April 2024 arXiv papers — page 142

Dieter Bothe, Pierre-Etienne Druet, Robert Haller

We consider elliptic transmission problems in several space dimensions near an interface which is $C^{1,1}$ diffeomorphic to an axisymmetric reference-interface with a singular point of cusp type. We establish the regularity of the gradient and of the Hessian in $L^p$ spaces up to the cusp point for local weak solutions. We obtain regularity thresholds which

Sharif Ahmed, Rey Ortiz, Nasir U. Eisty

Context: Annually, ICSE acknowledges a range of papers, a subset of which are paired with research artifacts such as source code, datasets, and supplementary materials, adhering to the Open Science Policy. However, no prior systematic inquiry dives into gauging the influence of ICSE papers using artifact attributes. Objective: We explore the mutual impact be

Mingrui Wu, Sheng Cao

Recently embedding-based retrieval or dense retrieval have shown state of the art results, compared with traditional sparse or bag-of-words based approaches. This paper introduces a model-agnostic doc-level embedding framework through large language model (LLM) augmentation. In addition, it also improves some important components in the retrieval model train

Giuseppe Montalbano, Leonardo Banchi

We show that hybrid quantum classifiers based on quantum kernel methods and support vector machines are vulnerable against adversarial attacks, namely small engineered perturbations of the input data can deceive the classifier into predicting the wrong result. Nonetheless, we also show that simple defence strategies based on data augmentation with a few craf

Shariful Alam, Jidong Xiao, Nasir U. Eisty

Covert channel attacks represent a significant threat to system security, leveraging shared resources to clandestinely transmit information from highly secure systems, thereby violating the system's security policies. These attacks exploit shared resources as communication channels, necessitating resource partitioning and isolation techniques as countermeasu

Gabriel Regnault, Alexander A Doinikov, Gabrielle Laloy-Borgna, Cyril Mauger

Self-oscillations underlie many natural phenomena such as heartbeat, ocean waves, and the pulsation of variable stars. From pendulum clocks to the behavior of animal groups, self-oscillation is one of the keys to the understanding of synchronization phenomena and hence the collective behavior of interacting systems. In this study, we consider two closely spa

Claudio Corianò, Dario Melle

One of the most important and unanswered problems in particle physics is the origin of the three generations of quarks and leptons. The standard Model does not provide any hint regarding its sequential charge assignments, which remain a fundamental mystery of Nature. One possible solution of the puzzle is to look for charge assignments, in a given gauge theo

Dereje Bekele Tekliye, Gopalakrishnan Sai Gautam

Calcium batteries (CBs) are potential next-generation energy storage devices, offering a promising alternative to lithium-ion batteries due to their theoretically high energy density, better safety, and lower costs associated with the natural abundance of calcium. However, the limited availability of positive electrode (cathode) materials has constrained the

Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj

We study the problem of estimating the stationary mass -- also called the unigram mass -- that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has several applications -- for example, estimating the stationary missing mass is critical for accurately smoothing probability estimates in sequence models. While the class

Moustafa Ibrahim

Mersenne primes, renowned for their captivating form as $2^p - 1$, have intrigued mathematicians for centuries. In this paper, we embark on a captivating quest to unveil the intricate nature of Mersenne primes, seamlessly integrating methods with the Eight Levels Theorem. Initially, we extend the Eight Levels Theorem and introduce an innovative approach that

Junyang Gou, Lara Börger, Michael Schindelegger, Benedikt Soja

The gravimetry measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) satellite mission provide an essential way to monitor changes in ocean bottom pressure ($p_b$), which is a critical variable in understanding ocean circulation. However, the coarse spatial resolution of the GRACE(-FO) fields blurs important spati

Ming Zhong, Dehao Liu, Raymundo Arroyave, Ulisses Braga-Neto

This paper proposes a semi-supervised methodology for training physics-informed machine learning methods. This includes self-training of physics-informed neural networks and physics-informed Gaussian processes in isolation, and the integration of the two via co-training. We demonstrate via extensive numerical experiments how these methods can ameliorate the

Djemel Ziou

In this report, we explore the data selection leading to a family of estimators maximizing a centrality. The family allows a nice properties leading to accurate and robust probability density function fitting according to some criteria we define. We establish a link between the centrality estimator and the maximum likelihood, showing that the latter is a par

Oleksandr Diatlyk, Himanshu Khanchandani, Fedor K. Popov, Yifan Wang

We study the operator algebra of extended conformal defects in more than two spacetime dimensions. Such algebra structure encodes the combined effect of multiple impurities on physical observables at long distances as well as the interactions among the impurities. These features are formalized by a fusion product which we define for a pair of defects, after

Kui Qian, Litao Qiao, Beth Friedman, Edward O'Donnell

We present a novel method for quantifying the microscopic structure of brain tissue. It is based on the automated recognition of interpretable features obtained by analyzing the shapes of cells. This contrasts with prevailing methods of brain anatomical analysis in two ways. First, contemporary methods use gray-scale values derived from smoothed version of t

Paolo Perrone, Ruben Van Belle

We provide a categorical proof of convergence for martingales and backward martingales in mean, using enriched category theory. The enrichment we use is in topological spaces, with their canonical closed monoidal structure, which encodes a version of pointwise convergence. We work in a topologically enriched dagger category of probability spaces and Markov k

Bhavith Chandra Challagundla, Chakradhar Peddavenkatagari

Automatic text summarization (TS) plays a pivotal role in condensing large volumes of information into concise, coherent summaries, facilitating efficient information retrieval and comprehension. This paper presents a novel framework for abstractive TS of single documents, which integrates three dominant aspects: structural, semantic, and neural-based approa

Liding Yao

We construct a linear operator $T:\mathscr S'(\mathbb R^n)\to \mathscr S'(\mathbb R^n)$ such that $T:\mathscr B_{pq}^s(\mathbb R^n)\to\mathscr B_{pq}^s(\mathbb R^n)$ for all $0<p,q\le\infty$ and $s\in\mathbb R$, but $T(\mathscr F_{pq}^s(\mathbb R^n))\not\subset \mathscr F_{pq}^s(\mathbb R^n)$ unless $p=q$. As a result Triebel-Lizorkin spaces cannot be interp

Léo Bigorgne, Renato Velozo Ruiz

In this paper, we study the precise late-time asymptotic behaviour of small data solutions for the Vlasov-Poisson system in dimension three. First, we show that the spatial density and the force field satisfy asymptotic self-similar polyhomogeneous expansions. Moreover, we obtain an enhanced modified scattering result for this non-linear system. We show that

Zoltán Á. Milacski, Koichiro Niinuma, Ryosuke Kawamura, Fernando de la Torre

The connection between our 3D surroundings and the descriptive language that characterizes them would be well-suited for localizing and generating human motion in context but for one problem. The complexity introduced by multiple modalities makes capturing this connection challenging with a fixed set of descriptors. Specifically, closed vocabulary scene enco

Arnab Dhani, Sebastian H. Völkel, Alessandra Buonanno, Hector Estelles

Gravitational-wave (GW) observations of binary black-hole (BBH) coalescences are expected to address outstanding questions in astrophysics, cosmology, and fundamental physics. Realizing the full discovery potential of upcoming LIGO-Virgo-KAGRA (LVK) observing runs and new ground-based facilities hinges on accurate waveform models. Using linear-signal approxi

Danial Motlagh, Modjtaba Shokrian Zini, Juan Miguel Arrazola, Nathan Wiebe

Quantum algorithms for probing ground-state properties of quantum systems require good initial states. Projection-based methods such as eigenvalue filtering rely on inputs that have a significant overlap with the low-energy subspace, which can be challenging for large, strongly-correlated systems. This issue has motivated the study of physically-inspired dyn

Yutian Ren, Aaron Haohua Yen, G. P. Li

Adaptive machine learning (ML) aims to allow ML models to adapt to ever-changing environments with potential concept drift after model deployment. Traditionally, adaptive ML requires a new dataset to be manually labeled to tailor deployed models to altered data distributions. Recently, an interactive causality based self-labeling method was proposed to auton

Thomas M. Summe, Siddharth Joshi

Recent advances to algorithms for training spiking neural networks (SNNs) often leverage their unique dynamics. While backpropagation through time (BPTT) with surrogate gradients dominate the field, a rich landscape of alternatives can situate algorithms across various points in the performance, bio-plausibility, and complexity landscape. Evaluating and comp

Asimpunya Mitra, Daniel J. Schultz, Yong Baek Kim

The weak Mott insulating regime of the triangular lattice Hubbard model exhibits a rich magnetic phase diagram as a result of the ring exchange interaction in the spin Hamiltonian. These phases include the Kalmeyer-Laughlin type chiral spin liquid (CSL) and a valence bond solid (VBS). A natural question arises regarding the robustness of these phases in the

Ryoki Matsukoba, Eduard I. Vorobyov, Takashi Hosokawa

In the context of planet formation, growth from micron-sized grains to kilometer-sized planetesimals is a crucial question. Since the dust growth rate depends on the amount of dust, realizing planet formation scenarios based on dust growth is challenging in environments with low metallicity, i.e. less dust. We investigate dust growth during disk evolution, p

Paolo Bellingeri, Celeste Damiani, Oscar Ocampo, Charalampos Stylianakis

Following previous work on congruence subgroups and crystallographic braid groups, we study the lower central series of congruence braid groups related to the braid group $B_3$, showing in particular that corresponding quotients are almost crystallographic.

Robin Fritsch, Andrea Canidio

This paper presents the results of a comprehensive empirical study of losses to arbitrageurs (following the formalization of loss-versus-rebalancing by [Milionis et al., 2022]) incurred by liquidity providers on automated market makers (AMMs). We show that those losses exceed the fees earned by liquidity providers across many of the largest AMM liquidity poo

Yunyi Zhao, Wei Zhang, Erhai Hu, Qingyu Yan

Battery recycling is a critical process for minimizing environmental harm and resource waste for used batteries. However, it is challenging, largely because sorting batteries is costly and hardly automated to group batteries based on battery types. In this paper, we introduce a machine learning-based approach for battery-type classification and address the d

Ralph Blumenhagen, Niccolò Cribiori, Aleksandar Gligovic, Antonia Paraskevopoulou

In a pedagogical manner, we review recent developments in the investigation of the Emergence Proposal. Although it is fair to say that this idea is still at an exploratory level and a fully coherent picture has yet to be developed, we put it into perspective to previous work on the swampland program and on emergence in QG. In view of the emergent string conj

Jan Cepila, Jesus Guillermo Contreras, Matej Vaculciak

In this proceedings a solution of the target-rapidity Balitsky-Kovchegov (BK) equation is presented considering the complete impact-parameter dependence, including the orientation of the dipole with respect to the impact-parameter vector. The target-rapidity formulation of the BK equation introduces non-locality in rapidity. Three different prescriptions are

Brij Mohan, Rajeev Gangwar, Tanmoy Pandit, Mohit Lal Bera

Conventional continuous quantum heat engines with incoherent heat transfer perform poorly as they exploit two-body interactions between the system and hot or cold baths, thus having limited capability to outperform their classical counterparts. We introduce distinct continuous quantum heat engines that utilize coherent heat transfer with baths, yielding genu

Tancredi Salamone, Henning G. Hugdal, Morten Amundsen, Sol H. Jacobsen

The geometrical properties of a helical ferromagnet are shown theoretically to control the critical temperature of a proximity-coupled superconductor. Using the Usadel equation for diffusive spin transport, we provide self-consistent analysis of how curvature and torsion modulate the proximity effect. When the helix is attached to a piezoelectric actuator, t

Alice Zurlo

Over the past 4 decades, the exploration of planets beyond our solar system has yielded the discovery of over 5600 exoplanets orbiting different stars. Continuous advancements in instrumentation and cutting-edge techniques empower astronomers to unveil and characterize new exoworlds with increasing frequency. Notably, direct imaging, also called high-contras

Jackson Walters

We describe the discrete Fourier transform (DFT) for a cyclic group when $p|N$ by factoring $x^N-1$ over finite fields and constructing the Fourier transform and its inverse using B\'{e}zout's identity for polynomials. For the symmetric group, in the modular case when $p|n!$ we construct the Peirce decomposition using central primitive orthogonal idempotents

Shaowu Huang, Hemant K. Mishra

Symplectic eigenvalues are known to satisfy analogs of several classic eigenvalue inequalities. Of these is a set of weak supermajorization relations concerning symplectic eigenvalues that are weaker analogs of some majorization relations corresponding to eigenvalues. The aim of this letter is to establish necessary and sufficient conditions for the saturati

Fahad Nasir, Prakash Gaikwad, Frederick B. Davies, James S. Bolton

Unveiling the thermal history of the intergalactic medium (IGM) at $4 \leq z \leq 5$ holds the potential to reveal early onset HeII reionization or lingering thermal fluctuations from HI reionization. We set out to reconstruct the IGM gas properties along simulated Lyman-alpha forest data on pixel-by-pixel basis, employing deep Bayesian neural networks. Our

Mar Mezcua, Fabio Pacucci, Hyewon Suh, Malgorzata Siudek

We report for the first time a sample of 12 supermassive black holes (SMBHs) hosted by low-mass galaxies at cosmic noon, i.e., in a redshift range consistent with the peak of star formation history: $z \sim 1-3$. These black holes are two orders of magnitude too massive for the stellar content of their hosts when compared with the local relation for active g

Elizaveta Sazonova, Cameron R Morgan, Michael Balogh, Katherine Alatalo

Structural disturbances, such as galaxy mergers or instabilities, are key candidates for driving galaxy evolution, so it is important to detect and quantify galaxies hosting these disturbances spanning a range of masses, environments, and cosmic times. Traditionally, this is done by quantifying the asymmetry of a galaxy as part of the concentration-asymmetry

Zahra Afkanpour, Sareh Ataiee, Alexandros Ziampras, Anna B. T. Penzlin

Resonant planetary migration in protoplanetary discs can lead to an interplay between the resonant interaction of planets and their disc torques called overstability. While theoretical predictions and N-body simulations hinted at its existence, there was no conclusive evidence until hydrodynamical simulations were performed. Our primary purpose is to find a

Vitor Cardoso, Shinji Mukohyama, Naritaka Oshita, Kazufumi Takahashi

The Standard Model of particle physics predicts the speed of light to be a universal speed of propagation of massless carriers. However, other possibilities exist -- including Lorentz-violating theories -- where different fundamental fields travel at different speeds. Black holes are interesting probes of such physics, as distinct fields would probe differen

Christoph Adam, Alberto Garcia Martin-Caro, Carlos Naya, Andrzej Wereszczynski

We show that any nonlinear field theory giving rise to static solutions with finite energy like, e.g., topological solitons, allows us to derive an infinite number of integral identities which any such solution has to obey. These integral identities can always be understood as being generated by field transformations and their related Noether currents. We al

Felipe Alarcón, Edwin Bergin, Gabriele Cugno

The upcoming new coronographs with deeper contrast limits, together with planned and current high-contrast imaging campaigns will push the detectability limit of protoplanets. These planet-hunting campaigns present a new opportunity to characterize protoplanets and their surrounding environments. However, there are clear uncertainties as to what are the exti

Shashikant Singh Kunwar, Madhuparna Karmakar

Taking cue from the recent experimental realization of metallic phases in Kagome materials we report the low temperature signatures and thermal scales of Kagome metals and insulators, determined in the framework of the Kagome Hubbard model, using a non perturbative numerical approach. In contrast to the existing consensus we establish a flat band localized i

Luca Broggi, Nicholas C. Stone, Taeho Ryu, Elisa Bortolas

Two-body relaxation may drive stars onto near-radial orbits around a massive black hole, resulting in a tidal disruption event (TDE). In some circumstances, stars are unlikely to undergo a single terminal disruption, but rather to have a sequence of many grazing encounters with the black hole. It has long been unclear what is the physical outcome of this seq

Hans Jockers, Sören Kotlewski, Pyry Kuusela, Andrew J. McLeod

It has long been known that the maximal cut of the equal-mass four-loop banana integral is a period of a family of Calabi-Yau threefolds that depends on the kinematic variable $z=m^2/p^2$. We show that it can also be interpreted as a period of a family of genus-two curves. We do this by introducing a general Calabi-Yau-to-curve correspondence, which in this

Julian Schuhmacher, Marco Ballarin, Alberto Baiardi, Giuseppe Magnifico

Hybrid Tensor Networks (hTN) offer a promising solution for encoding variational quantum states beyond the capabilities of efficient classical methods or noisy quantum computers alone. However, their practical usefulness and many operational aspects of hTN-based algorithms, like the optimization of hTNs, the generalization of standard contraction rules to an

Alberto Hojel, Yutong Bai, Trevor Darrell, Amir Globerson

Visual Prompting is a technique for teaching models to perform a visual task via in-context examples, without any additional training. In this work, we analyze the activations of MAE-VQGAN, a recent Visual Prompting model, and find task vectors, activations that encode task-specific information. Equipped with this insight, we demonstrate that it is possible

Lucas Lingle

Deep learning models have become a cornerstone of modern AI research, yet their initializations and learning rates may at times be set in an opaque or ad-hoc fashion due to the high cost of hyperparameter sweeps. The $\mu$-Parameterization ($\mu$P) offers a possible solution to this challenge, yielding scaling rules for model initialization and learning rate

Simon Cooper, Wushi Goldring

Given a connected, reductive $\mathbf{F}_p$-group $G$, a cocharacter $\mu \in X_*(G)$ and a smooth zip period map $\zeta:X \to \mathop{\text{$G$-{\tt Zip}}}\nolimits^{\mu}$, we study which classes in the Wedhorn-Ziegler tautological rings $T^*(X), T^*(Y)$ of $X$ and its flag space $Y \to G-ZipFlag^{\mu}$ are \textit{strata-effective}, meaning that they are n

Bo He, Hengduo Li, Young Kyun Jang, Menglin Jia

With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However, existing LLM-based large multimodal models (e.g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video understanding. In this study, we mainly focus

Andrew Weldon, Naveen A. Reddy, Alison L. Coil, Alice E. Shapley

We use the large spectroscopic data set of the MOSFIRE Deep Evolution Field survey to investigate the kinematics and energetics of ionised gas outflows. Using a sample of 598 star-forming galaxies at redshift 1.4 < $z$ < 3.8, we decompose $\rm{H}\alpha$ and [OIII] emission lines into narrow and broad components, finding significant detections of broad compon

Joris Witstok, Roberto Maiolino, Renske Smit, Gareth C. Jones

$\require{mediawiki-texvc}$Given the sensitivity of the resonant Lyman-$\mathrm{\alpha}$ (Ly$\mathrm{\alpha}$) transition to absorption by neutral hydrogen, observations of Ly$\mathrm{\alpha}$ emitting galaxies (LAEs) have been widely used to probe the ionising capabilities of reionisation-era galaxies and their impact on the intergalactic medium (IGM). Howe

Talha Hanif Butt, Prayag Tiwari, Fernando Alonso-Fernandez

Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes from CAN data. Three classifiers, Artificial Neural Networks (ANN), Random Forest, and Support Vector Machine

Yuichiro Tada, Takahiro Terada

The recent result of Dark Energy Spectroscopic Instrument (DESI) in combination with other cosmological data shows evidence of the evolving dark energy parameterized by $w_0w_a$CDM model. We interpret this result in terms of a quintessential scalar field and demonstrate that it can explain the DESI result even though it becomes eventually phantom in the past

Anastasiia Vladimirova, Guillermo Arregui, Sergei Lepeshov, Christian Anker Rosiek

We present a theoretical study on photonic topological crystals whose symmetry is governed by quantum valley-Hall topological insulators and whose propagating edge modes are strongly confined due to bowtie geometries. Dielectric bowtie structures exploit the field discontinuities at boundaries between materials with different refractive indices, and here bow

Vladimir Solovyev, Danni Liu, Jan Niehues

Finetuning pretrained models on downstream generation tasks often leads to catastrophic forgetting in zero-shot conditions. In this work, we focus on summarization and tackle the problem through the lens of language-independent representations. After training on monolingual summarization, we perform zero-shot transfer to new languages or language pairs. We f

Keen You, Haotian Zhang, Eldon Schoop, Floris Weers

Recent advancements in multimodal large language models (MLLMs) have been noteworthy, yet, these general-domain MLLMs often fall short in their ability to comprehend and interact effectively with user interface (UI) screens. In this paper, we present Ferret-UI, a new MLLM tailored for enhanced understanding of mobile UI screens, equipped with referring, grou

Jindong Gu

In recent years, generative AI (GenAI), like large language models and text-to-image models, has received significant attention across various domains. However, ensuring the responsible generation of content by these models is crucial for their real-world applicability. This raises an interesting question: What should responsible GenAI generate, and what sho

Jakub Ziembicki, Paweł Scharoch, Maciej P. Polak, Michał Wiśniewski

Nanotechnology's impact on semiconductor industry advancement, particularly through the engineering of nanostructures like nanowires, opens new possibilities for material functionality due to the tunable physical properties of nanostructures compared to bulk materials. This paper presents a comprehensive study on group IV semiconductors and their binaries ac

Jing Gu, Nanxuan Zhao, Wei Xiong, Qing Liu

Effective editing of personal content holds a pivotal role in enabling individuals to express their creativity, weaving captivating narratives within their visual stories, and elevate the overall quality and impact of their visual content. Therefore, in this work, we introduce SwapAnything, a novel framework that can swap any objects in an image with persona

Geng Li, Roshan Krishna Kumar, Petr Stepanov, Pierre A. Pantaleón

Twisted bilayer graphene (TBG) represents a highly tunable, strongly correlated electron system owed to its unique flat electronic bands. However, understanding the single-particle band structure alone has been challenging due to complex lattice reconstruction effects and a lack of spectroscopic measurements over a broad energy range. Here, we probe the band

Gabriel P. Lynch, Lloyd Knox, Jens Chluba

We develop and apply a new framework for reconstructing the ionization history during the epoch of recombination with combinations of cosmic microwave background (CMB), baryon acoustic oscillation (BAO) and supernova data. We find a wide range of ionization histories that are consistent with current CMB data, and also that cosmological parameter constraints

Nengkun Yu

The conventional approach to understanding the characteristics of an unknown quantum state involves having numerous identical independent copies of the system in that state. However, we demonstrate that gleaning insights into specific properties is feasible even with a single-state sample. Perhaps surprisingly, the confidence level of our findings increases

Xin Chen

As the electrification process advances, enormous power flexibility is becoming available on the demand side, which can be harnessed to facilitate power system decarbonization. Hence, this paper studies the carbon-aware demand response (C-DR) paradigm, where individual users aim to minimize their carbon footprints through the optimal scheduling of flexible l

Scott Melville, Guilherme L. Pimentel

Extending scattering to states with unphysical mass values (particles ``off their mass shell'') has been instrumental in developing modern amplitude technology for Minkowski spacetime. Here, we study the off-shell correlators which underpin the recently proposed S-matrix for scattering on de Sitter spacetime. By labelling each particle with both a spatial mo

T. J. Vongkovit, Hansveer Singh, Romain Vasseur, Sarang Gopalakrishnan

The ground states of interacting one-dimensional metals are generically Luttinger liquids. Luttinger liquid theory is usually considered for translation invariant systems. The Luttinger liquid description remains valid for weak quasiperiodic modulations; however, as the quasiperiodic modulation gets increasingly strong, it is increasingly renormalized and ev

Natanael Bort-Soldevila, Jaume Cunill-Subiranas, Nuria Del-Valle, Witlef Wieczorek

We theoretically investigate the properties of magnetically-levitated superconducting rotors confined in anti-Helmholtz traps, for application in magnetomechanical experiments. We study both the translational modes and a librational mode. The librational mode gives an additional degree of freedom that levitated spheres do not have access to. We compare rotor

Will Brian, Rene Gril Rogina

A fan $F$ is \emph{endpoint-homogeneous} if for any two endpoints $e,e'$ of $F$, there is a homeomorphism $h: F \rightarrow F$ such that $h(e) = e'$. We prove there are uncountably many distinct homeomorphism types of endpoint-homogeneous smooth fans. To do this, we associate to each such fan $F$ a topological invariant, in the form of a characteristic subse

Nis Meinert

Given two polygonal curves, there are many ways to define a notion of similarity between them. One popular measure is the Fr\'echet distance which has many desirable properties but is notoriously expensive to calculate, especially for non-trivial metrics. In 1994, Eiter and Mannila introduced the discrete Fr\'echet distance which is much easier to implement

Wushi Goldring, Stefan Reppen

This paper generalizes a result of Ogus that, under certain technical conditions, the vanishing order of the Hasse invariant of a family $Y/X$ of $n$-dimensional Calabi-Yau varieties in characteristic $p$ at a point $x$ of $X$ equals the "conjugate line position" of $H^n_{\text{dR}}(Y/X)$ at $x$, i.e. the largest $i$ such that the line of the conjugate filtr

Valery V. Ryzhikov

The function $P(T)=\sum_{i=0}^\infty c_i T^i$ is admissible if $c_i\geq 0$, $\sum_{i=0}^\infty c_i\leq 1$. For any given set of admissible functions $P_1,\dots, P_k$ there is a unitary operator $T$ of dynamic origin such that the weak closure of its powers is a semigroup generated by the operators $0$, $T$, $P_1(T),\dots, P_k(T)$, $T^\ast$, $P_1(T^\ast ),\do

Xinya Chen, Hanlei Guo, Yanrui Bin, Shangzhan Zhang

Collecting accurate camera poses of training images has been shown to well serve the learning of 3D-aware generative adversarial networks (GANs) yet can be quite expensive in practice. This work targets learning 3D-aware GANs from unposed images, for which we propose to perform on-the-fly pose estimation of training images with a learned template feature fie

Andrei T. Patrascu

In this article I am arguing in favour of the hypothesis that the origin of gauge and string dualities in general can be found in a higher-categorical interpretation of basic quantum mechanics. It is interesting to observe that the Galilei group has a non-trivial cohomology, while the Lorentz/Poincare group has trivial cohomology. When we constructed quantum

Spyros Argyropoulos, Ulrich Haisch, Ilia Kalaitzidou

The 2HDM+$a$ model is one of the main models used in the interpretations of dark matter searches at the LHC. So far, all the 2HDM+$a$ benchmarks considered by the ATLAS and CMS experiments are limited to a type-II Yukawa sector, in which the Higgs bosons $A$, $H$, and $H^\pm$ are all constrained to be mass-degenerate and heavier than around $600 \, {\rm GeV}

Preston K. Robinette, Diego Manzanas Lopez, Serena Serbinowska, Kevin Leach

Malware, or software designed with harmful intent, is an ever-evolving threat that can have drastic effects on both individuals and institutions. Neural network malware classification systems are key tools for combating these threats but are vulnerable to adversarial machine learning attacks. These attacks perturb input data to cause misclassification, bypas

Vasilis Chasiotis, Dimitris Karlis

The importance of exploring a potential integration among surveys has been acknowledged in order to enhance effectiveness and minimize expenses. In this work, we employ the alignment method to combine information from two different surveys for the estimation of complex statistics. The derivation of the alignment weights poses challenges in case of complex st

Dayanand Kumar, Hanrui Li, Amit Singh, Manoj Kumar Rajbhar

Photoresponsivity studies of wide-bandgap oxide-based devices have emerged as a vibrant and popular research area. Researchers have explored various material systems in their quest to develop devices capable of responding to illumination. In this study, we engineered a mature wide bandgap oxide-based bilayer heterostructure synaptic memristor to emulate the

Kaloyan Danovski, Miguel C. Soriano, Lucas Lacasa

The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network's prediction, when confronted with a learning task. This iterative change can be naturally interpreted as a trajectory in network space -- a time series of networks -- and thus the training algorithm (e.g. gradient desc

Hugo Duminil-Copin, Romain Panis

We study the nearest-neighbour Ising and $\varphi^4$ models on $\mathbb Z^d$ with $d\geq 3$ and obtain new lower bounds on their two-point functions at (and near) criticality. Together with the classical infrared bound, these bounds turn into up-to constant estimates when $d\geq 5$. When $d=4$, we obtain an ''almost'' sharp lower bound corrected by a logarit

Joris Verstraten, Kunlun Dai, Maxime Dixmerias, Bruno Peaudecerf

The wave nature of matter remains one of the most striking aspects of quantum mechanics. Since its inception, a wealth of experiments has demonstrated the interference, diffraction or scattering of massive particles. More recently, experiments with ever increasing control and resolution have allowed imaging the wavefunction of individual atoms. Here, we use

Roberto Ognibene, Bozhidar Velichkov

We consider the problem of optimal partition of a domain with respect to the sum of the principal eigenvalues and we prove for the first time regularity results for the free interface up to fixed boundary. All our results are quantitative and, in particular, we obtain fine estimates on the continuity of the solutions and the oscillation of the free interface

Chong Wang, Xiao-Wei Zhang, Xiaoyu Liu, Jie Wang

Recent experimental discovery of fractional Chern insulators at zero magnetic field in moir\'e superlattices has sparked intense interests in bringing Landau level physics to flat Chern bands. In twisted MoTe$_2$ bilayers (tMoTe$_2$), recent theoretical and experimental studies have found three consecutive flat Chern bands at twist angle $\sim 2^\circ$. In t

Sujeevan Ratnasingham, Catherine Wei, Dean Chan, Jireh Agda

BOLD, the Barcode of Life Data System, supports the acquisition, storage, validation, analysis, and publication of DNA barcodes, activities requiring the integration of molecular, morphological, and distributional data. Its pivotal role in curating the reference library of DNA barcodes, coupled with its data management and analysis capabilities, make it a ce

Xinyang Gu, Yen-Jen Wang, Jianyu Chen

Humanoid-Gym is an easy-to-use reinforcement learning (RL) framework based on Nvidia Isaac Gym, designed to train locomotion skills for humanoid robots, emphasizing zero-shot transfer from simulation to the real-world environment. Humanoid-Gym also integrates a sim-to-sim framework from Isaac Gym to Mujoco that allows users to verify the trained policies in

Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer, Kamyar Arzideh

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, a

Ionut M. Motoi, Leonardo Saraceni, Daniele Nardi, Thomas A. Ciarfuglia

Satellite imagery is crucial for tasks like environmental monitoring and urban planning. Typically, it relies on semantic segmentation or Land Use Land Cover (LULC) classification to categorize each pixel. Despite the advancements brought about by Deep Neural Networks (DNNs), their performance in segmentation tasks is hindered by challenges such as limited a

Shijie Xia, Xuefeng Li, Yixin Liu, Tongshuang Wu

The leaderboard of Large Language Models (LLMs) in mathematical tasks has been continuously updated. However, the majority of evaluations focus solely on the final results, neglecting the quality of the intermediate steps. This oversight can mask underlying problems, such as logical errors or unnecessary steps in the reasoning process. To measure reasoning b

Qing-Guo Huang, Chen Yuan, Zu-Cheng Chen, Lang Liu

During the fourth observing run of the LIGO-Virgo-KAGRA detector network, the LIGO Livingston observatory detected a coalescing compact binary, GW230529_181500, with component masses of $2.5-4.5\, M_\odot$ and $1.2-2.0\, M_\odot$ at the $90\%$ credible level. The gravitational-wave data alone is insufficient to determine whether the components are neutron st

P. Padovani, R. Gilli, E. Resconi, C. Bellenghi

Aims. We calculate the contribution to the neutrino background from the non-jetted active galactic nuclei (AGN) population following the recent IceCube association of TeV neutrinos with NGC 1068. Methods. We exploit our robust knowledge of the AGN X-ray luminosity function and evolution and convert it to the neutrino band by using NGC 1068 as a benchmark and

Sannyuya Liu, Qing Li, Xiaoxuan Shen, Jianwen Sun

Skill acquisition is a key area of research in cognitive psychology as it encompasses multiple psychological processes. The laws discovered under experimental paradigms are controversial and lack generalizability. This paper aims to unearth the laws of skill learning from large-scale training log data. A two-stage algorithm was developed to tackle the issues

Miguel Costa, Sandro Pinto

ML is shifting from the cloud to the edge. Edge computing reduces the surface exposing private data and enables reliable throughput guarantees in real-time applications. Of the panoply of devices deployed at the edge, resource-constrained MCUs, e.g., Arm Cortex-M, are more prevalent, orders of magnitude cheaper, and less power-hungry than application process

Jooyeon Kim, Eulrang Cho, Sehyung Kim, Hyunwoo J. Kim

Open-vocabulary object detection (OVD) has been studied with Vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve the generalization ability to expand the knowledge of the detector, using 'positive' pseudo-labels with additional 'class' names, e.g., sock, iPod, and alligator. To extend the previ

Pei-Yuan Zhao, Ke Ding, Shuo Yang

We introduce the Chebyshev pseudosite matrix product state approach (ChePSMPS) as a solver for cluster perturbation theory (CPT), crucial for simulating spectral functions in two-dimensional electron-phonon ($e$-ph) coupling systems. ChePSMPS distinguishes itself from conventional exact diagonalization solvers by supporting larger clusters, thereby significa

Zheng-Tao Xu, Zheng-Cheng Gu, Shuo Yang

It has long been believed that doped quantum spin liquids (QSLs) can give rise to fascinating quantum phases, including the possibility of high-temperature superconductivity (SC) as proposed by P. W. Anderson's resonating valence bond (RVB) scenario. The Kagome lattice $t$-$J$ model is known to exhibit spin liquid behavior at half-filling, making it an ideal

Bakul Agarwal, Stephen Jones, Matthias Kerner, Andreas von Manteuffel

We calculate the complete NLO QCD corrections to loop-induced $gg \rightarrow ZZ$ production including full top-quark mass effects. The two-loop virtual corrections are obtained by combining analytic results for the massless, Higgs-mediated, and one-loop factorizable contributions with numerically computed amplitudes containing the top-quark mass. We show th

Anna de Graaff, David J. Setton, Gabriel Brammer, Sam Cutler

Within the established framework of structure formation, galaxies start as systems of low stellar mass and gradually grow into far more massive galaxies. The existence of massive galaxies in the first billion years of the Universe, suggested by recent observations, appears to challenge this model, as such galaxies would require highly efficient conversion of

Nanna Inie, Stefania Druga, Peter Zukerman, Emily M. Bender

This paper investigates the influence of anthropomorphized descriptions of so-called "AI" (artificial intelligence) systems on people's self-assessment of trust in the system. Building on prior work, we define four categories of anthropomorphization (1. Properties of a cognizer, 2. Agency, 3. Biological metaphors, and 4. Properties of a communicator). We use

Simon Zacharias Lahme, Jasper Ole Cirkel, Larissa Hahn, Julia Hofmann

The current dropout rate in physics studies in Germany is about 60\%, with the majority of dropouts occurring in the first year. Consequently, the physics study entry phase poses a significant challenge for many students. Students' stress perceptions can provide more profound insights into the processes and challenges during that period. In a panel study fea

Karl Bringmann, Anita Dürr, Adam Polak

We present a pseudopolynomial-time algorithm for the Knapsack problem that has running time $\widetilde{O}(n + t\sqrt{p_{\max}})$, where $n$ is the number of items, $t$ is the knapsack capacity, and $p_{\max}$ is the maximum item profit. This improves over the $\widetilde{O}(n + t \, p_{\max})$-time algorithm based on the convolution and prediction technique