October 2023 arXiv papers — page 165

Bruno Bouchard, Xiaolu Tan

We study a class of linear parabolic path-dependent PDEs (PPDEs) defined on the space of c\`adl\`ag paths $x \in D([0,T])$, in which the coefficient functions at time $t$ depend on $x(t)$ and $\int_{0}^{t}x(s)dA_{s}$, for some (deterministic) continuous function $A$ with bounded variations. Under uniform ellipticity and H\"older regularity conditions on the

Tim R. Würfel, Mark J. Crumpton, Yan V. Fyodorov

We study analytically the Chalker-Mehlig mean diagonal overlap $\mathcal{O}(z)$ between left and right eigenvectors associated with a complex eigenvalue $z$ of $N\times N$ matrices in the real Ginibre ensemble (GinOE). We first derive a general finite $N$ expression for the mean overlap and then investigate several scaling regimes in the limit $N\rightarrow

Diego Saldaña Ulloa

This work combines algorithms based on word embeddings, dimensionality reduction, and clustering. The objective is to obtain topics from a set of unclassified texts. The algorithm to obtain the word embeddings is the BERT model, a neural network architecture widely used in NLP tasks. Due to the high dimensionality, a dimensionality reduction technique called

Qing Zhu, Qirong Mao, Jialin Zhang, Xiaohua Huang

Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group

Shiv Krishna Jaiswal, Karthik S. Gurumoorthy, Etika Agarwal, Shantala Manchenahally

We design an Inventory Allocation System (INVALS) that, for each item-store combination, plans the quantity to be allocated from a warehouse that replenishes multiple stores using trailers, while respecting typical operational constraints. We formulate a linear objective function which, when maximized, determines the allocation plan by considering not only t

Ahmed R. Sadik, Sebastian Brulin, Markus Olhofer

Generating code from a natural language using Large Language Models (LLMs) such as ChatGPT, seems groundbreaking. Yet, with more extensive use, it's evident that this approach has its own limitations. The inherent ambiguity of natural language presents challenges for complex software designs. Accordingly, our research offers an Agile Model-Driven Development

Bart M. P. Jansen, Bart van der Steenhoven

A kernelization for a parameterized decision problem $\mathcal{Q}$ is a polynomial-time preprocessing algorithm that reduces any parameterized instance $(x,k)$ into an instance $(x',k')$ whose size is bounded by a function of $k$ alone and which has the same yes/no answer for $\mathcal{Q}$. Such preprocessing algorithms cannot exist in the context of countin

Scott G. Engle

The vast majority of stars in the nearby stellar neighborhood are M dwarfs. Their low masses and luminosities result in slow rates of nuclear evolution and minimal changes to the star's observable properties, even along astronomical timescales. However, they possess relatively powerful magnetic dynamos and resulting X-ray to UV activity, compared to their bo

Madona Mekhael, Timo Stolt, Anna Vesala, Heikki Rekola

We demonstrate a simple and scalable approach to increase conversion efficiencies of nonlinear metasurfaces by incorporating them into multipass cells and by letting the pump beam to interact with the metasurfaces multiple times. We experimentally show that by metasurface design, the associated phase-matching criteria can be fulfilled. As a proof of principl

Diego Tancara, José Fredes, Ariel Norambuena

Dynamical quantum phase transition is a critical phenomenon involving out-of-equilibrium states and broken symmetries without classical analogy. However, when finite-sized systems are analyzed, dynamical singularities of the rate function can appear, leading to a challenging physical characterization when parameters are changed. Here, we report a quantum sup

Florent Sureau, Mahdi Latreche, Marion Savanier, Claude Comtat

In this work, we investigate hybrid PET reconstruction algorithms based on coupling a model-based variational reconstruction and the application of a separately learnt Deep Neural Network operator (DNN) in an ADMM Plug and Play framework. Following recent results in optimization, fixed point convergence of the scheme can be achieved by enforcing an additiona

M. Baker, V. Chelnokov, L. Cosmai, F. Cuteri

We report on the results of measuring the chromoelectric fields in a flux tube created by a static quark-antiquark pair in the finite-temperature SU(3) gauge theory. Below the deconfinement temperature the field behavior is similar to the zero-temperature case. Above the deconfinement temperature the field shape remains the same, but the field values drop wh

Junhao Hu, Weijie Gan, Zhixin Sun, Hongyu An

Deformable image registration (DIR) is an active research topic in biomedical imaging. There is a growing interest in developing DIR methods based on deep learning (DL). A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images. While conceptually simple, this approa

Antony Jerald, A. N. Madhavanunni, Gayathri Malamal, Mahesh Raveendranatha Panicker

Objective: The objective of this work is an attempt towards non-contact freehand 3D ultrasound imaging with minimal complexity added to the existing point of care ultrasound (POCUS) systems. Methods: This study proposes a novel approach of using a mechanical track for non-contact ultrasound (US) scanning. The approach thus restricts the probe motion to a lin

Sorawit Saengkyongam, Elan Rosenfeld, Pradeep Ravikumar, Niklas Pfister

The premise of identifiable and causal representation learning is to improve the current representation learning paradigm in terms of generalizability or robustness. Despite recent progress in questions of identifiability, more theoretical results demonstrating concrete advantages of these methods for downstream tasks are needed. In this paper, we consider t

Francesca Pistilli, Giuseppe Averta

Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely recognised in the machine learning community, graph learning is largely unexplored for downstream tasks such as robotics applic

F. Roeder, R. Pollmann, M. Stefszky, M. Santandrea

The bi-photon correlation time, a measure for the conditional uncertainty in the temporal arrival of two photons from a photon pair source, is a key performance identifier for many quantum spectroscopy applications, with shorter correlation times typically yielding better performance. Furthermore, it provides fundamental insight into the effects of dispersio

Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li

Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models. In this work, we pres

Yi-Ming Ding, Yan-Cheng Wang, Shi-Xin Zhang, Zheng Yan

Optimization problems are the core challenge in many fields of science and engineering, yet general and effective methods are scarce for searching optimal solutions. Quantum computing has been envisioned to help solve such problems, for example, the quantum annealing (QA) method based on adiabatic evolution has been extensively explored and successfully impl

Simona Cucchiara, Angelo Iollo, Tommaso Taddei, Haysam Telib

We present a nonlinear interpolation technique for parametric fields that exploits optimal transportation of coherent structures of the solution to achieve accurate performance. The approach generalizes the nonlinear interpolation procedure introduced in [Iollo, Taddei, J. Comput. Phys., 2022] to multi-dimensional parameter domains and to datasets of several

Thomas Guilmeau, Emilie Chouzenoux, Víctor Elvira

The {\lambda}-exponential family has recently been proposed to generalize the exponential family. While the exponential family is well-understood and widely used, this it not the case of the {\lambda}-exponential family. However, many applications require models that are more general than the exponential family. In this work, we propose a theoretical and alg

Inbar Fried, Janine Hoelscher, Jason A. Akulian, Ron Alterovitz

Anatomical models of a medical robot's environment can significantly help guide design and development of a new robotic system. These models can be used for benchmarking motion planning algorithms, evaluating controllers, optimizing mechanical design choices, simulating procedures, and even as resources for data generation. Currently, the time-consuming task

Maxime Lenormand

The ubiquitous use of mobile devices and associated Internet services generates vast volumes of geolocated data, offering valuable insights into human behaviors and their interactions with urban environments. Over the past decade, mobile phone data have proven indispensable in various fields such as demography, geography, transport planning, and epidemiology

Kristina Miller, Christopher K. Zeitler, William Shen, Kerianne Hobbs

A runtime assurance system (RTA) for a given plant enables the exercise of an untrusted or experimental controller while assuring safety with a backup (or safety) controller. The relevant computational design problem is to create a logic that assures safety by switching to the safety controller as needed, while maximizing some performance criteria, such as t

A. R. Derkink, M. C. Ramírez-Tannus, L. Kaper, A. de Koter

It is a challenge to study the formation process of massive stars: their formation time is short, they are few, often deeply embedded, and at relatively large distances. Our strategy is to study the outcome of the star formation process and to look for signatures remnant of the formation. We have access to a unique sample of (massive) pre-main-sequence (PMS)

Bahador Bahmani, WaiChing Sun

We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we propose a polyconvex neural additive model (PNAM) that enables us to express the hyperelastic model in a learnable feature

Marcel Kollovieh, Lukas Gosch, Marten Lienen, Yan Scholten

Most adversarial attacks and defenses focus on perturbations within small $\ell_p$-norm constraints. However, $\ell_p$ threat models cannot capture all relevant semantics-preserving perturbations, and hence, the scope of robustness evaluations is limited. In this work, we introduce Score-Based Adversarial Generation (ScoreAG), a novel framework that leverage

Jose A. Solano-Castellanos

In this paper a framework for solving the time optimal control (TOC) using Galerkin's Weighted Residuals Method (GWRM) and Sequential Convex Programming (SCP) is proposed. The proposed method solves the two-point boundary value problem, avoiding the use of shooting methods that rely heavily on the appropriate initialization of the adjoint state and optimal t

Fangshuo Liao, Junhyung Lyle Kim, Cruz Barnum, Anastasios Kyrillidis

Principal Component Analysis (PCA) aims to find subspaces spanned by the so-called principal components that best represent the variance in the dataset. The deflation method is a popular meta-algorithm that sequentially finds individual principal components, starting from the most important ones and working towards the less important ones. However, as deflat

Taofeng Wang, Ziming Li, R. B. Wiringa, Minliang Liu

The experimental evidence for the $\alpha$ Boson gas state in the $^{11}$C+$^{12}$C$\rightarrow$$^{23}$Mg$^{\ast}$ fusion evaporation reaction is presented. By measuring the $\alpha$ emission spectrum with multiplicity 2 and 3, we provide insight into the existence of a three-body force among $\alpha$ particles. The observed spectrum exhibited distinct tails

Alexandr Filyaev, Anton Losev, Vladimir Zavodilenko, Igor Pavlov

A sine-gated single-photon detector (SPD) intended for use in a quantum key distribution (QKD) system is considered in this paper. An "avalanche delay" effect in the sine-gated SPD is revealed. This effect consists in the appearance of an avalanche triggered at the next gate after the photon arrival gate. It has been determined experimentally that the nature

Maksim Papenkov, Chris Meredith, Claire Noel, Jai Padalkar

Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to

Zi-Kui Liu

Density functional theory (DFT) is the de facto approach for predicting self-consistent-field electronic structures of ground-state configurations of complex atoms, molecules, and solids and providing their property data for materials discovery and design. This capability is greatly enabled by the generalized gradient approximation for exchange-correlation i

Lorenzo A. Mariano, Sourav Mondal, Alessandro Lunghi

Vibronic coupling has a dramatic influence over a large number of molecular processes, ranging from photo-chemistry, to spin relaxation and electronic transport. The simulation of vibronic coupling with multi-reference wavefunction methods has been largely applied to organic compounds, and only early efforts are available for open-shell systems such as trans

J. S. Vorotyntseva, M. G. Kozlov, S. A. Levshakov

We present results on numerical calculations of the sensitivity coefficients, Qmu, of microwave molecular transitions in (13C)H3OH and CH3(18O)H to the hypothetical variation in the fundamental physical constant mu - the electron-to-proton mass ratio. The invariability of mu in time and space is one of the basic assumptions of the Standard Model of particle

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A measurement of the $CP$-violating observables from $B^{\pm}\rightarrow D^* K^{\pm}$ and $B^{\pm}\rightarrow D^* \pi^{\pm}$ decays is presented, where $D^* (D) $ is an admixture of $D^{*0}$ and $\bar{D}^{*0}$ ($D^0$ and $\bar{D}^0$) states and is reconstructed through the decay chains $ D^* \rightarrow D\pi^0/\gamma$ and $D \to K_S^0 \pi^+\pi^-/K_S^0 K^+K^-

Ilker Yildirim, L. A. Paul

In what sense does a large language model have knowledge? The answer to this question extends beyond the capabilities of a particular AI system, and challenges our assumptions about the nature of knowledge and intelligence. We answer by granting LLMs "instrumental knowledge"; knowledge defined by a certain set of abilities. We then ask how such knowledge is

Simone Zoia

I discuss the recent advances in the computation of two-loop scattering amplitudes for five-particle processes. The latter are fundamental ingredients to obtain predictions at the next-to-next-to-leading order (NNLO) in QCD for many interesting LHC processes. I discuss the state-of-the-art technology for computing scattering amplitudes analytically, and pres

Ziming Li, Jie Zhu, Taofeng Wang, Minliang Liu

The correlation of $\alpha\alpha$ was probed via measuring the transverse momentum $p_{T}$ and width $\delta p_{T}$ of one $\alpha$, for the first time, which represents the spatial and dynamical essentialities of the initial coupling state in $^{8}$Be nucleus. The weighted interaction vertex of 3$\alpha$ reflected by the magnitudes of their relative momentu

Zhikun Xu, Tianyou Wang, Zhizhao Che

The atomisation of suspension containing liquid and dispersed particles is prevalent in many applications. Previous studies of droplet breakup mainly focused on homogeneous fluids, and the heterogeneous effect of particles on the breakup progress is unclear. In this study, the breakup of particle-laden droplets in airflow is investigated experimentally. Comb

Johannes C. Krämer, Edouard Hannezo, Gerhard Gompper, Jens Elgeti

The homeostasis of epithelial tissue relies on a balance between the self-renewal of stem cell populations, cellular differentiation, and loss. Although this balance needs to be tightly regulated to avoid pathologies, such as tumor growth, the regulatory mechanisms, both cell-intrinsic and collective, which ensure tissue steady-state are still poorly underst

Max Argus, Abhijeet Nayak, Martin Büchner, Silvio Galesso

Learning-based manipulation policies from image inputs often show weak task transfer capabilities. In contrast, visual servoing methods allow efficient task transfer in high-precision scenarios while requiring only a few demonstrations. In this work, we present a framework that formulates the visual servoing task as graph traversal. Our method not only exten

Israt Jahan, Md Tahmid Rahman Laskar, Chun Peng, Jimmy Huang

Recently, Large Language Models (LLM) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks. For this purpose, we co

Galileu Santos de Jesus, Paulo Borba, Rodrigo Bonifácio, Matheus Barbosa de Oliveira

Version control system tools empower developers to independently work on their development tasks. These tools also facilitate the integration of changes through merging operations, and report textual conflicts. However, when developers integrate their changes, they might encounter other types of conflicts that are not detected by current merge tools. In this

Sachith Pai, Michael Mathioudakis, Yanhao Wang

Learned indexes fit machine learning (ML) models to the data and use them to make query operations more time and space-efficient. Recent works propose using learned spatial indexes to improve spatial query performance by optimizing the storage layout or internal search structures according to the data distribution. However, only a few learned indexes exploit

Noé Ensarguet, Paolo Perrone

We study a category of probability spaces and measure-preserving Markov kernels up to almost sure equality. This category contains, among its isomorphisms, mod-zero isomorphisms of probability spaces. It also gives an isomorphism between the space of values of a random variable and the sigma-algebra that it generates on the outcome space, reflecting the stan

Matteo El-Hariry, Antoine Richard, Vivek Muralidharan, Matthieu Geist

This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments. Floating platforms serve as versatile test-beds to emulate micro-gravity environments on Earth, useful to test autonomous navigation systems for space applications. Our approach addresses the system and en

Giuseppe Bruni, Sepehr Maleki, Senthil K. Krishnababu

Applications of deep learning to physical simulations such as Computational Fluid Dynamics have recently experienced a surge in interest, and their viability has been demonstrated in different domains. However, due to the highly complex, turbulent, and three-dimensional flows, they have not yet been proven usable for turbomachinery applications. Multistage a

Valter Borges, Hector Rosero-Garcia, João Paulo dos Santos

In this paper, we study ends of complete gradient non-trivial Schouten solitons. Without any additional assumptions, we show the shrinking ones have finitely many ends, and the expanding ones are connected at infinity. We also provide information regarding the non-parabolicity of the ends in the shrinking setting, and on the behavior of the potential functio

L. Yu. Ovchinnikova, A. P. Durkin, A. S. Kurilik, V. V. Paramonov

In proton therapy, depth scanning of the irradiated object is performed by changing the Output Energy (OE) of the accelerated beam. In pulsed linear accelerators, adjustment of the OE is usually by changing the amplitude and/or phase of the field in the accelerating elements from one RF pulse to another. The application of non-inertial traveling wave acceler

Ziming Li, Jie Zhu, Taofeng Wang, Minliang Liu

The enhancement of $J^{\pi}(T)$=3$^{+}$(0) state with isospin $T=0$ excited by the tensor force in the free $^{6}$Li nucleus has been observed, for the first time, relative to a shrinkable excitation in the $^{6}$Li cluster component inside its host nucleus. Comparatively, the excitation of $J^{\pi}(T)$=0$^{+}$(1) state with isospin $T=1$ for these two $^{6}

Frédéric Marin, Thibault Barnouin, Steven R. Ehlert, Abel Lawrence Peirson

Centaurus A is one of the closest radio-galaxies to Earth. Its proximity allowed us to extensively study its active galactic nucleus but the core emission mechanism remains elusive because of local strong dust and gas obscuration. The capability of polarimetry to shave-off contaminating emission has been exploited without success in the near-infrared by prev

Kingsley Adjenughwure, Arturo Tejada, Pedro F. V. Oliveira, Jeroen Hogema

Many car-following models like the Intelligent Driver Model (IDM) incorporate important aspects of safety in their definitions, such as collision-free driving and keeping safe distances, implying that drivers are safety conscious when driving. Despite their safety-oriented nature, when calibrating and evaluating these models, the main objective of most studi

Xue-Li Hua, Yi-Yao Li, Qian Wang, Shuai Yang

A novel approach is proposed to probe the nature of the double charm tetraquark through the prompt production asymmetry between $T_{\bar{c}\bar{c}}^-$ and $T_{cc}^+$ in $pp$ collisions. When comparing two theoretical pictures, i.e. the compact tetraquark and hadronic molecular pictures, we find that the former one exhibits a significantly larger production a

Qiuchen Qian, Yanran Wang, David Boyle

Close Enough Traveling Salesman Problem (CETSP) is a well-known variant of TSP whereby the agent may complete its mission at any point within a target neighborhood. Heuristics based on overlapped neighborhoods, known as Steiner Zones (SZ), have gained attention in addressing CETSP. While SZs offer effective approximations to the original graph, their inheren

Jasper Verbree, Ashish Cherukuri

This work explores the relationship between the set of Wardrop equilibria~(WE) of a routing game, the total demand of that game, and the occurrence of Braess's paradox~(BP). The BP formalizes the counter-intuitive fact that for some networks, removing a path from the network decreases congestion at WE. For a single origin-destination routing games with affin

Lennart Binkowski, Gereon Koßmann, Tobias J. Osborne, René Schwonnek

Optimisation via parameterised quantum circuits is the prevalent technique of near-term quantum algorithms. However, the omnipresent phenomenon of barren plateaus - parameter regions with vanishing gradients - sets a persistent hurdle that drastically diminishes its success in practice. In this work, we introduce an approach - based on non-unitary operations

Yiwei Gao, Santiago Ricoy, Addison Cobb, Ryan Phung

Atmospheric water harvesting is urgently needed given increasing global water scarcity. Current sorbent-based devices that cycle between water capture and release have low harvesting rates. We envision a radically different multi-material architecture with segregated and simultaneous capture and release. This way, proven fast-release mechanisms that approach

Sam Hainsworth

Media hype and technological breakthroughs are fuelling the race to adopt Artificial Intelligence amongst the business community, but is there evidence to suggest this will increase productivity? This paper uses 2015-2019 microdata from the UK Office for National Statistics to identify if the adoption of Artificial Intelligence techniques increases labour pr

Cong Zhang, Hongbo Bi, Tian-Zhu Xiang, Ranwan Wu

In this paper, we provide a comprehensive study on a new task called collaborative camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images. To this end, we meticulously construct the first large-scale dataset, termed CoCOD8K, which consists of 8,528 high-quality an

Luiz Renato Fontes, Mariela Pentón Machado, Leonel Zuaznábar

We consider the equilibrium surface of the Random Average Process started from an inclined plane, as seen from the height of the origin, obtained in [Ferrari & Fontes, 1998], where its fluctuations were shown to be of order of the square root of the distance to the origin in one dimension, and the square root of the log of that distance in two dimensions (an

Vane Jacky III Batkam Mbatchou, Calvin Tcheka

In these notes, we define a new simplicial structure on a connected multiplicative operad and call it connected multiplicative simplicial operad (for short; simplicial operad). Next we introduce on this simplicial operad a brace algebra structure analogous to that of Gerstenhaber-Voronov that we call right brace algebra structure. This permits us to obtain o

Petr Slaný

Both circular and epicyclic motion of test particles along equatorial circular orbits in the revisited Kerr-de Sitter spacetime is analyzed. We present relations for specific energy, specific angular momentum and Keplerian angular velocity of particles on equatorial circular orbits, and discuss criteria for the existence and stability of such orbits giving l

Guo Yu

The efficacy of active noise control technology in mitigating urban noise, particularly in relation to low-frequency components, has been well-established. In the realm of traditional academic research, adaptive algorithms, such as the filtered reference least mean square method, are extensively employed to achieve real-time noise reduction in many applicati

Vitaly Chernik, Pavel Buklemishev

The cell motility problem has been investigated for a long time. Today, many biologists, physicists, and mathematicians are looking for new research instruments for this process. A simple 2D model of a free-boundary cell moving on a homogeneous isotropic surface is presented in the paper. It describes the dynamics of the complex actomyosin liquid, whose spec

Yu-Chen Wei, Liang-Yan Hsu

Catalyzing reactions effectively by vacuum fluctuations of electromagnetic fields is a significant challenge within the realm of chemistry. Different from most studies based on vibrational strong coupling, we introduce an innovative catalytic mechanism driven by weakly coupled polaritonic fields. Through the amalgamation of macroscopic quantum electrodynamic

Vasantha Ramani, Pandarasamy Arjunan, Kameshwar Poolla, Clayton Miller

This work presents the analysis of semantically segmented, longitudinally, and spatially rich thermal images collected at the neighborhood scale to identify hot and cool spots in urban areas. An infrared observatory was operated over a few months to collect thermal images of different types of buildings on the educational campus of the National University of

Łukasz Płociniczak, Kacper Taźbierski

We couple the L1 discretization of the Caputo fractional derivative in time with the Galerkin scheme to devise a linear numerical method for the semilinear subdiffusion equation. Two important points that we make are: nonsmooth initial data and time-dependent diffusion coefficient. We prove the stability and convergence of the method under weak assumptions c

Andreas Belaey, Francesca Mariani, Thomas G. Mertens

In this work, we revisit the end-of-the-world (EOW) brane amplitudes in JT gravity from a BF gauge theoretic perspective. Observing and identifying the correct group theoretic ingredient for a closed EOW brane as a discrete series character, we use the group theory framework as a guide towards formulating the analogous supersymmetric problem. We compute thes

Mojtaba Moradi-Sepahvand, Simon H. Tindemans

The growing penetration of renewable energy sources (RESs) is inevitable to reach net zero emissions. In this regard, optimal planning and operation of power systems are becoming more critical due to the need for modeling the short-term variability of RES output power and load demand. Considering hourly time steps of one or more years to model the operationa

Wanyun Cui, Qianle Wang

Instructions augmentation is a crucial step for unleashing the full potential of large language models (LLMs) in downstream tasks. Existing Self-Instruct methods primarily simulate new instructions from a few initial instructions with in-context learning. However, our study identifies a critical flaw in this approach: even with GPT4o, Self-Instruct cannot ge

Suhan Zhong, Ying Cui, Jiawang Nie

This paper tackles the challenging problem of finding global optimal solutions for two-stage stochastic programs with continuous decision variables and nonconvex recourse functions. We introduce a two-phase approach. The first phase involves the construction of a polynomial lower bound for the recourse function through a linear optimization problem over a no

Zhenkun Zhou, Zikun Song, Tao Ren

Scanner big data has potential to construct Consumer Price Index (CPI). The study introduces a new weighted price index called S-FCPIw, which is constructed using scanner big data from retail sales in China. We address the limitations of China's CPI especially for its high cost and untimely release, and demonstrate the reliability of S-FCPIw by comparing it

Moritz Lange, Noah Krystiniak, Raphael C. Engelhardt, Wolfgang Konen

Real-world reinforcement learning (RL) environments, whether in robotics or industrial settings, often involve non-visual observations and require not only efficient but also reliable and thus interpretable and flexible RL approaches. To improve efficiency, agents that perform state representation learning with auxiliary tasks have been widely studied in vis

Pier Luigi Novi Inverardi, Aldo Tagliani, Jordan M. Stoyanov

We summarize significant classical results on (in)determinacy of measures in terms of their finite positive integer order moments. Well-known is the role of the smallest eigenvalues of Hankel matrices, starting from Hamburger's results a century ago and ending with the great progress made only in recent times by C. Berg and collaborators. We describe here kn

Mojtaba Moradi-Sepahvand, Simon H. Tindemans

Electric demand and renewable power are highly variable, and the solution of a planning model relies on capturing this variability. This paper proposes a hybrid multi-area method that effectively captures both the intraday and interday chronology of real data considering extreme values, using a limited number of representative days, and time points within ea

Dennis Klau, Marc Zöller, Christian Tutschku

This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the AutoML framing and b) solving a set of industrial use-cases with different ML problem types by benchmarking their most important characterist

Ng Bee Chin, Ng Zhi Ee Nicole, Kyla Kwan, Lee Yong Han Dylann

This study investigates the linguistic traits of fake news and real news. There are two parts to this study: text data and speech data. The text data for this study consisted of 6420 COVID-19 related tweets re-filtered from Patwa et al. (2021). After cleaning, the dataset contained 3049 tweets, with 2161 labeled as 'real' and 888 as 'fake'. The speech data f

Benjamin Aram Berendsohn, László Kozma, Michal Opler

Permutation pattern-avoidance is a central concept of both enumerative and extremal combinatorics. In this paper we study the effect of permutation pattern-avoidance on the complexity of optimization problems. In the context of the dynamic optimality conjecture (Sleator, Tarjan, STOC 1983), Chalermsook, Goswami, Kozma, Mehlhorn, and Saranurak (FOCS 2015) con

Dmitry Kudryavtsev

In this paper the concept of local embeddability into finite structures (being LEF) for the class of semigroups is expanded with investigations of non-LEF structures, a closely related generalising property of local wrapping of finite structures (being LWF) and inverse semigroups. The established results include a description of a family of non-LEF semigroup

Andrea Francesco Battaglia, Hugh Hudson, Alexander Warmuth, Hannah Collier

It is well known among the scientific community that solar flare activity often begins well before the main impulsive energy release. Our aim is to investigate the earliest phase of four distinct flares observed by Solar Orbiter/STIX and determine the relationships of the newly heated plasma to flare structure and dynamics. The analysis focuses on four event

Victor V. Golovko

An evaluation method supported by robust statistical analysis was used to analyze historical measurements of $^{39}$Ar half-life. The method, which combines the most frequent value (MFV) approach with bootstrap analysis, provides a more reliable way to estimate the half-life of $^{39}$Ar. The results show that the half-life is T1/2(MFV) = 268.2 + (3.1) - (2.

Sanya Gupta, Navin Sridhar, Lorenzo Sironi

We perform two-dimensional particle-in-cell simulations of magnetic reconnection for various strengths of the guide field (perpendicular to the reversing field), in magnetically-dominated electron-positron plasmas. Magnetic reconnection under such conditions could operate in accretion disk coronae around black holes. There, it has been suggested that the tra

Kay Scheerer, Thomas Michalke, Juergen Mathes

One of the key challenges for autonomous vehicles is the ability to accurately predict the motion of other objects in the surrounding environment, such as pedestrians or other vehicles. In this contribution, a novel motion forecasting approach for autonomous vehicles is developed, inspired by the work of Gilles et al. [1]. We predict multiple heatmaps with a

Pascal Schlachter, Zhibin Yu, Naveed Iqbal, Xiaofeng Wu

To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy, usability or are too expensive. Thus, we propose a novel concept called local reference point assisted active radar positioning, which is able to overcome these drawbacks. It is b

Jiarui Jin, Xianyu Chen, Fanghua Ye, Mengyue Yang

Recommender systems trained on offline historical user behaviors are embracing conversational techniques to online query user preference. Unlike prior conversational recommendation approaches that systemically combine conversational and recommender parts through a reinforcement learning framework, we propose CORE, a new offline-training and online-checking p

Ross Pallister, Natasha L. S. Jeffrey

Solar flare electron acceleration is an efficient process, but its properties (mechanism, location) are not well constrained. Via hard X-ray (HXR) emission, we routinely observe energetic electrons at the Sun, and sometimes we detect energetic electrons in interplanetary space. We examine if the plasma properties of an acceleration region (size, temperature,

Stancho G. Stanchev, Nikolay V. Vitanov

Raman qubits, represented by two ground or metastable quantum states coupled via an intermediate state, hold some advantages over directly coupled qubits, most notably much longer radiative lifetimes, shorter gate duration and lower radiation intensity due to using electric-dipole allowed optical transitions. They are also relatively simple to implement and

Gabriel F. Machado, Morgan Jones

Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side

Brennen T. Fagan, Niall J. MacKay, A. Jamie Wood

Processes of coalescence and fragmentation are used to understand the time-evolution of the mass distribution of various systems and may result in a steady state or in stable deterministic or stochastic cycles. Motivated by applications in insurgency warfare we investigate coalescence-fragmentation systems. We begin with a simple model of size-biased coalesc

Piet Groeneboom

We analyze nonparametric estimators for the distribution function of the incubation time in the singly and doubly interval censoring model. The classical approach is to use parametric families like Weibull, log-normal or gamma distributions in the estimation procedure. We propose nonparametric estimates which stay closer to the data than the classical parame

Zhengyu Yin, Zubiao Xiao

Let $r\geq 2$ and $(X_i,G)$ $(i=1,\cdots,r)$ be topological dynamical systems with $G$ being an infinite discrete amenable group. Suppose that $\pi_i:(X_i,G)\to (X_{i+1},G)$ are factor maps and $0\leq w_i\leq 1$. In this article, for $f\in C(X_1)$, we introduce the weighted topological pressure $P^{\textbf{a}}(f,G)$ for higher dimensions (not only for $r=2$)

Jérémie Chalopin, Victor Chepoi

In this note, we prove that finite CAT(0) cube complexes can be reconstructed from their boundary distances (computed in their 1-skeleta). This result was conjectured by Haslegrave, Scott, Tamitegama, and Tan (2023). The reconstruction of a finite cell complex from the boundary distances is the discrete version of the boundary rigidity problem, which is a cl

Tenglong Lu, Sheng Meng, Miao Liu

Sodium-ion batteries are a viable alternative to lithium-ion technology due to the plentiful sodium resources. However, certain commercialization challenges, such as low specific energies and poor cycling performance of current Na-ion cathodes, still need to be addressed. To overcome these hurdles, this study explored the potential of a novel class of fluori

Chatchai Promsiri, Weerawit Horinouchi, Ekapong Hirunsirisawat

The behaviors of null geodesics in the spherical symmetric black holes in Einstein-Maxwell-dilaton (EMD) theory with coupling function $f(\Phi)=e^{-2\alpha \Phi}$ are meticulously analyzed. We investigate the effects of coupling constant $\alpha$ on the effective potential of photon trajectories within three ranges, namely $0<\alpha <1$, $\alpha =1$ and $\al

Xiaojing Xu, Song Jiang, Wenjun Sun

A spatial second-order scheme for the nonlinear radiative transfer equations is introduced in this paper. The discretization scheme is based on the filtered spherical harmonics ($FP_N$) method for the angular variable and the unified gas kinetic scheme (UGKS) framework for the spatial and temporal variables respectively. In order to keep the scheme positive

Philippe Ben-Abdallah

The local density of states (LDOS) of electromagnetic field drives many basic processes associated to light-matter interaction such as the thermal emission of object, the spontaneous emission of quantum systems or the fluctuation-induced electromagnetic forces on molecules. Here, we study the LDOS in the close vicinity of magneto-optical metamaterials under

Vidya Sagar Sharma

Causal DAGs (also known as Bayesian networks) are a popular tool for encoding conditional dependencies between random variables. In a causal DAG, the random variables are modeled as vertices in the DAG, and it is stipulated that every random variable is independent of its ancestors conditioned on its parents. It is possible, however, for two different causal

Robert Samuel Simon

A game has approximate equilibria if for every $\epsilon >0$ there is an $\epsilon$-equilibrium. We show that there is a stochastic game that lacks approximate equilibria. This game has finitely many players and actions, their payoffs are Borel measurable functions on the pathways of play, and all players have perfect knowledge of the past histories and the

Ananth Mahadevan, Michael Mathioudakis

It is important to retrain a machine learning (ML) model in order to maintain its performance as the data changes over time. However, this can be costly as it usually requires processing the entire dataset again. This creates a trade-off between retraining too frequently, which leads to unnecessary computing costs, and not retraining often enough, which resu