July 2023 arXiv papers — page 149

A. Adıgüzel, H. Çetinkaya, Ş. Esen, D. Halis

High gradient accelerator injectors have been widely studied throughout the world-leading accelerator facilities. The demand for high frequency cavities have led the Detector, Accelerator and instrumentation laboratory (KAHVELab) in Istanbul to deploy a four-vane Radio Frequency Quadrupole (RFQ) operating at 800~MHz to accelerate 20~keV protons to 2~MeV. The

Mohamed Abouelnaga, Christine Jakobs

Nowadays, systematic security risk analysis plays a vital role in the automotive domain. The demand for advanced driver assistance systems and connectivity of vehicles to the internet makes cyber-security a crucial requirement for vehicle manufacturers. This paper summarizes the risk analysis method stated in the recently released automotive security standar

Nandini Iyer, Ronaldo Menezes, Hugo Barbosa

With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and safe. In this paper, we unveil how urban planning, not just

Lars Martin Sektnan, Carl Tipler

We study the zero locus of the Futaki invariant on K-polystable Fano threefolds, seen as a map from the K\"ahler cone to the dual of the Lie algebra of the reduced automorphism group. We show that, apart from families 3.9, 3.13, 3.19, 3.20, 4.2, 4.4, 4.7 and 5.3 of the Iskovskikh-Mori-Mukai classification of Fano threefolds, the Futaki invariant of such mani

Jiayi Lei, Tiankui Zhang, Yuanwei Liu

In this paper, a hybrid non-orthogonal multiple access (NOMA) framework for the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhanced cell-edge communication is investigated. Specifically, one transmitted user and one reflected user are paired as one NOMA-pair, while multiple NOMA-pairs are served via time division m

J Dedecker, F Merlevède, Emmanuel Rio

In this paper, we give precise rates of convergence in the strong invariance principle for stationary sequences of bounded real-valued random variables satisfying weak dependence conditions. One of the main ingredients is a new Fuk-Nagaev type inequality for a class of weakly dependent sequences. We describe also several classes of processes to which our res

Suvojit Dhara, Adrijit Goswami

In many real-world multi-attribute decision-making (MADM) problems, mining the inter-relationships and possible hierarchical structures among the factors are considered to be one of the primary tasks. But, besides that, one major task is to determine an optimal strategy to work on the factors to enhance the effect on the goal attribute. This paper proposes t

Mohamed Abouelnaga, Julien Vitay, Aida Farahani

This paper investigates different methods and various neural network architectures applicable in the time series classification domain. The data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and sound. With the help of this data, we can detect events such as occupancy in a specific environment. At first, we analyze

Giuseppe Emanuele Lio, Antonio Ferraro, Bruno Zappone, Janusz Parka

Epsilon-near-zero (ENZ) metamaterials represent a powerful toolkit for selectively transmitting and localizing light through cavity resonances, enabling the study of mesoscopic phenomena and facilitating the design of photonic devices. In this experimental study, we demonstrate the feasibility of engineering and actively controlling cavity modes, as well as

Mark D. McDonnell, Dong Gong, Amin Parveneh, Ehsan Abbasnejad

Continual learning (CL) aims to incrementally learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. Most CL works focus on tackling catastrophic forgetting under a learning-from-scratch paradigm. However, with the increasing prominence of foundation models, pre-trained models equipped with informative rep

Hannah Schuster, Axel Polleres, Johannes Wachs

This research studies how populations depend on road networks for access to health care during crises or natural disasters. So far, most researchers rather studied the accessibility of the whole network or the cost of network disruptions in general, rather than as a function of the accessibility of specific priority destinations like hospitals. Even short de

Linhao Qu, Yingfan Ma, Xiaoyuan Luo, Manning Wang

Weakly supervised whole slide image classification is usually formulated as a multiple instance learning (MIL) problem, where each slide is treated as a bag, and the patches cut out of it are treated as instances. Existing methods either train an instance classifier through pseudo-labeling or aggregate instance features into a bag feature through attention m

Saketh Virupakshi, Katarzyna Kowalczyk-Gajewska

The crystal plasticity finite element method (CPFEM) is used to investigate the coupling between the cylindrical void growth or collapse and grain refinement in face-centered cubic (FCC) single crystals. A 2D plane strain model with one void is used. The effect of the initial lattice orientation, similarities, and differences between stress- and strain-drive

Guangqiang Mei, Wei Tan, Xingxia Cui, Cong Wang

The quest for pragmatic room-temperature (RT) magnetic semiconductors (MSs) with a suitable bandgap constitutes one of the contemporary opportunities to be exploited. This may provide a materials platform for to bring new-generation ideal information device technologies into real-world applications where the otherwise conventionally separately utilized charg

Jayateja Kalla, Soma Biswas

Few-shot class-incremental learning (FSCIL) aims to learn progressively about new classes with very few labeled samples, without forgetting the knowledge of already learnt classes. FSCIL suffers from two major challenges: (i) over-fitting on the new classes due to limited amount of data, (ii) catastrophically forgetting about the old classes due to unavailab

Hongmin Cai, Xiaoke Huang, Zhengliang Liu, Wenxiong Liao

Alzheimer's disease (AD) is a common form of dementia that severely impacts patient health. As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease. This study investigates various methods for detecting AD using patients' speech and transcripts data from the DementiaBank P

Lukas Muttenthaler, Robert A. Vandermeulen, Qiuyi Zhang, Thomas Unterthiner

Model overconfidence and poor calibration are common in machine learning and difficult to account for when applying standard empirical risk minimization. In this work, we propose a novel method to alleviate these problems that we call odd-$k$-out learning (OKO), which minimizes the cross-entropy error for sets rather than for single examples. This naturally

Sandipana Dowerah, Ajinkya Kulkarni, Romain Serizel, Denis Jouvet

The paper introduces Diff-Filter, a multichannel speech enhancement approach based on the diffusion probabilistic model, for improving speaker verification performance under noisy and reverberant conditions. It also presents a new two-step training procedure that takes the benefit of self-supervised learning. In the first stage, the Diff-Filter is trained by

Garrett Allen, Gaole He, Ujwal Gadiraju

We are amidst an explosion of artificial intelligence research, particularly around large language models (LLMs). These models have a range of applications across domains like medicine, finance, commonsense knowledge graphs, and crowdsourcing. Investigation into LLMs as part of crowdsourcing workflows remains an under-explored space. The crowdsourcing resear

Yuan Fang, Siyao Zhang, Xinmin Li, Xianghao Yu

This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which ea

Stefan Kratsch, Pascal Kunz

An $\alpha$-approximate polynomial Turing kernelization is a polynomial-time algorithm that computes an $(\alpha c)$-approximate solution for a parameterized optimization problem when given access to an oracle that can compute $c$-approximate solutions to instances with size bounded by a polynomial in the parameter. Hols et al. [ESA 2020] showed that a wide

Cyril Laplane, Peng Ren, Reece P. Roberts, Yiqing Lu

We report on a study exploring the design of nanoparticles that can enhance their laser refrigeration efficiency for applications in levitated optomechanics. In particular, we developed lanthanide-doped nanocrystals with an inert shell coating and compared their performance with bare nanocrystals. While optically levitated, we studied the refrigeration of bo

Jiayi Lei, Tiankui Zhang, Xidong Mu, Yuanwei Liu

This paper proposes a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) emergency communication network. Multiple STAR-RISs are deployed to provide additional and intelligent transmission links between trapped users and UAV-mounted base s

Paul Schwenteck, Sandra Zimmermann, Caspar von Lengerke, Giang T. Nguyen

This paper provides an overview of the hardware and software components used in our test bed project the NET Playground. All source information is stored in the GitLab repository (https://gitlab.com/Paulteck/net-playground). In the Hardware section, we present sketches and 3D views of mechanical parts and technical drawings of printed boards. The Software se

Shuai Chen, Subhradeep Kayal, Marleen de Bruijne

The paradigm of self-supervision focuses on representation learning from raw data without the need of labor-consuming annotations, which is the main bottleneck of current data-driven methods. Self-supervision tasks are often used to pre-train a neural network with a large amount of unlabeled data and extract generic features of the dataset. The learned model

Fernando León-Saavedra, M. del Carmen Listán-García, María Pilar Romero de la Rosa

We survey some results that provide different versions of classical results through different summability methods. Specifically, in order to adapt such classical results, we analyze which properties should satisfy the summability methods. Sometimes very sharp conditions are obtained, giving a focused view of the subject and from which new problems emerge.

Torsten Glemser, Rainer Schwabe

Data reduction is a fundamental challenge of modern technology, where classical statistical methods are not applicable because of computational limitations. We consider multiple linear regression for an extraordinarily large number of observations, but only a few covariates. Subsampling aims at the selection of a given proportion of the existing original dat

Muzaffar M. Rahmatullaev, Obid Sh. Karshiboev

In this paper, we consider a three-state solid-on-solid (SOS) model with two competing interactions (nearest-neighbour, one-level next-nearest-neighbour) on the Cayley tree of order two. We show that at some values of parameters the model exhibits a phase transition. We prove that for the model under some conditions there is no antiferromagnetic phases.

G. Arunkumar, Narayanan Narayanan, Raghavendra Rao B. V., Sagar S. Sawant

Stanley's Tree Isomorphism Conjecture posits that the chromatic symmetric function can distinguish non-isomorphic trees. While already established for caterpillars and other subclasses of trees, we prove the conjecture's validity for a new class of trees that generalize proper caterpillars, thus confirming the conjecture for a broader class of trees.

Dirk - André Deckert, Franz Merkl, Markus Nöth

In honour of Detlef D\"urr, we report on a mathematical rigorous computation of the electric vacuum polarisation current and extract the well-known expression for the second order perturbation. Intermediate steps in the presented calculation demonstrate, to the knowledge of the authors for the first time, mathematical rigorous versions of the combined dimens

Tristan Joachim Winkel, Tahereh Sadat Parvini, Finn-Frederik Stiewe, Jakob Walowski

Spintronic THz emitters have attracted much attention due to their desirable properties, such as affordability, ultra-wideband capability, high efficiency, and tunable polarization. In this study, we investigate the characteristics of THz signals, including their frequency, bandwidth, and amplitude, emitted from a series of heterostructures with ferromagneti

Vahid Heidaripour Lakhani, Arman Babaei, Leander Jehl, Georgy Ishmaev

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. The challenge is to conceive incentives that are fair in regard to the income distribution among peers. Despite many systems using tokens to incentivize forwarding data, like Swarm, little is known about the interplay between

Yu-Bin Wang, Abdusattar Kurban, Xia Zhou, Yun-Wei Yu

FRB 180916.J0158+65 is a well-known repeating fast radio burst with a period ($16.35~\rm days$) and an active window ($5.0~\rm days$). We give out the statistical results of the dispersion measures and waiting times of bursts of FRB 180916.J0158+65. We find the dispersion measures at the different frequencies show a bimodal distribution. The peaking dispersi

Yann Claes, Vân Anh Huynh-Thu, Pierre Geurts

Learning processes by exploiting restricted domain knowledge is an important task across a plethora of scientific areas, with more and more hybrid training methods additively combining data-driven and model-based approaches. Although the obtained models are more accurate than purely data-driven models, the optimization process usually comes with sensitive re

Andris P. Stikuts, Abdelqader Zaben, Ivars Driķis, Māra Šmite

Effective mixing of fluids at the microfluidic scale is important for future applications in biology, medicine, and chemistry. A promising type of micromixers are magnetic filaments, which can be activated by an external magnetic field. However, there is a lack of research that combines experiments and numerical modelling of hydrodynamics around such filamen

Licai Sun, Zheng Lian, Bin Liu, Jianhua Tao

Dynamic facial expression recognition (DFER) is essential to the development of intelligent and empathetic machines. Prior efforts in this field mainly fall into supervised learning paradigm, which is severely restricted by the limited labeled data in existing datasets. Inspired by recent unprecedented success of masked autoencoders (e.g., VideoMAE), this pa

Laixiang Qin, Chunlai Li, Yiqun Wei, Zhangwei Xu

We demonstrate an exotic doubled-channeled NT GAAFET (DC NT GAAFET) structure with Ion boost in comparison with NT GAAFET and NW GAAFET with the same footprint. Ion gains of 64.8% and 1.7 times have been obtained in DC NT GAAFET in compared with NT GAAFET and NW GAAFET. Ioff of DC NT GAAFET degrades by 61.8% than that of NT GAAFET, SS is almost comparable in

Ronny Müller, Domenico Ribezzo, Mujtaba Zahidy, Leif Katsuo Oxenløwe

The Information Reconciliation phase in quantum key distribution has significant impact on the range and throughput of any QKD system. We explore this stage for high-dimensional QKD implementations and introduce two novel methods for reconciliation. The methods are based on nonbinary LDPC codes and the Cascade algorithm, and achieve efficiencies close the th

Janus Collaboration, I. Paga, J. He, M. Baity-Jesi

Rejuvenation and memory, long considered the distinguishing features of spin glasses, have recently been proven to result from the growth of multiple length scales. This insight, enabled by simulations on the Janus~II supercomputer, has opened the door to a quantitative analysis. We combine numerical simulations with comparable experiments to introduce two c

Hamza Kebiri, Ali Gholipour, Meritxell Bach Cuadra, Davood Karimi

The brain white matter consists of a set of tracts that connect distinct regions of the brain. Segmentation of these tracts is often needed for clinical and research studies. Diffusion-weighted MRI offers unique contrast to delineate these tracts. However, existing segmentation methods rely on intermediate computations such as tractography or estimation of f

Shiyu Liu, Shaogao Lv, Dun Zeng, Zenglin Xu

Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data. However, the presence of statistical heterogeneity among clients poses a challenge, as the global model may struggle to perform well on each client's specific task. To a

Maria del Pilar Romero de la Rosa

We explore some convergence notions for set-convergence coming from modern summability methods. Specifically we will see the connections between Wijsman $f$-statistical convergence and Wijsman $f$-strong Ces\`aro convergence, when $f$ is a modulus function that induces a density in $\mathbb{N}$.

Christian Gerhards, Xinpeng Huang

Subspaces obtained by the orthogonal projection of locally supported square-integrable vector fields onto the Hardy spaces $H_+(\mathbb{S})$ and $H_-(\mathbb{S})$, respectively, play a role in various inverse potential field problems since they characterize the uniquely recoverable components of the underlying sources. Here, we consider approximation in thes

Jonas M. Schneider, Christoph Burkhardt, Thorsten Kleine

Nucleosynthetic isotope anomalies in meteorites allow distinguishing between the non-carbonaceous (NC) and carbonaceous (CC) meteorite reservoirs and show that correlated isotope anomalies exist in both reservoirs. It is debated, however, whether these anomalies reflect thermal processing of presolar dust in the disk or are primordial heterogeneities inherit

F. Conte, S. Massucco, F. Silvestro, D. Cirio

This paper describes an intelligent management algorithm for an aggregate of domestic electric water heaters called to provide a demand response service. This algorithm is developed using Model Predictive Control. The model of the entire aggregate is dynamically identified using a recursive polynomial model estimation technique. This allows the control to be

Ritika Singhal, N. Shravan Kumar

In this paper, we prove several versions of the classical Paley inequality for the Weyl transform. As an application, we discuss $L^p$-$L^q$ boundedness of the Weyl multipliers and prove a version of the H\"ormander's multiplier theorem. We also prove Hardy-Littlewood inequality. Finally, we study vector-valued versions of these inequalities. In particular,

Shengfeng Deng, Géza Ódor

Partial, frustrated synchronization and chimera-like states are expected to occur in Kuramoto-like models if the spectral dimension of the underlying graph is low: $d_s < 4$. We provide numerical evidence that this really happens in case of the high-voltage power grid of Europe ($d_s < 2$), a large human connectome (KKI113) and in case of the largest, exactl

Shrobona Bagchi, Dimpi Thakuria, Arun Kumar Pati

We derive a quantum speed limit for mixed quantum states using the stronger uncertainty relation for mixed quantum states and unitary evolution. We also show that this bound can be optimized over different choices of operators for obtaining a better bound. We illustrate this bound with some examples and show its better performance with respect to some earlie

A. Arbuzov, D. Kuznetsov, B. Latosh, V. Shmidt

We study inflation in scalar-tensor perturbative quantum gravity driven by a one-loop effective potential. We consider effective potentials generated by three models. The first model describes a single scalar field with a non-vanishing mass. The second model describes a massless scalar field with non-minimal coupling to the Einstein tensor. The third model g

P. Tozman, S. Isogami, I. Suzuki, A. Bolyachkin

Multi-level magnetic recording is a new concept for increasing the data storage capacity of hard disk drives. However, its implementation has been limited by a lack of suitable media capable of storing information at multiple levels. Herein, we overcome this problem by developing dual FePt-C nanogranular films separated by a Ru-C breaking layer with a cubic

Xiang Li, Yubing Qian, Ji Chen

Ab initio calculation of dielectric response with high-accuracy electronic structure methods is a long-standing problem, for which mean-field approaches are widely used and electron correlations are mostly treated via approximated functionals. Here we employ a neural network wavefunction ansatz combined with quantum Monte Carlo to incorporate correlations in

Souayah Abdelkader, Mokretar Kraroubi Abderrahmene, Slimane Larabi

People with visual impairments face numerous challenges when interacting with their environment. Our objective is to develop a device that facilitates communication between individuals with visual impairments and their surroundings. The device will convert visual information into auditory feedback, enabling users to understand their environment in a way that

Bahaeddine Gannouni, Victor Réville, Alexis Rouillard

We investigate the origin of mesoscale structures in the solar wind called microstreams defined as enhancements in solar wind speed and temperature that last several hours. They were first clearly detected in Helios and Ulysses solar wind data and are now omnipresent in the "young" solar wind measured by Parker Solar Probe and Solar Orbiter. These recent dat

Rudra Kamat

Williamson's theorem is well known for symmetric matrices. In this paper, we state and re-derive some of the cases of Williamson's theorem for symmetric positive-semi definite matrices and symmetric matrices having negative index 1, due to H\"ormander. We prove theorems that guarantee conditions under which two symmetric positive-definite matrices can be sim

Stefano Biagi, Giulia Meglioli, Fabio Punzo

We study mixed local and nonlocal elliptic equation with a variable coefficient $\rho$. Under suitable assumptions on the behaviour at infinity of $\rho$, we obtain uniqueness of solutions belonging to certain weighted Lebsgue spaces, with a weight depending on the coefficient $\rho$. The hypothesis on $\rho$ is optimal; indeed, when it fails we get nonuniqu

Thomas Schnappinger, Dominik Sidler, Michael Ruggenthaler, Angel Rubio

Experimental studies indicate that optical cavities can affect chemical reactions, through either vibrational or electronic strong coupling and the quantized cavity modes. However, the current understanding of the interplay between molecules and confined light modes is incomplete. Accurate theoretical models, that take into account inter-molecular interactio

Riccardo Catena, Taylor R. Gray

The current framework for dark matter searches at beam dump and fixed target experiments relies on four benchmark models, the complex scalar, inelastic scalar, pseudo-Dirac and finally, Majorana DM models. While this approach has so far been successful in the interpretation of the available data, it a priori excludes the possibility that DM is made of spin-1

Gandhali D. Joshi, Andrew Pontzen, Oscar Agertz, Martin P. Rey

We investigate the impact of a galaxy's merger history on its system of satellites using the new \textsc{vintergatan-gm} suite of zoom-in hydrodynamical simulations of Milky Way-mass systems. The suite simulates five realizations of the same halo with targeted `genetic modifications' (GMs) of a $z \approx 2$ merger, but resulting in the same halo mass at $z=

Anita Dürr, Nicolas El Maalouly, Lasse Wulf

In 1982 Papadimitriou and Yannakakis introduced the Exact Matching problem, in which given a red and blue edge-colored graph $G$ and an integer $k$ one has to decide whether there exists a perfect matching in $G$ with exactly $k$ red edges. Even though a randomized polynomial-time algorithm for this problem was quickly found a few years later, it is still un

Bogdan Grechuk, Tetiana Grechuk, Ashleigh Wilcox

We present a general algorithm for solving all two-variable polynomial Diophantine equations consisting of three monomials. Before this work, even the existence of an algorithm for solving the one-parameter family of equations $x^4+axy+y^3=0$ has been an open question. We also present an elementary method that reduces the task of finding all integer solution

Aiman Khan, Francesco Albarelli, Animesh Datta

It depends. For a single molecule interacting with one mode of a biphoton probe, we show that the spectroscopic information has three contributions, only one of which is a genuine two-photon contribution. When all the scattered light can be measured, solely this contribution exists and can be fully extracted using unentangled measurements. Furthermore, this

Fatemeh Farokhmanesh, Kevin Höhlein, Christoph Neuhauser, Tobias Necker

We present the first neural network that has learned to compactly represent and can efficiently reconstruct the statistical dependencies between the values of physical variables at different spatial locations in large 3D simulation ensembles. Going beyond linear dependencies, we consider mutual information as a measure of non-linear dependence. We demonstrat

Mingfei Lu, Badong Chen

The generative autoencoders, such as the variational autoencoders or the adversarial autoencoders, have achieved great success in lots of real-world applications, including image generation, and signal communication. However, little concern has been devoted to their robustness during practical deployment. Due to the probabilistic latent structure, variationa

Mustafa Sencer Aydin, Igor Kukavica, Wojciech S. Ożański, Amjad Tuffaha

We consider the three-dimensional Euler equations in a domain with a free boundary with no surface tension. We construct unique local-in-time solutions in the Lagrangian setting for $u_0 \in H^{2.5+\delta }$ such that the Rayleigh-Taylor condition holds and $\mathrm{curl}\,u_0 \in H^{2+\delta }$ in an arbitrarily small neighborhood of the free boundary. We s

Shanqi Liu, Weiwei Liu, Wenzhou Chen, Guanzhong Tian

The field of cooperative multi-agent reinforcement learning (MARL) has seen widespread use in addressing complex coordination tasks. While value decomposition methods in MARL have been popular, they have limitations in solving tasks with non-monotonic returns, restricting their general application. Our work highlights the significance of joint intentions in

Asif Manzoor Hasan, Johan Larsson, Sergio Pirozzoli, Rene Pecnik

Accurately predicting drag and heat transfer for compressible high-speed flows is of utmost importance for a range of engineering applications. This requires the precise knowledge of the entire velocity and temperature profiles. A common approach is to use compressible velocity scaling laws (transformation), that inverse transform the velocity profile of an

Piotr Gaiński, Michał Koziarski, Jacek Tabor, Marek Śmieja

Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics. However, typical GNNs cannot capture the concept of chirality, which means they do not distinguish between the 3D graph of a chemical compound and its mirror image (enantiomer). The ability to distinguish between enantiomers is important esp

Vincenzo Antonelli, Francesco Malaspina, Simone Marchesi, Joan Pons-Llopis

In this work we study the moduli spaces of instanton bundles on the flag twistor space $F:=F(0,1,2)$. We stratify them in terms of the minimal twist supporting global sections and we introduce the notion of (special) 't Hooft bundle on $F$. In particular we prove that there exist $\mu$-stable 't Hooft bundles for each admissible charge $k$. We completely des

Fabio Villa

EuPRAXIA@SPARC_LAB is a new multi-disciplinary user-facility that is currently under construction at the Laboratori Nazionali di Frascati of the INFN in the framework of the EuPRAXIA collaboration. The electron beam will be accelerated by an X-band normal conducting linac followed by a Plasma WakeField Acceleration (PWFA) stage. It will be characterized by a

Sascha Mücke, Thore Gerlach, Nico Piatkowski

Quadratic unconstrained binary optimization (QUBO) problems are well-studied, not least because they can be approached using contemporary quantum annealing or classical hardware acceleration. However, due to limited precision and hardware noise, the effective set of feasible parameter values is severely restricted. As a result, otherwise solvable problems be

Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst

Large Language Models (LLMs) are capable of answering questions in natural language for various purposes. With recent advancements (such as GPT-4), LLMs perform at a level comparable to humans for many proficient tasks. The analysis of business processes could benefit from a natural process querying language and using the domain knowledge on which LLMs have

Renato Ferreira Pinto

We study the connection between directed isoperimetric inequalities and monotonicity testing. In recent years, this connection has unlocked breakthroughs for testing monotonicity of functions defined on discrete domains. Inspired the rich history of isoperimetric inequalities in continuous settings, we propose that studying the relationship between directed

Norbert Tihanyi, Tamas Bisztray, Ridhi Jain, Mohamed Amine Ferrag

This paper presents the FormAI dataset, a large collection of 112, 000 AI-generated compilable and independent C programs with vulnerability classification. We introduce a dynamic zero-shot prompting technique constructed to spawn diverse programs utilizing Large Language Models (LLMs). The dataset is generated by GPT-3.5-turbo and comprises programs with va

David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy, Tatiana Matejovicova

For safety, medical AI systems undergo thorough evaluations before deployment, validating their predictions against a ground truth which is assumed to be fixed and certain. However, this ground truth is often curated in the form of differential diagnoses. While a single differential diagnosis reflects the uncertainty in one expert assessment, multiple expert

Henryk Gzyl

Consider the linear equation $\mathbf{A}\mathbf{x}=\mathbf{y}$, where $\mathbf{A}$ is a $k\times N$-matrix, $\mathbf{x}\in\mathcal{K}\subset \mathbb{R}^N$ and $\mathbf{y}\in\mathbb{R}^M$ a given vector. When $\mathcal{K}$ is a convex set and $M\not= N$ this is a typical ill-posed, linear inverse problem with convex constraints. Here we propose a new way to s

Irina Afanasieva, Valery Murzin, Valery Ardilanov, Nikolai Ivaschenko

Herein, we describe the design, implementation and operation principles of an astronomical camera system, based on a large-format CCD261-84 detector with an extremely thick 200 mkm substrate. The DINACON-V controller was used with the CCD to achieve high performance and low noise. The CCD system photometric characteristics are presented. A spatial autocorrel

Si Chen, Li-Chao Peng, Yong-Peng Guo, Xue-Mei Gu

In the quest to build general-purpose photonic quantum computers, fusion-based quantum computation has risen to prominence as a promising strategy. This model allows a ballistic construction of large cluster states which are universal for quantum computation, in a scalable and loss-tolerant way without feed-forward, by fusing many small n-photon entangled re

Zach Rosenof

Fantasy basketball has a rich underlying mathematical structure which makes optimal drafting strategy unclear. A central issue for category leagues is how to aggregate a player's statistics from all categories into a single number representing general value. It is shown that under a simplified model of fantasy basketball, a novel metric dubbed the "G-score"

Yanda Wu, Wenxing Zhang, Michael J. Ramsey-Musolf

We perform a comprehensive analysis of baryon number violation during an electroweak phase transition (EWPT) within the framework of a scalar electroweak multiplet extension of the Standard Model. We classify the multiplet representations, topological properties, and corresponding thermal histories. Sphaleron or monopole topological field solutions emerge du

Maokai Hu, Lifan Wang, Xiaofeng Wang, Lingzhi Wang

Type Ia supernovae (SNe Ia) arise from the thermonuclear explosion in binary systems involving carbon-oxygen white dwarfs (WDs). The pathway of WDs acquiring mass may produce circumstellar material (CSM). Observing SNe Ia within a few hours to a few days after the explosion can provide insight into the nature of CSM relating to the progenitor systems. In thi

Jie Huang, Kevin Chen-Chuan Chang

Large Language Models (LLMs) bring transformative benefits alongside unique challenges, including intellectual property (IP) and ethical concerns. This position paper explores a novel angle to mitigate these risks, drawing parallels between LLMs and established web systems. We identify "citation" - the acknowledgement or reference to a source or evidence - a

Ioannis Zachos, Theodoros Damoulas, Mark Girolami

A key challenge in agent-based mobility simulations is the synthesis of individual agent socioeconomic profiles. Such profiles include locations of agent activities, which dictate the quality of the simulated travel patterns. These locations are typically represented in origin-destination matrices that are sampled using coarse travel surveys. This is because

Guangrui Yang, Lihua Yang, Qing Zhang, Zhihua Yang

Recommendation algorithm plays an important role in recommendation system (RS), which predicts users' interests and preferences for some given items based on their known information. Recently, a recommendation algorithm based on the graph Laplacian regularization was proposed, which treats the prediction problem of the recommendation system as a reconstructi

Hsin-Lun Li

A money transfer involves a buyer and a seller. A buyer buys goods or services from a seller. The money the buyer decreases is the same as that the seller increases. At each time step, a pair of socially connected agents are selected and transact in agreed money. We evolve the Deffuant model to a money exchange system and study circumstances under which asym

Subrata Nandi, Srinivasan Krishnaswamy, Pinaki Mitra

In LFSR-based stream ciphers, the knowledge of the feedback equation of the LFSR plays a critical role in most attacks. In word-based stream ciphers such as those in the SNOW series, even if the feedback configuration is hidden, knowing the characteristic polynomial of the state transition matrix of the LFSR enables the attacker to create a feedback equation

Cornelis J. G. Mommers, Marc Vanderhaeghen

We propose a novel direct search experiment for X17 using the reaction $\gamma D \to e^+ e^- pn$. X17 is a hypothetical particle conjectured by the ATOMKI collaboration to explain anomalous signals around 17 MeV in excited ${}^8$Be, ${}^4$He and ${}^{12}$C nuclear decays via internal pair creation. It has been subject to a global experimental and theoretical

Thom Fruehwirth

We introduce a just-in-time runtime program transformation strategy based on repeated recursion unfolding. Our online program optimization generates several versions of a recursion differentiated by the minimal number of recursive steps covered. The base case of the recursion is ignored in our technique. Our method is introduced here on the basis of single l

Meysam Alizadeh, Maël Kubli, Zeynab Samei, Shirin Dehghani

This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research. By examining tasks like stance, topic, and relevance classification, we aim to guide scholars in making informed decisions about their use of LLMs for text analysis. Specifically, we conduct an assessment of both

Shuaijie Qian, Chen Yang

This paper studies a finite-horizon portfolio selection problem with non-concave terminal utility and proportional transaction costs, in which the commonly used concavification principle for terminal value is no longer applicable. We establish a proper theoretical characterization of this problem via a two-step procedure. First, we examine the asymptotic ter

Juami H. M. van Gils, Maurits Dijkstra, Halima Mouhib, Arriën Symon Rauh

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Halima Mouhib, Juami H. M. van Gils, Jose Gavaldá-Garciá, Qingzhen Hou

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Juami H. M. van Gils, Halima Mouhib, Erik van Dijk, Maurits Dijkstra

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Juami H. M. van Gils, Erik van Dijk, Ali May, Halima Mouhib

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Bas Stringer, Annika Jacobsen, Qingzhen Hou, Hans de Ferrante

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Maurits Dijkstra, Punto Bawono, Isabel Houtkamp, Jose Gavaldá-Garciá

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Jose Gavaldá-Garciá, Bas Stringer, Olga Ivanova, Sanne Abeln

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Olga Ivanova, Jose Gavaldá-Garciá, Dea Gogishvili, Isabel Houtkamp

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Annika Jacobsen, Erik van Dijk, Halima Mouhib, Bas Stringer

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensio

Fan Chen, Yiqing Lin, Zhenjie Ren, Songbo Wang

We study the kinetic mean field Langevin dynamics under the functional convexity assumption of the mean field energy functional. Using hypocoercivity, we first establish the exponential convergence of the mean field dynamics and then show the corresponding $N$-particle system converges exponentially in a rate uniform in $N$ modulo a small error. Finally we s

A. V. Bobakov, A. V. Karpova, S. V. Zharikov, A. Yu. Kirichenko

We present the results of optical spectroscopy of stellar companions to three binary millisecond pulsars, PSRs J0621$+$2514, J2317$+$1439 and J2302$+$4442, obtained with the Gran Telescopio Canarias. The spectrum of the J0621$+$2514 companion shows a blue continuum and prominent Balmer absorption lines. The latter are also resolved in the spectrum of the J23