April 2023 arXiv papers — page 12

Guilherme Nakassima, Marcio Gameiro

This work presents a framework for a-posteriori error-estimating algorithms for differential equations which combines the radii polynomial approach with Haar wavelets. By using Haar wavelets, we obtain recursive structures for the matrix representations of the differential operators and quadratic nonlinearities, which can be exploited for the radii polynomia

Hamza Kheddar, Yassine Himeur, Somaya Al-Maadeed, Abbes Amira

Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input

E. Aguilar-Ruiz, N. Fraija, A. Galván-G ámez

The BL Lac Markarian 501 exhibited two flaring activities in the very-high-energy (VHE) band in May 2009. The lack of correlation between X-rays and TeV gamma-rays without increasing in other bands suggested that more than one emission zone could be involved. Moreover, fast variability in the flaring state was observed, indicating that the emission zones res

Md Masudur Rahman, Yexiang Xue

The policy represented by the deep neural network can overfit the spurious features in observations, which hamper a reinforcement learning agent from learning effective policy. This issue becomes severe in high-dimensional state, where the agent struggles to learn a useful policy. Data augmentation can provide a performance boost to RL agents by mitigating t

Rui Zu, Gyunghyun Ryu, Kyle P. Kelley, Steven M. Baksa

Multifunctionality as a paradigm requires materials exhibiting multiple superior properties. Integrating second-order optical nonlinearity and large bandgap with piezoelectricity could, for example, enable broadband, strain-tunable photonics. Though very different phenomena at distinct frequencies, both second-order optical nonlinearity and piezoelectricity

Tianyi Huang, Shenghui Cheng, Stan Z. Li, Zhengjun Zhang

Clustering aims to group unlabelled samples based on their similarities. It has become a significant tool for the analysis of high-dimensional data. However, most of the clustering methods merely generate pseudo labels and thus are unable to simultaneously present the similarities between different clusters and outliers. This paper proposes a new framework c

Dvir Samuel, Rami Ben-Ari, Simon Raviv, Nir Darshan

Text-to-image diffusion models can synthesize high-quality images, but they have various limitations. Here we highlight a common failure mode of these models, namely, generating uncommon concepts and structured concepts like hand palms. We show that their limitation is partly due to the long-tail nature of their training data: web-crawled data sets are stron

Rishita Das, Sharath S. Girimaji

Velocity gradient tensor, $A_{ij}\equiv \partial u_i/\partial x_j$, in a turbulence flow field is modeled by separating the treatment of intermittent magnitude ($A = \sqrt{A_{ij}A_{ij}}$) from that of the more universal normalized velocity gradient tensor, $b_{ij} \equiv A_{ij}/A$. The boundedness and compactness of the $b_{ij}$-space along with its universa

Jennifer Biermann, Beth Anne Castellano, Marcella Manivel, Eden Petrucelli

We introduce a family of graphs, which we call down-left graphs, and study their combinatorial and algebraic properties. We show that members of this family are well-covered, $C_5$-free, and vertex decomposable. By applying a result of H\`a-Woodroofe and Moradi--Khosh-Ahang, the (Castelnuovo-Mumford) regularity of the associated edge ideals is the induced ma

Jana Maříková

We consider bipartite graphs definable in o-minimal structures, in which the edge relation $G$ is a finite union of graphs of certain measure-preserving maps. We establish a fact on the existence of definable matchings with few short augmenting paths. Under the additional assumptions that $G\subseteq [0,1]^n$ and 2-regularity, this yields the existence of de

Jeffrey Lazar, Stephan Meighen-Berger, Christian Haack, David Kim

Neutrino telescopes are gigaton-scale neutrino detectors comprised of individual light-detection units. Though constructed from simple building blocks, they have opened a new window to the Universe and are able to probe center-of-mass energies that are comparable to those of collider experiments. \prometheus{} is a new, open-source simulation tailored for th

Brenden W Hamilton, Timothy C. Germann

The rapid compaction of granular media results in localized heating that can induce chemical reactions, phase transformations, and melting. However, there are numerous mechanisms in play that can be dependent on a variety of microstructural features. Machine learning techniques such as neural networks offer a ubiquitous method to develop models for physical

Oscar Bulancea-Lindvall, Joel Davidsson, Rickard Armiento, Igor A. Abrikosov

The diamond nitrogen vacancy (NV) center remains an ever increasing topic of interest. At present, it is considered an ideal example of a solid-state qubit applicable in quantum communication, computing, and sensing alike. With its success, the search for defects that share or improve upon its advantageous features is an ongoing endeavor. By performing large

Nathaniel Eldredge, Maria Gordina, Enrico Le Donne, Sean Li

We study several notions of null sets on infinite-dimensional Carnot groups. We prove that a set is Aronszajn null if and only if it is null with respect to measures that are convolutions of absolutely continuous (CAC) measures on Carnot subgroups. The CAC measures are the non-abelian analogue of cube measures. In the case of infinite-dimensional Heisenberg-

Calford Otieno

Efficient third-order nonlinearities of the Zinc Oxide and Al-doped Zinc Oxide were studied by Third Harmonic Generation (Third Harmonic Generation) Maker fringes to establish the effect Aluminum of Aluminum doping(Al-doping) on the cubic nonlinearities. The addition of the Al-dopant to the Zinc Oxide crystal structure results in changes that affected the op

Hamed Zamani, Michael Bendersky

Dense retrieval models use bi-encoder network architectures for learning query and document representations. These representations are often in the form of a vector representation and their similarities are often computed using the dot product function. In this paper, we propose a new representation learning framework for dense retrieval. Instead of learning

Nur Imtiazul Haque, Maurice Ngouen, Mohammad Ashiqur Rahman, Selcuk Uluagac

Modern smart home control systems utilize real-time occupancy and activity monitoring to ensure control efficiency, occupants' comfort, and optimal energy consumption. Moreover, adopting machine learning-based anomaly detection models (ADMs) enhances security and reliability. However, sufficient system knowledge allows adversaries/attackers to alter sensor m

Sergey S. Kharintsev, Aleksey I. Noskov, Elina I. Battalova, Liat Katrivas

Photons do not carry sufficient momentum to induce indirect optical transitions in semiconducting materials such as silicon, necessitating the assistance of lattice phonons to conserve momentum. Compared to direct bandgap semiconductors, this renders silicon a less attractive material for a wide variety of optoelectronic applications. In this work, we introd

Alexander Kyuroson, Niklas Dahlquist, Nikolaos Stathoulopoulos, Vignesh Kottayam Viswanathan

Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing

Jiuyu Liu, Yi Ma, Rahim Tafazolli

We focus on the signal detection for large quasi-symmetric (LQS) multiple-input multiple-output (MIMO) systems, where the numbers of both service (M) and user (N) antennas are large and N/M tends to 1. It is challenging to achieve maximum-likelihood detection (MLD) performance with square-order complexity due to the ill-conditioned channel matrix. In the eme

Francesco Pogliano, Ann-Cecilie Larsen, Stephane Goriely, Lionel Siess

The $\gamma$-ray strength function and the nuclear level density of $^{167}$Ho have been extracted using the Oslo method from a $^{164}\text{Dy}(\alpha,p\gamma)^{167}$Ho experiment carried out at the Oslo Cyclotron Laboratory. The level density displays a shape that is compatible with %can be approximated with the constant temperature model in the quasiconti

Valdecy Pereira, Marcio Pereira Basilio, Carlos Henrique Tarjano Santos

Bibliometric and Scientometric analyses offer invaluable perspectives on the complex research terrain and collaborative dynamics spanning diverse academic disciplines. This paper presents pyBibX, a python library devised to conduct comprehensive bibliometric and scientometric analyses on raw data files sourced from Scopus, Web of Science, and PubMed, seamles

Gary Wang, Kyle Kastner, Ankur Bapna, Zhehuai Chen

Recently, a number of approaches to train speech models by incorpo-rating text into end-to-end models have been developed, with Mae-stro advancing state-of-the-art automatic speech recognition (ASR)and Speech Translation (ST) performance. In this paper, we expandour understanding of the resulting shared speech-text representationswith two types of analyses.

Shuangjiu Fu, Shabnam Raayai-Ardakani

Investigation of external flows past arbitrary objects requires access to the information in the boundary layer and the inviscid flow to paint a full picture of their characteristics. However, in laser diagnostic techniques such as particle image velocimetry (PIV), limitations like the size of the sample, field of view and magnification of the camera, and th

M. A. Shishkin, K. S. Kolegov, S. A. Pikin, B. I. Ostrovskii

We study theoretically internal flows in a small oblate droplet suspended on the circular frame. Marangoni convection arises due to a vertical temperature gradient across the drop and is driven by the surface tension variations at the free drop interface. Using the analytical basis for the solutions of Stokes equation in coordinates of oblate spheroid we hav

Kevin Denamganaï, Sondess Missaoui, James Alfred Walker

Natural languages are powerful tools wielded by human beings to communicate information. Among their desirable properties, compositionality has been the main focus in the context of referential games and variants, as it promises to enable greater systematicity to the agents which would wield it. The concept of disentanglement has been shown to be of paramoun

Guru Khalsa, Jeffrey Z. Kaaret, Nicole A. Benedek

We use theory and first-principles calculations to explore mechanisms for control of the translational and point group symmetries of crystals in ultrafast optical experiments. We focus in particular on mechanisms that exploit anharmonic (biquadratic) lattice couplings between a driven infrared-active phonon mode and other modes at arbitrary wave vector, whic

Martin Will, Michael Fleischhauer

We discuss the dynamics of the formation of a Bose polaron when an impurity is injected into a weakly interacting one-dimensional Bose condensate. While for small impurity-boson couplings this process can be described within the Froehlich model as generation, emission and binding of Bogoliubov phonons, this is no longer adequate if the coupling becomes stron

Bishal Deb

In the combinatorial theory of continued fractions, the Foata--Zeilberger bijection and its variants have been extensively used to derive various continued fractions enumerating several (sometimes infinitely many) simultaneous statistics on permutations (combinatorial model for factorials) and D-permutations (combinatorial model for Genocchi and median Genoc

Jose González-Abad, Jorge Baño-Medina

Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate change conditions remains questionable, mainly due to the stationarity assumption. We propose deep ensembles as a simpl

Dhyey Manish Rajani, Surya Pratap Singh, Rahul Kashyap Swayampakula

In this paper, we propose an advanced methodology for the detection of 3D objects and precise estimation of their spatial positions from a single image. Unlike conventional frameworks that rely solely on center-point and dimension predictions, our research leverages a deep convolutional neural network-based 3D object weighted orientation regression paradigm.

Lars Magnus Øverlier

The main result of this thesis is to show that there are only finitely many integers $n$ such that both $n$ and $d(n)$ are highly composite numbers at the same time, where $d(n)$ is the divisor function. Bertrand's postulate [4] is used many times throughout the thesis and allows us to write a proof that is as simple (and as short) as possible. This thesis i

Muhammad Umer, Robi Polikar

Class incremental learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, it has been shown that such approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of

Y. Shvartzvald, E. Waxman, A. Gal-Yam, E. O. Ofek

The Ultraviolet Transient Astronomy Satellite (ULTRASAT) is scheduled to be launched to geostationary orbit in 2026. It will carry a telescope with an unprecedentedly large field of view (204 deg$^2$) and NUV (230-290nm) sensitivity (22.5 mag, 5$\sigma$, at 900s). ULTRASAT will conduct the first wide-field survey of transient and variable NUV sources and wil

Michael P. Landry, Chi Cheuk Tsang

We strengthen the unpublished theorem of Gabai and Mosher that every depth one sutured manifold contains a very full dynamic branched surface by showing that the branched surface can be chosen to satisfy an additional property we call veering. To this end we prove that every endperiodic map admits a periodic splitting sequence of train tracks carrying its po

Héctor Martínez, Sandra Catalán, Francisco D. Igual, José R. Herrero

This paper advocates for an intertwined design of the dense linear algebra software stack that breaks down the strict barriers between the high-level, blocked algorithms in LAPACK (Linear Algebra PACKage) and the low-level, architecture-dependent kernels in BLAS (Basic Linear Algebra Subprograms). Specifically, we propose customizing the GEMM (general matrix

Nicholas A. Peters, Muneer Alshowkan, Joseph C. Chapman, Raphael C. Pooser

If continental-scale quantum networks are realized, they will provide the resources needed to fulfill the potential for dramatic advances in cybersecurity through quantum-enabled cryptography applications. We describe recent progress and where the US is headed as well as argue that we go one step further and jointly develop quantum and conventional cryptogra

Ian C. Matheson, Renu Malhotra, James T. Keane

Small solar system bodies have widely dispersed orbital poles, posing challenges to dynamical models of solar system origin and evolution. To characterize the orbit pole distribution of dynamical groups of small bodies it helps to have a functional form for a model of the distribution function. Previous studies have used the small-inclination approximation a

Dufour Gwenaëlle, Steven B. Charnley

We have investigated the chemistry of dense interstellar clouds and found new bistable solutions in the nitrogen and carbon chemistries. We identify the autocatalytic processes that are present in the pure, reduced, chemical networks and, as previously found for oxygen chemistry, that He$^+$ plays an important role. The applicability of these results to astr

Junxin Chen, Benjamin B. Lane, Su Direkci, Dhruva Ganapathy

The observables of a noisy quantum system can be estimated by appropriately filtering the records of their continuous measurement. Such filtering is relevant for state estimation and measurement-based quantum feedback control. It is therefore imperative that the observables estimated through a causal filter satisfy the Heisenberg uncertainty principle. In th

Jiazhao Li, Yijin Yang, Zhuofeng Wu, V. G. Vinod Vydiswaran

Textual backdoor attacks pose a practical threat to existing systems, as they can compromise the model by inserting imperceptible triggers into inputs and manipulating labels in the training dataset. With cutting-edge generative models such as GPT-4 pushing rewriting to extraordinary levels, such attacks are becoming even harder to detect. We conduct a compr

Yifeng Chu, Maxim Raginsky

We obtain an upper bound on the expected supremum of a Bernoulli process indexed by the image of an index set under a uniformly Lipschitz function class in terms of properties of the index set and the function class, extending an earlier result of Maurer for Gaussian processes. The proof makes essential use of recent results of Bednorz and Latala on the boun

Guandao Yang, Abhijit Kundu, Leonidas J. Guibas, Jonathan T. Barron

Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data. Generating NeRFs, however, remains difficult in many scenarios. For instance, training a NeRF with only a small number of views as supervision remains challenging since it is an under-constrained problem. In such settings, it calls

D. Matus Carrillo, M. Fellhauer, T. Boekholt, A. Stutz

We investigate the effects of an oscillating gas filament on the dynamics of its embedded stellar clusters. Motivated by recent observational constraints, we model the host gas filament as a cylindrically symmetrical potential, and the star cluster as a Plummer sphere. In the model, the motion of the filament will produce star ejections from the cluster, lea

Kangning Liu, Yu-Chuan Su, Wei, Hong

The one-shot talking-head synthesis task aims to animate a source image to another pose and expression, which is dictated by a driving frame. Recent methods rely on warping the appearance feature extracted from the source, by using motion fields estimated from the sparse keypoints, that are learned in an unsupervised manner. Due to their lightweight formulat

Martina Hofmanová, Umberto Pappalettera, Rongchan Zhu, Xiangchan Zhu

We identify a sufficient condition under which solutions to the 3D forced Navier--Stokes equations satisfy an $L^p$-in-time version of the Kolmogorov 4/5 law for the behavior of the averaged third order longitudinal structure function along the vanishing viscosity limit. The result has a natural probabilistic interpretation: the predicted behavior is observe

D. J. McLeod, C. T. Donnan, R. J. McLure, J. S. Dunlop

We present a new determination of the evolving galaxy UV luminosity function (LF) over the redshift range $9.5<z<12.5$ based on a wide-area ($>250$ arcmin$^2$) data set of JWST NIRCam near-infrared imaging assembled from thirteen public JWST surveys. Our relatively large-area search allows us to uncover a sample of 61 robust $z>9.5$ candidates detected at $\

Marcin Bownik, John Jasper

Given a self-adjoint operator $T$ on a separable infinite-dimensional Hilbert space we study the problem of characterizing the set $\mathcal D(T)$ of all possible diagonals of $T$. For operators $T$ with at least two points in their essential spectrum $\sigma_{ess}(T)$, we give a complete characterization of $\mathcal D(T)$ for the class of self-adjoint oper

Chen Quan, Yunghsiang S. Han, Baocheng Geng, Pramod K. Varshney

This paper investigates distributed detection of sparse stochastic signals with quantized measurements under Byzantine attacks. Under this type of attack, sensors in the networks might send falsified data to degrade system performance. The Bernoulli-Gaussian (BG) distribution in terms of the sparsity degree of the stochastic signal is utilized for modeling t

Hamam Mokayed, Amirhossein Nayebiastaneh, Kanjar De, Stergios Sozos

Vehicle detection and recognition in drone images is a complex problem that has been used for different safety purposes. The main challenge of these images is captured at oblique angles and poses several challenges like non-uniform illumination effect, degradations, blur, occlusion, loss of visibility, etc. Additionally, weather conditions play a crucial rol

Genly Leon, Miguel A. García-Aspeitia, Guillermo Fernandez-Anaya, Alberto Hernández-Almada

Recently, a new field of study called fractional cosmology has emerged. It uses fractional calculus to modify the standard derivative equations and change the Friedmann equations. The evolution of cosmic species densities is also affected by the $\mu$ fractional parameter and the age of the Universe $t_0$. This new approach to cosmology modifies the Friedman

Benedikt Dornauer, Michael Felderer, Johannes Weinzerl, Mircea-Cristian Racasan

Technical debt is often the result of Short Run decisions made during code development, which can lead to long-term maintenance costs and risks. Hence, evaluating the progression of a project and understanding related code quality aspects is essential. Fortunately, the prioritization process for addressing technical debt can be expedited with code analysis t

Burak Bartan, Haoming Li, Harris Teague, Christopher Lott

The deployment and training of neural networks on edge computing devices pose many challenges. The low memory nature of edge devices is often one of the biggest limiting factors encountered in the deployment of large neural network models. Tensor rematerialization or recompute is a way to address high memory requirements for neural network training and infer

Hamam Mokayed, Palaiahnakote Shivakumara, Lama Alkhaled, Rajkumar Saini

Vehicle detection in real-time scenarios is challenging because of the time constraints and the presence of multiple types of vehicles with different speeds, shapes, structures, etc. This paper presents a new method relied on generating a confidence map-for robust and faster vehicle detection. To reduce the adverse effect of different speeds, shapes, structu

Gaston Giribet, Juan Laurnagaray, Pedro Schmied

In many cases, the near-horizon geometry encodes sufficient information to compute conserved charges of a gravitational solution, including thermodynamic quantities. These charges are Noether charges associated to asymptotic isometries that preserve appropriate boundary conditions at the future horizon. For isolated, compact horizons these charges turn out t

Barza Nisar, Hruday Vishal Kanna Anand, Steven L. Waslander

Accurate 3D object detection in all weather conditions remains a key challenge to enable the widespread deployment of autonomous vehicles, as most work to date has been performed on clear weather data. In order to generalize to adverse weather conditions, supervised methods perform best if trained from scratch on all weather data instead of finetuning a mode

Jose González-Abad, Jorge Baño-Medina, Ignacio Heredia Cachá

Deep Learning has recently emerged as a perfect prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data. Despite their promising results to reproduce the observed local variability, they are based on the estimation of independent distributions at each location, which leads to deficient spatial structures, es

Sriniwas Pandey, Yiding Cao, Yingjun Dong, Minjun Kim

Studying extreme ideas in routine choices and discussions is of utmost importance to understand the increasing polarization in society. In this study, we focus on understanding the generation and influence of extreme ideas in routine conversations which we label "eccentric" ideas. The eccentricity of any idea is defined as the deviation of that idea from the

Are Austad, Eduard Ortega, Mathias Palmstrøm

We investigate property $RD_p$ for \'etale groupoids and apply it to $K$-theory of reduced groupoid $L^p$-operator algebras. In particular, under the assumption of polynomial growth, we show that the $K$-theory groups for a reduced groupoid $L^p$-operator algebra are independent of $p\in (1, \infty)$. We apply the results to coarse groupoids and graph groupo

Shaul Barkan

Fix a prime $p$ and a chromatic height $h$. We prove that the homotopy $(k,1)$-category of $L_h$-local spectra $\mathrm{h}_k\big(\mathrm{Sp}_{p,h}\big)$ is algebraic as a symmetric monoidal category when $p > O(h^2+kh)$. To achieve this, we develop a general tool for investigating such algebraicity questions, based on an operadic variant of Goerss-Hopkins ob

David Alonso del Barrio, Daniel Gatica-Perez

Identifying the frames of news is important to understand the articles' vision, intention, message to be conveyed, and which aspects of the news are emphasized. Framing is a widely studied concept in journalism, and has emerged as a new topic in computing, with the potential to automate processes and facilitate the work of journalism professionals. In this p

Nhat-Minh Le-Phan, Minh Hoang Trinh, Phuoc Doan Nguyen

In this paper, we consider a randomized gossip algorithm for the bearing-based network localization problem. Let each sensor node be able to obtain the bearing vectors and communicate its position estimates with several neighboring agents. Each update involves two agents, and the update sequence follows a stochastic process. Under the assumption that the net

Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, Ya Zhang

Recently, Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this

Ramakanta Chapai, P. V. Sreenivasa Reddy, Lingyi Xing, David E. Graf

PdTe is a superconductor with Tc ~4.25 K. Recently, evidence for bulk-nodal and surface-nodeless gap features has been reported in PdTe [Yang et al., Phys. Rev. Lett. 130, 046402 (2023)]. Here, we investigate the physical properties of PdTe in both the normal and superconducting states via specific heat and magnetic torque measurements and first-principles c

Wei-Chun Jao, Todd J. Henry, Russel J. White, Azmain H. Nisak

Since identifying the gap in the H-R Diagram (HRD) marking the transition between partially and fully convective interiors, a unique type of slowly pulsating M dwarf has been proposed. These unstable M dwarfs provide new laboratories in which to understand how changing interior structures can produce potentially observable activity at the surface. In this wo

Mahnoor Shahid

Detection and mitigation of critical web vulnerabilities and attacks like cross-site scripting (XSS), and cross-site request forgery (CSRF) have been a great concern in the field of web security. Such web attacks are evolving and becoming more challenging to detect. Several ideas from different perspectives have been put forth that can be used to improve the

Alice Zurlo, Raffaele Gratton, Sebastián Pérez, Lucas Cieza

The demographic of circumstellar disks, the birthplaces of planets, is diverse and rich in disks featuring rings, gaps, spirals, filaments, and arcs. Many studies revealing these disk structures have focused on objects around single stars and disks in isolation. The scenario is more complex if binarity or multiplicity is involved; most stars are part of mult

Liam O'Brien, Gil Refael

Identifying and measuring the "localization length'' in many-body systems in the vicinity of a many-body localization transition is difficult. Following Hatano and Nelson, a recent work (Heuben, White, Refael, PRB 103, 064201 (2021)) introduced an "imaginary vector potential'' to a disordered ring of interacting fermions, in order to define a many-body local

Jon Ortuzar, Jose Ignacio Pascual, F. Sebastian Bergeret, Miguel A. Cazalilla

We argue that the formation of Yu-Shiba-Rusinov excitations in proximitized thin films is largely mediated by a type of Andreev-bound state named after de Gennes and Saint James. This is shown by studying an experimentally motivated model and computing the overlap of the wave functions of these two subgap states. We find the overlap stays close to unity even

Nguyen Anh Tu, Hoang Thi Thu Uyen, Tu Minh Phuong, Ngo Xuan Bach

In this paper, we propose using deep neural networks to extract important information from Vietnamese legal questions, a fundamental task towards building a question answering system in the legal domain. Given a legal question in natural language, the goal is to extract all the segments that contain the needed information to answer the question. We introduce

Jenny Xu, Steven L. Waslander

Current LiDAR-based 3D object detectors for autonomous driving are almost entirely trained on human-annotated data collected in specific geographical domains with specific sensor setups, making it difficult to adapt to a different domain. MODEST is the first work to train 3D object detectors without any labels. Our work, HyperMODEST, proposes a universal met

Nathan Perchikov, Đorđe Vujić, Branimir Bajac, Andrea Alù

We study pressure acoustic propagation in asymmetric spoof-fluid-spoof acoustic waveguides and its potential application in acoustic gas sensors. First, a stable and efficient analytical method is established for fast calculation of the dispersion curves based on spectral expansion and enforcement of continuity between segments at suitable collocation points

Jakub Nevlacil, Simon Bilik, Karel Horak

A declining honeybee population could pose a threat to a food resources of the whole world one of the latest trend in beekeeping is an effort to monitor a health of the honeybees using various sensors and devices. This paper participates on a development on one of these devices. The aim of this paper is to make an upgrades and improvement of an in-developmen

E. Cabrera, D. Cogollo, C. A. de S. Pires

Seesaw mechanisms are the simplest and the most elegant way of generating small masses for the active neutrinos $(m_\nu)$. In these mechanisms $m_\nu$ is inversely proportional to the lepton number breaking scale $(M)$ that, in the particular case of the type I seesaw mechanism, is the Majorana mass of the right-handed neutrinos. In the canonical case right-

Ivar Svalheim Haugerud, Pranay Jaiswal, Christoph A. Weber

Recent experimental studies suggest that wet-dry cycles and coexisting phases can each strongly alter chemical processes. The mechanisms of why and to which degree chemical processes are altered when subject to evaporation and condensation are unclear. To close this gap, we developed a theoretical framework for non-dilute chemical reactions subject to non-eq

Stefanos Giaremis, Maria Clelia Righi

Boron doped diamond is extensively studied for its use in tribological and electrochemical applications due to its remarkable physical and chemical properties. However, ambient conditions play a major role to its macroscopically observed behavior. In this study, the fundamental interactions between the low Miller index (001), (110) and (111) B-doped diamond

Óscar del Río, Alexey Vladimirov

The partons' transverse momentum can be explored with QCD lattice simulations by studying the quasi-transverse-momentum-dependent parton distribution functions (qTMDPDFs), which are factorized in terms of physical TMDPDFs and soft factors in the limit of the large hadron's momentum. We present the next-to-next-to-leading order (NNLO) calculation of the coeff

Elie Bermot, Christa Zoufal, Michele Grossi, Julian Schuhmacher

The standard model (SM) of particle physics represents a theoretical paradigm for the description of the fundamental forces of nature. Despite its broad applicability, the SM does not enable the description of all physically possible events. The detection of events that cannot be described by the SM, which are typically referred to as anomalous, and the rela

Yu-Min Hu, Zhong Wang

Green's functions of non-Hermitian systems play a fundamental role in various dynamical processes. Because non-Hermitian systems are sensitive to boundary conditions due to the non-Hermitian skin effect, open-boundary Green's functions are closely related to the non-Bloch band theory. While the exact formula of open-boundary Green's functions in single-band

Victor Knapp-Perez, Xiang-Gan Liu, Hans Peter Nilles, Saul Ramos-Sanchez

Modular flavor symmetries provide us with a very compelling approach to the flavor problem. It has been argued that moduli values close to some special values like $\tau=i$ or $\tau=\omega$ provide us with the best fits to data. We point out that the presence of hidden "matter" fields, needed to uplift symmetric AdS vacua, gives rise to a dynamical mechanism

Aamna Ahmed, Abee Nelson, Ankur Raina, Auditya Sharma

In this work, we map the phase diagrams of one-dimensional quasiperiodic models using artificial neural networks. We observe that the multi-class classifier precisely distinguishes the various phases, namely the delocalized, multifractal, and localized phases, when trained on the eigenstates of the long-range Aubry-Andr\'e Harper (LRH) model. Additionally, w

Simon-Dominik Börner, Christoph Berke, David P. DiVincenzo, Simon Trebst

The development of quantum computing hardware is facing the challenge that current-day quantum processors, comprising 50-100 qubits, already operate outside the range of quantum simulation on classical computers. In this paper we demonstrate that the simulation of classical limits can be a potent diagnostic tool potentially mitigating this problem. As a test

Giorgos Korkidis, Vasiliki Pavlidou, Konstantinos Tassis

The mean matter density within the turnaround radius, which is the boundary that separates a nonexpanding structure from the Hubble flow, was recently proposed as a novel cosmological probe. According to the spherical collapse model, the evolution with cosmic time of this turnaround density, $\rm \rho_{ta}(z)$, can be used to determine both $\rm \Omega_m$ an

Kaixiang Su, Nayan Myerson-Jain, Chong Wang, Chao-Ming Jian

We aim to address the following question: if we start with a quantum state with a spontaneously broken higher-form symmetry, what is the fate of the system under weak local quantum measurements? We demonstrate that under certain conditions, a phase transition can be driven by weak measurements, which suppresses the spontaneous breaking of the 1-form symmetry

Jonah C. Rose, Paul Torrey, Francisco Villaescusa-Navarro, Mark Vogelsberger

We present a new suite of over 1,500 cosmological N-body simulations with varied Warm Dark Matter (WDM) models ranging from 2.5 to 30 keV. We use these simulations to train Convolutional Neural Networks (CNNs) to infer WDM particle masses from images of DM field data. Our fiducial setup can make accurate predictions of the WDM particle mass up to 7.5 keV at

Jinning Liang, Fangzhou Jiang, Shany Danieli, Andrew Benson

The properties of globular clusters (GCs) contain valuable information of their host galaxies and dark-matter halos. In the remarkable example of ultra-diffuse galaxy, NGC5846-UDG1, the GC population exhibits strong radial mass segregation, indicative of dynamical-friction-driven orbital decay, which opens the possibility of using imaging data alone to const

Hiroki Takeda, Yusuke Manita, Hidetoshi Omiya, Takahiro Tanaka

Scalar polarization modes of gravitational waves, which are often introduced in the context of the viable extension of gravity, have been actively searched. However, couplings of the scalar modes to the matter are strongly constrained by the fifth-force experiments. Thus, the amplitude of scalar polarization in the observed gravitational-wave signal must be

Eric R. Coughlin, Matt Nicholl

Tidal disruption events (TDEs), in which a star is destroyed by the gravitational field of a supermassive black hole (SMBH), are being observed at a high rate owing to the advanced state of survey science. One of the properties of TDEs that is measured with increasing statistical reliability is the TDE luminosity function, $d\dot{N}_{\rm TDE}/dL$, which is t

Junke Wang, Dongdong Chen, Chong Luo, Xiyang Dai

Existing deep video models are limited by specific tasks, fixed input-output spaces, and poor generalization capabilities, making it difficult to deploy them in real-world scenarios. In this paper, we present our vision for multimodal and versatile video understanding and propose a prototype system, \system. Our system is built upon a tracklet-centric paradi

Sumith Kulal, Tim Brooks, Alex Aiken, Jiajun Wu

We study the problem of inferring scene affordances by presenting a method for realistically inserting people into scenes. Given a scene image with a marked region and an image of a person, we insert the person into the scene while respecting the scene affordances. Our model can infer the set of realistic poses given the scene context, re-pose the reference

Ta Duc Huy, Nguyen Anh Tu, Tran Hoang Vu, Nguyen Phuc Minh

Existing medical text datasets usually take the form of question and answer pairs that support the task of natural language generation, but lacking the composite annotations of the medical terms. In this study, we publish a Vietnamese dataset of medical questions from patients with sentence-level and entity-level annotations for the Intent Classification and

Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng

Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation, with the usual approach being to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. A recent seminal work (InvLINT) demons

Tsai-Shien Chen, Chieh Hubert Lin, Hung-Yu Tseng, Tsung-Yi Lin

Recent advancements in diffusion models have greatly improved the quality and diversity of synthesized content. To harness the expressive power of diffusion models, researchers have explored various controllable mechanisms that allow users to intuitively guide the content synthesis process. Although the latest efforts have primarily focused on video synthesi

Anand Bhattad, Viraj Shah, Derek Hoiem, D. A. Forsyth

StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables (GAN inversion) remains a challenge. Existing GAN inversion methods struggle to maintain editing directions and produce realistic results. To address these limitations, we propose

Minghao Wu, Abdul Waheed, Chiyu Zhang, Muhammad Abdul-Mageed

Large language models (LLMs) with instruction fine-tuning demonstrate superior generative capabilities. However, these models are resource-intensive. To alleviate this issue, we explore distilling knowledge from instruction-tuned LLMs into much smaller ones. To this end, we carefully develop a large set of 2.58M instructions based on both existing and newly-

Jiteng Mu, Shen Sang, Nuno Vasconcelos, Xiaolong Wang

While NeRF-based human representations have shown impressive novel view synthesis results, most methods still rely on a large number of images / views for training. In this work, we propose a novel animatable NeRF called ActorsNeRF. It is first pre-trained on diverse human subjects, and then adapted with few-shot monocular video frames for a new actor with u

Ronghuan Wu, Wanchao Su, Kede Ma, Jing Liao

Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG content can be challenging for users due to the steep learning curve required to understand SVG grammars or get familiar with professional editing software. Recent advancements in

Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr

Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators and revise our interpretations as listeners. As language models (LMs) are increasingly employed as dialogue interfaces and writing aids, handling ambiguous language is critical to

Matthew Russell, Peng Wang

Deep Learning (DL) can diagnose faults and assess machine health from raw condition monitoring data without manually designed statistical features. However, practical manufacturing applications remain extremely difficult for existing DL methods. Machine data is often unlabeled and from very few health conditions (e.g., only normal operating data). Furthermor

Sajani Vithana, Zhusheng Wang, Sennur Ulukus

Private information retrieval (PIR) is a privacy setting that allows a user to download a required message from a set of messages stored in a system of databases without revealing the index of the required message to the databases. PIR was introduced under computational privacy guarantees, and is recently re-formulated to provide information-theoretic guaran