February 2024 arXiv papers — page 91

Haiyan Zhao, Fan Yang, Bo Shen, Himabindu Lakkaraju

Large language models (LLMs) have led to breakthroughs in language tasks, yet the internal mechanisms that enable their remarkable generalization and reasoning abilities remain opaque. This lack of transparency presents challenges such as hallucinations, toxicity, and misalignment with human values, hindering the safe and beneficial deployment of LLMs. This

Zhangjie Peng, Zhibo Zhang, Cunhua Pan, Marco Di Renzo

In this paper, we consider an active reconfigurable intelligent surface (RIS) to assist the multiuser downlink transmission in the presence of practical hardware impairments (HWIs), including the HWIs at the transceivers and the phase noise at the active RIS. The active RIS is deployed to amplify the incident signals to alleviate the multiplicative fading ef

Meiyi Zhu, Caili Guo, Chunyan Feng, Osvaldo Simeone

In a membership inference attack (MIA), an attacker exploits the overconfidence exhibited by typical machine learning models to determine whether a specific data point was used to train a target model. In this paper, we analyze the performance of the likelihood ratio attack (LiRA) within an information-theoretical framework that allows the investigation of t

Steinn Ymir Agustsson, Alfred J. H. Jones, Davide Curcio, Søren Ulstrup

Angle-resolved photoemission spectroscopy (ARPES) is a technique used to map the occupied electronic structure of solids. Recent progress in X-ray focusing optics has led to the development of ARPES into a microscopic tool, permitting the electronic structure to be spatially mapped across the surface of a sample. This comes at the expense of a time-consuming

Yi Lu, Xin Zhou, Wei He, Jun Zhao

Large language models (LLMs) have achieved impressive performance in numerous domains but often struggle to process lengthy inputs effectively and efficiently due to limited length generalization and attention's quadratic computational demands. Many sought to mitigate this by restricting the attention window within the pre-trained length. However, these meth

Bernhard Steffen, Tiziana Margaria, Alexander Bainczyk, Steve Boßelmann

We illustrate how purpose-specific, graphical modeling enables application experts with different levels of expertise to collaboratively design and then produce complex applications using their individual, purpose-specific modeling language. Our illustration includes seven graphical Integrated Modeling Environments (IMEs) that support full code generation, a

Takuya Hatomura

We propose energy-saving fast-forward scaling. Fast-forward scaling is a method which enables us to speed up (or slow down) given dynamics in a certain measurement basis. We introduce energy costs of fast-forward scaling, and find possibility of energy-saving speedup for time-independent measurement bases. As concrete examples, we show such energy-saving fas

Lucas Hedström, Peter Olsson

We study the jamming transition in a model of elastic particles under shear at zero temperature, with a focus on the relaxation time $\tau_1$. This relaxation time is from two-step simulations where the first step is the ordinary shearing simulation and the second step is the relaxation of the energy after stopping the shearing. $\tau_1$ is determined from t

Tobias Würth, Niklas Freymuth, Clemens Zimmerling, Gerhard Neumann

Engineering components must meet increasing technological demands in ever shorter development cycles. To face these challenges, a holistic approach is essential that allows for the concurrent development of part design, material system and manufacturing process. Current approaches employ numerical simulations, which however quickly becomes computation-intens

Anas Jnini, Flavio Vella, Marius Zeinhofer

We propose Gauss-Newton's method in function space for the solution of the Navier-Stokes equations in the physics-informed neural network (PINN) framework. Upon discretization, this yields a natural gradient method that provably mimics the function space dynamics. Our computational results demonstrate close to single-precision accuracy measured in relative $

Marjorie Galinier, Marco Delbo, Chrysa Avdellidou, Laurent Galluccio

The classical theory of differentiation states that due to the heat generated by the decay of radioactive elements, some asteroids form an iron core, an olivine-rich mantle, and a crust. The collisional breakup of these differentiated bodies is expected to lead to exposed mantle fragments, creating families of newly-formed asteroids. Among these new objects,

Nathan Claudet, Simon Perdrix

Local sets, a graph structure invariant under local complementation, have been originally introduced in the context of quantum computing for the study of quantum entanglement within the so-called graph state formalism. A local set in a graph is made of a non-empty set of vertices together with its odd neighborhood. We show that any graph can be covered by mi

Hugo Lebeau, Mohamed El Amine Seddik, José Henrique de Morais Goulart

We study the estimation of a planted signal hidden in a recently introduced nested matrix-tensor model, which is an extension of the classical spiked rank-one tensor model, motivated by multi-view clustering. Prior work has theoretically examined the performance of a tensor-based approach, which relies on finding a best rank-one approximation, a problem know

Heidi Van den Camp, Brendan D. McKay

Lopsp-operations are operations on maps that are applied locally and are guaranteed to preserve all the orientation-preserving symmetries of maps. Well-known examples of such operations are dual, ambo, truncation, and leapfrog. They are described by plane 3-coloured triangulations with specific properties. We developed and implemented a program that can gene

Lars Klöser, Mika Beele, Jan-Niklas Schagen, Bodo Kraft

This study pioneers the use of synthetically generated data for training generative models in document-level text simplification of German texts. We demonstrate the effectiveness of our approach with real-world online texts. Addressing the challenge of data scarcity in language simplification, we crawled professionally simplified German texts and synthesized

Benjamin Biaggi, Chia-Yu Chang, Jan Draisma, Filip Rupniewski

We show that the border subrank of a sufficiently general tensor in $(\mathbb{C}^n)^{\otimes d}$ is $\mathcal{O}(n^{1/(d-1)})$ for $n \to \infty$. Since this matches the growth rate $\Theta(n^{1/(d-1)})$ for the generic (non-border) subrank recently established by Derksen-Makam-Zuiddam, we find that the generic border subrank has the same growth rate. In our

Alekhya Ghosh, Arghadeep Pal, Lewis Hill, Graeme N Campbell

Within optical microresonators, the Kerr interaction of photons can lead to symmetry breaking of optical modes. In a ring resonator, this leads to the interesting effect that light preferably circulates in one direction or in one polarization state. Applications of this effect range from chip-integrated optical diodes to nonlinear polarization controllers an

François Dufour, Tomás Prieto-Rumeau

We consider an absorbing Markov decision process with Borel state and action spaces. We study conditions under which the MDP is uniformly absorbing and the set of occupation measures of the MDP is compact in the usual weak topology. These include suitable continuity requirements on the transition kernel and conditions on the dynamics of the system at the bou

Yuanzhen Xie, Xinzhou Jin, Tao Xie, MingXiong Lin

In-context learning of large-language models (LLMs) has achieved remarkable success in the field of natural language processing, while extensive case studies reveal that the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. To improve the contextual learn

Yuxuan Kuang, Hai Lin, Meng Jiang

Object navigation (ObjectNav) requires an agent to navigate through unseen environments to find queried objects. Many previous methods attempted to solve this task by relying on supervised or reinforcement learning, where they are trained on limited household datasets with close-set objects. However, two key challenges are unsolved: understanding free-form n

Guiming Hardy Chen, Shunian Chen, Ziche Liu, Feng Jiang

Adopting human and large language models (LLM) as judges (a.k.a human- and LLM-as-a-judge) for evaluating the performance of LLMs has recently gained attention. Nonetheless, this approach concurrently introduces potential biases from human and LLMs, questioning the reliability of the evaluation results. In this paper, we propose a novel framework that is fre

Rudi Coppola, Andrea Peruffo, Manuel Mazo

At the intersection of dynamical systems, control theory, and formal methods lies the construction of symbolic abstractions: these typically represent simpler, finite-state models whose behavior mimics that of an underlying concrete system but are easier to analyse. Building an abstraction usually requires an accurate knowledge of the underlying model: this

Yingru Li

We introduce the first probabilistic framework tailored for sequential random projection, an approach rooted in the challenges of sequential decision-making under uncertainty. The analysis is complicated by the sequential dependence and high-dimensional nature of random variables, a byproduct of the adaptive mechanisms inherent in sequential decision process

Murat Altunbulak, Fatma Altunbulak Aksu, Roghayeh Hafezieh, İpek Tuvay

A binary linear code whose permutation automorphism group has a fixed point free permutation of order $3$ is called a binary cubic code. The scope of this paper is to investigate the structural properties of binary cubic codes. Let $C$ be a binary cubic $[n,k]$ code. In this paper, we prove that if $n\geq 30$ and $C$ has permutation automorphism group of ord

Xuanliang Zhang, Dingzirui Wang, Longxu Dou, Qingfu Zhu

The open-domain text-to-SQL task aims to retrieve question-relevant tables from massive databases and generate SQL. However, the performance of current methods is constrained by single-hop retrieval, and existing multi-hop retrieval of open-domain question answering is not directly applicable due to the tendency to retrieve tables similar to the retrieved on

Patrick Ebel

As the automotive world moves toward higher levels of driving automation, Level 3 automated driving represents a critical juncture. In Level 3 driving, vehicles can drive alone under limited conditions, but drivers are expected to be ready to take over when the system requests. Assisting the driver to maintain an appropriate level of Situation Awareness (SA)

Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu

Currently, the in-context learning method based on large language models (LLMs) has become the mainstream of text-to-SQL research. Previous works have discussed how to select demonstrations related to the user question from a human-labeled demonstration pool. However, human labeling suffers from the limitations of insufficient diversity and high labeling ove

Aravinth Sivaganeshan, Nisansa de Silva

Named Entity Recognition (NER) is a sequence classification Natural Language Processing task where entities are identified in the text and classified into predefined categories. It acts as a foundation for most information extraction systems. Dungeons and Dragons (D&D) is an open-ended tabletop fantasy game with its own diverse lore. DnD entities are domain-

Patrik L. Ferrari, Min Liu

We consider the Airy$_1$ process, which is the limit process in KPZ growth models with flat and non-random initial conditions. We study the persistence probability, namely the probability that the process stays below a given threshold $c$ for a time span of length $L$. This is expected to decay as $e^{-\kappa(c) L}$. We determine an analytic expression for $

Maximiliano Rivera Figueroa, Pradyumna Kumar Bishoyi, Marina Petrova

Integrated Sensing and Communication (ISAC) is considered one of the crucial technologies in the upcoming sixth-generation (6G) mobile communication systems that could facilitate ultra-precise positioning of passive and active targets and extremely high data rates through spectrum coexistence and hardware sharing. Such an ISAC network offers a lot of benefit

Marios Papachristou, Yuan Yuan

Social networks profoundly influence how humans form opinions, exchange information, and organize collectively. As large language models (LLMs) are increasingly embedded into social and professional environments, it is critical to understand whether their interactions approximate human-like network dynamics. We develop a framework to study the network format

Nicolas Sadoune, Ke Liu, Han Yan, Ludovic D. C. Jaubert

Machine learning algorithms thrive on large data sets of good quality. Here we show that they can also excel in a typical research setting with little data of limited quality, through an interplay of insights coming from machine, and human researchers. The question we address is the unsolved problem of ordering out of a spin-liquid phase described by an emer

Håkan Andréasson, Markus Kunze

In 2001 Wolansky \cite{Wol} introduced a particle number-Casimir functional for the Einstein-Vlasov system. Two open questions are associated with this functional. First, a meaningful variational problem should be formulated and the existence of a minimizer to this problem should be established. The second issue is to show that a minimizer, for some choice o

Margherita Solci

We develop a general approach, using local interpolation inequalities, to non-convex integral functionals depending on the gradient with a singular perturbation by derivatives of order $k\ge 2$. When applied to functionals giving rise to free-discontinuity energies, such methods permit to change boundary values for derivatives up to order $k-1$ in problems d

C. Erdogan, T. Bode, P. Junker

Shape memory alloys are remarkable 'smart' materials used in a broad spectrum of applications, ranging from aerospace to robotics, thanks to their unique thermomechanical coupling capabilities. Given the complex properties of shape memory alloys, which are largely influenced by thermal and mechanical loads, as well as their loading history, predicting their

Dingzirui Wang, Longxu Dou, Xuanliang Zhang, Qingfu Zhu

Numerical reasoning is an essential ability for NLP systems to handle numeric information. Recent research indicates that fine-tuning a small-scale model to learn generating reasoning processes alongside answers can significantly enhance performance. However, current methods have the limitation that most methods generate reasoning processes with large langua

Lingxiao Zhang

We establish the $L^p$ boundedness of Hilbert transforms and maximal functions along flat curves in the Heisenberg group. This generalizes the $\mathbb{R}^n$ result by Carbery, Christ, Vance, Wainger, and Watson. What is new about our result compared to the Heisenberg group generalization by Carbery, Wainger, and Wright is that we allow all three components

V. Klippenstein, N. Wolf, N. F. A. van der Vegt

In molecular dynamics simulations, dynamically consistent coarse-grained (CG) models commonly use stochastic thermostats to model friction and fluctuations that are lost in a CG description. While Markovian, i.e., time-local, formulations of such thermostats allow for an accurate representation of diffusivities/long-time dynamics, a correct description of th

Alexander Malik, Geralf Hütter, Martin Abendroth, Bjoern Kiefer

Capturing and predicting the effective mechanical properties of highly porous cellular media still represents a significant challenge for the research community, due to their complex structural interdependencies and known size effects. Micromorphic theories are often applied in this context to model the inelastic deformation behavior of foam-like structures,

Zhongda Zeng, Giuliano Giudici, Hannes Pichler

The Rydberg blockade mechanism is an important ingredient in quantum simulators based on neutral atom arrays. It enables the emergence of a rich variety of quantum phases of matter, such as topological spin liquids. The typically isotropic nature of the blockade effect, however, restricts the range of natively accessible models and quantum states. In this wo

A. Lysak, E. Przeździecka, A. Wierzbicka, R. Jakiela

In situ Eu-doped {ZnCdO/ZnO}30 multilayer systems were grown on p-type Si-substrates and on quartz substrates by plasma-assisted molecular beam epitaxy. Various Eu concentrations in the samples were achieved by controlling temperature of the europium effusion cell. The properties of as-grown and annealed {ZnCdO/ZnO}30:Eu multilayers were investigated using s

Kourosh Parand, Aida Pakniyat

The Schrodinger equation is a mathematical equation describing the wave function's behavior in a quantum-mechanical system. It is a partial differential equation that provides valuable insights into the fundamental principles of quantum mechanics. In this paper, the aim was to solve the Schrodinger equation with sufficient accuracy by using a mixture of neur

Teresa Yu

We study generalizations of Schur functors from categories consisting of flags of vector spaces. We give different descriptions of the category of such functors in terms of representations of certain combinatorial categories and infinite rank groups, and we apply these descriptions to study polynomial representations and representation stability of parabolic

Robert Nieuwenhuis, Albert Oliveras, Enric Rodriguez-Carbonell

State-of-the-art SAT solvers are nowadays able to handle huge real-world instances. The key to this success is the so-called Conflict-Driven Clause-Learning (CDCL) scheme, which encompasses a number of techniques that exploit the conflicts that are encountered during the search for a solution. In this article we extend these techniques to Integer Linear Prog

Andreas Apseros, Valerio Scagnoli, Mirko Holler, Manuel Guizar-Sicairos

The functionality of materials is determined by their composition and microstructure, that is, the distribution and orientation of crystalline grains, grain boundaries and the defects within them. The characterisation of the material's microstructure is therefore critical for materials applications such as catalysis, energy storage and buildings. Until now,

Zhaowei Wang, Wei Fan, Qing Zong, Hongming Zhang

Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study. Existing work shows that LLMs are deficient in abstract ability, and how to improve it remains unexplored. In this work, we design the framework AbsInstruct to enhance LLMs' abstraction ability through instruction tuning. The framework builds instructions

Yoonjeong Park, Hyunjin Kim, Chanyeol Choi, Junseong Kim

Chain-of-thought (CoT) prompting is a simple and effective method for improving the reasoning capabilities of Large Language Models (LLMs). The basic idea of CoT is to let LLMs break down their thought processes step-by-step by putting exemplars in the input prompt. However, the densely structured prompt exemplars of CoT may cause the cognitive overload of L

Yaroslav Aksenov, Nikita Balagansky, Sofia Maria Lo Cicero Vaina, Boris Shaposhnikov

Advancing the frontier of subquadratic architectures for Language Models (LMs) is crucial in the rapidly evolving field of natural language processing. Current innovations, including State Space Models, were initially celebrated for surpassing Transformer performance on language modeling tasks. However, these models have revealed deficiencies in essential In

Ronald Cardenas, Matthias Galle, Shay B. Cohen

Extractive summaries are usually presented as lists of sentences with no expected cohesion between them. In this paper, we aim to enforce cohesion whilst controlling for informativeness and redundancy in summaries, in cases where the input exhibits high redundancy. The pipeline controls for redundancy in long inputs as it is consumed, and balances informativ

Xiangyu Zhang, Daijiao Liu, Hexin Liu, Qiquan Zhang

Recently, Denoising Diffusion Probabilistic Models (DDPMs) have attained leading performances across a diverse range of generative tasks. However, in the field of speech synthesis, although DDPMs exhibit impressive performance, their long training duration and substantial inference costs hinder practical deployment. Existing approaches primarily focus on enh

Sajad Salavatidezfouli, Saeid Rakhsha, Armin Sheidani, Giovanni Stabile

This paper aims to comprehensively investigate the efficacy of various Model Order Reduction (MOR) and deep learning techniques in predicting heat transfer in a pulsed jet impinging on a concave surface. Expanding on the previous experimental and numerical research involving pulsed circular jets, this investigation extends to evaluate Predictive Surrogate Mo

Benedikt Fröhlich, Lyne Moser

We study (vertically) normal lax double functors valued in the weak double category $\mathbb{C}\mathrm{at}$ of small categories, functors, profunctors and natural transformations, which we refer to as lax double presheaves. We show that for the theory of double categories they play a similar role as 2-functors valued in $\mathrm{Cat}$ for 2-categories. We fi

Tuc Nguyen, Thai Le

Several parameter-efficient fine-tuning methods based on adapters have been proposed as a streamlined approach to incorporate not only a single specialized knowledge into existing Pre-Trained Language Models (PLMs) but also multiple of them at once. Recent works such as AdapterSoup propose to mix not all but only a selective sub-set of domain-specific adapte

Dionn Hargreaves, Sarah Woolner, Oliver E. Jensen

During cell division, the mitotic spindle moves dynamically through the cell to position the chromosomes and determine the ultimate spatial position of the two daughter cells. These movements have been attributed to the action of cortical force generators which pull on the astral microtubules to position the spindle, as well as pushing events by these same m

Valery E. Lobanov, Olga V. Borovkova, Alexander K. Vorobyev, Vladislav I. Pavlov

Stability domains (i.e. pump frequency detuning range) of a single dark soliton (or platicon) and dark breather in high-Q Kerr optical microresonators with normal group velocity dispersion is studied for a wide range of pump amplitudes within the framework of the Lugiato-Lefever model. The effect of the significant fragmentation of the stability domains at h

Hyunsoo Cha, Byungjun Kim, Hanbyul Joo

We present PEGASUS, a method for constructing a personalized generative 3D face avatar from monocular video sources. Our generative 3D avatar enables disentangled controls to selectively alter the facial attributes (e.g., hair or nose) while preserving the identity. Our approach consists of two stages: synthetic database generation and constructing a persona

Yuqi Chen, Kan Ren, Yansen Wang, Yuchen Fang

Modeling continuous-time dynamics on irregular time series is critical to account for data evolution and correlations that occur continuously. Traditional methods including recurrent neural networks or Transformer models leverage inductive bias via powerful neural architectures to capture complex patterns. However, due to their discrete characteristic, they

Ivan Marisca, Cesare Alippi, Filippo Maria Bianchi

Given a set of synchronous time series, each associated with a sensor-point in space and characterized by inter-series relationships, the problem of spatiotemporal forecasting consists of predicting future observations for each point. Spatiotemporal graph neural networks achieve striking results by representing the relationships across time series as a graph

Youngsik Huh, Ryo Nikkuni

The crossing number of a graph is the minimum number of double points over all generic immersions of the graph into the plane. In this paper we investigate the behavior of crossing number under a graph transformation, called $\mathsf{\Delta Y}$-move, on the complete graph $K_n$. Concretely it is shown that for any $k\in \mathbb{N}$, there exist a natural num

Damiano Rossi

In this paper, we show that the Character Triple Conjecture holds for all finite groups once assumed for all quasi-simple groups. This answers the question on the existence of a self-reducing form of Dade's conjecture, a problem that was long investigated by Dade in the 1990s. Our result shows that this role is played by the Character Triple Conjecture, rece

Dayou Du, Yijia Zhang, Shijie Cao, Jiaqi Guo

The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantization has emerged as a widely embraced solution to reduce memory and computational demands. This paper introduces BitDistiller, a framework that synergizes Quantization-Aware Training

Tuomas P. Hytönen

Building on the notion of convex body domination introduced by Nazarov, Petermichl, Treil, and Volberg, we provide a general principle of bootstrapping bilinear estimates for scalar-valued functions into vector-valued versions with a reduced right-hand side involving iterated norms of a pointwise dot product $\vec f(x)\cdot\vec g(y)$ instead of the product o

Zhi-bin Gao, Yan-yue Fan, Hao Chen, Cheng-qun Pang

In this paper, we mainly predict the rates of $M$1 radiative and spin-nonflip $\pi\pi$ transitions of the $B_{c}$-meson under the nonrelativistic Cornell potential model with a screening potential effect. We employ the numerical wave function to determine the $M$1 radiative transition widths of $B_c$ excited states and utilize the Kuang-Yan proposed method f

Chen Xu, Jun Xu, Yiming Ding, Xiao Zhang

In pursuit of fairness and balanced development, recommender systems (RS) often prioritize group fairness, ensuring that specific groups maintain a minimum level of exposure over a given period. For example, RS platforms aim to ensure adequate exposure for new providers or specific categories of items according to their needs. Modern industry RS usually adop

Naoto Ohsaka

We present a reconfiguration analogue of alphabet reduction \`a la Dinur (J. ACM, 2007) and its applications. Given a binary constraint graph $G$ and its two satisfying assignments $\psi^\mathsf{ini}$ and $\psi^\mathsf{tar}$, the Maxmin Binary CSP Reconfiguration problem requests to transform $\psi^\mathsf{ini}$ into $\psi^\mathsf{tar}$ by repeatedly changin

Fenghao Zhu, Xinquan Wang, Chongwen Huang, Zhaohui Yang

Reconfigurable intelligent surface (RIS) has become a promising technology to realize the programmable wireless environment via steering the incident signal in fully customizable ways. However, a major challenge in RIS-aided communication systems is the simultaneous design of the precoding matrix at the base station (BS) and the phase shifting matrix of the

Subhodeep Dey, Antik Bhattacharya, Smarajit Karmakar

In two-dimensions (2D), the Mermin-Wagner-Hohenberg (MWH) fluctuation plays a significant role, giving rise to striking dimensionality effects marked by long-range density fluctuations leading to the singularities of various dynamical properties. According to the MWH theorem, a 2D equilibrium system with continuous degrees of freedom cannot achieve long-rang

Corentin Ségalas, Catherine Helmer, Robin Genuer, Cécile Proust-Lima

Analyzing longitudinal data in health studies is challenging due to sparse and error-prone measurements, strong within-individual correlation, missing data and various trajectory shapes. While mixed-effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, Functional Principal Compo

Paula Benaglia

Here I present a somewhat personal review of the results of my research carried out in the last couple of decades, mostly focused on the fields of stellar astrophysics, the interstellar medium, and the high energy sources. They have been obtained mainly from dedicated interferometric observations in the radio band at low frequencies of the electromagnetic sp

Hanno Sahlmann, Waleed Sherif

In the canonical approach of loop quantum gravity, arguably the most important outstanding problem is finding and interpreting solutions to the Hamiltonian constraint. In this work, we demonstrate that methods of machine learning are in principle applicable to this problem. We consider $U(1)$ BF theory in 3 dimensions, quantized with loop quantum gravity met

Yi Geng

Communication systems operating at high frequency bands must use narrow beams to compensate the high path loss. However, it is incredibly time-consuming to achieve beam alignment between the transmitter and receiver due to the large volume of beam space with narrow beams. The high latency of initial beam establishment will challenge the implementation of fut

Subhajit Sarkar, Pratim Banerjee, Molly De Raychaudhury

We report here perhaps the first successful synthesis and structural characterization of the n=3 family member of Tin+1AlCn, i. e. Ti4AlC3. X-ray Powder diffraction (XRD) data shows characteristic reflections of from corresponding to reflections from the (002), (004), (006), (008), (100), (102), (104), (0010), (105), (106), (0012), (1011) and (1012) planes a

Ruth Gregory, Shi-Qian Hu

Higher derivative terms in the gravitational action are natural from the perspective of quantum gravity, but are perceived as leading to a lack of well-posedness. The Gauss Bonnet term has second-order equations of motion, but does not impact gravitational dynamics in 4D, so one might expect that it is not physically relevant. We discuss how signatures can s

Shu-Qing Zhong, Long Li, Di Xiao, Hui Sun

Very recently, a particularly long gamma-ray burst (GRB) 230307A was reported and proposed to originate from a compact binary merger based on its host galaxy property, kilonova, and heavy elements. More intriguingly, a very early plateau followed by a rapid decline in soft X-ray band was detected in its light curve by the Lobster Eye Imager for Astronomy, in

Yihong Tang, Jiao Ou, Che Liu, Fuzheng Zhang

The advent of Large Language Models (LLMs) has propelled dialogue generation into new realms, particularly in the field of role-playing systems (RPSs). While enhanced with ordinary role-relevant training dialogues, existing LLM-based RPSs still struggle to align with roles when handling intricate and trapped queries in boundary scenarios. In this paper, we d

Michelangelo Diligenti, Marco Gori, Marco Maggini, Leonardo Rigutini

This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines. The logic propositions provide a partial representation of the environment, in which the learner operates, that is exploited by the learning algorithm together with the information available in the supervis

Changxin Xu, Yuxin Qiao, Zhanxin Zhou, Fanghao Ni

Federated Learning (FL) is a distributed machine learning paradigm that allows clients to train models on their data while preserving their privacy. FL algorithms, such as Federated Averaging (FedAvg) and its variants, have been shown to converge well in many scenarios. However, these methods require clients to upload their local updates to the server in a s

Francesco Galimberti, Stephanie Bopp, Alessandro Carletti, Rui Catarino

The increase in global pesticide use has mirrored the rising demand for food over the last decades, resulting in a boost in crop yields. However, concerns about the impact of pesticides on biodiversity, ecosystems, and human health, especially for populations residing close to cultivated areas, are growing. This study investigates how exposure and possible r

Huifeng Jiao, Nathapon Udomlertsakul, Anukul Tamprasirt

Blockchain market value peaked at $3 trillion, fell to $1 trillion, then recovered to $1.5 trillion and is rising again. Blockchain accounts secure most on-chain assets in this huge market (Web-12). This paper initiates a universal blockchain account model from a comprehensive review of blockchain account development, encompassing both academic and industry

Wietse M. Boon, Dennis Gläser, Rainer Helmig, Kilian Weishaupt

A discretization method with non-matching grids is proposed for the coupled Stokes-Darcy problem that uses a mortar variable at the interface to couple the marker and cell (MAC) method in the Stokes domain with the Raviart-Thomas mixed finite element pair in the Darcy domain. Due to this choice, the method conserves linear momentum and mass locally in the St

Ming Li, Jiuhai Chen, Lichang Chen, Tianyi Zhou

Making LLMs speak for different, especially minority groups of people, and generate statements supporting their diverse or even controversial perspectives is critical to creating an inclusive environment. However, existing LLMs lack sufficient controllability to the stance of their generated content, which often contains inconsistent, neutral, or biased stat

Bing-bing Wang, Zheyu Cheng, Hong-yu Zou, Yong Ge

Bloch wavefunctions in crystals experience localization within the bulk when disorder is introduced, a phenomenon commonly known as Anderson localization. This effect is considered universal, being applicable to all types of waves, quantum or classical. However, the interaction between disorder and topology -- a concept that has profoundly transformed many b

Miles M. Turner

Minimising opposing forces is a matter of interest to most cyclists. These forces arise from passage through air ("drag") and interaction with the road surface ("resistance"). Recent work recognises that resistance forces arise not only from the deformation of the tyre ("rolling resistance") but also from irregularities in the road surface ("roughness resist

Adam Olejniczak, Yury Rakovich, Victor Krivenkov

The Nobel Prizes in Physics (2022) and Chemistry (2023) heralded the recognition of quantum information science and the synthesis of quantum dots, respectively. This acknowledgment has propelled colloidal quantum dots and perovskite nanocrystals to the forefront of quantum technologies. Their distinct emission properties, facilitating the efficient generatio

Hanxing Ding, Liang Pang, Zihao Wei, Huawei Shen

Hallucinations present a significant challenge for large language models (LLMs). The utilization of parametric knowledge in generating factual content is constrained by the limited knowledge of LLMs, potentially resulting in internal hallucinations. While incorporating external information can help fill knowledge gaps, it also introduces the risk of irreleva

A. G. M. Pietrow, M. K. Druett, V. Singh

Solar flare ribbons are intense brightenings of principally chromospheric material that are responsible for a large fraction of the chromospheric emission in solar and stellar flares. We present an on-disc observation of flare ribbon substructures in an X9.3-class flare observed by the Swedish 1-m Solar Telescope. We identify categories of ribbon substructur

Clemens Eisenhofer, Michael Rawson, Laura Kovács

We propose a new encoding of the first-order connection method as a Boolean satisfiability problem. The encoding eschews tree-like presentations of the connection method in favour of matrices, as we show that tree-like calculi have a number of drawbacks in the context of satisfiability solving. The matrix setting permits numerous global refinements of the ba

Ziqi Gao, S. Kevin Zhou

Implicit visual knowledge in a large latent diffusion model (LLDM) pre-trained on natural images is rich and hypothetically universal to natural and medical images. To test this hypothesis from a practical perspective, we propose a novel framework for undersampled MRI Reconstruction by Prompting a large latent Diffusion model (MRPD). While the existing metho

Babak M. S. Arani

Literature is full of inference techniques developed to estimate the parameters of stochastic dynamical systems driven by the well-known Brownian noise. Such diffusion models are often inappropriate models to properly describe the dynamics reflected in many real-world data which are dominated by jump discontinuities of various sizes and frequencies. To accou

ATLAS Collaboration

A combination of searches for new heavy spin-1 resonances decaying into different pairings of $W$, $Z$, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb$^{-1}$ of proton-proton collisions at $\sqrt{s}$ = 13 TeV collected during 2015-2018 with the ATLAS detector at the CERN Large Hadron Coll

Alexander Makin

We study the spectral problem for the Dirac operator with degenerate boundary conditions and a complex-valued summable potential. Sufficient conditions are found under which the spectrum of the problem under consideration coincides with the spectrum of the corresponding unperturbed operator.

Kamila Zaman, Tasnim Ahmed, Muhammad Kashif, Muhammad Abdullah Hanif

In current noisy intermediate-scale quantum devices, hybrid quantum-classical neural networks (HQNNs) represent a promising solution that combines the strengths of classical machine learning with quantum computing capabilities. Compared to classical deep neural networks (DNNs), HQNNs present an additional set of hyperparameters, which are specific to quantum

J. Arias de Reyna

We show that the Riemann hypothesis is true if and only if the measure $$\mu=-\sum_{n=1}^\infty\frac{\Lambda(n)}{\sqrt{n}}(\delta_{\log n}+\delta_{-\log n})+2\cosh(x/2)\,dx$$ is a tempered distribution. In this case it is the Fourier transform of another measure $$\mathcal{F}\Bigl(\sum_{\gamma}\delta_{\gamma/2\pi}-2\vartheta'(2\pi t)\,dt\Bigr)=\mu.$$ We prop

Sérgio Vinha, Gabriel M. Fernandes, Huu Thien Nguyen, Manuel C. R. M. Fernandes

We consider a motorized aircraft tethered to a central anchorage point in a configuration similar to a control line model airplane. For this system, we address the problem of automatic take-off and landing (ATOL) with a circular path, whose center and radius are defined by the anchorage point and the tether length, respectively. We propose a hierarchical con

Lingzi Zhang, Xin Zhou, Zhiwei Zeng, Zhiqi Shen

Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance. Despite their effectiveness, the expressive power of text features in these models remains largely unexplored. While most existing models emphasize the importance of ID embeddings in recommendation

Divij Handa, Zehua Zhang, Amir Saeidi, Shrinidhi Kumbhar

Recent advancements in Large Language Model (LLM) safety have primarily focused on mitigating attacks crafted in natural language or common ciphers (e.g. Base64), which are likely integrated into newer models' safety training. However, we reveal a paradoxical vulnerability: as LLMs advance in reasoning, they inadvertently become more susceptible to novel jai

Ahmed Alhammadi, Anuj Abraham, Aymen Fakhreddine, Yu Tian

In an era marked by unprecedented climatic upheavals and evolving urban landscapes, the role of advanced communication networks in disaster prevention and management is becoming increasingly critical. This paper explores the transformative potential of 3D wireless networks, an innovative amalgamation of terrestrial, aerial, and satellite technologies, in enh

Roman Bychkov, Andrey Dergachev, Alexander Osinsky, Vladimir Lyashev

Hybrid beamforming (HBF) is a key technology to enable mm-wave Massive multiple-input multiple-output (mMIMO) receivers for future-generation wireless communications. It combines beamforming in both analog (via phase shifters) and digital domains, resulting in low power consumption and high spectral efficiency. In practice, the problem of joint beamforming i

Alberto Barlini, Andrea Bianchi, Enrico Ronca, Henrik Koch

In this work, we present ab initio cavity QED methods which include interactions with a static magnetic field and nuclear spin degrees of freedom using different treatments of the quantum electromagnetic field. We derive explicit expressions for QED-HF magnetizability, nuclear shielding, and spin-spin coupling tensors. We apply this theory to explore the inf

Giovanni Chesi

We address the quantum dynamics of second harmonic generation with a perturbative approach. By inspecting the Taylor expansion of the unitary evolution, we identify the subsequent application of annihilation and creation operators as elementary processes and find out how the expansion of the second-harmonic photon-number probability distribution can be expre