May 2024 arXiv papers — page 5

Ke Song, Zimo Hao

In this paper, we establish the weak convergence rate of density-dependent stochastic differential equations with bounded drift driven by $\alpha$-stable processes with $\alpha\in(1,2)$. The well-posedness of these equations has been previously obtained in \cite{wu2023well}. We derive an explicit convergence rate in total variation for the Euler-Maruyama sch

Suhail Khan, Ajay Bassi, Rathin Adhikari

We show that gravitational leptogenesis with dynamical $CPT$ breaking in an expanding universe can be reconciled with the exponential $f(R)$ gravity model, which introduces only one additional parameter $\beta$ compared to the standard $\Lambda$CDM cosmology. This model incorporated axions as cold dark matter. For $L$ violating interactions, we consider both

Philip Kennerberg, Magnus Wiktorsson

Consider a sequence of cadlag processes $\{X^n\}_n$, and some fixed function $f$. If $f$ is continuous then under several modes of convergence $X^n\to X$ implies corresponding convergence of $f(X^n)\to f(X)$, due to continuous mapping. We study conditions (on $f$, $\{X^n\}_n$ and $X$) under which convergence of $X^n\to X$ implies $\left[f(X^n)-f(X)\right]\to

Linus Ericsson, Miguel Espinosa, Chenhongyi Yang, Antreas Antoniou

Neural architecture search (NAS) finds high performing networks for a given task. Yet the results of NAS are fairly prosaic; they did not e.g. create a shift from convolutional structures to transformers. This is not least because the search spaces in NAS often aren't diverse enough to include such transformations a priori. Instead, for NAS to provide greate

J. Plo, A. Pershin, S. Li, T. Poirier

Defects in crystals can have a transformative effect on the properties and functionalities of solid-state systems. Dopants in semiconductors are core components in electronic and optoelectronic devices. The control of single color centers is at the basis of advanced applications for quantum technologies. Unintentional defects can also be detrimental to the c

Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun

Solving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions, their accuracy and training speed are limited by two core barriers: gradient-descent-based iterative optimization over complex l

Davide Paglieri, Saurabh Dash, Tim Rocktäschel, Jack Parker-Holder

Post-Training Quantization (PTQ) enhances the efficiency of Large Language Models (LLMs) by enabling faster operation and compatibility with more accessible hardware through reduced memory usage, at the cost of small performance drops. We explore the role of calibration sets in PTQ, specifically their effect on hidden activations in various notable open-sour

Cheng Tan, Jingxuan Wei, Linzhuang Sun, Zhangyang Gao

Large language models equipped with retrieval-augmented generation (RAG) represent a burgeoning field aimed at enhancing answering capabilities by leveraging external knowledge bases. Although the application of RAG with language-only models has been extensively explored, its adaptation into multimodal vision-language models remains nascent. Going beyond mer

Ella Rabinovich

The Uniform Information Density (UID) hypothesis posits that speakers optimize the communicative properties of their utterances by avoiding spikes in information, thereby maintaining a relatively uniform information profile over time. This paper investigates the impact of UID principles on syntactic reduction, specifically focusing on the optional omission o

István Vona, Márton Mestyán, Balázs Pozsgay

We consider quantum spin chains with a hidden free fermionic structure, distinct from the Jordan-Wigner transformation and its generalizations. We express selected local operators with the hidden fermions. This way we can exactly solve the real time dynamics in various physical scenarios, including the computation of selected dynamical two point functions, i

Eva Löcherbach, Dasha Loukianova, Elisa Marini

We consider a system of $N$ interacting particles, described by SDEs driven by Poisson random measures, where the coefficients depend on the empirical measure of the system. Every particle jumps with a jump rate depending on its position. When this happens, all the other particles of the system receive a small random kick which is distributed according to a

Yueqin Yin, Zhendong Wang, Yujia Xie, Weizhu Chen

Traditional language model alignment methods, such as Direct Preference Optimization (DPO), are limited by their dependence on static, pre-collected paired preference data, which hampers their adaptability and practical applicability. To overcome this limitation, we introduce Self-Augmented Preference Optimization (SAPO), an effective and scalable training p

Seongheon Park, Hyuk Kwon, Kwanghoon Sohn, Kibok Lee

Open-world semi-supervised learning (OWSSL) extends conventional semi-supervised learning to open-world scenarios by taking account of novel categories in unlabeled datasets. Despite the recent advancements in OWSSL, the success often relies on the assumptions that 1) labeled and unlabeled datasets share the same balanced class prior distribution, which does

Erik Weiss, Marcel Cech, Stanislaw Soltan, Martin Koppenhöfer

With the growing number of qubits of quantum information processing devices, the task of fully characterizing these processors becomes increasingly unfeasible. From a practical perspective, one wants to find possible errors in the functioning of the device as quickly as possible, or otherwise establish its correct functioning with high confidence. In respons

Sumin Lim, Mikhail V. Vaganov, Junjie Liu, Arzhang Ardavan

The realization of effective quantum error correction protocols remains a central challenge in the development of scalable quantum computers. Employing high-dimensional quantum systems (qudits) can offer more hardware-efficient protocols than qubit-based approaches. Using electron-nuclear double resonance, we implement a logical qubit encoded on the four sta

Ruizhi Gong, Deng-Shan Wang

For the KdV equation with box type initial data, the interaction between a trial soliton and large-scale dispersive mean flow is studied theoretically and numerically. The pure box initial value can cause rarefaction wave and dispersive shock wave, and can create an area of soliton train. The key to the interaction of soliton and mean flow is that the dynami

Nanna E. Hartong, Ilias Sachpazidis, Oliver Blanck, Lucas Etzel

Background: This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM), using clinical, dosimetric, and pretherapeutic MRI data. Methods: In this multicenter retrospective study, 517 MBM from 130 patients treated with single-fraction or hypofractionated SRT at thr

Stephen Pasteris, Chris Hicks, Vasilios Mavroudis, Mark Herbster

We consider the classic problem of online convex optimisation. Whereas the notion of static regret is relevant for stationary problems, the notion of switching regret is more appropriate for non-stationary problems. A switching regret is defined relative to any segmentation of the trial sequence, and is equal to the sum of the static regrets of each segment.

Riccardo Piovani

Given a compact complex manifold, we study the cohomology and the Hodge theory for the elliptic complex of differential forms defined by Bigolin in 1969 and recently referred to as the Schweitzer complex. Recall that the double complex of a compact complex manifold decomposes into a direct sum of so-called squares and zigzags, and the zigzags are the only co

Pablo de la Vega, Guido Zack, Jimena Calvo, Emiliano Libman

This paper analyzes the empirical relationship between the inflation rate and its proximate determinants in Argentina, using quarterly data over the period 2004-2022 and an error-correction vector model approach. Unlike previous literature, this paper uses a theoretical framework to motivate the inclusion of variables that are expected to contribute to expla

Seok-Ju Hahn, Gi-Soo Kim, Junghye Lee

In traditional federated learning, a single global model cannot perform equally well for all clients. Therefore, the need to achieve the client-level fairness in federated system has been emphasized, which can be realized by modifying the static aggregation scheme for updating the global model to an adaptive one, in response to the local signals of the parti

Ajay Suresha Sathya

Efficient robot dynamics simulation is a fundamental problem key for robot control, identification, design and analysis. This research statement explores my current progress in this field and future research directions.

Alejandra Martinez, Laura Tovar, Carla Irigoyen Amparan, Karen Gonzalez

Occupational exoskeletons promise to reduce the incidence of musculoskeletal injuries; however, we do not know if their designs allow universal use by all workers. We also do not know how easy the tasks of assembling, donning, doffing, and disassembling exoskeletons are. The purpose of our study was to heuristically evaluate a back support, a shoulder suppor

Jack Bunyan, Seth Bullock, Conor Houghton

The iterated learning model is an agent model which simulates the transmission of of language from generation to generation. It is used to study how the language adapts to pressures imposed by transmission. In each iteration, a language tutor exposes a na\"ive pupil to a limited training set of utterances, each pairing a random meaning with the signal that c

Maria Laura Battagliola, Martin Bladt

Extremiles provide a generalization of quantiles which are not only robust, but also have an intrinsic link with extreme value theory. This paper introduces an extremile regression model tailored for functional covariate spaces. The estimation procedure turns out to be a weighted version of local linear scalar-on-function regression, where now a double kerne

Po-Hsun Tseng, H. -Y. Karen Yang, Chun-Yen Chen, Hsi-Yu Schive

The Fermi Gamma-Ray Space Telescope reveals two large bubbles in the Galaxy, extending nearly symmetrically $\sim50^{\circ}$ above and below the Galactic center (GC). Previous simulations of bubble formation invoking active galactic nucleus (AGN) jets have assumed that the jets are vertical to the Galactic disk; however, in general, the jet orientation does

Zhuoyao Huang, Nan Zhang, Jingran Shen, Georgios Diamantopoulos

Digital Twin technology facilitates the monitoring and online analysis of large-scale communication networks. Faster predictions of network performance thus become imperative, especially for analysing Quality of Service (QoS) parameters in large-scale city networks. Discrete Event Simulation (DES) is a standard network analysis technology, and can be further

Paweł Pietrzycki, Jan Stochel

Although Arveson's hyperrigidity conjecture was recently resolved negatively by B. Bilich and A. Dor-On, the problem remains open for commutative $C^*$-algebras. Relatively few examples of hyperrigid sets are known in the commutative case. The main goal of this paper is to determine which sets of monomials in $t$ and $t^*$, where $t$ is a generator of a comm

Ilia Tutunnikov, Jianshu Cao

Recent advances in transport properties measurements of disordered materials and lattice simulations, using superconducting qubits, have rekindled interest in Anderson localization, motivating our study of highly disordered quantum lattices. Initially, our statistical analysis of localized eigenstates reveals a distinct transition between weak and strong dis

Ole Sönnerborn

Nonadiabatic holonomic quantum computation is a promising approach for implementing quantum gates that offers both efficiency and robustness against certain types of errors. A key element of this approach is a geometric constraint known as the parallel transport condition. According to the principle of covariance, this condition must be appropriately modifie

Jürgen Fuchs, Gregor Schaumann, Christoph Schweigert, Simon Wood

We develop the theory of module categories over a Grothendieck-Verdier category, i.e. a monoidal category with a dualizing object and hence a duality structure more general than rigidity. Such a category C comes with two monoidal structures which are related by non-invertible morphisms and which we treat on an equal footing. Quite generally, non-invertible s

Yunbin Tu, Liang Li, Li Su, Zheng-Jun Zha

Multi-change captioning aims to describe complex and coupled changes within an image pair in natural language. Compared with single-change captioning, this task requires the model to have higher-level cognition ability to reason an arbitrary number of changes. In this paper, we propose a novel context-aware difference distilling (CARD) network to capture all

Navnath Daundkar

In this paper, we define and study an equivariant analogue of Cohen, Farber and Weinberger's parametrized topological complexity. We show that several results in the non-equivariant case can be extended to the equivariant case. For example, we establish the fibrewise equivariant homotopy invariance of the sequential equivariant parametrized topological compl

Shahrzad Haddadan, Cheng Xin, Jie Gao

We consider a cooperative learning scenario where a collection of networked agents with individually owned classifiers dynamically update their predictions, for the same classification task, through communication or observations of each other's predictions. Clearly if highly influential vertices use erroneous classifiers, there will be a negative effect on t

Maoyin Lv, Hao Wu

We consider a class of Cahn-Hilliard equation that characterizes phase separation phenomena of binary mixtures in a bounded domain $\Omega \subset \mathbb{R}^d$ $(d\in \{2,3\})$ with non-permeable boundary. The equations in the bulk are subject to kinetic rate dependent dynamic boundary conditions with possible boundary diffusion acting on the boundary chemi

Robert West, Roland Aydin

The field of AI alignment aims to steer AI systems toward human goals, preferences, and ethical principles. Its contributions have been instrumental for improving the output quality, safety, and trustworthiness of today's AI models. This perspective article draws attention to a fundamental challenge we see in all AI alignment endeavors, which we term the "AI

Sourabrata Mukherjee, Atul Kr. Ojha, Akanksha Bansal, Deepak Alok

Text style transfer (TST) involves altering the linguistic style of a text while preserving its core content. This paper focuses on sentiment transfer, a popular TST subtask, across a spectrum of Indian languages: Hindi, Magahi, Malayalam, Marathi, Punjabi, Odia, Telugu, and Urdu, expanding upon previous work on English-Bangla sentiment transfer (Mukherjee e

Stephan Spengler

We consider games played on the transtion graph of concurrent programs running under the Total Store Order (TSO) weak memory model. Games are frequently used to model the interaction between a system and its environment, in this case between the concurrent processes and the nondeterminisitic TSO buffer updates. The game is played by two players, who alternat

Jonathan Freundlich

The interstellar medium of galaxies is composed of multiple phases, including molecular, atomic, and ionized gas, as well as dust. Stars are formed within this medium from cold molecular gas clouds, which collapse due to their gravitational attraction. Throughout their life, stars emit strong radiation fields and stellar winds, and they can also explode as s

S. Frauendorf

The appearance of wobbling motion and chirality in rotating triaxial nuclei is explained. The discovery of a new mode, chiral wobbling, in $^{74}$Br, is commented.

Patrick J. Park, Sebastian Herzele, Timothy W. Koeth

We re-examine a common narrative that experimental errors by Walther Bothe in 1941 led Germany to abandon graphite as a reactor moderator during World War II. Using document-based nuclear archaeology, we first show that both American and German scientists used an incorrect carbon scattering cross section, thereby undermining the accuracy of all wartime data,

Viktor Martinek, Julia Reuter, Ophelia Frotscher, Sanaz Mostaghim

We study the addition of shape constraints (SC) and their consideration during the parameter identification step of symbolic regression (SR). SC serve as a means to introduce prior knowledge about the shape of the otherwise unknown model function into SR. Unlike previous works that have explored SC in SR, we propose minimizing SC violations during parameter

Fernando Moreno-Pino, Álvaro Arroyo, Harrison Waldon, Xiaowen Dong

Time-series data in real-world settings typically exhibit long-range dependencies and are observed at non-uniform intervals. In these settings, traditional sequence-based recurrent models struggle. To overcome this, researchers often replace recurrent architectures with Neural ODE-based models to account for irregularly sampled data and use Transformer-based

Vladyslav M. Kuchkin, Andreas Haller, Štefan Liščák, Michael P. Adams

A Bloch point represents a three-dimensional hedgehog singularity of a magnetic vector field in which the magnetization vanishes. However, standard micromagnetic theory, developed for magnetic moments of fixed lengths, lacks full applicability in studying such singularities. To address this gap, we study a Bloch point in a quantum Heisenberg model for the ca

Shiyin Lu, Yang Li, Qing-Guo Chen, Zhao Xu

Current Multimodal Large Language Models (MLLMs) typically integrate a pre-trained LLM with another pre-trained vision transformer through a connector, such as an MLP, endowing the LLM with visual capabilities. However, the misalignment between two embedding strategies in MLLMs -- the structural textual embeddings based on an embedding look-up table and the

Luigi De Masi, Nick Edelen, Carlo Gasparetto, Chao Li

We prove $\varepsilon$-regularity theorems for varifolds with capillary boundary condition in a Riemannian manifold. These varifolds were first introduced by Kagaya-Tonegawa \cite{KaTo}. We establish a uniform first variation control for all such varifolds (and free-boundary varifolds generally) satisfying a sharp density bound and prove that if a capillary

Huaxiang Zhang, Yaojia Mu, Guo-Niu Zhu, Zhongxue Gan

Accurate visual understanding is imperative for advancing autonomous systems and intelligent robots. Despite the powerful capabilities of vision-language models (VLMs) in processing complex visual scenes, precisely recognizing obscured or ambiguously presented visual elements remains challenging. To tackle such issues, this paper proposes InsightSee, a multi

Yunling Ma, Chaojun Zhang, Xiaochuan Wang, Qianqian Wang

Major depressive disorder (MDD) is a common mental disorder that typically affects a person's mood, cognition, behavior, and physical health. Resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used for computer-aided diagnosis of MDD. While multi-site fMRI data can provide more data for training reliable diagnostic models, signific

Rodrigo M. C. Bernardo, Delfim F. M. Torres, Carlos A. R. Herdeiro, Marco P. Soares dos Santos

Control algorithms have been proposed based on knowledge related to nature-inspired mechanisms, including those based on the behavior of living beings. This paper presents a review focused on major breakthroughs carried out in the scope of applied control inspired by the gravitational attraction between bodies. A control approach focused on Artificial Potent

Donald Kridel, Jacob Dineen, Daniel Dolk, David Castillo

Explainable AI (XAI) has a counterpart in analytical modeling which we refer to as model explainability. We tackle the issue of model explainability in the context of prediction models. We analyze a dataset of loans from a credit card company and apply three stages: execute and compare four different prediction methods, apply the best known explainability te

Rebecca M. Bowen, Sadie Pruitt, Douglas A. Torrance

A Tangle is a smooth simple closed curve formed from arcs (or ``links'') of circles with fixed radius. Most previous study of Tangles has dealt with the case where these arcs are quarter-circles, but Tangles comprised of thirds and sixths of circles are introduced. Together, these three families of Tangles are related to the three regular tilings of the plan

Wolfram Bauer, Robert Fulsche, Miguel Angel Rodriguez Rodriguez

We consider various classes of bounded operators on the Fock space $F^2$ of Gaussian square integrable entire functions over the complex plane. These include Toeplitz (type) operators, weighted composition operators, singular integral operators, Volterra-type operators and Hausdorff operators and range from classical objects in harmonic analysis to more rece

Yumeng He, Yunbo Wang, Xiaokang Yang

Out-of-distribution (OOD) 3D relighting requires novel view synthesis under unseen lighting conditions that differ significantly from the observed images. Existing relighting methods, which assume consistent light source distributions between training and testing, often degrade in OOD scenarios. We introduce MetaGS to tackle this challenge from two perspecti

Gezheng Xu, Qi Chen, Charles Ling, Boyu Wang

AI systems have been shown to produce unfair results for certain subgroups of population, highlighting the need to understand bias on certain sensitive attributes. Current research often falls short, primarily focusing on the subgroups characterized by a single sensitive attribute, while neglecting the nature of intersectional fairness of multiple sensitive

Tirthankar Roy, Shivendra Srivastava, Beichen Zhang

In this study, we discuss how reinforcement learning (RL) provides an effective and efficient framework for solving sociohydrology problems. The efficacy of RL for these types of problems is evident because of its ability to update policies in an iterative manner - something that is also foundational to sociohydrology, where we are interested in representing

Eran Ben-Haim, Sefi Givli, Yizhar Or, Amir Gat

Artificial neural networks (ANNs), which are inspired by the brain, are a central pillar in the ongoing breakthrough in artificial intelligence. In recent years, researchers have examined mechanical implementations of ANNs, denoted as Physical Neural Networks (PNNs). PNNs offer the opportunity to view common materials and physical phenomena as networks, and

Ping Xu, Huiqiu Lin, Longfei Fang

There is a rich history of studying the existence of cycles in planar graphs. The famous Tutte theorem on the Hamilton cycle states that every 4-connected planar graph contains a Hamilton cycle. Later on, Thomassen (1983), Thomas and Yu (1994) and Sanders (1996) respectively proved that every 4-connected planar graph contains a cycle of length $n-1, n-2$ and

Luca Giannoni, Marta Marradi, Kevin Scibilia, Ivan Ezhov

Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long times required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. We propose here a transportable, high-density, spectral-scanning based hypers

Zhiming Meng, Hui Li, Zeyang Zhang, Zhongwei Shen

Generative models are widely utilized to model the distribution of fused images in the field of infrared and visible image fusion. However, current generative models based fusion methods often suffer from unstable training and slow inference speed. To tackle this problem, a novel fusion method based on consistency model is proposed, termed as CoMoFusion, whi

Mingze Wang, Jinbo Wang, Haotian He, Zilin Wang

In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence. Specifically, IRE decouples the dynamics of flat and sharp directions, which boosts the sharpness reduction along flat directions while maintaining the training stabi

Aya Mohamed, Dagmar Auer, Daniel Hofer, Josef Kueng

Access control is the enforcement of the authorization policy, which defines subjects, resources, and access rights. Graph-structured data requires advanced, flexible, and fine-grained access control due to its complex structure as sequences of alternating vertices and edges. Several research works focus on protecting property graph-structured data, enforcin

Aditya Shankar, Jérémie Decouchant, Dimitra Gkorou, Rihan Hai

Vertical federated learning (VFL) is a promising area for time series forecasting in many applications, such as healthcare and manufacturing. Critical challenges to address include data privacy and over-fitting on small and noisy datasets during both training and inference. Additionally, such forecasting models must scale well with the number of parties whil

Avnish K. Sharma, Mamta Rani, Sharwan K. Tiwari, Anupama Panigrahi

This article investigates the existence of an $r$-primitive $k$-normal polynomial, defined as the minimal polynomial of an $r$-primitive $k$-normal element in $\mathbb{F}_{q^n}$, with a specified degree $n$ and two given coefficients over the finite field $\mathbb{F}_{q}$. Here, $q$ represents an odd prime power, and $n$ is an integer. The article establishe

Jens Decke, Olaf Wünsch, Bernhard Sick, Christian Gruhl

This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models again

Chao Wang, Giulio Franzese, Alessandro Finamore, Massimo Gallo

Diffusion models for Text-to-Image (T2I) conditional generation have recently achieved tremendous success. Yet, aligning these models with user's intentions still involves a laborious trial-and-error process, and this challenging alignment problem has attracted considerable attention from the research community. In this work, instead of relying on fine-grain

Ana Carolina da Cruz, Camila P. E. de Souza, Pedro H. T. O. Sousa

Functional data analysis finds widespread application across various fields. While functional data are intrinsically infinite-dimensional, in practice, they are observed only at a finite set of points, typically over a dense grid. As a result, smoothing techniques are often used to approximate the observed data as functions. In this work, we propose a novel

Jens Decke, Arne Jenß, Bernhard Sick, Christian Gruhl

This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that t

Miguel Huidobro, Paul Leask, Carlos Naya, Andrzej Wereszczynski

We show that coupling the $\textrm{SU}(2)$-valued Skyrme field to the $\rho$-meson solves the long-standing issue of (in)compressibility in the solitonic Skyrme model. Even by including only one $\rho\pi$ interaction term, motivated by a holographic-like reduction of Yang-Mills action by Sutcliffe, reduces the compression modulus from $K_0 \simeq 1080$ MeV,

Daniel Faílde, Víctor Ocampo-Zalvide, David Serantes, Òscar Iglesias

Careful determination of the heating performance of magnetic nanoparticles under AC fields is critical for magnetic hyperthermia applications. However, most interpretations of experimental data are based on the uniaxial anisotropy approximation, which in first instance can be correlated with particle aspect ratio. This is to say, the intrinsic magnetocrystal

Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro

Hate detection has long been a challenging task for the NLP community. The task becomes complex in a code-mixed environment because the models must understand the context and the hate expressed through language alteration. Compared to the monolingual setup, we see much less work on code-mixed hate as large-scale annotated hate corpora are unavailable for the

Xinliang Li, Zhong Tan

We study the non-uniqueness of weak solutions for the two-dimensional hyper-dissipative Navier-Stokes equations in the super-critical spaces $L_{t}^{\gamma}L_{x}^{p}$ when $\alpha\in[1,\frac{3}{2})$, and obtain the conclusion that the non-uniqueness of the weak solutions at the endpoint $(\gamma,p)=(\infty, \frac{2}{2\alpha-1})$ is sharp in view of the gener

Manfred Droste, Zoltán Fülöp, Andreja Tepavčević, Heiko Vogler

We consider the images of the initial algebra semantics of weighted tree automata over strong bimonoids (hence also over semirings). These images are subsets of the carrier set of the underlying strong bimonoid. We consider locally finite, weakly locally finite, and bi-locally finite strong bimonoids. We show that there exists a strong bimonoid which is weak

Ghulam Hussain, Jianbo Zhang, Man Zhang, Lalit Yadav

Drawing inspiration from the recent breakthroughs in the \ce{Na_{2}BaCo(PO_{4})_{2}} quantum magnet, renowned for its spin supersolidity phase and its potential for revolutionary cooling applications, our study delves into the intricate interplay among lattice, spin, and orbital degrees of freedom within this intriguing compound. Using meticulous temperature

D. Lasagna, G. Zampino, B. Ganapathisubramani

Prandtl's secondary flows of the second kind generated by laterally-varying roughness are studied using the linearised Reynolds-Averaged Navier-Stokes approach proposed in Zampino et al (2022). The momentum equations are coupled to the Spalart-Allmaras model while the roughness is captured by adapting established strategies for homogeneous roughness to heter

Bowen Zheng, Tianming Yang

Diffusion Models (DMs) have achieved great success in image generation and other fields. By fine sampling through the trajectory defined by the SDE/ODE solver based on a well-trained score model, DMs can generate remarkable high-quality results. However, this precise sampling often requires multiple steps and is computationally demanding. To address this pro

Sumio Watanabe

Mathematical equivalence between statistical mechanics and machine learning theory has been known since the 20th century, and research based on this equivalence has provided novel methodologies in both theoretical physics and statistical learning theory. It is well known that algebraic approaches in statistical mechanics such as operator algebra enable us to

Gabriele Maroni, Filip Stojceski, Lorenzo Pallante, Marco A. Deriu

Cell-penetrating peptides (CPPs) are powerful vectors for the intracellular delivery of a diverse array of therapeutic molecules. Despite their potential, the rational design of CPPs remains a challenging task that often requires extensive experimental efforts and iterations. In this study, we introduce an innovative approach for the de novo design of CPPs,

Sili Huang, Jifeng Hu, Zhejian Yang, Liwei Yang

Recent works have shown the remarkable superiority of transformer models in reinforcement learning (RL), where the decision-making problem is formulated as sequential generation. Transformer-based agents could emerge with self-improvement in online environments by providing task contexts, such as multiple trajectories, called in-context RL. However, due to t

Joseph Chapman, Bruno Tomasello, Sam T. Carr

The classical Ising chain is the paradigm for the non-existence of phase transitions in 1D systems and was solved by Ernst Ising one hundred years ago. More recently, a decorated two leg Ising ladder has received interest for the curious thermodynamics that resemble a phase transition; a sharp peak in the specific heat at low, but finite temperature. We use

Yiwen Sun, Wenye Li

OpenTensor is a reproduction of AlphaTensor, which discovered a new algorithm that outperforms the state-of-the-art methods for matrix multiplication by Deep Reinforcement Learning (DRL). While AlphaTensor provides a promising framework for solving scientific problems, it is really hard to reproduce due to the massive tricks and lack of source codes. In this

Julio R. Banga, Sebastian Sager

Living organisms exhibit remarkable adaptations across all scales, from molecules to ecosystems. We believe that many of these adaptations correspond to optimal solutions driven by evolution, training, and underlying physical and chemical laws and constraints. While some argue against such optimality principles due to their potential ambiguity, we propose ge

Wenchao Liu, Xuhui Zhang, Huijun Xing, Jinke Ren

Recently, movable antenna (MA) array becomes a promising technology for improving the communication quality in wireless communication systems. In this letter, an unmanned aerial vehicle (UAV) enabled multi-user multi-input-single-output system enhanced by the MA array is investigated. To enhance the throughput capacity, we aim to maximize the achievable data

Blair Archibald, Muffy Calder, Michele Sevegnani

Bigraphs are a versatile modelling formalism that allows easy expression of placement and connectivity relations in a graphical format. System evolution is user defined as a set of rewrite rules. This paper presents a practical, yet detailed guide to developing, executing, and reasoning about bigraph models, including recent extensions such as parameterised,

Riccardo Benaglia, Angelo Porrello, Pietro Buzzega, Simone Calderara

Trajectory forecasting is crucial for video surveillance analytics, as it enables the anticipation of future movements for a set of agents, e.g. basketball players engaged in intricate interactions with long-term intentions. Deep generative models offer a natural learning approach for trajectory forecasting, yet they encounter difficulties in achieving an op

Raphael Wieland, Olcay Kizilaslan, Nickolay Kinev, Eric Dorsch

Suitably patterned single crystals made of the cuprate superconductor Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ (BSCCO), intrinsically forming a stack of Josephson junctions, can generate electromagnetic radiation in the lower terahertz regime. Due to Joule heating the emission power of single stacks seems to be limited to values below 100 $\mu$W. To increase the radiat

M. Amar, D. Andreucci, E. N. M. Cirillo

We study the Fokker-Planck diffusion equation with diffusion coefficient depending periodically on the space variable. Inside a periodic array of inclusions the diffusion coefficient is reduced by a factor called the diffusion magnitude. We find the upscaled equations obtained by taking both the degeneration and the homogenization limits in which the diffusi

Robin K. S. Hankin

Discrete Lanchester-type attrition models describe many types of antagonistic situations; the preferred interpretation is two fleets of battleships, each trying to sink the other. Such models may be characterised by a bivariate recurrence relation. Here I consider a restricted case in which a fleet that finds itself two or three units behind its opponent imm

Tigran Simonian, Ahin Roy, Akash Bajaj, Rui Dong

Thermoelectric materials are of great interest for heat energy harvesting applications. One such promising material is TlGaSe$_{2}$, a p-type semiconducting ternary chalcogenide. Recent reports show it can be processed as a thin film, opening the door for large-scale commercialization. However, TlGaSe$_{2}$ is prone to stacking faults along the [001] stackin

Youngjun Park, Cord Eric Schmidt, Benedikt Marcel Batton, Anne-Christin Hauschild

In the healthcare sector, a consciousness surrounding data privacy and corresponding data protection regulations, as well as heterogeneous and non-harmonized data, pose huge challenges to large-scale data analysis. Moreover, clinical data often involves partially overlapping features, as some observations may be missing due to various reasons, such as differ

Fernando Acebes, M Pereda, David Poza, Javier Pajares

The aim of this paper is to describe a new an integrated methodology for project control under uncertainty. This proposal is based on Earned Value Methodology and risk analysis and presents several refinements to previous methodologies. More specifically, the approach uses extensive Monte Carlo simulation to obtain information about the expected behavior of

Juan De Anton, Juan J Senovilla, Jose M Gonzalez-Varona, Fernando Acebes

Production planning in 3D printing factories brings new challenges among which the scheduling of parts to be produced stands out. A main issue is to increase the efficiency of the plant and 3D printers productivity. Planning, scheduling, and nesting in 3D printing are recurrent problems in the search for new techniques to promote the development of this tech

Le Phuoc Hai, Felipe Lara, Boris S. Mordukhovich

The paper addresses the study and applications of a broad class of extended-real-valued functions, known as optimal value or marginal functions, which are frequently appeared in variational analysis, parametric optimization, and a variety of applications. Functions of this type are intrinsically nonsmooth and require the usage of tools of generalized differe

Kai Matsunaga, Hiroyuki Uchida, Rei Enokiya, Toshiki Sato

It is generally hard to put robust constraints on progenitor masses of supernovae (SNe) and remnants (SNRs) observationally, while they offer tantalizing clues to understanding explosion mechanisms and mass distribution. Our recent study suggests that ``shell merger'', which is theoretically expected for stellar evolution, can appreciably affect final yields

Mansi Kakkar, Dattesh Shanbhag, Chandan Aladahalli, Gurunath Reddy M

Vision-language models have emerged as a powerful tool for previously challenging multi-modal classification problem in the medical domain. This development has led to the exploration of automated image description generation for multi-modal clinical scans, particularly for radiology report generation. Existing research has focused on clinical descriptions f

Jose M Gonzalez-Varona, Adolfo Lopez-Paredes, Javier Pajares, Fernando Acebes

Large companies are fully engaged in their digital transformation, specifically in developing strategic Business Intelligence (BI) projects. They have a Digital Strategy and top-level executives managing the change. BI projects are also being carried out in SMEs. In this paper, we present the results of an interpretative study conducted on a sample of SMEs f

P. Thalhammer, R. Ballhausen, E. Sokolova-Lapa, J. Stierhof

The Be X-ray binary EXO 2030+375 went through its third recorded giant outburst from June 2021 to early 2022. We present the results of both spectral and timing analysis based on NICER monitoring, covering the 2-10 keV flux range from 20 to 310 mCrab. Dense monitoring with observations carried out about every second day and a total exposure time of 160 ks al

Jose M Gonzalez-Varona, Adolfo Lopez-Paredes, David Poza, Fernando Acebes

Purpose: The new competitive environment characterized by innovation and constant change is forcing a new organizational behavior. This requires a digital transformation of SMEs based on collective performance determinants. SMEs have particular characteristics that differentiate them from large companies and a model that allows them to identify, leverage and

Ziang Liu, Sheng Cai, Qiuwei Wu, Xinwei Shen

The increasing frequency of extreme weather events has posed significant risks to the operation of power grids. During long-duration extreme weather events, microgrid formation (MF) is an essential solution to enhance the resilience of the distribution systems by proactively partitioning the distribution system into several microgrids to mitigate the impact

Fernando Acebes, Javier Pajares, Jose M Gonzalez-Varona, Adolfo Lopez-Paredes

Project managers need to manage risks throughout the project lifecycle and, thus, need to know how changes in activity durations influence project duration and risk. We propose a new indicator (the Activity Risk Index, ARI) that measures the contribution of each activity to the total project risk while it is underway. In particular, the indicator informs us

Jian-Hua Zeng, Qizhong Zhu, Liang He

Moir\'e related physics in twisted bilayer two-dimensional (2D) materials has attracted widespread interest in condensed matter physics. Simulation of moir\'e related physics in cold atom platform is expected to outperform the 2D materials thanks to its advantage of higher tunablility. Here, we demonstrate that, the cold atom platform enables a new mechanism