February 2023 arXiv papers — page 3

Kiyoshi Igusa, Job Daisie Rock

We introduce stability conditions (in the sense of King) for representable modules of continuous quivers of type A along with a special criteria called the four point condition. The stability conditions are defined using a generalization of delta functions, called half-delta functions. We show that for a continuous quiver of type A with finitely many sinks a

A. Schmitt, M. Rosticher, T. Taniguchi, K. Watanabe

One of the intrinsic drift velocity limit of the quantum Hall effect is the collective magneto-exciton (ME) instability. It has been demonstrated in bilayer graphene (BLG) using noise measurements. We reproduce this experiment in monolayer graphene (MLG), and show that the same mechanism carries a direct relativistic signature on the breakdown velocity. Base

Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou

A sequential quadratic optimization algorithm for minimizing an objective function defined by an expectation subject to nonlinear inequality and equality constraints is proposed, analyzed, and tested. The context of interest is when it is tractable to evaluate constraint function and derivative values in each iteration, but it is intractable to evaluate the

Alfredo Deaño, Lucía Morey, Pablo Román

In this paper, we study parameter deformations of matrix valued orthogonal polynomials (MVOPs). These deformations are built on the use of certain matrix valued operators which are symmetric with respect to the matrix valued inner product defined by the orthogonality weight. We show that the recurrence coefficients associated with these operators satisfy gen

Liam Connor, Vikram Ravi, Morgan Catha, Ge Chen

The hot gas that constitutes the intracluster medium (ICM) has been studied at X-ray and millimeter/sub-millimeter wavelengths (Sunyaev-Zeldovich effect) for decades. Fast radio bursts (FRBs) offer an additional method of directly measuring the ICM and gas surrounding clusters, via observables such as dispersion measure (DM) and Faraday rotation measure (RM)

Saudamini Nayak

We define global and local Weyl modules for $q \otimes A$, where $q$ is the queer Lie superalgebra and $A$ is an associative commutative unital $\mathbb{C}-$algebra. We prove that global Weyl modules are universal highest weight objects in certain category upto parity reversing functor $\Pi$. Then with the assumption that $A$ is finitely generated and with a

Franklyn Quinlan

Electrical signals derived from optical sources have achieved record-low levels of phase noise, and have demonstrated the highest frequency stability yet achieved in the microwave domain. Attaining such ultrastable phase and frequency performance requires high-fidelity optical-to-electrical conversion, typically performed via a high-speed photodiode. This pa

Teven Le Scao, Claire Gardent

A key feature of neural models is that they can produce semantic vector representations of objects (texts, images, speech, etc.) ensuring that similar objects are close to each other in the vector space. While much work has focused on learning representations for other modalities, there are no aligned cross-modal representations for text and knowledge base (

Juan Marcelo Virdis, Fernando Delbianco, María Eugenia Elorza

We conducted regression discontinuity design models in order to evaluate changes in access to healthcare services and financial protection, using as a natural experiment the age required to retire in Argentina, the moment in which people are able to enroll in the free social health insurance called PAMI. The dependent variables were indicators of the populat

Karla Alejandra Cutiva-Alvarez, Roger Coziol, Juan Pablo Torres-Papaqui

Using the modelling code X-CIGALE, we reproduced the SEDs of 1,359 SDSS QSOs within the redshift range 0 < z < 4, for which we have NIR/MIR fluxes with the highest quality and spectral data characterizing their SMBHs. Consistent with a rapid formation of the host galaxies, the star formation histories (SFHs) have small e-folding, at most 750 Myrs using an SF

Likang Wang, Lei Chen

Existing one-shot neural architecture search (NAS) methods have to conduct a search over a giant super-net, which leads to the huge computational cost. To reduce such cost, in this paper, we propose a method, called FTSO, to divide the whole architecture search into two sub-steps. Specifically, in the first step, we only search for the topology, and in the s

Mostafa Maslouhi, Kasso A. Okoudjou

The HRT conjecture states that any finite collection of time-frequency shifts of a non-zero square-integrable function on the real line is linearly independent. In this paper, we establish the linear independence of finite systems of time-frequency shifts of a non-zero meromorphic function. Consequently, we prove that the conjecture is true for any square-in

Kritti Sharma, Jean Somalwar, Casey Law, Vikram Ravi

The stellar population environments associated with fast radio burst (FRB) sources provide important insights for developing their progenitor theories. We expand the diversity of known FRB host environments by reporting two FRBs in massive galaxy clusters discovered by the Deep Synoptic Array (DSA-110) during its commissioning observations. FRB 20220914A has

Sarah Greer, Laurent Demanet

Wave-based imaging techniques use wavefield data from receivers on the boundary of a domain to produce an image of the underlying structure in the domain of interest. These images are defined by the imaging condition, which maps recorded data to their reflection points in the domain. In this paper, we introduce a nonlinear modification to the standard imagin

Somayeh Zamani, Hamed Talebi, Gunnar Stevens

Fixing energy leakage caused by different anomalies can result in significant energy savings and extended appliance life. Further, it assists grid operators in scheduling their resources to meet the actual needs of end users, while helping end users reduce their energy costs. In this paper, we analyze the patterns pertaining to the power consumption of dishw

Lewis Croney, Sarben Sarkar

The renormalisation group flow of a Hermitian field theory is shown to have trajectories which lead to a non-Hermitian Parity-Time ($\mathcal{PT}$) symmetric field theory for an axion coupled to a fermion in spacetime dimensions $D=4-\epsilon$, where $\epsilon >0 $. In this renormalisable field theory, the Dirac fermion field has a Yukawa coupling $g$ to a p

Hannes Knötzele, Christoph Schweigert, Matthias Traube

We discuss a string-net construction on 2-framed surfaces, taking as algebraic input a finite, rigid tensor category, which is neither assumed to be pivotal nor semi-simple. It is shown that circle categories of our framed string-net construction essentially compute Drinfeld centers twisted by powers of the double dual functor.

Aamir Sahil Chandroth, Nithya Ramakrishnan, Sanjay Chandrasekharan

When biological communities use signaling structures for complex coordination, 'free-riders' emerge. The free-riding agents do not contribute to the community resources (signals), but exploit them. Most models of such 'selfish' behavior consider free-riding as evolving through mutation and selection. Over generations, the mutation -- which is considered to c

Aakansha Mishra, Ashish Anand, Prithwijit Guha

The use of complex attention modules has improved the performance of the Visual Question Answering (VQA) task. This work aims to learn an improved multi-modal representation through dense interaction of visual and textual modalities. The proposed model has an attention block containing both self-attention and co-attention on image and text. The self-attentio

Abdelatif Gadoum, Djilali Benyoucef, Rachid Taleb

The electrical discharge plasma in methane is used in many fields of technology and the knowledge of the electron impact cross sections with the molecule of this gas is necessary for the modeling, which represents a good tool for understanding this phenomenon. The main object of this work is a finding the important electron impact cross sections with the met

J A Sellwood, R G Carlberg

The problem of how some disk galaxies avoid forming bars remains unsolved. Many galaxy models having reasonable properties continue to manifest vigorous instabilities that rapidly form strong bars and no widely-accepted idea has yet been advanced to account for how some disk galaxies manage to avoid this instability. It is encouraging that not all galaxies f

Jacek Dobrzyniecki, Michał Tomza

Central spin models, where a single spinful particle interacts with a spin environment, find wide application in quantum information technology and can be used to describe, e.g., the decoherence of a qubit over time. We propose a method of realizing an ultracold quantum simulator of a central spin model with XX (spin-exchanging) interactions. The proposed sy

David Cabezas, Antonio M. Peralta

We prove that every commutative JB$^*$-triple has numerical index one. We also revisit the notion of commutativity in JB$^*$-triples to show that a JBW$^*$-triple $M$ has numerical index one precisely when it is commutative, while $e^{-1}\leq n(M) \leq 2^{-1}$ otherwise. Consequently, a JB$^*$-triple $E$ is commutative if and only if $n(E^*) =1$ (equivalentl

Shun Lu, Yu Hu, Longxing Yang, Zihao Sun

Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer from different gradient descent directions during training. And we further find that large gradient variance occu

Wei Huang, Zhiliang Peng, Li Dong, Furu Wei

Large vision Transformers (ViTs) driven by self-supervised pre-training mechanisms achieved unprecedented progress. Lightweight ViT models limited by the model capacity, however, benefit little from those pre-training mechanisms. Knowledge distillation defines a paradigm to transfer representations from large (teacher) models to small (student) ones. However

Jigyasa Nigam, Sergey N. Pozdnyakov, Kevin K. Huguenin-Dumittan, Michele Ceriotti

In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with the widespread adoption of machine-learning techniques in science, as it underpins the capacity of model

Voncarlos Marcelo Araújo, Sébastien Gambs, Clément Chion, Robert Michaud

To efficiently monitor the growth and evolution of a particular wildlife population, one of the main fundamental challenges to address in animal ecology is the re-identification of individuals that have been previously encountered but also the discrimination between known and unknown individuals (the so-called "open-set problem"), which is the first step to

Shan Wang, Hanxiang Peng

In this article, we construct semiparametrically efficient estimators of linear functionals of a probability measure in the presence of side information using an easy empirical likelihood approach. We use estimated constraint functions and allow the number of constraints to grow with the sample size. Considered are three cases of information which can be cha

Erik Piatti, Daniele Torsello, Gianluca Ghigo, Dario Daghero

The iron-based compounds of the so-called 12442 family are very peculiar in various respects. They originate from the intergrowth of 122 and 1111 building blocks, display a large in-plane vs. out-of-plane anisotropy, possess double layers of FeAs separated by insulating layers, and are generally very similar to double-layer cuprates. Moreover, they are stoic

Konstantina Chalkou, Tasnim Hamza, Pascal Benkert, Jens Kuhle

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types

Luciano Miuccio, Salvatore Riolo, Mehdi Bennis, Daniela Panno

In this paper, we propose a novel framework for designing a fast convergent multi-agent reinforcement learning (MARL)-based medium access control (MAC) protocol operating in a single cell scenario. The user equipments (UEs) are cast as learning agents that need to learn a proper signaling policy to coordinate the transmission of protocol data units (PDUs) to

Xiao Tang, Hongliang He, Limeng Dong, Lixin Li

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this paper, we consider the anti-eavesdropping communication established through a RIS at a fixed location, as well as an aerial platform mounti

Kévin Cortacero, Brienne McKenzie, Sabina Müller, Roxana Khazen

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. Crucially however, these frameworks

Xiaoyu Zhang

We give an elementary proof of the convergence of indefinite theta series associated to an inner space of signature $(n,2)$ conjectured in the work of Alexandrov,Banerjee,Manschot and Pioline (2018) and show that the incidence conditions are also necessary for the convergence.

Imran Khan, Vikrant Saxena

The interaction of a high-intensity ultrashort laser pulse with a few microns-thick hydrocarbon target is known to accelerate protons/ions to multi-MeV, on the rear side of the target, via the mechanism of target normal sheath acceleration. Micro-structuring the target front is one of the promising approaches to enhance the cut-off energy as well as to reduc

Samuel Ranz, Lauran Toussaint

Novikov's theorem states that, given a taut (codimension-one) foliation on a closed 3-manifold M, the fundamental group of any leaf injects into the fundamental group of M. We use foliated branched covers to give a simple proof of this result.

Marin Bužančić, Elisa Davoli, Igor Velčić

We identify effective models for thin, linearly elastic and perfectly plastic plates exhibiting a microstructure resulting from the periodic alternation of two elastoplastic phases. We study here both the case in which the thickness of the plate converges to zero on a much faster scale than the periodicity parameter and the opposite scenario in which homogen

Prachi Singh, Srikrishna Karanam, Sumit Shekhar

We consider and propose a new problem of retrieving audio files relevant to multimodal design document inputs comprising both textual elements and visual imagery, e.g., birthday/greeting cards. In addition to enhancing user experience, integrating audio that matches the theme/style of these inputs also helps improve the accessibility of these documents (e.g.

Matan Yah Ben Zion, Naomi Oppenheimer

Short-range repulsion governs the dynamics of matter from atoms to animals. Using theory, simulations, and experiments, we find that an ensemble of repulsive particles spreads compactly with a sharp boundary, in contrast to the diffusive spreading of Brownian particles. Starting from the pair interactions, at high densities, the many-body dynamics follow non

Nolan J. Coble, Matthew Coudron, Jon Nelson, Seyed Sajjad Nezhadi

The recently-defined No Low-energy Sampleable States (NLSS) conjecture of Gharibian and Le Gall [GL22] posits the existence of a family of local Hamiltonians where all states of low-enough constant energy do not have succinct representations allowing perfect sampling access. States that can be prepared using only Clifford gates (i.e. stabilizer states) are a

Ahmed Hossain, Xiaoduan Sun, Shahrin Islam, Shah Alam

More than half of all fatalities on U.S. highways occur due to roadway departure (RwD) each year. Previous research has explored various risk factors that contribute to RwD crashes, however, a comprehensive investigation considering the effect of lighting conditions has been insufficiently addressed. Using the Louisiana Department of Transportation and Devel

Sham M. Kakade, Akshay Krishnamurthy, Gaurav Mahajan, Cyril Zhang

This paper is concerned with the computational complexity of learning the Hidden Markov Model (HMM). Although HMMs are some of the most widely used tools in sequential and time series modeling, they are cryptographically hard to learn in the standard setting where one has access to i.i.d. samples of observation sequences. In this paper, we depart from this s

Tongjia Zheng, Zhenyuan Yuan, Mollik Nayyar, Alan R. Wagner

Emergency evacuation describes a complex situation involving time-critical decision-making by evacuees. Mobile robots are being actively explored as a potential solution to provide timely guidance. In this work, we study a robot-guided crowd evacuation problem where a small group of robots is used to guide a large human crowd to safe locations. The challenge

Niklas Fahse, Matthew Millard, Fabian Kempter, Steffen Maier

Significant trends in the vehicle industry are autonomous driving, micromobility, electrification and the increased use of shared mobility solutions. These new vehicle automation and mobility classes lead to a larger number of occupant positions, interiors and load directions. As safety systems interact with and protect occupants, it is essential to place th

J. C. Hood, A. Simpson, A. McDaniel, A. Foster

Including millimeter-wave (mm-wave) data in multi-wavelength studies of the variability of active galactic nuclei (AGN) can provide insights into AGN physics that are not easily accessible at other wavelengths. We demonstrate in this work the potential of cosmic microwave background (CMB) telescopes to provide long-term, high-cadence mm-wave AGN monitoring o

Bunlong Lay, Simon Welker, Julius Richter, Timo Gerkmann

Recently, score-based generative models have been successfully employed for the task of speech enhancement. A stochastic differential equation is used to model the iterative forward process, where at each step environmental noise and white Gaussian noise are added to the clean speech signal. While in limit the mean of the forward process ends at the noisy mi

C. Gomez

Infinite entanglement fluctuations appear when a quantum field theory on a causally complete domain of space-time is a type $III$ factor. In the weak gravity limit $G_N=0$ this factor can be transformed into a crossed product type $II$ factor with finite entanglement fluctuations by adding a physical reference frame system (observer). The use of a physical r

Ji Hou, Xiaoliang Dai, Zijian He, Angela Dai

Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understand 3D structural priors in 2D backbones, we propose Mask3D to leverage existing large-scale RGB-D data in a self-supervised pre-training to embed these 3D priors into 2D learned fea

Movaffaq Kateb, Jon Tomas Gudmundsson, Snorri Ingvarsson

We present optimization of [(15 $\unicode{x212B}$) Ni$_{80}$Fe$_{20}$/(5 $\unicode{xC5}$) M]$_{20}$ single crystal multilayers on (001) MgO, with M being Cu, Cu$_{50}$Pt$_{50}$ and Pt. These superlattices were characterized by high-resolution X-ray reflectivity (XRR) and diffraction (XRD) as well as polar mapping of important crystal planes. It is shown that

Max Biggs, Georgia Perakis

We focus on modeling the relationship between an input feature vector and the predicted outcome of a trained decision tree using mixed-integer optimization. This can be used in many practical applications where a decision tree or tree ensemble is incorporated into an optimization problem to model the predicted outcomes of a decision. We propose tighter mixed

Mudasser Seraj

Insight into individual driving behavior and habits is essential in traffic operation, safety, and energy management. With Connected Vehicle (CV) technology aiming to address all three of these, the identification of driving patterns is a necessary component in the design of personalized Advanced Driver Assistance Systems (ADAS) for CVs. Our study aims to ad

Aliakbar Kabiri, Aref Darzi, Saeed Saleh Namadi, Yixuan Pan

Mobile Device Location Data (MDLD) has been popularly utilized in various fields. Yet its large-scale applications are limited because of either biased or insufficient spatial coverage of the data from individual data vendors. One approach to improve the data coverage is to leverage the data from multiple data vendors and integrate them to build a more repre

Nicolas Amat, Silvano Dal Zilio

SMPT (for Satisfiability Modulo Petri Net) is a model checker for reachability problems in Petri nets. It started as a portfolio of methods to experiment with symbolic model checking, and was designed to be easily extended. Some distinctive features are its ability to benefit from structural reductions and to generate verdict certificates. Our tool is quite

Harsh Vardhan, Peter Volgyesi, Janos Sztipanovits

In computer-aided engineering design, the goal of a designer is to find an optimal design on a given requirement using the numerical simulator in loop with an optimization method. In this design optimization process, a good design optimization process is one that can reduce the time from inception to design. In this work, we take a class of design problem, t

Sebastian Baudelet, Brieuc Frénais, Mathieu Laurière, Amal Machtalay

Mean field control (MFC) problems have been introduced to study social optima in very large populations of strategic agents. The main idea is to consider an infinite population and to simplify the analysis by using a mean field approximation. These problems can also be viewed as optimal control problems for McKean-Vlasov dynamics. They have found application

Wiillem A. Baan, Tao AN, Christian Henkel, Hiroshi Imai

H$_2$O MegaMaser emission may arise from thin gas discs surrounding the massive nuclei of galaxies such as NGC\,4258, but the physical conditions responsible for the amplified emission are unclear. A detailed view of these regions is possible using the very high angular resolution afforded by space very long baseline interferometry (SVLBI). Here we report SV

Lisa Sauermann, Dmitrii Zakharov

The Erd\H{o}s--Ginzburg--Ziv Problem is a classical extremal problem in discrete geometry. Given $m$ and $n$, the problem asks about the smallest number $s$ such that among any $s$ points in the integer lattice $\mathbb{Z}^n$ one can find $m$ points whose centroid is again a lattice point. Despite of a lot of attention over the last 50 years, this problem is

Yunpeng Bai, Cairong Wang, Shuzhao Xie, Chao Dong

Most existing image restoration methods use neural networks to learn strong image-level priors from huge data to estimate the lost information. However, these works still struggle in cases when images have severe information deficits. Introducing external priors or using reference images to provide information also have limitations in the application domain.

Sandipan Das, Bengt Boberg, Maurice Fallon, Saikat Chatterjee

Modern autonomous systems typically use several sensors for perception. For best performance, accurate and reliable extrinsic calibration is necessary. In this research, we propose a reliable technique for the extrinsic calibration of several lidars on a vehicle without the need for odometry estimation or fiducial markers. First, our method generates an init

David Jordan

I introduce new Langlands duality conjectures concerning skein modules of 3-manifolds, which we have made recently with David Ben-Zvi, Sam Gunningham, and Pavel Safronov. I recount some historical motivation and some recent special cases where the conjecture is confirmed. The proofs in these cases combine the representation theory of double affine Hecke alge

Johannes Zirkelbach, Burak Gurlek, Masoud Mirzaei, Alexey Shkarin

The small cross section of Raman scattering poses a great challenge for its direct study at the single-molecule level. By exploiting the high Franck-Condon factor of a common-mode resonance, choosing a large vibrational frequency difference in electronic ground and excited states and operation at T < 2K, we succeed at driving a coherent stimulated Raman tran

Harsh Vardhan, Peter Volgyesi, Will Hedgecock, Janos Sztipanovits

Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optimization framework with the integrated to

Alessandro Candido, Luigi Del Debbio, Tommaso Giani, Giacomo Petrillo

The determination of Parton Distribution Functions from a finite set of data is a typical example of an inverse problem. Inverse problems are notoriously difficult to solve, in particular when a robust determination of the uncertainty in the result is needed. We present a Bayesian framework to deal with this problem and discuss first results from a closure t

Callum Cuttle, Liam C. Morrow, Christopher W. MacMinn

The displacement of a viscous liquid by a gas within a Hele-Shaw cell is a classical problem. The gas-liquid interface is hydrodynamically unstable, forming striking finger-like patterns that have attracted research interest for decades. Generally, both the gas and liquid phases are taken to be incompressible, with the capillary number being the key paramete

E. F. Talantsev

The relationship between the long-range antiferromagnetic order in cuprates and the high-temperature superconductivity in these compounds represents unresolved, nearly four-decades long scientific problem. Because recently discovered nickelate superconductors are crystallographical counterparts of cuprates, many properties and difficulties into describing th

Prasun Roy, Saumik Bhattacharya, Subhankar Ghosh, Umapada Pal

We propose a data-driven approach for context-aware person image generation. Specifically, we attempt to generate a person image such that the synthesized instance can blend into a complex scene. In our method, the position, scale, and appearance of the generated person are semantically conditioned on the existing persons in the scene. The proposed technique

Anna Koufakou, Jairo Garciga, Adam Paul, Joseph Morelli

Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three datasets that were recently presented i

Elias Arnold, Georg Böcherer, Florian Strasser, Eric Müller

Neuromorphic computing implementing spiking neural networks (SNN) is a promising technology for reducing the footprint of optical transceivers, as required by the fast-paced growth of data center traffic. In this work, an SNN nonlinear demapper is designed and evaluated on a simulated intensity-modulation direct-detection link with chromatic dispersion. The

Jakob Baumann, Matthias Pfretzschner, Ignaz Rutter

Motivated by the planarization of 2-layered straight-line drawings, we consider the problem of modifying a graph such that the resulting graph has pathwidth at most 1. The problem Pathwidth-One Vertex Explosion (POVE) asks whether such a graph can be obtained using at most $k$ vertex explosions, where a vertex explosion replaces a vertex $v$ by deg$(v)$ degr

P. Diego-Palazuelos, R. B. Barreiro, P. Vielva, D. Balbás

We propose a machine learning approach to the blind detection of extragalactic point sources on maps of the temperature anisotropies of the cosmic microwave background. Using realistic simulations of the microwave sky as seen by Planck, we train a convolutional neural network (CNN) that solves source detection as an image segmentation problem. We divide the

Carlos Lassance

This paper describes our participation in the 2023 WSDM CUP - MIRACL challenge. Via a combination of i) document translation; ii) multilingual SPLADE and Contriever; and iii) multilingual RankT5 and many other models, we were able to get first place in both the known and surprise languages tracks. Our strategy mostly revolved around getting the most diverse

Zeus S. Moreira, Haroldo C. D. Lima Junior, Luís C. B. Crispino, Carlos A. R. Herdeiro

We analyze the quasinormal modes (QNMs) of a recently obtained solution of a Schwarzschild black hole (BH) with corrections motivated by Loop Quantum Gravity (LQG). This spacetime is regular everywhere and presents the global structure of a wormhole, with a minimal surface whose radius depends on a LQG parameter. We focus on the investigation of massless sca

Adrian Bach

To date, the mechanisms underlying the diversity of the emergent patterns of collective motion in locust hopper bands remain to be unveiled. This study investigates the role of speed heterogeneity in the emergence of the most common patterns (frontal and columnar), following the Self-Organization framework. To address whether marching activity intermittency

Rahul Jain, Marco Ricci, Jonathan Rollin, André Schulz

A graph is 2-degenerate if every subgraph contains a vertex of degree at most 2. We show that every 2-degenerate graph can be drawn with straight lines such that the drawing decomposes into 4 plane forests. Therefore, the geometric arboricity, and hence the geometric thickness, of 2-degenerate graphs is at most 4. On the other hand, we show that there are 2-

Tristan Protzman, Joel Giedt

When simulating a lattice system near its critical temperature, local algorithms for modeling the system's evolution can introduce very large autocorrelation times into sampled data. This critical slowing down places restrictions on the analysis that can be completed in a timely manner of the behavior of systems around the critical point. Because it is often

Kai Sun, Richong Zhang, Samuel Mensah, Nikolaos Aletras

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adver

Francesco Mattiotti, Jérôme Dubail, David Hagenmüller, Johannes Schachenmayer

We analyze the spectral and transport properties of the interacting disordered Tavis-Cummings model at half excitation filling. We demonstrate that a Poissonian level statistics coexists with eigenfunctions that are multifractal (extended, but non-ergodic) in the Hilbert space, for all strengths of light-matter interactions. This is associated with a lack of

Xiaoying Wei, Yizheng Gu, Emily Kuang, Xian Wang

When living apart, grandparents and grandchildren often use audio-visual communication approaches to stay connected. However, these approaches seldom provide sufficient companionship and intimacy due to a lack of co-presence and spatial interaction, which can be fulfilled by immersive virtual reality (VR). To understand how grandparents and grandchildren mig

Sylvio R. Bistafa

Variational formulations for viscous flows which lead to the Navier-Stokes equation are examined. Since viscosity leads to dissipation and, therefore, to the irreversible transfer of mechanical energy to heat, thermal degrees of freedom have been included in the construction of viscous dissipative Lagrangians, by embedding of thermodynamics aspects of the fl

Mujan N. Seif, Jake Puppo, Metodi Zlatinov, Denver Schaffarzick

Metallic foams are crucial to many emerging applications, among them shielding against hypervelocity impacts caused by micrometeoroids and orbital debris. The variability of properties at feature-scale and mesoscale lengths originating from the foam's inherently random microstructure makes predictive models of their properties challenging. It also hinders th

Vincent Corlay, Jean-Christophe Sibel

Standard Markov decision process (MDP) and reinforcement learning algorithms optimize the policy with respect to the expected gain. We propose an algorithm which enables to optimize an alternative objective: the probability that the gain is greater than a given value. The algorithm can be seen as an extension of the value iteration algorithm. We also show ho

Simon Tschirner, Katharina Zeuch, Sascha Kaven, Lorenz Bornholdt

Proof-of-Location (PoL) is a lightweight security concept for Internet-of-Things (IoT) networks, focusing on the sensor nodes as the least performant and most vulnerable parts of IoT networks. PoL builds on the identification of network participants based on their physical location. It introduces a secondary message type to exchange location information. Via

Sazia Mahfuz

Lifelong machine learning (LML) is an area of machine learning research concerned with human-like persistent and cumulative nature of learning. LML system's objective is consolidating new information into an existing machine learning model without catastrophically disrupting the prior information. Our research addresses this LML retention problem for creatin

Ali Binai-Motlagh, Matthew Day, Nikolay Videnov, Noah Greenberg

Trapped ions are one of the leading platforms for quantum information processing, exhibiting the highest gate and measurement fidelities of all contending hardware. In order to realize a universal quantum computer with trapped ions, independent and parallel control over the state of each qubit is necessary. The manipulation of individual qubit states in an i

Daisuke Bekki, Hitomi Yanaka

The Japanese CCGBank serves as training and evaluation data for developing Japanese CCG parsers. However, since it is automatically generated from the Kyoto Corpus, a dependency treebank, its linguistic validity still needs to be sufficiently verified. In this paper, we focus on the analysis of passive/causative constructions in the Japanese CCGBank and show

Alexander Simm, Shai Machnes, Frank K. Wilhelm

We show that it is theoretically possible to use higher energy levels for storing and controlling two qubits within a superconducting transmon. This is done by identifying energy levels as product states between multiple effecitve qubits. As a proof of concept we realise a complete set of gates necessary for universal computing by numerically optimising cont

Shikhar Tuli, Niraj K. Jha

Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is challenging due to its quadratic computational complexity and large activation sizes. Existing transformer accelerators attempt to prune its tokens to reduce memory access, albeit with

Yamuna Krishnamurthy, Chris Watkins, Thomas Gaertner

Mixture of experts (MoE), introduced over 20 years ago, is the simplest gated modular neural network architecture. There is renewed interest in MoE because the conditional computation allows only parts of the network to be used during each inference, as was recently demonstrated in large scale natural language processing models. MoE is also of potential inte

Marios Mavronicolas, Paul G. Spirakis

We consider a contest game modelling a contest where reviews for $m$ proposals are crowdsourced from $n$ strategic agents} players. Player $i$ has a skill $s_{i\ell}$ for reviewing proposal $\ell$; for her review, she strategically chooses a quality $q \in \{ 1, 2, \ldots, Q \}$ and pays an effort ${\sf f}_{q} \geq 0$, strictly increasing with $q$. For her e

Benjamin Allen, Abdur-Rahman Khwaja, James L. Donahue, Cassidy Lattanzio

Organisms from microbes to humans engage in a variety of social behaviors, which affect fitness in complex, often nonlinear ways. The question of how these behaviors evolve has consequences ranging from antibiotic resistance to human origins. However, evolution with nonlinear social interactions is challenging to model mathematically, especially in combinati

Marc Hermes, Dominik Kirst

Tennenbaum's theorem states that the only countable model of Peano arithmetic (PA) with computable arithmetical operations is the standard model of natural numbers. In this paper, we use constructive type theory as a framework to revisit, analyze and generalize this result. The chosen framework allows for a synthetic approach to computability theory, exploit

Samin Aref, Mahdi Mostajabdaveh, Hriday Chheda

Community detection is a fundamental problem in computational sciences with extensive applications in various fields. The most commonly used methods are the algorithms designed to maximize modularity over different partitions of the network nodes. Using 80 real and random networks from a wide range of contexts, we investigate the extent to which current heur

Viktoriia Borovik, Paul Breiding

The Parameter Continuation Theorem is the theoretical foundation for polynomial homotopy continuation, which is one of the main tools in computational algebraic geometry. In this note, we give a short proof using Gr\"obner bases. Our approach gives a method for computing discriminants.

Jian Shi, Pengyi Zhang, Ni Zhang, Hakim Ghazzai

Medical imaging often contains critical fine-grained features, such as tumors or hemorrhages, crucial for diagnosis yet potentially too subtle for detection with conventional methods. In this paper, we introduce \textit{DIA}, dissolving is amplifying. DIA is a fine-grained anomaly detection framework for medical images. First, we introduce \textit{dissolving

Xiang Li, Xinrui Wang, Songcan Chen

In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternative should be Single Positive Multi-Label Learning (SPMLL), where only one positive label needs to be provided per sample. Existing SPMLL meth

Jonathan Gräfe, Falk Adamietz, Ralf Schützhold

We study the interaction between gravitational waves and quantum matter such as Bose-Einstein condensates, super-fluid Helium, or ultra-cold solids, explicitly taking into account the changes of the trapping potential induced by the gravitational wave. As a possible observable, we consider the change of energy due to the gravitational wave, for which we deri

Heather M. Hill, Ching-Chen Yeh, Swapnil M. Mhatre, Ngoc Thanh Mai Tran

We demonstrate the preparation of both armchair and zigzag epitaxial graphene nanoribbons (GNRs) on 4H-SiC using a polymer-assisted, sublimation growth method. Historically, the preparation of GNRs depended on the quality, or smoothness, of the surface changes during growth. The physical phenomenon of terrace step formation introduces the risk of GNR deforma

Orr Fischer, Adi Horowitz, Rotem Oshman

Massively-parallel graph algorithms have received extensive attention over the past decade, with research focusing on three memory regimes: the superlinear regime, the near-linear regime, and the sublinear regime. The sublinear regime is the most desirable in practice, but conditional hardness results point towards its limitations. In this work we study a \e

Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang, Hyeongu Yun

In this paper, we present our finding that prepending a Task-Agnostic Prefix Prompt (TAPP) to the input improves the instruction-following ability of various Large Language Models (LLMs) during inference. TAPP is different from canonical prompts for LLMs in that it is a fixed prompt prepended to the beginning of every input regardless of the target task for

Steffen Dereich, Arnulf Jentzen, Sebastian Kassing

In this article, we show existence of minimizers in the loss landscape for residual artificial neural networks (ANNs) with multi-dimensional input layer and one hidden layer with ReLU activation. Our work contrasts earlier results in [D. Gallon, A. Jentzen, and F. Lindner, preprint, arXiv:2211.15641, 2022] and [P. Petersen, M. Raslan, and F. Voigtlaender, Fo