October 2023 arXiv papers — page 178

Aurélie Astoul, Adrian J. Barker

Most prior works studying tidal interactions in tight star/planet or star/star binary systems have employed linear theory of a viscous fluid in a uniformly-rotating two-dimensional spherical shell. However, compact systems may have sufficiently large tidal amplitudes for nonlinear effects to be important. We compute tidal flows subject to nonlinear effects i

Jenna M. Cann, Kimberly A. Weaver, Ryan W. Pfeifle, Nathan J. Secrest

One of the primary outstanding questions in extragalactic astronomy is the formation and early evolution of the supermassive black holes that are seen in nearly every massive galaxy. Low metallicity dwarf galaxies may offer the most representative local analogs to pristine early galaxies, making them a vital tool in probing black hole seed models through the

Yi-Lin Sung, Jaehong Yoon, Mohit Bansal

Large Vision-Language Models (LVLMs) can understand the world comprehensively by integrating rich information from different modalities, achieving remarkable advancements on various multimodal downstream tasks. However, deploying LVLMs is often problematic due to their massive computational/energy costs and carbon consumption. Such issues make it infeasible

Daile Osorio-Roig, Mahdi Ghafourian, Christian Rathgeb, Ruben Vera-Rodriguez

Nowadays, facial recognition systems are still vulnerable to adversarial attacks. These attacks vary from simple perturbations of the input image to modifying the parameters of the recognition model to impersonate an authorised subject. So-called privacy-enhancing facial recognition systems have been mostly developed to provide protection of stored biometric

Shahram Yadollahi, Hamed Kebriaei, Sadegh Soudjani

In this paper, we focus on modeling and analysis of demand-side management in a microgrid where agents utilize grid energy and a shared battery charged by renewable energy sources. We model the problem as a generalized stochastic dynamic aggregative game with chance constraints that capture the effects of uncertainties in the renewable generation and agents'

Pengyuan Lu, Michele Caprio, Eric Eaton, Insup Lee

Like generic multi-task learning, continual learning has the nature of multi-objective optimization, and therefore faces a trade-off between the performance of different tasks. That is, to optimize for the current task distribution, it may need to compromise performance on some previous tasks. This means that there exist multiple models that are Pareto-optim

Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker, Ruben Ohana

We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers. In MPP, rather than training one model on a specific physical system, we train a backbone model to predict the dynamics of multiple heterogeneous physical systems simultaneously in

Iiro Kumpulainen, Nikolaj Tatti

Grouping the nodes of a graph into clusters is a standard technique for studying networks. We study a problem where we are given a directed network and are asked to partition the graph into a sequence of coherent groups. We assume that nodes in the network have features, and we measure the group coherence by comparing these features. Furthermore, we incorpor

Xichen Pan, Li Dong, Shaohan Huang, Zhiliang Peng

Recent advancements in subject-driven image generation have made significant strides. However, current methods still fall short in diverse application scenarios, as they require test-time tuning and cannot accept interleaved multi-image and text input. These limitations keep them far from the ultimate goal of "image as a foreign language in image generation.

D. V. Khveshchenko

The single-particle behavior in $d\geq 1$-dimensional Fermi gases with a large number $N$ of species and strong short-range $s$-wave scattering is discussed in the $2d$ 'tomographic' framework of a (pseudo)holographic correspondence with a certain $3d$ gravity of the $AdS_3$ type. However, due to the intrinsically topological nature of such a bulk theory its

Nwosu Obinnaya Chikezie Victor

Fieldwire, a cloud-based construction management software, has become a pivotal tool in the construction industry. It offers a comprehensive suite of features encompassing project management, task tracking, document management, and collaboration. With the rise of Application Programming Interfaces (APIs) in the software industry, Fieldwire has harnessed this

Siavash Golkar, Mariel Pettee, Michael Eickenberg, Alberto Bietti

Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific datasets. Rendering datasets as text, however, could help aggregate diverse and multi-modal scientific data into a single training corpus, thereby potentially facilitating the deve

Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno

While vision-language models (VLMs) have achieved remarkable performance improvements recently, there is growing evidence that these models also posses harmful biases with respect to social attributes such as gender and race. Prior studies have primarily focused on probing such bias attributes individually while ignoring biases associated with intersections

Xufeng Cai, Ahmet Alacaoglu, Jelena Diakonikolas

Machine learning approaches relying on such criteria as adversarial robustness or multi-agent settings have raised the need for solving game-theoretic equilibrium problems. Of particular relevance to these applications are methods targeting finite-sum structure, which generically arises in empirical variants of learning problems in these contexts. Further, m

Luigi Palmieri, Chiara Boldrini, Lorenzo Valerio, Andrea Passarella

Fully decentralized learning enables the distribution of learning resources and decision-making capabilities across multiple user devices or nodes, and is rapidly gaining popularity due to its privacy-preserving and decentralized nature. Importantly, this crowdsourcing of the learning process allows the system to continue functioning even if some nodes are a

Giuseppe Bisicchia, Stefano Forti, Ernesto Pimentel, Antonio Brogi

The problem of managing multi-service applications on top of Cloud-Edge networks in a QoS-aware manner has been thoroughly studied in recent years from a decision-making perspective. However, only a few studies addressed the problem of actively enforcing such decisions while orchestrating multi-service applications and considering infrastructure and applicat

Vivien Cabannes, Elvis Dohmatob, Alberto Bietti

Learning arguably involves the discovery and memorization of abstract rules. The aim of this paper is to study associative memory mechanisms. Our model is based on high-dimensional matrices consisting of outer products of embeddings, which relates to the inner layers of transformer language models. We derive precise scaling laws with respect to sample size a

Walter Schirmacher, Matteo Paoluzzi, Felix Cosmin Mocanu, Dmytro Khomenko

Using heterogeneous-elasticity theory (HET) and a generalisation of HET theory (GHET), obtained by applying a newly developed procedure for obtaining the continuum limit of the glass's Hessian, we investigate the nature of vibrational excitations, which are present in small systems, which do not allow for low-frequency phonons. We identify two types of such

James Q. Feng

Zeno's ancient paradox depicts a race between swift Achilles and a slow tortoise with a head start. Zeno argued that Achilles could never overtake the tortoise, as at each step Achilles arrived at the tortoise's former position, the tortoise had already moved ahead. Though Zeno's premise is valid, his conclusion that Achilles can "never" pass the tortoise re

Jun Ho Choi, Oliver Garrod, Paul Atherton, Andrew Joyce-Gibbons

Education systems in developing countries have few resources to serve large, poor populations. How might generative AI integrate into classrooms? This paper introduces an AI chatbot designed to assist teachers in Sierra Leone with professional development to improve their instruction. We describe initial findings from early implementation across 122 schools

Ivan D. Markozov, Alexander A. Mushtukov

The luminosity of X-ray pulsars is their key parameter determining the geometry and physical conditions of the accretion flow both on the spatial scales of a binary system and on much smaller scales of emitting regions located close to the stellar surface. Traditionally, the luminosity of X-ray pulsars is estimated out of the X-ray energy flux averaged over

Ido Amos, Jonathan Berant, Ankit Gupta

Modeling long-range dependencies across sequences is a longstanding goal in machine learning and has led to architectures, such as state space models, that dramatically outperform Transformers on long sequences. However, these impressive empirical gains have been by and large demonstrated on benchmarks (e.g. Long Range Arena), where models are randomly initi

Richard Bi, Peter Bradshaw

In the flexible list coloring problem, we consider a graph $G$ and a color list assignment $L$ on $G$, as well as a subset $U \subseteq V(G)$ for which each $u \in U$ has a preferred color $p(u) \in L(u)$. Our goal is to find a proper $L$-coloring $\phi$ of $G$ such that $\phi(u) = p(u)$ for at least $\epsilon|U|$ vertices $u \in U$. We say that $G$ is $\eps

S. Arora, T. Bauer, R. Barczyk, E. Verhagen

We present an experimental investigation of multiple scattering in photonic-crystal-based topological edge states with and without engineered random disorder. We map the spatial distribution of light as it propagates along a so-called bearded interface between two valley photonic crystals which supports both trivial and non-trivial edge states. As the light

Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao

Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF. Notably, these methods are able to produce high-quality 3D scenes without training on 3D data. Due to the open-ended nature of the task, most studies evaluate their results with subjective case studies and user experiments, thereby presenting a challenge in quantitat

Harikrishnan G, Shashwata Chattopadhyay, K. Bandopadhyay, K. Kolodziejak

We investigate anomalous electrical transport properties of a Ni-TiO2 hybrid system displaying a unique nanostructured morphology. The system undergoes an insulator to metal transition below 150 K with a low temperature metallic phase that shows negative resistance in a four-probe configuration. Temperature dependent transport measurements and numerical mode

Gianmarco Genalti, Lupo Marsigli, Nicola Gatti, Alberto Maria Metelli

Heavy-tailed distributions naturally arise in several settings, from finance to telecommunications. While regret minimization under subgaussian or bounded rewards has been widely studied, learning with heavy-tailed distributions only gained popularity over the last decade. In this paper, we consider the setting in which the reward distributions have finite a

E. Vogel, J. Sekutowicz, D. Bazyl, T. Büttner

In recent years we increased the typical maximum peak field on axis gradients obtained in L-band superconducting RF (SRF) photoinjector cavities at vertical tests to around 55 MV/m. This was achieved with niobium cathode plugs directly screwed to the cavity back wall omitting an RF choke filter and a load lock system for cathodes. Copper demonstrated being a

Siddhant Arora, Hayato Futami, Jee-weon Jung, Yifan Peng

Recent studies leverage large language models with multi-tasking capabilities, using natural language prompts to guide the model's behavior and surpassing performance of task-specific models. Motivated by this, we ask: can we build a single model that jointly performs various spoken language understanding (SLU) tasks? We start by adapting a pre-trained autom

Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu

Target segmentation in CT images of Head&Neck (H&N) region is challenging due to low contrast between adjacent soft tissue. The SegRap 2023 challenge has been focused on benchmarking the segmentation algorithms of Nasopharyngeal Carcinoma (NPC) which would be employed as auto-contouring tools for radiation treatment planning purposes. We propose a fully-auto

Kai-Wei Chang, Ming-Hsin Chen, Yun-Ping Lin, Jing Neng Hsu

Prompting and adapter tuning have emerged as efficient alternatives to fine-tuning (FT) methods. However, existing studies on speech prompting focused on classification tasks and failed on more complex sequence generation tasks. Besides, adapter tuning is primarily applied with a focus on encoder-only self-supervised models. Our experiments show that prompti

Erik J Bekkers, Sharvaree Vadgama, Rob D Hesselink, Putri A van der Linden

Based on the theory of homogeneous spaces we derive geometrically optimal edge attributes to be used within the flexible message-passing framework. We formalize the notion of weight sharing in convolutional networks as the sharing of message functions over point-pairs that should be treated equally. We define equivalence classes of point-pairs that are ident

Guancheng Qiu, Mathieu Tanneau, Pascal Van Hentenryck

In recent years, there has been significant interest in the development of machine learning-based optimization proxies for AC Optimal Power Flow (AC-OPF). Although significant progress has been achieved in predicting high-quality primal solutions, no existing learning-based approach can provide valid dual bounds for AC-OPF. This paper addresses this gap by t

Akira Horiguchi, Li Ma, Botond T. Szabó

Many modern statistical applications involve a two-level sampling scheme that first samples subjects from a population and then samples observations on each subject. These schemes often are designed to learn both the population-level functional structures shared by the subjects and the functional characteristics specific to individual subjects. Common wisdom

Linjin Zheng

Dr. Brizard's comment on my work is based on a conceived procedure that does not come from my work. The defense of his claim that the modification of the so-called standard Lie's transform theory is unnecessary is also unsupported. This response reveals in detail the inconsistency issues in the so-called standard Lie's transform theory by analyzing both its

Maria Teresa Chiri, Kenneth D Czuprynski, Ludmil T Zikatanov

In this paper, we study solutions for a weakly coupled system of eikonal equations arising in an optimal path-planning problem with random breakdown. The model considered takes into account two types of breakdown for the vehicle, partial and total, which happen at a known, spatially inhomogeneous rate. In particular, we analyze the complications due to the d

Zachary Morgan, Iris Ye, Colin L. Sarkis, Xiaoping Wang

We report magnetization and neutron diffraction studies on crystal and magnetic structures of Ir- and Rh-substituted honeycomb lattice $\alpha$-RuCl$_3$. The iridium or rhodium atoms are distributed at the Ru site with little structural modification. Both systems undergo a room-temperature monoclinic $C2/m$ to low-temperature trigonal $R\bar{3}$ phase transf

Zihan Liu, Ge Wang, Jiaqi Wang, Jiangbin Zheng

Peptides are formed by the dehydration condensation of multiple amino acids. The primary structure of a peptide can be represented either as an amino acid sequence or as a molecular graph consisting of atoms and chemical bonds. Previous studies have indicated that deep learning routes specific to sequential and graphical peptide forms exhibit comparable perf

Yifeng Xiong, Fan Liu

Location-awareness is essential in various wireless applications. The capability of performing precise ranging is substantial in achieving high-accuracy localization. Due to the notorious ambiguity phenomenon, optimal ranging waveforms should be adaptive to the signal-to-noise ratio (SNR). In this letter, we propose to use the Ziv-Zakai bound (ZZB) as the ra

Jennifer Li

Let $Y$ be a smooth projective $3$-fold admitting a K3 fibration $f : Y \rightarrow \mathbb{P}^1$ with $-K_Y = f^*\mathcal{O}(1)$. We show that the pseudoautomorphism group of $Y$ acts with finitely many orbits on the codimension one faces of the movable cone if $H^3(Y,\mathbb{C})=0$, confirming a special case of the Kawamata--Morrison--Totaro cone conjectur

Masoud Mansoury, Finn Duijvestijn, Imane Mourabet

Popularity bias is a well-known issue in recommender systems where few popular items are over-represented in the input data, while majority of other less popular items are under-represented. This disparate representation often leads to bias in exposure given to the items in the recommendation results. Extensive research examined this bias from item perspecti

Yang Cao, Yihan Zeng, Hang Xu, Dan Xu

Open-vocabulary 3D Object Detection (OV-3DDet) aims to detect objects from an arbitrary list of categories within a 3D scene, which remains seldom explored in the literature. There are primarily two fundamental problems in OV-3DDet, i.e., localizing and classifying novel objects. This paper aims at addressing the two problems simultaneously via a unified fra

Binqi Sun, Debayan Roy, Tomasz Kloda, Andrea Bastoni

Cache partitioning techniques have been successfully adopted to mitigate interference among concurrently executing real-time tasks on multi-core processors. Considering that the execution time of a cache-sensitive task strongly depends on the cache available for it to use, co-optimizing cache partitioning and task allocation improves the system's schedulabil

Yiwen Tang, Ray Zhang, Zoey Guo, Dong Wang

The popularity of pre-trained large models has revolutionized downstream tasks across diverse fields, such as language, vision, and multi-modality. To minimize the adaption cost for downstream tasks, many Parameter-Efficient Fine-Tuning (PEFT) techniques are proposed for language and 2D image pre-trained models. However, the specialized PEFT method for 3D pr

Aron C. Wall

I suggest the possibility that holographic quantum gravity is, in some sense, equivalent to quantum information theory. Some radical implications would follow. First, the theory of quantum gravity should have no adjustable coupling constants, similar to string theory. Thus, all complete bulk theories of quantum gravity are dual to each other. By setting up a

Robert Mieth, H. Vincent Poor

For a timely decarbonization of our economy, power systems need to accommodate increasing numbers of clean but stochastic resources. This requires new operational methods that internalize this stochasticity to ensure safety and efficiency. This paper proposes a novel approach to compute adaptive safety intervals for each stochastic resource that internalize

Anas Arram, Masri Ayob, Musatafa Abbas Abbood Albadr, Alaa Sulaiman

The use of credit cards has recently increased, creating an essential need for credit card assessment methods to minimize potential risks. This study investigates the utilization of machine learning (ML) models for credit card default prediction system. The main goal here is to investigate the best-performing ML model for new proposed credit card scoring dat

Miša Korać, Corentin Salaün, Iliyan Georgiev, Pascal Grittmann

Independently estimating pixel values in Monte Carlo rendering results in a perceptually sub-optimal white-noise distribution of error in image space. Recent works have shown that perceptual fidelity can be improved significantly by distributing pixel error as blue noise instead. Most such works have focused on static images, ignoring the temporal perceptual

Jing Xiong, Zixuan Li, Chuanyang Zheng, Zhijiang Guo

Recent advances in natural language processing, primarily propelled by Large Language Models (LLMs), have showcased their remarkable capabilities grounded in in-context learning. A promising avenue for guiding LLMs in intricate reasoning tasks involves the utilization of intermediate reasoning steps within the Chain-of-Thought (CoT) paradigm. Nevertheless, t

Chang Gao, Wenxuan Zhang, Guizhen Chen, Wai Lam

Instruction tuning is vital for enhancing the performance of large language models (LLMs), but existing text-to-text methods, referred to as TextTuning, struggle with issues such as generalization, robustness, and controllability due to their lack of explicit task structures. We introduce JsonTuning, a structure-to-structure approach that uses JSON structure

Carlos Caleiro, Sérgio Marcelino, Umberto Rivieccio

We extend classical work by Janusz Czelakowski on the closure properties of the class of matrix models of entailment relations - nowadays more commonly called multiple-conclusion logics - to the setting of non-deterministic matrices (Nmatrices), characterizing the Nmatrix models of an arbitrary logic through a generalization of the standard class operators t

Bekzhan Kerimkulov, James-Michael Leahy, David Siska, Lukasz Szpruch

We study the global convergence of a Fisher-Rao policy gradient flow for infinite-horizon entropy-regularised Markov decision processes with Polish state and action space. The flow is a continuous-time analogue of a policy mirror descent method. We establish the global well-posedness of the gradient flow and demonstrate its exponential convergence to the opt

Samuel Mansfield, Akshat Mudgal

Let $p$ be a prime, let $s \geq 3$ be a natural number and let $A \subseteq \mathbb{F}_p$ be a non-empty set satisfying $|A| \ll p^{1/2}$. Denoting $J_s(A)$ to be the number of solutions to the system of equations \[ \sum_{i=1}^{s} (x_i - x_{i+s}) = \sum_{i=1}^{s} (x_i^2 - x_{i+s}^2) = 0, \] with $x_1, \dots, x_{2s} \in A$, our main result implies that \[ J_

Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold

Warning: This paper contains examples of harmful language, and reader discretion is recommended. The increasing open release of powerful large language models (LLMs) has facilitated the development of downstream applications by reducing the essential cost of data annotation and computation. To ensure AI safety, extensive safety-alignment measures have been c

Meghna Gummadi, Cassandra Kent, Karl Schmeckpeper, Eric Eaton

Despite outstanding semantic scene segmentation in closed-worlds, deep neural networks segment novel instances poorly, which is required for autonomous agents acting in an open world. To improve out-of-distribution (OOD) detection for segmentation, we introduce a metacognitive approach in the form of a lightweight module that leverages entropy measures, segm

Katikapalli Subramanyam Kalyan

Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities which allow them to achieve remarkable performances without any task-specific training in many of the natu

Arthur Desbois, Tristan Venot, Fabrizio De Vico Fallani, Marie-Constance Corsi

Brain-Computer Interface (BCI) systems allow users to perform actions by translating their brain activity into commands. Such systems usually need a training phase, consisting in training a classification algorithm to discriminate between mental states using specific features from the recorded signals. This phase of feature selection and training is crucial

Hannah Markwig, Lukas Ristau, Victoria Schleis

We provide explicit faithful re-embeddings for all hyperelliptic curves of genus at most three and an algorithmic way to construct them. Both in the faithful tropicalization algorithm and the proofs of correctness, we showcase OSCAR-methods for commutative algebra, polyhedral and tropical geometry.

Olivier Peltre

A comprehensive picture of three Bethe-Kikuchi variational principles including their relationship to belief propagation (BP) algorithms on hypergraphs is given. The structure of BP equations is generalized to define continuous-time diffusions, solving localized versions of the max-entropy principle (A), the variational free energy principle (B), and a less

Utsab Saha, Atik Jawad, Shakib Shahria, A. B. M Harun-Ur Rashid

This article proposes a proximal policy optimization (PPO)-based reinforcement learning (RL) approach for DC-DC boost converter control that is compared with traditional control methods. The performance of the PPO algorithm is evaluated using MATLAB Simulink co-simulation, and the results demonstrate that the most efficient approach for achieving short settl

Corey Knutson, Zhipeng Cao, Junaed Sattar

Autonomous Underwater Vehicles (AUVs) conduct missions underwater without the need for human intervention. A docking station (DS) can extend mission times of an AUV by providing a location for the AUV to recharge its batteries and receive updated mission information. Various methods for locating and tracking a DS exist, but most rely on expensive acoustic se

Aleksandr Meister, Matvei Novikov, Nikolay Karpov, Evelina Bakhturina

Traditional automatic speech recognition (ASR) models output lower-cased words without punctuation marks, which reduces readability and necessitates a subsequent text processing model to convert ASR transcripts into a proper format. Simultaneously, the development of end-to-end ASR models capable of predicting punctuation and capitalization presents several

Alexandre Capone, Tim Brüdigam, Sandra Hirche

Solving chance-constrained stochastic optimal control problems is a significant challenge in control. This is because no analytical solutions exist for up to a handful of special cases. A common and computationally efficient approach for tackling chance-constrained stochastic optimal control problems consists of reformulating the chance constraints as hard c

Hao Chen, Abhishek Gupta, Yin Sun, Ness Shroff

This paper studies Hoeffding's inequality for Markov chains under the generalized concentrability condition defined via integral probability metric (IPM). The generalized concentrability condition establishes a framework that interpolates and extends the existing hypotheses of Markov chain Hoeffding-type inequalities. The flexibility of our framework allows

Alexander C. Murph, Curtis B. Storlie, Patrick M. Wilson, Jonathan P. Williams

When a predictive model is in production, it must be monitored in real-time to ensure that its performance does not suffer due to drift or abrupt changes to data. Ideally, this is done long before learning that the performance of the model itself has dropped by monitoring outcome data. In this paper we consider the problem of monitoring a predictive model th

M. Tiwari, R. Kievit, S. Kabanovic, L. Bonne

We explore the potential of the Gaussian Mixture Model (GMM), an unsupervised machine learning method, to identify coherent physical structures in the ISM. The implementation we present can be used on any kind of spatially and spectrally resolved data set. We provide a step-by-step guide to use these models on different sources and data sets. Following the g

Kei Funano

In this short survey, we derive some weyl-type universal inequalities of eigenvalues of the Laplacian on a closed Riemannian manifold of nonnegative Ricci curvature. We also give upper bounds for the $L_{\infty}$ norm of eigenfunctions of the Laplacian in the same setting. A detailed proof of these results did not seem to appear in the literature but the res

Lorenzo Sponza, Van Binh Vu, Elisa Serrano Richaud, Hakim Amara

We investigate the electronic structure and the optical absorption onset of close-to-30\degree twisted hexagonal boron nitride bilayers. Our study is carried out with a purposely developed tight-binding model validated against DFT simulations. We demonstrate that approaching 30\degree (quasicrystal limit), all bilayers sharing the same moir\'e supercell deve

Angela Aguglia, Luca Giuzzi, Alessandro Montinaro, Viola Siconolfi

In this paper we study the BM quasi-Hermitian varieties introduced in [A. Aguglia, A. Cossidente, G. Korchm\`aros, On quasi-Hermitian Varieties, J. Combin. Des. 20 (2012) 433-447.] in characteristc $2$ and dimension $3$. After a brief investigation of their combinatorial properties, we first show that all of these varieties are projectively equivalent, exhib

Antonio Corbo Esposito, Luisa Faella, Vincenzo Mottola, Gianpaolo Piscitelli

This paper is focused on the Monotonicity Principle (MP) for nonlinear materials with piecewise growth exponent. This results are relevant because enables the use of a fast imaging method based on MP, to the wide class of problems with two or more materials, where at least one is nonlinear. The treatment is very general and allows to model a wide variety of

Jan Burczak, László Székelyhidi, Bian Wu

We show anomalous dissipation of scalars advected by weak solutions to the incompressible Euler equations with $C^{(\sfrac{1}{3})^-}$ regularity, for an arbitrary initial datum in $\dot H^1 (\T^3)$. This is the first rigorous derivation of zeroth law of scalar turbulence, where the scalar is advected by solution to an equation of hydrodynamics (unforced and

Peter Zalom, Martin Žonda, T. Novotný

We study a multi-terminal Josephson junction based on an interacting quantum dot coupled to $n$ superconducting BCS leads. Using an Anderson type model of a local level with an arbitrary onsite Coulomb repulsion, we uncover its surprising equivalence with an effective two-terminal junction with symmetric couplings to appropriately phase-biased leads. Regardl

Jannis Bulian, Mike S. Schäfer, Afra Amini, Heidi Lam

As Large Language Models (LLMs) rise in popularity, it is necessary to assess their capability in critically relevant domains. We present a comprehensive evaluation framework, grounded in science communication research, to assess LLM responses to questions about climate change. Our framework emphasizes both presentational and epistemological adequacy, offeri

Nithya Bhasker, Stefan Leger, Alexander Zwanenburg, Chethan Babu Reddy

Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. Since patient data is not readily available as a graph, most existing methods either manually define a patient graph, or learn a latent graph based on pairwise similarities between the patients. There are also h

Leilei Cui, Zhong-Ping Jiang, Eduardo D. Sontag

This paper studies the effect of perturbations on the gradient flow of a general nonlinear programming problem, where the perturbation may arise from inaccurate gradient estimation in the setting of data-driven optimization. Under suitable conditions on the objective function, the perturbed gradient flow is shown to be small-disturbance input-to-state stable

Davide Breoni, Christina Kurzthaler, Benno Liebchen, Hartmut Löwen

We study the viscoelastic properties of highly entangled, flexible, self-propelled polymers using Brownian dynamics simulations. Our results show that the active motion of the polymer increases the height of the stress plateau by orders of magnitude due to the emergence of grip forces at entanglement points. Identifying the activity-induced energy of a singl

C. E. Kester, A. Bernui, W. S. Hipólito-Ricaldi

We study the angular distribution of temperature fluctuations in the cosmic microwave background (CMB) to probe the statistical isotropy of the universe by using precise full-sky CMB data with a model-independent approach. We investigated the temperature-temperature angular correlations in the four Planck foreground-cleaned CMB maps that were released recent

Z. Mohammadi, M. Soleimanpour-Moghadam, S. Talebi, H. Ahmadi

The paper proposes a new approach to minimize the number of relays while maximizing the lifetime of underwater acoustic sensor networks (UASNs). This involves formulating the relay node placement (RNP) problem as a multi-objective optimization problem and employing the multi-objective lexico-graphic method (MOLM) to solve it. To achieve the optimal solution,

Burlen Loring, E. Wes Bethel, Gunther H. Weber, Michael W. Mahoney

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectu

Hugues Van Assel, Titouan Vayer, Remi Flamary, Nicolas Courty

Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan. Many formulations impose a global constraint on the transport plan, for instance by relying on entropic regularisation. As it is more expensive to diffuse mass for outlier points compared to central one

Esteban Alvarez Seoane, Alessandro Cattaneo, Fabien Neuenschwander, Lucien Blanchard

The widespread use of disposable facemasks during the COVID-19 pandemic has led to environmental widespread concern due to microplastic pollution. Biodegradable disposable facemasks are a first step to reducing the environmental impact of pandemics. In this paper we present high-performance facemask components based on novel poly(ester amide) (PEA) grades sy

Pranav Bidve, Shalini Mishra, Annapurna J

The identification of Prakriti types for the human body is a long-lost medical practice in finding the harmony between the nature of human beings and their behaviour. There are 3 fundamental Prakriti types of individuals. A person can belong to any Dosha. In the existing models, researchers have made use of SVM, KNN, PCA, Decision Tree, and various other alg

Amina Mollaysa, Ahmed Allam, Michael Krauthammer

Human genetic diseases often arise from point mutations, emphasizing the critical need for precise genome editing techniques. Among these, base editing stands out as it allows targeted alterations at the single nucleotide level. However, its clinical application is hindered by low editing efficiency and unintended mutations, necessitating extensive trial-and

Mohamed-Khalil Bouzidi, Yue Yao, Daniel Goehring, Joerg Reichardt

Model Predictive Control lacks the ability to escape local minima in nonconvex problems. Furthermore, in fast-changing, uncertain environments, the conventional warmstart, using the optimal trajectory from the last timestep, often falls short of providing an adequately close initial guess for the current optimal trajectory. This can potentially result in con

Yu Qiu, Xiaoting Zhang

Let $\mathcal{G}$ be a fusion category acting on a triangulated category $\mathcal{D}$, in the sense that $\mathcal{D}$ is a $\mathcal{G}$-module category. Our motivation example is fusion-weighted species, which is essentially Heng's construction. We study $\mathcal{G}$-stable tilting, cluster and stability structures on $\mathcal{D}$. In particular, we pro

E. Russeil, K. L. Malanchev, P. D. Aleo, E. E. O. Ishida

We present Rainbow, a physically motivated framework which enables simultaneous multi-band light curve fitting. It allows the user to construct a 2-dimensional continuous surface across wavelength and time, even in situations where the number of observations in each filter is significantly limited. Assuming the electromagnetic radiation emission from the tra

Chen Lu, Zhiming Pan, Fan Yang, Congjun Wu

The discovery of high-$T_c$ superconductivity (SC) in La$_3$Ni$_2$O$_7$ (LNO) has aroused a great deal of interests. Previously, it was proposed that the Ni-$3d_{z^2}$ orbital is crucial to realize the high-$T_c$ SC in LNO: The preformed Cooper pairs therein acquire coherence via hybridization with the $3d_{x^2-y^2}$ orbital to form the SC. However, we held

Cemsinan Deliduman, Oguzhan Kasikci, Vildan Keles Tugyanoglu

We propose a new model of cosmology based on an anisotropic background and a specific $f(R)$ theory of gravity. It is shown that field equations of $f(R)$ gravity in a Bianchi type I background give rise to a modified Friedmann equation. This model contains two important parameters: $\gamma$ and $\delta$. We, thus, simply call our model $\gamma\delta$CDM. It

Cristiano Fanelli, James Giroux

We introduce a physics-informed Bayesian Neural Network (BNN) with flow approximated posteriors using multiplicative normalizing flows (MNF) for detailed uncertainty quantification (UQ) at the physics event-level. Our method is capable of identifying both heteroskedastic aleatoric and epistemic uncertainties, providing granular physical insights. Applied to

Tanguy Vernet

We prove that the polynomials counting locally free, absolutely indecomposable, rank 1 representations of quivers over rings of truncated power series have non-negative coefficients. This is a generalisation to higher depth of positivity for toric Kac polynomials. The proof goes by inductively contracting/deleting arrows of the quiver and is inspired from a

Massimiliano Silenzi, Umut Can Cabuk, Enis Karaarslan, Omer Aydin

The fast-growing cryptocurrency sector presents both challenges and opportunities for businesses and consumers alike. This study investigates the knowledge, expertise, and buying habits of people who shop using cryptocurrencies. Our survey of 516 participants shows that knowledge levels vary from beginners to experts. Interestingly, a segment of respondents,

Lingfei Zhao, Ethan G. Arnault, Trevyn F. Q. Larson, Kenji Watanabe

There has been a growing interest in hybrid quantum Hall (QH) superconductor devices, driven by the prospect to realize exotic ground states and excitations with non-abelian exchange statistics. While the existing experiments clearly demonstrate Andreev coupling between the edge states and the superconductors, the question remains whether the quantum coheren

János Barát, Andrzej Grzesik, Attila Jung, Zoltán Lóránt Nagy

In a graph $G$, the $2$-neighborhood of a vertex set $X$ consists of all vertices of $G$ having at least $2$ neighbors in $X$. We say that a bipartite graph $G(A,B)$ satisfies the double Hall property if $|A|\geq2$, and every subset $X \subseteq A$ of size at least $2$ has a $2$-neighborhood of size at least $|X|$. Salia conjectured that any bipartite graph

H. S. Xu, L. Jin

Symmetry plays a fundamentally important role in physics. In this work, we find a conservation law, $S^{\dagger}(H_{c}^{\dagger})S(H_{c})=I$, which is valid for any non-Hermitian scattering center $H_c$. As a result, the reflections and transmissions of a non-Hermitian system $\left\{ r,t\right\}$ and its Hermitian conjugation system $\left\{ \bar{r},\bar{t}

Ioannis Dadiotis, Arturo Laurenzi, Nikos Tsagarakis

Recent progress in legged locomotion has rendered quadruped manipulators a promising solution for performing tasks that require both mobility and manipulation (loco-manipulation). In the real world, task specifications and/or environment constraints may require the quadruped manipulator to be equipped with high redundancy as well as whole-body motion coordin

Shiyi Du, Xiaosong Wang, Yongyi Lu, Yuyin Zhou

Image synthesis approaches, e.g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks. It is primarily beneficial to overcome the shortage of publicly accessible data and associated quality annotations. However, the current techniques often lack control over the detailed contents in generated imag

Joshua A. Vita, Dallas R. Trinkle

While machine learning (ML) interatomic potentials (IPs) are able to achieve accuracies nearing the level of noise inherent in the first-principles data to which they are trained, it remains to be shown if their increased complexities are strictly necessary for constructing high-quality IPs. In this work, we introduce a new MLIP framework which blends the si

Oscar Skean, Aayush Dhakal, Nathan Jacobs, Luis Gonzalo Sanchez Giraldo

Self-supervised learning (SSL) is a popular paradigm for representation learning. Recent multiview methods can be classified as sample-contrastive, dimension-contrastive, or asymmetric network-based, with each family having its own approach to avoiding informational collapse. While these families converge to solutions of similar quality, it can be empiricall

Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp

Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs. However, RL requires careful structuring of the search space and algorithm design to be effective in this challenge. Through extensive experiments, we explore how different design choices for text grammar and algorithmic choices

Mathew Mithra Noel, Venkataraman Muthiah-Nakarajan, Yug D Oswal

Higher order artificial neurons whose outputs are computed by applying an activation function to a higher order multinomial function of the inputs have been considered in the past, but did not gain acceptance due to the extra parameters and computational cost. However, higher order neurons have significantly greater learning capabilities since the decision b