arXiv papers from November 2023

Tonatiuh Matos, Luis A. Ureña-López, Jae-Weon Lee

The Scalar Field Dark Matter model has been known in various ways throughout its history; Fuzzy, BEC, Wave, Ultralight, Axion-like Dark Matter, etc. All of them consist in proposing that the dark matter of the universe is a spinless field $\Phi$ that follows the Klein-Gordon (KG) equation of motion $\Box\Phi-dV/d\Phi=0$, for a given scalar field potential $V

Nathan Smith, Jennifer E. Andrews, Peter Milne, Alexei V. Filippenko

We present photometry and spectroscopy of the slowly evolving superluminous Type IIn SN2015da. SN2015da is extraordinary for its very high peak luminosity, and also for sustaining a high luminosity for several years. Even at 8\,yr after explosion, SN2015da remains as luminous as the peak of a normal SNII-P. The total radiated energy integrated over this time

Quang-Hung Luu, Thai M. Nguyen, Nan Zheng, Hai L. Vu

Connected and automated vehicles (CAV) are expected to deliver a much safer, more efficient, and eco-friendlier mobility. Being an indispensable component of the future transportation, their key driving features of CAVs include not only the automated functionality but also the cooperative capability. Despite the CAVs themselves are emerging and active resear

Haithem Turki, Michael Zollhöfer, Christian Richardt, Deva Ramanan

Neural Radiance Fields (NeRFs) can be dramatically accelerated by spatial grid representations. However, they do not explicitly reason about scale and so introduce aliasing artifacts when reconstructing scenes captured at different camera distances. Mip-NeRF and its extensions propose scale-aware renderers that project volumetric frustums rather than point s

H. A. Vinutha, Manon Marchand, Marco Caggioni, Vishwas V. Vasisht

Cessation of flow in simple yield stress fluids results in a complex stress relaxation process that depends on the preceding flow conditions and leads to finite residual stresses. To assess the microscopic origin of this phenomenon, we combine experiments with largescale computer simulations, exploring the behavior of jammed suspensions of soft repulsive par

Zhuoran Zheng, Boxue Xiao

Currently, to further improve visual enjoyment, Ultra-High-Definition (UHD) images are catching wide attention. Here, UHD images are usually referred to as having a resolution greater than or equal to $3840 \times 2160$. However, since the imaging equipment is subject to environmental noise or equipment jitter, UHD images are prone to contrast degradation, b

Jinhua Liang, Xubo Liu, Wenwu Wang, Mark D. Plumbley

The auditory system plays a substantial role in shaping the overall human perceptual experience. While prevailing large language models (LLMs) and visual language models (VLMs) have shown their promise in solving a wide variety of language and vision understanding tasks, only a few of them can be generalised to the audio domain without compromising their dom

Alex Havrilla, Kevin Rojas, Wenjing Liao, Molei Tao

Diffusion generative models have achieved remarkable success in generating images with a fixed resolution. However, existing models have limited ability to generalize to different resolutions when training data at those resolutions are not available. Leveraging techniques from operator learning, we present a novel deep-learning architecture, Dual-FNO UNet (D

Alex Lewandowski, Haruto Tanaka, Dale Schuurmans, Marlos C. Machado

Loss of plasticity is a phenomenon in which neural networks lose their ability to learn from new experience. Despite being empirically observed in several problem settings, little is understood about the mechanisms that lead to loss of plasticity. In this paper, we offer a consistent explanation for loss of plasticity: Neural networks lose directions of curv

Yaman Jandali, Nojan Sheybani, Farinaz Koushanfar

With the rising use of aircrafts for operations ranging from disaster-relief to warfare, there is a growing risk of adversarial attacks. Malicious entities often only require the location of the aircraft for these attacks. Current satellite-aircraft communication and tracking protocols put aircrafts at risk if the satellite is compromised, due to computation

Dániel Gábor Simon

Let $P$ be a set of $n$ points in $\mathbb{R}^d$, in general position. We remove all of them one by one, in each step erasing one vertex of the convex hull of the current remaining set. Let $g_d(P)$ denote the number of different removal orders we can attain while erasing all points of $P$ this way, and let $g_d(n)$ be the \emph{minimum} of $g_d(P)$ over all

Martin Bichler, Alok Gupta, Matthias Oberlechner

The transition of display ad exchanges from second-price auctions (SPA) to first-price auctions (FPA) has raised questions about its impact on revenue. Auction theory predicts the revenue equivalence between these two auction formats. However, display ad auctions are different from standard models in auction theory. First, automated bidding agents cannot eas

Ji Hoon Lee

We study to what extent, and in what form, the notion of gauge-string duality may persist at finite $N$. It is shown, in the half-BPS sector, that the states of D3 giant graviton branes in $\mathrm{AdS}_5 \times S^5$ are holographically dual to certain auxiliary ghosts that compensate for finite $N$ trace relations in $U(N)$ $\mathcal{N}=4$ super Yang-Mills.

Aaron Dunton

In this paper, we test various models of wastewater infrastructure for risk analysis and compare their performance. While many representations are available, existing studies do not consider selection of the appropriate model for risk analysis. In this paper, we define two characteristics of wastewater models: the network granularity and the fidelity of the

Abel de Burgos, Sergio Simón-Díaz, Miguel A. Urbaneja, Joachim Puls

Blue supergiants (BSGs) are key objects for understanding the evolution of massive stars. However, discrepancies between theoretical predictions and empirical observations have opened up important questions yet to be answered. Studying statistically significant and unbiased samples of BSGs can help to improve the situation. We aim to perform a homogeneous an

Anton Ratnarajah, Sreyan Ghosh, Sonal Kumar, Purva Chiniya

Accurate estimation of Room Impulse Response (RIR), which captures an environment's acoustic properties, is important for speech processing and AR/VR applications. We propose AV-RIR, a novel multi-modal multi-task learning approach to accurately estimate the RIR from a given reverberant speech signal and the visual cues of its corresponding environment. AV-R

Scott T. Chapman, Joshua Jang, Jason Mao, Skyler Mao

Let $M$ be a Puiseux monoid, that is, a monoid consisting of nonnegative rationals (under addition). A nonzero element of $M$ is called an atom if its only decomposition as a sum of two elements in $M$ is the trivial decomposition (i.e., one of the summands is $0$), while a nonzero element $b \in M$ is called atomic if it can be expressed as a sum of finitel

Dina Bashkirova, Arijit Ray, Rupayan Mallick, Sarah Adel Bargal

Professional artists, photographers, and other visual content creators use object relighting to establish their photo's desired effect. Unfortunately, manual tools that allow relighting have a steep learning curve and are difficult to master. Although generative editing methods now enable some forms of image editing, relighting is still beyond today's capabi

Jamie M. Taylor, Thomas G. Fai, Epifanio G. Virga, Xiaoyu Zheng

In this paper, we model the configurations of a system of hard rods by viewing each rod in a cell formed by its neighbors. By minimizing the free energy in the model and performing molecular dynamics, where, in both cases, the shape of the cell is a free parameter, we obtain the equilibrium orientational order parameter, free energy and pressure of the syste

Jordan Barrett, Bogumil Kaminski, Pawel Pralat, Francois Theberge

The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs similar to the well-known LFR model but it is faster and can be investigated analytically. In this paper, we show that the ABCD model exhibits some interesti

Nuri Korhan, Ceren Öner

Catastrophic forgetting is a significant challenge in the field of machine learning, particularly in neural networks. When a neural network learns to perform well on a new task, it often forgets its previously acquired knowledge or experiences. This phenomenon occurs because the network adjusts its weights and connections to minimize the loss on the new task

Mark Ariel Levin

Quantum algorithms for unstructured search problems rely on the preparation of a uniform superposition, traditionally achieved through Hadamard gates. However, this incidentally creates an auxiliary search space consisting of nonsensical answers that do not belong in the search space and reduce the efficiency of the algorithm due to the need to neglect, un-c

Xuan-Bac Nguyen, Xin Li, Pawan Sinha, Samee U. Khan

Human perception plays a vital role in forming beliefs and understanding reality. A deeper understanding of brain functionality will lead to the development of novel deep neural networks. In this work, we introduce a novel framework named Brainformer, a straightforward yet effective Transformer-based framework, to analyze Functional Magnetic Resonance Imagin

Fritz Grimpen, Anastasios Stefanou

Given a multiparameter filtration of simplicial complexes, we consider the problem of explicitly constructing generators for the multipersistent homology groups with arbitrary PID coefficients. We propose the use of spanning trees as a tool to identify such generators by introducing a condition for persistent spanning trees, which is accompanied by an existe

Tanya Marwah, Ashwini Pokle, J. Zico Kolter, Zachary C. Lipton

Data-driven machine learning approaches are being increasingly used to solve partial differential equations (PDEs). They have shown particularly striking successes when training an operator, which takes as input a PDE in some family, and outputs its solution. However, the architectural design space, especially given structural knowledge of the PDE family of

Maddalena Torricelli, Mauro Martino, Andrea Baronchelli, Luca Maria Aiello

Generative AI for the creation of images is becoming a staple in the toolkit of digital artists and visual designers. The interaction with these systems is mediated by \emph{prompting}, a process in which users write a short text to describe the desired image's content and style. The study of prompts offers an unprecedented opportunity to gain insight into t

Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin

Graph contrastive learning has shown great promise when labeled data is scarce, but large unlabeled datasets are available. However, it often does not take uncertainty estimation into account. We show that a variational Bayesian neural network approach can be used to improve not only the uncertainty estimates but also the downstream performance on semi-super

Charles C. Onu, Hemanth K. Sheetha, Arsenii Gorin, Doina Precup

The issue of domain shift remains a problematic phenomenon in most real-world datasets and clinical audio is no exception. In this work, we study the nature of domain shift in a clinical database of infant cry sounds acquired across different geographies. We find that though the pitches of infant cries are similarly distributed regardless of the place of bir

Jeffrey Brock, Franco Vargas Pallete

We extend the notion of Epstein maps to conformal metrics on submanifolds of the unit sphere $\mathbb{S}^n=\partial_\infty\mathbb{H}^{n+1}$. Using this construction for curves in $\mathbb{S}^2$, we define the W-volume for conformal metrics on domains in $\overline{\mathbb{C}}=\mathbb{S}^2$ with round circles as boundaries. We show that the W-volume is a real

Bojan Crnković, Jerko Škifić, Tina Bosner

Image zooming or upsampling is a widely used tool in image processing and an essential step in many algorithms. Upsampling increases the number of pixels and introduces new information into the image, which can lead to numerical effects such as ringing artifacts, aliasing effects, and blurring of the image. In this paper, we propose an efficient polynomial i

Xin Yang, Elyssa Sliheet, Reece Iriye, Daniel Reynolds

The Poisson-Boltzmann (PB) model governs the electrostatics of solvated biomolecules, i.e., potential, field, energy, and force. These quantities can provide useful information about protein properties, functions, and dynamics. By considering the advantages of current algorithms and computer hardware, we focus on the parallelization of the treecode-accelerat

Ergun Akleman, Alan Freed

In this preliminary study, we provide two methods for estimating the gradients of functions of real value. Both methods are built on derivative estimations that are calculated using the standard method or the Squire-Trapp method for any given direction. Gradients are computed as the average of derivatives in uniformly sampled directions. The first method use

Aranya Lahiri, Claus Sorensen, Matthias Strauch

Let $(G,\omega)$ be a $p$-saturated group and $K/\mathbb{Q}_p$ a finite extension. In this paper we introduce the space of $K$-valued overconvergent functions $\mathcal{C}^\dagger(G,K)$. In the process we promote the rigid analytic group attached to $(G,\omega)$ in a previous work of the first two authors to a dagger group. A main result of this article is t

Thomas G. Rizzo

Kinetic mixing of the dark photon, the gauge boson of a hidden $U(1)_D$, with the Standard Model (SM) gauge fields to induce an interaction between ordinary matter and dark matter (DM) at 1-loop requires the existence of portal matter (PM) fields having both dark and SM charges. As discussed in earlier work, these same PM fields can also lead to other loop-l

Orestis Loukas, Ho Ryun Chung

Experimental and observational studies often lead to spurious association between the outcome and independent variables describing the intervention, because of confounding to third-party factors. Even in randomized clinical trials, confounding might be unavoidable due to small sample sizes. Practically, this poses a problem, because it is either expensive to

Imane Koulali, M. Taner Eskil

In this study, we propose a novel motif-based approach for unsupervised textile anomaly detection that combines the benefits of traditional convolutional neural networks with those of an unsupervised learning paradigm. It consists of five main steps: preprocessing, automatic pattern period extraction, patch extraction, features selection and anomaly detectio

Mena Shenouda, Eyjólfur Gudmundsson, Feng Li, Christopher M. Straus

Malignant pleural mesothelioma (MPM) is the most common form of mesothelioma. To assess response to treatment, tumor measurements are acquired and evaluated based on a patient's longitudinal computed tomography (CT) scans. Tumor volume, however, is the more accurate metric for assessing tumor burden and response. Automated segmentation methods using deep lea

Naoki Kikugawa, Shinya Uji, Taichi Terashima

In the $R$Al(Si,Ge) ($R$: lanthanides) family, both spatial inversion and time-reversal symmetries are broken. This may offer opportunities to study Weyl-fermion physics in nontrivial spin structures emerging from a noncentrosymmetric crystal structure. In this study, we investigated the anomalous Hall effect (AHE) in NdAlGe via magnetotransport, magnetizati

Yoo Jung Kim, Michael P. Fitzgerald, Jonathan Lin, Steph Sallum

Photonic lanterns (PLs) are tapered waveguides that gradually transition from a multi-mode fiber geometry to a bundle of single-mode fibers. In astronomical applications, PLs can efficiently couple multi-mode telescope light into a multi-mode fiber entrance and convert it into multiple single-mode beams. The output beams are highly stable and suitable for fe

Linzi Xing, Quan Tran, Fabian Caba, Franck Dernoncourt

Video topic segmentation unveils the coarse-grained semantic structure underlying videos and is essential for other video understanding tasks. Given the recent surge in multi-modal, relying solely on a single modality is arguably insufficient. On the other hand, prior solutions for similar tasks like video scene/shot segmentation cater to short videos with c

Shane Sparkes, Erika Garcia, Lu Zhang

This paper establishes the functional average as an important estimand for causal inference. The significance of the estimand lies in its robustness against traditional issues of confounding. We prove that this robustness holds even when the probability distribution of the outcome, conditional on treatment or some other vector of adjusting variables, differs

Thais Maria Dalbelo, Regilene Oliveira, Otavio Henrique Perez

Given a planar polynomial vector field $X$ with a fixed Newton polytope $\mathcal{P}$, we prove (under some non degeneracy conditions) that the monomials associated to the upper boundary of $\mathcal{P}$ determine (under topological equivalence) the phase portrait of $X$ in a neighbourhood of boundary of the Poincar\'e--Lyapunov disk. This result can be seen

Jean-François Tremblay, David Meger, Francois Hogan, Gregory Dudek

Robots operating in an open world will encounter novel objects with unknown physical properties, such as mass, friction, or size. These robots will need to sense these properties through interaction prior to performing downstream tasks with the objects. We propose a method that autonomously learns tactile exploration policies by developing a generative world

Isidora Chara Tourni, Derry Wijaya

With the advent of the Transformer architecture, Neural Machine Translation (NMT) results have shown great improvement lately. However, results in low-resource conditions still lag behind in both bilingual and multilingual setups, due to the limited amount of available monolingual and/or parallel data; hence, the need for methods addressing data scarcity in

A. Rikhter, D. N. Basov, M. M. Fogler

We present a basic framework for modeling collective mode effects in photocurrent measurements performed on two-dimensional materials using nano-optical scanned probes. We consider photothermal, photovoltaic, and bolometric contributions to the photocurrent. We show that any one of these can dominate depending on frequency, temperature, applied bias, and sam

Jinxin Zhou, Tianyu Ding, Tianyi Chen, Jiachen Jiang

We present DREAM, a novel training framework representing Diffusion Rectification and Estimation Adaptive Models, requiring minimal code changes (just three lines) yet significantly enhancing the alignment of training with sampling in diffusion models. DREAM features two components: diffusion rectification, which adjusts training to reflect the sampling proc

Vincent Roulet, Atish Agarwala, Fabian Pedregosa

Recent empirical work has revealed an intriguing property of deep learning models by which the sharpness (largest eigenvalue of the Hessian) increases throughout optimization until it stabilizes around a critical value at which the optimizer operates at the edge of stability, given a fixed stepsize (Cohen et al, 2022). We investigate empirically how the shar

Neetal Neel

Kakimizu complexes have been found for several classes of links. O.Kakimizu found the Kakimizu complexes of knots with crossing number less than or equal to 10. Hatcher and Thurston found the 0-skeleton of the Kakimizu complex of 2-bridge links. Sakuma later generalized the result for special arborescent links and found the Kakimizu complexes for the same. J

Chengjie Lu, Shaukat Ali, Tao Yue

Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, named EpiTESTER, by taking inspiration from epigenetics, wh

Adam Kwela

We study Egorov ideals, that is ideals on $\omega$ for which the Egorov's theorem for ideal versions of pointwise and uniform convergences holds. We show that a non-pathological $\bf{\Sigma^0_2}$ ideal is Egorov if and only if it is countably generated. In particular, up to isomorphism, there are only three non-pathological $\bf{\Sigma^0_2}$ Egorov ideals. O

Kunyi Li, Michael Niemeyer, Nassir Navab, Federico Tombari

In recent years, coordinate-based neural implicit representations have shown promising results for the task of Simultaneous Localization and Mapping (SLAM). While achieving impressive performance on small synthetic scenes, these methods often suffer from oversmoothed reconstructions, especially for complex real-world scenes. In this work, we introduce DNS SL

Vikram Plomp, Xu-Dong Wang, Jacek Kłos, Paul J. Dagdigian

An intriguing phenomenon in molecular collisions is the occurrence of scattering resonances, which originate from bound and quasi-bound states supported by the interaction potential at low collision energies. The resonance effects in the scattering behaviour are extraordinarily sensitive to the interaction potential, and their observation provides one of the

Zhi Chen

Whether the Refinitiv provide a reliable and trusted methodology in the process of aggregating 10 category scores to overall score?

Eleni Dimitriadou, Andreas Lanitis

The aim of the work presented in this paper is to develop and evaluate an integrated system that provides automated lecture style evaluation, allowing teachers to get instant feedback related to the goodness of their lecturing style. The proposed system aims to promote improvement of lecture quality, that could upgrade the overall student learning experience

Igor Gaidai, Rebekah Herrman

In this paper we consider the scalability of Multi-Angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. However, MA-QAOA is not optimal for minimization of the total QPU time. Different optimization initialization strategies

Ismail Ahmed, Matthieu Davy, Hugo Prod'homme, Philippe Besnier

We experimentally investigate the feasibility of evaluating multiple-input multiple-output (MIMO) radio equipment under adjustable Rician fading channel conditions in a programmable-metasurface-stirred (PM-stirred) reverberation chamber (RC). Whereas within the "smart radio environment" paradigm PMs offer partial control over the channels to the wireless sys

Gyeong-Geon Lee, Ehsan Latif, Xuansheng Wu, Ninghao Liu

This study investigates the application of large language models (LLMs), specifically GPT-3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written responses to science assessments. We focused on overcoming the challenges of accessibility, technical complexity, and lack of explainability that have previously limited the use of art

Jeremy McMahan, Young Wu, Xiaojin Zhu, Qiaomin Xie

To ensure the usefulness of Reinforcement Learning (RL) in real systems, it is crucial to ensure they are robust to noise and adversarial attacks. In adversarial RL, an external attacker has the power to manipulate the victim agent's interaction with the environment. We study the full class of online manipulation attacks, which include (i) state attacks, (ii

Jan Kyzioł, Andrzej Okniński

In this work, we investigate the period doubling phenomenon in the periodically forced asymmetric Duffing oscillator. We use the known steady-state asymptotic solution -- the amplitude-frequency implicit function -- and known criterion for the existence of period doubling. Working in the framework of differential properties of implicit functions we derive an

Shohreh Gholizadeh Siahmazgi, Stephen B. Robinson

In this paper, we give a formulation of the variational iteration method that makes it suitable for the analysis of the solutions of Klein-Gordon equations with variable coefficients. We particularly study a Klein-Gordon problem which has solutions in terms of Airy functions. We prove that the sequence of approximate solutions generated by the variational it

Siddhi Krishna

We study positive braid knots (the knots in the three-sphere realized as positive braid closures) through the lens of the L-space conjecture. This conjecture predicts that if $K$ is a non-trivial positive braid knot, then for all $r < 2g(K)-1$, the 3-manifold obtained via $r$-framed Dehn surgery along $K$ admits a taut foliation. Our main result provides som

Davide Cozzolino, Giovanni Poggi, Riccardo Corvi, Matthias Nießner

The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide variety of challenging scenarios. We find that, contrary to previous beliefs, it is neither necessary nor convenien

Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey, Amr Ahmed

Distributed representations provide a vector space that captures meaningful relationships between data instances. The distributed nature of these representations, however, entangles together multiple attributes or concepts of data instances (e.g., the topic or sentiment of a text, characteristics of the author (age, gender, etc), etc). Recent work has propos

Aleksandar Minja, Vojin Šenk

Some nonlinear codes, such as Kerdock and Preparata codes, can be represented as binary images under the Gray map of linear codes over rings. This paper introduces MAP decoding of Kerdock and Preparata codes by working with their quaternary representation (linear codes over Z4 ) with the complexity of O(N2log2N), where N is the code length in Z4. A sub-optim

Renos Zabounidis, Ini Oguntola, Konghao Zhao, Joseph Campbell

Concept bottleneck models (CBMs) are interpretable models that first predict a set of semantically meaningful features, i.e., concepts, from observations that are subsequently used to condition a downstream task. However, the model's performance strongly depends on the engineered features and can severely suffer from incomplete sets of concepts. Prior works

Patricia Suriana, Ron O. Dror

Deep learning promises to dramatically improve scoring functions for molecular docking, leading to substantial advances in binding pose prediction and virtual screening. To train scoring functions-and to perform molecular docking-one must generate a set of candidate ligand binding poses. Unfortunately, the sampling protocols currently used to generate candid

Katherine E. Lukens, Kevin Garrett, Kayo Ide, David Santek

Accurate atmospheric 3D wind observations are a high priority in the science community. To address this requirement and to support researchers' needs to acquire and analyze wind data from multiple sources, the System for Analysis of Wind Collocations (SAWC) was jointly developed by NOAA/NESDIS/STAR, UMD/ESSIC/CISESS, and UW-Madison/CIMSS. SAWC encompasses a

Farhan Tanvir, Khaled Mohammed Saifuddin, Tanvir Hossain, Arunkumar Bagavathi

Modeling the interactions between drugs, targets, and diseases is paramount in drug discovery and has significant implications for precision medicine and personalized treatments. Current approaches frequently consider drug-target or drug-disease interactions individually, ignoring the interdependencies among all three entities. Within human metabolic systems

Robert Dawson, Vivek Aji

Inducing superconductivity in systems with unconventional band structures is a promising approach for realising unconventional superconductivity. Of particular interest are single interface or Josephson Junction architectures involving Weyl semimetals, which are predicted to host odd parity, potentially topological, superconducting states. These expectations

Simin Zheng, Lu Lu, Yili Hong, Jian Liu

Artificial intelligence (AI) technology has become increasingly prevalent and transforms our everyday life. One important application of AI technology is the development of autonomous vehicles (AV). However, the reliability of an AV needs to be carefully demonstrated via an assurance test so that the product can be used with confidence in the field. To plan

Marcos H. Maruo, José Carlos M. Bermudez

Most studies of adaptive algorithm behavior consider performance measures based on mean values such as the mean-square error. The derived models are useful for understanding the algorithm behavior under different environments and can be used for design. Nevertheless, from a practical point of view, the adaptive filter user has only one realization of the alg

Anusha Guruprasad

The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now have access to images of a vast number of galaxies. However, the visual inspection of these images is an impossible task f

Emanuele Cavalleri, Alberto Cabri, Mauricio Soto-Gomez, Sara Bonfitto

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to the patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are continuously produced and available from public repositories, the

Hongzhi Huang

In this article, we prove that the fundamental group $\pi_1(M)$ of a complete open manifold $M$ with nonnegative Ricci curvature is finitely generated, under the condition that the Riemannian universal cover $\tilde M$ satisfies an "almost $k$-polar at infinity" condition. Additionally, such $\pi_1(M)$ is virtually abelian. Furthermore, we demonstrate that t

E. M. Varvarin, G. V. Osipov

This article suggests ways to implement sequential, parallel and in the form of a given configuration of the movement of an ensemble (swarm) of mobile agents using the effect of chaotic phase synchronization. The possibility of controlling the movement of the ensemble is shown and the stability conditions of the obtained structures are determined.

Jussi Behrndt, Pavel Exner, Markus Holzmann, Matěj Tušek

In this paper we study the self-adjointness and spectral properties of two-dimensional Dirac operators with electrostatic, Lorentz scalar, and anomalous magnetic $\delta$-shell interactions with constant weights that are supported on a smooth unbounded curve that is straight outside a compact set and whose ends are rays that are not parallel to each other. F

Yuhan Mei

The coherent quantum Zeno dynamics (QZD) is a special unitary time evolution in which a quantum population transition gets constrained in a subspace of the entire Hilbert space. We show that coherent QZD can be categorized by orders for the first time, where only the zeroth-order type has been well investigated. In this paper, we focus on the little-known fi

Fernando M. Belchior, Roberto V. Maluf

This work aims to investigate the classical-level duality between the $SIM(1)$-Maxwell-Chern-Simons (MCS) model and its self-dual counterpart. Initially, our focus is on free-field cases to establish equivalence through two distinct approaches: comparing the equations of motion and utilizing the master Lagrangian method. In both instances, the classical corr

Mario Motta, William Kirby, Ieva Liepuoniute, Kevin J. Sung

Quantum subspace methods (QSMs) are a class of quantum computing algorithms where the time-independent Schrodinger equation for a quantum system is projected onto a subspace of the underlying Hilbert space. This projection transforms the Schrodinger equation into an eigenvalue problem determined by measurements carried out on a quantum device. The eigenvalue

A. C. McRae, G. Wei, L. Huang, S. Yigen

Two-dimensional materials (2DMs) are fundamentally electro-mechanical systems. Their environment unavoidably strains them and modifies their quantum transport properties. For instance, a simple uniaxial strain could completely turn off the conductivity of ballistic graphene or switch on/off the superconducting phase of magic-angle bilayer graphene. Here we r

Rajat Bhattacharjya, Alish Kanani, A Anil Kumar, Manoj Nambiar

In recent times, orthogonal frequency-division multiplexing (OFDM)-based radar has gained wide acceptance given its applicability in joint radar-communication systems. However, realizing such a system on hardware poses a huge area and power bottleneck given its complexity. Therefore it has become ever-important to explore low-power OFDM-based radar processor

Ali Leylavi Shoushtari

Agricultural robotics and automation are facing some challenges rooted in the high variability 9 of products, task complexity, crop quality requirement, and dense vegetation. Such a set of 10 challenges demands a more versatile and safe robotic system. Soft robotics is a young yet 11 promising field of research aimed to enhance these aspects of current rigid

Gokul Srinivasagan, Michael Deisher, Munir Georges

People with visual impairments have difficulty accessing touchscreen-enabled personal computing devices like mobile phones and laptops. The image-to-speech (ITS) systems can assist them in mitigating this problem, but their huge model size makes it extremely hard to be deployed on low-resourced embedded devices. In this paper, we aim to overcome this challen

Bilel Tarchoun, Quazi Mishkatul Alam, Nael Abu-Ghazaleh, Ihsen Alouani

Adversarial patches exemplify the tangible manifestation of the threat posed by adversarial attacks on Machine Learning (ML) models in real-world scenarios. Robustness against these attacks is of the utmost importance when designing computer vision applications, especially for safety-critical domains such as CCTV systems. In most practical situations, monito

Benjamin Carrel, Bart Vandereycken

The numerical integration of stiff equations is a challenging problem that needs to be approached by specialized numerical methods. Exponential integrators form a popular class of such methods since they are provably robust to stiffness and have been successfully applied to a variety of problems. The dynamical low- \rank approximation is a recent technique f

Cliff B. Jones, Alan Burns

The reference point for developing any artefact is its specification; to develop software formally, a formal specification is required. For sequential programs, pre and post conditions (together with abstract objects) suffice; rely and guarantee conditions extend the scope of formal development approaches to tackle concurrency. In addition, real-time systems

Anna B. Romanowska, Jonathan D. H. Smith, Anna Zamojska-Dzienio

Each point of a simplex is expressed as a unique convex combination of the vertices. The coefficients in the combination are the barycentric coordinates of the point. For each point in a general convex polytope, there may be multiple representations, so its barycentric coordinates are not necessarily unique. There are various schemes to fix particular baryce

Zhou Lu

Can a physicist make only a finite number of errors in the eternal quest to uncover the law of nature? This millennium-old philosophical problem, known as inductive inference, lies at the heart of epistemology. Despite its significance to understanding human reasoning, a rigorous justification of inductive inference has remained elusive. At a high level, ind

Shaina Raza

The proliferation of biased news narratives across various media platforms has become a prominent challenge, influencing public opinion on critical topics like politics, health, and climate change. This paper introduces the "Navigating News Narratives: A Media Bias Analysis Dataset", a comprehensive dataset to address the urgent need for tools to detect and

Frank Schlawin

Entangled two-photon absorption (ETPA) could form the basis of nonlinear quantum spectroscopy at very low photon fluxes, since, at sufficiently low photon fluxes, ETPA scales linearly with the photon flux. When different pairs start to overlap temporally, accidental coincidences are thought to give rise to a 'classical' quadratic scaling which dominates the

Emanuel A. Fronhofer, Dries Bonte, Elvire Bestion, Julien Cote

Dispersal is a well recognized driver of ecological and evolutionary dynamics, and simultaneously an evolving trait. Dispersal evolution has traditionally been studied in single-species metapopulations so that it remains unclear how dispersal evolves in spatially structured communities and food webs. Since most natural systems are biodiverse and spatially st

Daniel McDuff, Mike Schaekermann, Tao Tu, Anil Palepu

An accurate differential diagnosis (DDx) is a cornerstone of medical care, often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by Large Language Models (LLMs) present new opportunities to both assist and automate aspects of this proces

Erik Chi, Gaukas Wang, J. Alex Halderman, Eric Wustrow

As Internet censors rapidly evolve new blocking techniques, circumvention tools must also adapt and roll out new strategies to remain unblocked. But new strategies can be time consuming for circumventors to develop and deploy, and usually an update to one tool often requires significant additional effort to be ported to others. Moreover, distributing the upd

Xzavier Herbert, Jonathan Gross, Michael Newman

We describe a quantum error-detecting and error-correcting code embedded within irreducible representations of SU(3). These logical qutrits inherit the He(3) symmetries induced by the representation, while protecting against small SU(3) displacements. We explore the general methodology for finding codes from structure-inducing representations of groups, toge

Xin Zhang, Andreas Klümper, Vladislav Popkov

A chiral coordinate Bethe ansatz method is developed to study the periodic XYZ chain. We construct a set of chiral vectors with fixed number of kinks. All vectors are factorized and have simple structures. Under roots of unity conditions, the Hilbert space has an invariant subspace and our vectors form a basis of this subspace. We propose a Bethe ansatz sole

Fangzhi Wang, Hua Liao, Richard S. J. Tol

We investigate optimal carbon abatement in a dynamic general equilibrium climate-economy model with endogenous structural change. By differentiating the production of investment from consumption, we show that social cost of carbon can be conceived as a reduction in physical capital. In addition, we distinguish two final sectors in terms of productivity growt

Apostolos Apostolakis, Guillaume Aoust, Grégory Maisons, Ludovic Laurent

Ammonia (NH$_3$) toxicity, stemming from nitrification, can adversely affect aquatic life and influence the taste and odor of drinking water. This underscores the necessity for highly responsive and accurate sensors to continuously monitor NH$_3$ levels in water, especially in complex environments where reliable sensors have been lacking until this point. He

Benjamin Schneider, Nils Lukas, Florian Kerschbaum

Web-scraped datasets are vulnerable to data poisoning, which can be used for backdooring deep image classifiers during training. Since training on large datasets is expensive, a model is trained once and re-used many times. Unlike adversarial examples, backdoor attacks often target specific classes rather than any class learned by the model. One might expect

Andrea Sciandra

In a previous paper we showed that the category of cocommutative color Hopf algebras is semi-abelian in case the group $G$ is abelian and finitely generated and the characteristic of the base field is different from 2 (not needed if $G$ is finite of odd cardinality). Here we describe the commutator of cocommutative color Hopf algebras and we explain the Hall

Wenjing Chen, Shuo Xing, Victoria G. Crawford

We consider the maximization of a submodular objective function $f:2^U\to\mathbb{R}_{\geq 0}$, where the objective $f$ is not accessed as a value oracle but instead subject to noisy queries. We introduce a versatile adaptive sampling procedure called which determines whether the marginal gain of the function $f$ is approximately above or below an input thres