration, and quantum radar cross-section, QRCS based on quantum electrodynamics and interferometric considerations. Quantum radar systems supported by quantum measurement can fulfill not only conventional target detection and recognition tasks but are also

Peipei Du, Gendi Wang

In this paper, we study some properties such as the monotonicity, logarithmically complete monotonicity, logarithmic convexity, and geometric convexity, of the combinations of gamma function and power function. The results we obtain generalize some related known results for parameters with specific values.

Xiao Guo, Qing Yang, Wei Yu, Qiuhao Zhu

Understanding the intermolecular interactions and utilize these interactions to effectively control the transport behavior of single molecule is the key step from single molecule device to molecular circuits1-6. Although many single molecule detection techniques are used to detect the molecular interaction at single-molecule level1,4,5,7,8, probing and tunin

Dheeraj Mekala, Chengyu Dong, Jingbo Shang

Weakly supervised text classification methods typically train a deep neural classifier based on pseudo-labels. The quality of pseudo-labels is crucial to final performance but they are inevitably noisy due to their heuristic nature, so selecting the correct ones has a huge potential for performance boost. One straightforward solution is to select samples bas

Nada Aldarrab, Jonathan May

Deciphering historical substitution ciphers is a challenging problem. Example problems that have been previously studied include detecting cipher type, detecting plaintext language, and acquiring the substitution key for segmented ciphers. However, attacking unsegmented, space-free ciphers is still a challenging task. Segmentation (i.e. finding substitution

Xiang Wei, Qing-Hua Shen, Ju-Jun Xie

We perform a theoretical study on the $D\bar{D}K$ three body system, using the fixed center approximation to the Faddeev equations, considering the interaction between $D$ and $K$, $D$ and $\bar{D}$ from the chiral unitary approach. We assume the scattering of $K$ meson on a clusterized system $D\bar{D}$, where a scalar meson $X(3720)$ could be formed. Thank

Airat M. Bikchentaev

Let $\mathcal{M}$ be a semifinite von Neumann algebra on a Hilbert space $\mathcal{H}$ equipped with a faithful normal semifinite trace $\tau$, $S(\mathcal{M},\tau)$ be the ${}^*$-algebra of all $\tau$-measurable operators. Let $S_0(\mathcal{M},\tau)$ be the ${}^*$-algebra of all $\tau$-compact operators and $T(\mathcal{M},\tau)=S_0(\mathcal{M},\tau)+\mathbb

Zhaoyang Lyu, Xudong XU, Ceyuan Yang, Dahua Lin

Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressive performance on various generation tasks. By modeling the reverse process of gradually diffusing the data distribution into a Gaussian distribution, generating a sample in DDPMs can be regarded as iteratively denoising a randomly sampled Gaussian noise. However, in practice DDPMs often

Rongjie Huang, Jinglin Liu, Huadai Liu, Yi Ren

Direct speech-to-speech translation (S2ST) with discrete units leverages recent progress in speech representation learning. Specifically, a sequence of discrete representations derived in a self-supervised manner are predicted from the model and passed to a vocoder for speech reconstruction, while still facing the following challenges: 1) Acoustic multimodal

Ashish V. Thapliyal, Jordi Pont-Tuset, Xi Chen, Radu Soricut

Research in massively multilingual image captioning has been severely hampered by a lack of high-quality evaluation datasets. In this paper we present the Crossmodal-3600 dataset (XM3600 in short), a geographically diverse set of 3600 images annotated with human-generated reference captions in 36 languages. The images were selected from across the world, cov

Cuiling Liu, Xingyong Zhang

We investigate the existence and multiplicity of weak solutions for a nonlinear Kirchhoff type quasilinear elliptic system on the whole space $\mathbb{R}^N$. We assume that the nonlinear term satisfies the locally super-$(m_1,m_2)$ condition, that is, $\lim_{|(u,v)|\rightarrow+\infty}\frac{F(x,u,v)}{|u|^{m_1}+|v|^{m_2}}=+\infty \mbox{ for a.e. } x \in G$ whe

Chong Han, Weijun Gao, Nan Yang, Josep M. Jornet

Communications in the terahertz band (THz) (0.1--10~THz) have been regarded as a promising technology for future 6G and beyond wireless systems, to overcome the challenges of evergrowing wireless data traffic and crowded spectrum. As the frequency increases from the microwave band to the THz band, new spectrum features pose unprecedented challenges to wirele

Sushruth Nagesh, Asfiya Baig, Savitha Srinivasan, Akshay Rangesh

3-D object detection is pivotal for autonomous driving. Point cloud based methods have become increasingly popular for 3-D object detection, owing to their accurate depth information. NuTonomy's nuScenes dataset greatly extends commonly used datasets such as KITTI in size, sensor modalities, categories, and annotation numbers. However, it suffers from severe

Derek Leinweber, James Biddle, Waseem Kamleh

The non-trivial ground-state vacuum fields of QCD form the foundation of matter. Using modern visualisation techniques, this presentation examines the microscopic structure of these fields. Of particular interest are the centre vortices identified within the ground-state fields of lattice QCD. Our current focus is on understanding the manner in which dynamic

Ichiro Oda

We revisit the issue that the Noether current associated with a local scale symmtery, or equivalently the Weyl symmetry, identically vanishes. Based on only the second Noether theorem for a local symmetry, we prove that the Noether current associated with the Weyl symmetry is in general vanishing in any Weyl invariant gravitational theories in four dimension

Neeraj Prakash, Shu-Wei Huang, Bowen Li

Counter-propagating all-normal dispersion (CANDi) fiber laser is an emerging high-energy single-cavity dual-comb laser source. Its relative timing jitter (RTJ), a critical parameter for dual-comb timing precision and spectral resolution, has not been comprehensively investigated. In this paper, we enhance the state-of-the-art CANDi fiber laser pulse energy f

Yi Wan, Richard S. Sutton

We propose a new objective for option discovery that emphasizes the computational advantage of using options in planning. In a sequential machine, the speed of planning is proportional to the number of elementary operations used to achieve a good policy. For episodic tasks, the number of elementary operations depends on the number of options composed by the

Jacob Bremerman, Xiang Ren, Jonathan May

Current Machine Translation (MT) models still struggle with more challenging input, such as noisy data and tail-end words and phrases. Several works have addressed this robustness issue by identifying specific categories of noise and variation then tuning models to perform better on them. An important yet under-studied category involves minor variations in n

Jhih-Hong Lyu, Tai-Chia Lin

When ions are crowded, the effect of steric repulsion between ions becomes significant and the conventional Poisson--Boltzmann (PB) equation (without steric effect) should be modified. For this purpose, we study the asymptotic limit of steady state Poisson--Nernst--Planck equations with steric effects (PNP-steric equations). By the assumptions of steric effe

D. M. A. Ayanthi, Sarasi Munasinghe

Synthesizing images from text descriptions has become an active research area with the advent of Generative Adversarial Networks. The main goal here is to generate photo-realistic images that are aligned with the input descriptions. Text-to-Face generation (T2F) is a sub-domain of Text-to-Image generation (T2I) that is more challenging due to the complexity

Priyotosh Bandyopadhyay, Eung Jin Chun, Chandrima Sen

We investigate boosted displaced signatures in the Type-I seesaw mechanism associated with the $B-L$ gauge symmetry. Such events arise from decays of right-handed neutrinos depending on their Yukawa couplings and masses. Considering two scenarios: (a) three degenerate right-handed neutrinos whose Yukawa couplings are reconstructed from the observed neutrino

Liu Ziyin, Masahito Ueda

This work reports deep-learning-unique first-order and second-order phase transitions, whose phenomenology closely follows that in statistical physics. In particular, we prove that the competition between prediction error and model complexity in the training loss leads to the second-order phase transition for nets with one hidden layer and the first-order ph

Agnid Banerjee, Soumen Senapati

We study an inverse problem for variable coefficient fractional parabolic operators of the form $(\partial_t -\operatorname{div}(A(x) \nabla_x)^s + q(x,t)$ for $s\in(0,1)$ and show the unique recovery of $q$ from exterior measured data. Similar to the fractional elliptic case, we use Runge type approximation argument which is obtained via a global weak uniqu

Saikat Panja, Sachchidanand Prasad

It was conjectured that the augmentation ideal of a dihedral quandle of even order $n>2$ satisfies $|\Delta^k(\text{R}_n)/\Delta^{k+1}(\text{R}_{n})|=n$ for all $k\geq 2$. In this article we provide a counterexample against this conjecture.

M. Mirzakhani, N. Myoung, F. M. Peeters, H. C. Park

Graphene matter in a strong magnetic field, realizing one-dimensional quantum Hall channels, provides a unique platform for studying electron interference. Here, using the Landauer-B\"uttiker formalism along with the tight-binding model, we investigate the quantum Hall (QH) effects in unipolar and bipolar monolayer-bilayer graphene (MLG-BLG) junctions. We fi

Chenglei Si, Chen Zhao, Sewon Min, Jordan Boyd-Graber

For users to trust model predictions, they need to understand model outputs, particularly their confidence - calibration aims to adjust (calibrate) models' confidence to match expected accuracy. We argue that the traditional calibration evaluation does not promote effective calibrations: for example, it can encourage always assigning a mediocre confidence sc

Fatemehsadat Mireshghallah, Archit Uniyal, Tianhao Wang, David Evans

Large language models are shown to present privacy risks through memorization of training data, and several recent works have studied such risks for the pre-training phase. Little attention, however, has been given to the fine-tuning phase and it is not well understood how different fine-tuning methods (such as fine-tuning the full model, the model head, and

Tanmay Parekh, I-Hung Hsu, Kuan-Hao Huang, Kai-Wei Chang

Recent works in Event Argument Extraction (EAE) have focused on improving model generalizability to cater to new events and domains. However, standard benchmarking datasets like ACE and ERE cover less than 40 event types and 25 entity-centric argument roles. Limited diversity and coverage hinder these datasets from adequately evaluating the generalizability

Yukita Fujitani, Tomoki Yamauchi, Yuki Miyashita, Toshiharu Sugawara

This paper proposes a control method for the multi-agent pickup and delivery problem (MAPD problem) by extending the priority inheritance with backtracking (PIBT) method to make it applicable to more general environments. PIBT is an effective algorithm that introduces a priority to each agent, and at each timestep, the agents, in descending order of priority

Zhiqiang Zhuang, Kewen Wang, Zhe Wang, Junhu Wang

DeGroot-style opinion formation presumes a continuous interaction among agents of a social network. Hence, it cannot handle agents external to the social network that interact only temporarily with the permanent ones. Many real-world organisations and individuals fall into such a category. For instance, a company tries to persuade as many as possible to buy

Gi-Cheon Kang, Sungdong Kim, Jin-Hwa Kim, Donghyun Kwak

Visual dialog (VisDial) is a task of answering a sequence of questions grounded in an image, using the dialog history as context. Prior work has trained the dialog agents solely on VisDial data via supervised learning or leveraged pre-training on related vision-and-language datasets. This paper presents a semi-supervised learning approach for visually-ground

Zixiang Han, Shanpu Shen, Yujie Zhang, Shiwen Tang

A method for achieving the continuous-space electromagnetic channel capacity bound using loaded N-port structures is described. It is relevant for the design of compact multiple-input multiple-output (MIMO) antennas that can achieve channel capacity bounds when constrained by size. The method is not restricted to a specific antenna configuration and a closed

Konstantin M. Dyakonov

Given an inner function $\theta$ on the unit disk, let $K^p_\theta:=H^p\cap\theta\bar z\bar{H^p}$ be the associated star-invariant subspace of the Hardy space $H^p$. Also, we put $K_{*\theta}:=K^2_\theta\cap{\rm BMO}$. Assuming that $B=B_{\mathcal Z}$ is an interpolating Blaschke product with zeros $\mathcal Z=\{z_j\}$, we characterize, for a number of smoot

Sergei Agapov, Alexey Potashnikov, Vladislav Shubin

We study the magnetic geodesic flows on 2-surfaces having an additional first integral which is independent of the Hamiltonian at a fixed energy level. The following two cases are considered: when there exists a quadratic in momenta integral, and also the case of a rational in momenta integral with a linear numerator and denominator. In both cases certain se

Mohammad Pirani, Aritra Mitra, Shreyas Sundaram

As the scale of networked control systems increases and interactions between different subsystems become more sophisticated, questions of the resilience of such networks increase in importance. The need to redefine classical system and control-theoretic notions using the language of graphs has recently started to gain attention as a fertile and important are

M. M. Amiri, O. N. Ghodsi

In this study, the effects of repulsive nucleon-nucleon interactions arising from the Pauli exclusion principle were examined regarding the half-lives of heavy even-even nuclei with $84\leq Z\leq92$. The Pauli exclusion principle is applied to our investigations by renormalizing the nucleon-nucleon interactions according to the Bohr-Sommerfeld quantization c

Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal

Question-answering datasets require a broad set of reasoning skills. We show how to use question decompositions to teach language models these broad reasoning skills in a robust fashion. Specifically, we use widely available QDMR representations to programmatically create hard-to-cheat synthetic contexts for real questions in six multi-step reasoning dataset

Badr AlKhamissi, Faisal Ladhak, Srini Iyer, Ves Stoyanov

Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next. It is also difficult to collect a large-scale hate speech annotated dataset. In this work, we frame this problem as a few-shot learning task, and show significant gains with decomposi

Prayash Dutta, Albert Lee, Kang L. Wang, Alex K. Jones

Domain-wall memory (DWM) has SRAM class access performance, low energy, high endurance, high density, and CMOS compatibility. Recently, shift reliability and processing-using-memory (PuM) proposals developed a need to count the number of parallel or anti-parallel domains in a portion of the DWM nanowire. In this paper we propose a multi-domain magneto-tunnel

Disha Makhija, Nhat Ho, Joydeep Ghosh

Federated Learning has become an important learning paradigm due to its privacy and computational benefits. As the field advances, two key challenges that still remain to be addressed are: (1) system heterogeneity - variability in the compute and/or data resources present on each client, and (2) lack of labeled data in certain federated settings. Several rec

Adrian Hauffe-Waschbüsch, Aloys Krieg, Brandon Williams

We consider the Hermitian Eisenstein series $E^{(\mathbb{K})}_k$ of degree $2$ and weight $k$ associated with an imaginary-quadratic number field $\mathbb{K}$ and determine the influence of $\mathbb{K}$ on the arithmetic and the growth of its Fourier coefficients. We find that they satisfy the identity $E^{{(\mathbb{K})}^2}_4 = E^{{(\mathbb{K})}}_8$, which i

William Hogan, Jiacheng Li, Jingbo Shang

Recent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on gold labels. Existing methods typically assume all these silver labels are accurate and treat them equally; however, distant supervision is inevitably noisy -- some silver labels

Shuke Zhang, Yanzhao Jin, Tianmeng Liu, Qi Wang

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DT

Zhiyang Xu, Jay-Yoon Lee, Lifu Huang

Data scarcity has been the main factor that hinders the progress of event extraction. To overcome this issue, we propose a Self-Training with Feedback (STF) framework that leverages the large-scale unlabeled data and acquires feedback for each new event prediction from the unlabeled data by comparing it to the Abstract Meaning Representation (AMR) graph of t

Bo Y. -C. Ning, Ismaël Castillo

Piecewise constant priors are routinely used in the Bayesian Cox proportional hazards model for survival analysis. Despite its popularity, large sample properties of this Bayesian method are not yet well understood. This work provides a unified theory for posterior distributions in this setting, not requiring the priors to be conjugate. We first derive contr

Raj Kumar Sadhu, Christian Hernandez-Padilla, Yael Eshed Eisenbach, Lixia Zhang

Protrusions at the leading-edge of a cell play an important role in sensing the extracellular cues, during cellular spreading and motility. Recent studies provided indications that these protrusions wrap (coil) around the extra-cellular fibers. The details of this coiling process, and the mechanisms that drive it, are not well understood. We present a combin

Barry Menglong Yao, Aditya Shah, Lichao Sun, Jin-Hee Cho

We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (e.g., support, refute or not enough informatio

Manoj Mathews, B. Ramesh, T. Sreedhar

Multilevel inverter converts different level DC voltage to AC voltage. It has wide interest in power industry especially in high power applications. In power electronic equipment the major drawback is the harmonics. Several control strategies are available to reduce the harmonic content and the most widely used measure of Total Harmonic Distortion (THD). In