February 2024 arXiv papers — page 98

Leonidas Gee, Andrea Zugarini, Leonardo Rigutini, Paolo Torroni

Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression

Yiming Xu, Dongfang Xu, Shenghui Song

Simultaneous wireless information and power transfer (SWIPT) has been proposed to offer communication services and transfer power to the energy harvesting receiver (EHR) concurrently. However, existing works mainly focused on static EHRs, without considering the location uncertainty caused by the movement of EHRs and location estimation errors. To tackle thi

Ali Jafarizadeh, Shadi Farabi Maleki, Parnia Pouya, Navid Sobhi

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face cha

Dongfang Xu, Yiming Xu, Xin Zhang, Xianghao Yu

Future wireless networks are envisioned to simultaneously provide high data-rate communication and ubiquitous environment-aware services for numerous users. One promising approach to meet this demand is to employ network-level integrated sensing and communications (ISAC) by jointly designing the signal processing and resource allocation over the entire netwo

Prasasthy Balasubramanian, Sadaf Nazari, Danial Khosh Kholgh, Alireza Mahmoodi

The extraction of cyber threat intelligence (CTI) from open sources is a rapidly expanding defensive strategy that enhances the resilience of both Information Technology (IT) and Operational Technology (OT) environments against large-scale cyber-attacks. While previous research has focused on improving individual components of the extraction process, the com

Armin Tavakoli, Simon Morelli

The Schmidt number represents the genuine entanglement dimension of a bipartite quantum state. We derive simple criteria for the Schmidt number of a density matrix in arbitrary local dimensions. They are based on the trace norm of correlations obtained from seminal families of quantum measurements, specifically symmetric informationally complete measurements

Tesshu Hanaka, Michael Lampis, Manolis Vasilakis, Kanae Yoshiwatari

Vertex integrity is a graph parameter that measures the connectivity of a graph. Informally, its meaning is that a graph has small vertex integrity if it has a small separator whose removal disconnects the graph into connected components which are themselves also small. Graphs with low vertex integrity are extremely structured; this renders many hard problem

Zhiwei Tang, Jiasheng Tang, Hao Luo, Fan Wang

Diffusion models have emerged as state-of-the-art generative models for image generation. However, sampling from diffusion models is usually time-consuming due to the inherent autoregressive nature of their sampling process. In this work, we propose a novel approach that accelerates the sampling of diffusion models by parallelizing the autoregressive process

Hyungryul Baik, Wonyong Jang

Any finitely generated group $G$ acts on its asymptotic cones in natural ways. The purpose of this paper is to calculate the kernel of such actions. First, we show that when $G$ is acylindrically hyperbolic, the kernel of the natural action on every asymptotic cone coincides with the unique maximal finite normal subgroup $K(G)$ of $G$. Secondly, we use this

Flavien Mabilat

The $\lambda$-quiddities of size $n$ are $n$-tuples of elements of a fixed set, solutions of a matrix equation appearing in the study of Coxeter's friezes. Their number and their properties are closely linked to the structure and the cardinality of the chosen set. The main objective of this text is to obtain an explicit formula giving the number of $\lambda$

Min Zhang, Sato Takumi, Jack Zhang, Jun Wang

Large Language Models (LLMs) excel in generating personalized content and facilitating interactive dialogues, showcasing their remarkable aptitude for a myriad of applications. However, their capabilities in reasoning and providing explainable outputs, especially within the context of reasoning abilities, remain areas for improvement. In this study, we delve

Junjie Shentu, Matthew Watson, Noura Al Moubayed

Subject-driven text-to-image diffusion models empower users to tailor the model to new concepts absent in the pre-training dataset using a few sample images. However, prevalent subject-driven models primarily rely on single-concept input images, facing challenges in specifying the target concept when dealing with multi-concept input images. To this end, we i

Cel Thys, Rodney Martinez Alonso, Antoine Lhomel, Maxandre Fellmann

This paper investigates the use of Neural Network (NN) nonlinear modelling for Power Amplifier (PA) linearization in the Walsh-Hadamard transceiver architecture. This novel architecture has recently been proposed for ultra-high bandwidth systems to reduce the transceiver power consumption by extensive parallelization of the digital baseband hardware. The par

Michael Hahn, Mark Rofin

Empirical studies have identified a range of learnability biases and limitations of transformers, such as a persistent difficulty in learning to compute simple formal languages such as PARITY, and a bias towards low-degree functions. However, theoretical understanding remains limited, with existing expressiveness theory either overpredicting or underpredicti

Luca Colomba, Paolo Garza

Satellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings. Different EO missions enables the collection of information

Zead Saleh, Ahmad Al Hanbali, Ahmad Baubaid

Crowdsourced delivery platforms face complex scheduling challenges to match couriers and customer orders. We consider two types of crowdsourced couriers, namely, committed and occasional couriers, each with different compensation schemes. Crowdsourced delivery platforms usually schedule committed courier shifts based on predicted demand. Therefore, platforms

Olivier Dudas, Gunter Malle

We determine the rationality properties of unipotent characters of finite reductive groups arising as fixed points of disconnected reductive groups under a Frobenius map. In the proof we use realisations of characters in $\ell$-adic cohomology groups of Deligne--Lusztig varieties as well as block theoretic considerations.

Pengyang Shao, Yonghui Yang, Chen Gao, Lei Chen

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges

Jujia Zhao, Wenjie Wang, Chen Xu, See-Kiong Ng

Large Language Models (LLMs) have empowered generative recommendation systems through fine-tuning user behavior data. However, utilizing the user data may pose significant privacy risks, potentially leading to ethical dilemmas and violations of data protection regulations. To address the privacy concerns, Federated Learning for Recommendation (Fed4Rec) has b

Xianggui Ding, Zongliang Du, Jiachen Luo, Hui Chen

Utilizing synthetic dimensions generated by spatial or temporal modulation, topological pumping enables the exploration of higher-dimensional topological phenomena through lower-dimensional physical systems. In this letter, we propose a rational design paradigm of fast topological pumping based on 1D and 2D time-modulated discrete elastic lattices for the fi

Seetaram Maurya, Nishchal K. Verma

Various methods for designing input features have been proposed for fault recognition in rotating machines using one-dimensional raw sensor data. The available methods are complex, rely on empirical approaches, and may differ depending on the condition monitoring data used. Therefore, this article proposes a novel algorithm to design input features that unif

Theodore Andronikos, Alla Sirokofskich

In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. We propose a radically new, to the best of our knowledge, algorithm that uses quantum

Alexander Karlberg, Julie Malcles, Bernhard Mistlberger, Roberto Di Nardo

This note documents predictions for the inclusive production cross sections of the Standard Model Higgs boson at the Large Hadron Collider at a centre of mass energy of 13.6 TeV. The predictions here are based on simple extrapolations of previously documented predictions published in the CERN Yellow Report "Deciphering the Nature of the Higgs Sector". The pr

Jiashu Pu, Yajing Wan, Yuru Zhang, Jing Chen

Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce. Our work fills this gap by systematically investigating the ICL capabilities of large language models (LLMs) in persona-based dialogue generation, c

Lucrezia Guiotto Nai Fovino, Anita Zanella, Massimo Grassi

Astronomy is a discipline primarily reliant on visual data. However, alternative data representation techniques are being explored, in particular ''sonification'', namely, the representation of data into sound. While there is increasing interest in the astronomical community in using sonification in research and educational contexts, its full potential is st

Lekhashri Konwar, Bhavna Yadav

In this study, we investigate the impact of new physics on different measures of tripartite entanglement within the context of three-flavor neutrino oscillations. These measures encompass concurrence, entanglement of formation, and negativity. We analyze the influence of new physics on these measures across a range of experimental setups involving both react

Tomáš Nagy, Michael Pinsker

We investigate the `local consistency implies global consistency' principle of strict width among structures within the scope of the Bodirsky-Pinsker dichotomy conjecture for infinite-domain Constraint Satisfaction Problems (CSPs). Our main result implies that for certain CSP templates within the scope of that conjecture, having bounded strict width has a co

Jiayang Yu, Xu Zhang

In this work, we propose a convenient framework for infinite-dimensional analysis (including both real and complex analysis in infinite dimensions), in which differentiation (in some weak sense) and integration operations can be easily performed, integration by parts can be conveniently established under rather weak conditions, and especially some nice prope

Leonidas Gee, Leonardo Rigutini, Marco Ernandes, Andrea Zugarini

Large Language Models have proven highly successful at modelling a variety of tasks. However, this comes at a steep computational cost that hinders wider industrial uptake. In this paper, we present MWT: a Multi-Word Tokenizer that goes beyond word boundaries by representing frequent multi-word expressions as single tokens. MWTs produce a more compact and ef

Aleksandr Ermolov, Shreya Kadambi, Maximilian Arnold, Mohammed Hirzallah

Radio signals are well suited for user localization because they are ubiquitous, can operate in the dark and maintain privacy. Many prior works learn mappings between channel state information (CSI) and position fully-supervised. However, that approach relies on position labels which are very expensive to acquire. In this work, this requirement is relaxed by

Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias Seeger

A large branch of explainable machine learning is grounded in cooperative game theory. However, research indicates that game-theoretic explanations may mislead or be hard to interpret. We argue that often there is a critical mismatch between what one wishes to explain (e.g. the output of a classifier) and what current methods such as SHAP explain (e.g. the s

B. A. Polyak, V. S. Khrapai, E. S. Tikhonov

We critically address the recent experiment [Science 382, 907 (2023)] on nonequilibrium transport and noise in a strange metal YbRh2Si2 patterned into the nanowire shape. In the long device, resistivity, differential resistance and current noise data seem to be consistent allowing us to extract electron-phonon coupling and the temperature dependence of elect

P. Rocha-Rodrigues, I. P. Miranda, S. S. M. Santos, G. N. P. Oliveira

Perturbed angular correlation spectroscopy combined with $ab-initio$ electronic structure calculations is used to unravel the structural phase transition path from the low-temperature polar structure to the high-temperature structural phase in $Ca_3Ti_2O_7$, a hybrid improper ferroelectric. This procedure explores the unique features of a local probe environ

Beth A. Henderson, Sarah L. Casewell, Michael R. Goad, Jack S. Acton

We report the discovery of a brown dwarf orbiting a M1 host star. We first identified the brown dwarf within the Next Generation Transit Survey data, with supporting observations found in TESS sectors 11 and 38. We confirmed the discovery with follow-up photometry from the South African Astronomical Observatory, SPECULOOS-S, and TRAPPIST-S, and radial veloci

A. Choudhury, L. Duchemin, F. Lequeux, L. Talini

In binary mixtures, the lifetimes of surface bubbles can be five orders of magnitude longer than those in pure liquids because of slightly different compositions of the bulk and the surfaces, leading to a thickness-dependent surface tension of thin films. Taking profit of the resulting simple surface rheology, we derive the equations describing the thickness

Zhiwei Tang, Tsung-Hui Chang

In Federated Learning (FL), a framework to train machine learning models across distributed data, well-known algorithms like FedAvg tend to have slow convergence rates, resulting in high communication costs during training. To address this challenge, we introduce FedLion, an adaptive federated optimization algorithm that seamlessly incorporates key elements

Ridwan Taiwo, Idris Temitope Bello, Sulemana Fatoama Abdulai, Abdul-Mugis Yussif

The construction industry is a vital sector of the global economy, but it faces many productivity challenges in various processes, such as design, planning, procurement, inspection, and maintenance. Generative artificial intelligence (AI), which can create novel and realistic data or content, such as text, image, video, or code, based on some input or prior

Laura Etfer, James M. S. Wason, Michael J. Grayling

Under a generalised estimating equation analysis approach, approximate design theory is used to determine Bayesian D-optimal designs. For two examples, considering simple exchangeable and exponential decay correlation structures, we compare the efficiency of identified optimal designs to balanced stepped-wedge designs and corresponding stepped-wedge designs

Claude Carlet, Marko Ðurasevic, Domagoj Jakobovic, Stjepan Picek

Boolean functions are mathematical objects used in diverse applications. Different applications also have different requirements, making the research on Boolean functions very active. In the last 30 years, evolutionary algorithms have been shown to be a strong option for evolving Boolean functions in different sizes and with different properties. Still, most

Francisco C. Alcaraz, Lucas M. Ramos

We calculated the spectral properties of two related families of non-Hermitian free-particle quantum chains with $N$-multispin interactions ($N=2,3,\ldots$). The first family have a $Z(N)$ symmetry and are described by free parafermions. The second one has a $U(1)$ symmetry and are generalizations of $XX$ quantum chains described by free fermions. The eigens

Zahra Yazdanparast

A self-healing software system is an advanced computer program or system designed to detect, diagnose, and automatically recover from faults or errors without human intervention. These systems are typically employed in mission-critical applications where downtime can have significant financial or operational consequences. Failure detection is one of the impo

Sushanta Barman, Sudeep Bhattacharjee

The evolution from classical to quantum matter wave optics has been influenced by transformative optical devices. Fresnel zone plates (FZP), initially designed for light manipulation, have now found expanded applications in matter waves. In this study, focusing of helium atoms by an electrically biased FZP is investigated numerically. The $n$th opaque zone o

Olivia Bennett, Antonia Freibert, K. Eryn Spinlove, Graham A. Worth

Quantum dynamics simulations are becoming a standard tool for simulating photo-excited molecular systems involving a manifold of coupled states, known as non-adiabatic dynamics. While these simulations have had many successes in explaining experiments and giving details of non-adiabatic transitions, the question remains as to their predictive power. In this

CMS Collaboration

A search is presented for fractionally charged particles with charge below 1$e$, using their small energy loss in the tracking detector as a key variable to observe a signal. The analyzed data set corresponds to an integrated luminosity of 138 fb$^{-1}$ of proton-proton collisions collected at $\sqrt{s}$ = 13 TeV in 2016-2018 at the CERN LHC. This is the fir

L. G. Chej, A. G. Monastra, M. F. Carusela

Many computational studies on hotspot microfluidic cooling devices found in the literature rely on simplified assumptions and conventions that do not capture the full complexity of the conjugate thermal problem, such as constant thermophysical fluid properties, radiation and free air convection on the external walls. These assumptions are generally applied t

Man Yao, Jiakui Hu, Tianxiang Hu, Yifan Xu

Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on neuromorphic chips, is a promising energy-efficient alternative to traditional AI. CNN-based SNNs are the current mainstream of neuromorphic computing. By contrast, no neuromorphic chips are designed especially for Transformer-based SNNs, which have just emerged, and their performance i

Alessio Belenchia, Felix Spengler, Dennis Rätzel, Daniel Braun

That light propagating in a gravitational field gets frequency-shifted is one of the basic consequences of any metric theory of gravity rooted in the equivalence principle. At the same time, also a time dependent material's refractive index can frequency-shift light propagating in it. The mathematical analogy between the two effects is such that the latter h

Alice Pelosse, Élisabeth Guazzelli, Matthieu Roché

The spreading of large viscous drops of density-matched suspensions of non-Brownian spheres on a smooth solid surface is experimentally investigated at the global drop scale. The focus is on dense suspensions with a solid volume fraction equal to or greater than $40\%$ and for drops larger than the capillary length, i.e. for which the spreading is governed b

Tobias Rüttenauer, Ozan Aksoy

The conventional Two-Way Fixed-Effects (TWFE) estimator has come under scrutiny lately. Recent literature has revealed potential shortcomings of TWFE when the treatment effects are heterogeneous. Scholars have developed new advanced dynamic Difference-in-Differences (DiD) estimators to tackle these potential shortcomings. However, confusion remains in applie

Anthony Chagneau, Yousra Massaoudi, Imene Derbali, Linda Yahiaoui

Drug discovery has become a main challenge in our society, following the Covid-19 pandemic. Even pharmaceutical companies are already using computing to accelerate drug discovery. They are increasingly interested in Quantum Computing with a view to improve the speed of research and development process for new drugs. Here, the authors propose a quantum method

Matthias Kränzler, Christian Herglotz, André Kaup

Energy efficiency for video communications is essential for mobile devices with a limited battery capacity. Therefore, hardware decoder implementations are commonly used to significantly reduce the energetic load of video playback. The energy consumption of such a hardware implementation largely depends on a previously published specification of a video codi

Mary Sánchez-Gordón, Ricardo Colomo-Palacios, Alex Sanchez Gordon

A role model is a person who serves as an example for others to follow, especially in terms of values, behavior, achievements, and personal characteristics. In this paper, authors study how role models influence software practitioners careers, an aspect not studied in the literature before. By means of this study, authors aim to understand if there are any s

Zhu-Jian Wan, Rui Xue, Ze-Rui Wang, Hu-Bing Xiao

The blazar sequence, including negative correlations between radiative luminosity $L_{\rm rad}$ and synchrotron peak frequency $\nu$, and between Compton dominance $Y$ and $\nu$, is widely adopted as a phenomenological description of spectral energy distributions (SEDs) of blazars, although its underlying cause is hotly debated. In particular, these correlat

Zhiyi Luo, Yingying Zhang, Shuyun Luo, Ying Zhao

Multi-span answer extraction, also known as the task of multi-span question answering (MSQA), is critical for real-world applications, as it requires extracting multiple pieces of information from a text to answer complex questions. Despite the active studies and rapid progress in English MSQA research, there is a notable lack of publicly available MSQA benc

William K. Wootters

For the continuous Wigner function and for certain discrete Wigner functions, permuting the values of the Wigner function in accordance with a symplectic linear transformation is equivalent to performing a certain unitary transformation on the state. That is, performing this unitary transformation is simply a matter of moving Wigner-function values around in

Chloe Conrad, Carole Adam

This report presents results from an M1 internship dedicated to agent-based modelling and simulation of daily mobility choices. This simulation is intended to be realistic enough to serve as a basis for a serious game about the mobility transition. In order to ensure this level of realism, we conducted a survey to measure if real mobility choices are made ra

Tyler W. Neely, Guillaume Gauthier, Charles Glasspool, Matthew J. Davis

The two-dimensional One-Component Plasma (OCP) is a foundational model of the statistical mechanics of interacting particles, describing phenomena common to astrophysics, turbulence, and the Fractional Quantum Hall Effect (FQHE). Despite an extensive literature, the phase diagram of the 2D OCP is still a subject of some controversy. Here we develop a "vortex

Runbo Li

Let $N$ denotes a sufficiently large even integer, $p$ denotes a prime and $P_{r}$ denotes an integer with at most $r$ prime factors. In this paper, we study the solutions of the equation $N-p=P_3$ and consider two special cases where $p$ is small, and $p,P_3$ are within short intervals.

Katarzyna Michałowska, Helga Margrete Bodahl Holmestad, Signe Riemer-Sørensen

We propose a new method for inferring roads from GPS trajectories to map construction sites. This task presents a unique challenge due to the erratic and non-standard movement patterns of construction machinery, which significantly diverge from typical vehicular traffic on established roads. Our proposed method first identifies intersections in the road netw

R. Turcati, I. Soares, S. B. Duarte

We analyze the blackbody radiation problem in the presence of quantum gravity effects encoded in modified dispersion relations. The spectral radiance and the generalized Stefan-Boltzmann law are studied in this context. Furthermore, the regime of low temperatures is also contemplated, where features related to the blackbody thermal laws and the thermodynamic

Monroe Eskew, Masahiro Shioya

Using Easton collapses, we give a simplified construction of a model in which Chang's Conjecture for triples holds.

Andrea Zugarini, Andrew Zamai, Marco Ernandes, Leonardo Rigutini

Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reasons resides in the displacement between popular benchmarks and actual data. Lack of supervision, unbalanced classes, noisy data and long documents often affect real problems in ver

Nir Lev, Anton Tselishchev

For every $p > (1 + \sqrt{5})/2$ we construct a uniformly discrete real sequence $\{\lambda_n\}_{n=1}^\infty$ satisfying $|\lambda_n| = n + o(1)$, a function $g \in L^p(\mathbb{R})$, and continuous linear functionals $\{g^*_n\}_{n=1}^\infty$ on $L^p(\mathbb{R})$, such that every $f \in L^p(\mathbb{R})$ admits a series expansion \[ f(x) = \sum_{n=1}^{\infty}

Karla Leipold, Frank Vallentin

The Ekeland-Hofer-Zehnder capacity (EHZ capacity) is a fundamental symplectic invariant of convex bodies. We show that computing the EHZ capacity of polytopes is NP-hard. For this we reduce the feedback arc set problem in bipartite tournaments to computing the EHZ capacity of simplices.

Houshang Ardavan

In their catalogue of pulsars' radio spectra, Swainston et al. (2022, PASA, 39, e056) distinguish between five different forms of these spectra: those that can be fitted with (i) a simple power law, (ii) a broken power law, (iii) a low-frequency turn-over, (iv) a high-frequency turn-over or (v) a double turn-over spectrum. Here, we choose two examples from e

Victor Gracia, Pablo Krupa, Daniel Limon, Teodoro Alamo

Model Predictive Control (MPC) for tracking formulation presents numerous advantages compared to standard MPC, such as a larger domain of attraction and recursive feasibility even when abrupt changes in the reference are produced. As a drawback, it includes some extra decision variables in its related optimization problem, leading to a semi-banded structure

Jiaxiang Liu, Tong Zhou, Yubo Chen, Kang Liu

Mitigating the hallucinations of Large Language Models is a crucial task. Although some existing methods employ self-enhancement techniques, they fall short of effectively addressing unknown factual hallucinations. Meanwhile, Knowledge Graph (KG) enhancement approaches fail to address the generalization across different KG sources and the enhancement of open

André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira, Lei Li

How can we detect if copyrighted content was used in the training process of a language model, considering that the training data is typically undisclosed? We are motivated by the premise that a language model is likely to identify verbatim excerpts from its training text. We propose DE-COP, a method to determine whether a piece of copyrighted content was in

Harish Chandra, Anurag Kumar Patel

We give a characterization of zero divisors of the ring $C[a,b].$ Using the Weierstrass approximation theorem, we completely characterize topological divisors of zero of the Banach algebra $C[a,b].$ We also characterize the zero divisors and topological divisors of zero in $\ell^\infty.$ Further, we show that zero is the only zero divisor in the disk algebra

Mate Krezinger, Sandor Frey, Krisztina Perger, Krisztina E. Gabanyi

There is still a limited number of high-redshift ($z>3$) active galactic nuclei (AGN) whose jet kinematics have been studied with very long baseline interferometry (VLBI). Without a dedicated proper motion survey, regularly conducted astrometric VLBI observations of bright radio-emitting AGN with sensitive arrays can be utilized to follow changes in the jets

Carlos Alejandro Lopez, Jaume Riba

The Majorization-Minimization (MM) framework is widely used to derive efficient algorithms for specific problems that require the optimization of a cost function (which can be convex or not). It is based on a sequential optimization of a surrogate function over closed convex sets. A natural extension of this framework incorporates ideas of Block Coordinate D

Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang

All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to oth

Basile Coron

We introduce a new type of operad-like structure called a P-operad, which depends on the choice of some collection of posets P, and which is governed by chains in posets of P. We introduce several examples of such structures which are related to classical poset theoretic notions such as poset homology, Cohen--Macaulayness and lexicographic shellability. We t

M. C. Zerbo, M. E. De Rossi, M. A. Lara-López, S. A. Cora

Effective yields, $y_{\rm eff}$, are defined by fundamental galaxy properties (i.e., stellar mass -$M_{\star}$-, gas mass -$M_{\rm gas}$- and gas-phase metallicity). For a closed-box model, $y_{\rm eff}$ is constant and equivalent to the mass in metals returned to the gas per unit mass locked in long-lived stars. Deviations from such behaviour have been ofte

Yumin Cho, Jaehyun Kim, Jang Soo Kim, Nakyung Lee

In 2019, Butler, Choi, Kim, and Seo introduced a new type of juggling card that represents multiplex juggling patterns in a natural bijective way. They conjectured a formula for the generating function for the number of multiplex juggling cards with capacity 2. In this paper we prove their conjecture. More generally, we find an explicit formula for the gener

Leo Sünkel, Michael Kölle, Tobias Rohe, Thomas Gabor

While the majority of focus in quantum computing has so far been on monolithic quantum systems, quantum communication networks and the quantum internet in particular are increasingly receiving attention from researchers and industry alike. The quantum internet may allow a plethora of applications such as distributed or blind quantum computing, though researc

Kai-Feng Chen, Titus Mombächer, Umberto de Sanctis

This article reviews the most recent measurements of $B_{(s)}^0\to\mu^+\mu^-$ decay properties at the LHC, which are the most precise to date. The measurements of the branching fraction and effective lifetime of the $B_{s}^0\to\mu^+\mu^-$ decay by the ATLAS, CMS and LHCb collaborations, as well as the search for $B^0\to\mu^+\mu^-$ decays are summarised with

Michał Malinowski

The chapter aims to explore the application of graph theory and networks in the recommendation domain, encompassing the mathematical models that form the foundation for the algorithms and recommendation systems developed based on them. The initial section of the chapter provides a concise overview of the recommendation field, with a particular focus on the t

Steven Morad, Chris Lu, Ryan Kortvelesy, Stephan Liwicki

Memory models such as Recurrent Neural Networks (RNNs) and Transformers address Partially Observable Markov Decision Processes (POMDPs) by mapping trajectories to latent Markov states. Neither model scales particularly well to long sequences, especially compared to an emerging class of memory models called Linear Recurrent Models. We discover that the recurr

Dmitry Salnikov, Petr Satunin, Leysan Valeeva, D. V. Kirpichnikov

We address the radio frequency (RF) cavity experiment for probing dark photons, which is a modification of the light-shining-through-thin-wall (LSthinW) setup with a relatively thin conducting barrier between cylindrical emitter and hollow We address the radio frequency (RF) cavity experiment for probing dark photons, which is a modification of the light-shi

Singsong Li, Shu Liu, Liming Ma, Chaoping Xing

Locally repairable codes have been extensively investigated due to practical applications in distributed and cloud storage systems in recent years. However, not much work on asymptotic behavior of locally repairable codes has been done. In particular, there is few result on constructive lower bound of asymptotic behavior of locally repairable codes with mult

Mahathir Mohammad Bishal, Md. Rakibul Hassan Chowdory, Anik Das, Muhammad Ashad Kabir

The COVID-19 pandemic has had adverse effects on both physical and mental health. During this pandemic, numerous studies have focused on gaining insights into health-related perspectives from social media. In this study, our primary objective is to develop a machine learning-based web application for automatically classifying COVID-19-related discussions on

Anastasios Papazafeiropoulos, Hanxiao Ge, Pandelis Kourtessis, Tharmalingam Ratnarajah

Simultaneously transmitting and reflecting \textcolor{black}{reconfigurable intelligent surface} (STAR-RIS) is a promising implementation of RIS-assisted systems that enables full-space coverage. However, STAR-RIS as well as conventional RIS suffer from the double-fading effect. Thus, in this paper, we propose the marriage of active RIS and STAR-RIS, denoted

Tobias Rüttenauer

This handbook chapter provides an essential introduction to the field of spatial econometrics, offering a comprehensive overview of techniques and methodologies for analysing spatial data in the social sciences. Spatial econometrics addresses the unique challenges posed by spatially dependent observations, where spatial relationships among data points can be

Tao Long, Katy Ilonka Gero, Lydia B. Chilton

Generative AI brings novel and impressive abilities to help people in everyday tasks. There are many AI workflows that solve real and complex problems by chaining AI outputs together with human interaction. Although there is an undeniable lure of AI, it is uncertain how useful generative AI workflows are after the novelty wears off. Additionally, workflows b

James A. Brotherston

The $S$-model category structures on filtered chain complexes and bicomplexes were introduced by Cirici, Egas Santander, Livernet and Whitehouse and later generalised by this author. In this paper we show they are left proper, cellular and stable model categories. We use these properties and the Cellularization Principle of Greenlees and Shipley to show that

Alexey Bufetov, Kailun Chen

Q-exchangeable ergodic distributions on the infinite symmetric group were classified by Gnedin-Olshanski (2012). In this paper, we study a specific linear combination of the ergodic measures and call it the Mallows product measure. From a particle system perspective, the Mallows product measure is a reversible stationary blocking measure of the infinite-spec

Vivian Y. Nastl, Moritz Hardt

We study how well machine learning models trained on causal features generalize across domains. We consider 16 prediction tasks on tabular datasets covering applications in health, employment, education, social benefits, and politics. Each dataset comes with multiple domains, allowing us to test how well a model trained in one domain performs in another. For

Enrique Álvarez, Jesús Anero

Linear superposition of gravitational fields is shown to be possible for a large class of spacetimes, in some specific coordinates. Explicit examples are presented.

Saikat Mukherjee, Rafael S. Mattos, Josene M. Toldo, Hans Lischka

This research examines the nonadiabatic dynamics of cyclobutanone after excitation into the n-3s Rydberg S2 state. It stems from our contribution to the Special Topic of the Journal of Chemical Physics to test the predictive capability of computational chemistry against unseen experimental data. Decoherence-corrected fewest-switches surface hopping (DC-FSSH)

Krishna Sahithi, Prateek Gupta

We derive a nonlinear acoustic wave propagation model for analysing the thermoviscous dissipation in narrow pores with wavy walls. As the nonlinear waves propagate in the thermoviscous pores, the wave-steepening effect competes with the bulk dissipation, as well as the thermoviscous heat transfer and shear from the pore walls. Consequently, the length scale

Anna Nalpantidi, Dimitris Karlis, Panagiotis Papastamoulis

The purpose of this paper is to extend standard finite mixture models in the context of multinomial mixtures for spatial data, in order to cluster geographical units according to demographic characteristics. The spatial information is incorporated on the model through the mixing probabilities of each component. To be more specific, a Gibbs distribution is as

Keiichi Shigechi

The Jones--Wenzl projections are a special class of elements of the Temperley--Lieb algebra. We prove that the coefficient appearing in the Jones--Wenzl projection is given by a generating function of combinatorial objects, called Dyck tilings. We also show that this correspondence holds for type $B$ case.

Mirko Nava, Nicholas Carlotti, Luca Crupi, Daniele Palossi

We propose a novel self-supervised approach for learning to visually localize robots equipped with controllable LEDs. We rely on a few training samples labeled with position ground truth and many training samples in which only the LED state is known, whose collection is cheap. We show that using LED state prediction as a pretext task significantly helps to l

Marie S. Rider, Edwin C. Johnson, Demetris Bates, William P. Wardley

We provide a simple method that enables readily acquired experimental data to be used to predict whether or not a candidate molecular material may exhibit strong coupling. Specifically, we explore the relationship between the hybrid molecular/photonic (polaritonic) states and the bulk optical response of the molecular material. For a given material this appr

Raymond Cheng

For any power $q$ of the positive ground field characteristic, a smooth $q$-bic threefold -- the Fermat threefold of degree $q+1$ for example -- has a smooth surface $S$ of lines which behaves like the Fano surface of a smooth cubic threefold. I develop projective, moduli-theoretic, and degeneration techniques to study the geometry of $S$. Using, in addition

Ruben Tous

This article introduces Lester, a novel method to automatically synthetise retro-style 2D animations from videos. The method approaches the challenge mainly as an object segmentation and tracking problem. Video frames are processed with the Segment Anything Model (SAM) and the resulting masks are tracked through subsequent frames with DeAOT, a method of hier

Liam J Berrisford, Hugo Barbosa, Ronaldo Menezes

Global ambient air pollution, a transboundary challenge, is typically addressed through interventions relying on data from spatially sparse and heterogeneously placed monitoring stations. These stations often encounter temporal data gaps due to issues such as power outages. In response, we have developed a scalable, data-driven, supervised machine learning f

Kristof Meixner, Kevin Feichtinger, Hafiyyan Sayyid Fadhlillah, Sandra Greiner

Cyber-Physical Production Systems (CPPSs), such as automated car manufacturing plants, execute a configurable sequence of production steps to manufacture products from a product portfolio. In CPPS engineering, domain experts start with manually determining feasible production step sequences and resources based on implicit knowledge. This process is hard to r

Maximilian Fleissner, Leena Chennuru Vankadara, Debarghya Ghoshdastidar

Despite the growing popularity of explainable and interpretable machine learning, there is still surprisingly limited work on inherently interpretable clustering methods. Recently, there has been a surge of interest in explaining the classic k-means algorithm, leading to efficient algorithms that approximate k-means clusters using axis-aligned decision trees