February 2024 arXiv papers — page 148

Michael Oberguggenberger

The paper addresses the question whether a random functional, a map from a set $E$ into the space of real-valued measurable functions on a probability space, has a measurable version with values in ${\mathbb R}^E$. Similarly, one may ask whether linear random functionals have versions in the algebraic dual. Most importantly, it can be asked which locally con

Tianle Zhang, Yuchen Zhang, Kun Wang, Kai Wang

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. As one of the most promising directions, graph condensation methods address these issues by employing gradient matching, aiming to condense the full graph into a more concise yet information-rich synthetic se

O. Sefi, A. Ben Yehuda, Y. Klein, S. Bloch

Hard x-ray imaging is indispensable across diverse fields owing to its high penetrability. However, the resolution of traditional x-ray imaging modalities, such as computed tomography (CT) systems, is constrained by factors including beam properties, the absence of optical components, and detection resolution. As a result, typical resolution in commercial im

Ilia Grishmanovskii, Olga Soloveva, Taesoo Song, Carsten Greiner

We extend the investigation on jet transport coefficients within the effective Dynamical QuasiParticle Model (DQPM) -- constructed for the description of non-perturbative QCD phenomena of the strongly interacting quark-gluon plasma (sQGP) in line with the lattice QCD equation-of-state -- by accounting for inelastic $2\to 3$ reactions with gluon radiation add

Nathan Wycoff, John W. Smith, Annie S. Booth, Robert B. Gramacy

Bayesian optimization (BO) offers an elegant approach for efficiently optimizing black-box functions. However, acquisition criteria demand their own challenging inner-optimization, which can induce significant overhead. Many practical BO methods, particularly in high dimension, eschew a formal, continuous optimization of the acquisition function and instead

Jiajun Zeng, Dong Ni, Ruobing Huang

Breast lesion segmentation from breast ultrasound (BUS) videos could assist in early diagnosis and treatment. Existing video object segmentation (VOS) methods usually require dense annotation, which is often inaccessible for medical datasets. Furthermore, they suffer from accumulative errors and a lack of explicit space-time awareness. In this work, we propo

Jiakun Zhuang, Long Ma, Yinghua Qiu

As an important property of porous membranes, the surface charge property determines many ionic behaviors of nanopores, such as ionic conductance and selectivity. Based on the dependence of electric double layers on bulk concentrations, ionic conductance through nanopores at high and low concentrations is governed by the bulk conductance and surface charge d

Fabo Feng, Yicheng Rui, Yifan Xuan, Hugh R. A. Jones

Hidden within the Gaia satellite's multiple data releases lies a valuable cache of dark companions. To facilitate the efficient and reliable detection of these companions via combined analyses involving Gaia, Hipparcos, and Tycho-2 catalogs, we introduce an astrometric modeling framework. This method incorporates analytical least square minimization and nonl

Frances Yung, Mansoor Ahmad, Merel Scholman, Vera Demberg

Pre-trained large language models, such as ChatGPT, archive outstanding performance in various reasoning tasks without supervised training and were found to have outperformed crowdsourcing workers. Nonetheless, ChatGPT's performance in the task of implicit discourse relation classification, prompted by a standard multiple-choice question, is still far from s

Alexandre Legrand, Pascal Maillard

The $N$-particle branching Brownian motion ($N$-BBM) is a branching Markov process which describes the evolution of a population of particles undergoing reproduction and selection. It has attracted a lot of interest due to its relations to the study of front propagation phenomena on the one hand, and to (hierarchical) physical $p$-spin models on the other ha

Jarek Duda

While standard Weisfeiler-Leman vertex labels are not able to distinguish even vertices of regular graphs, there is proposed and tested family of inexpensive polynomial time vertex and edge invariants, distinguishing much more difficult SRGs (strongly regular graphs), also often their vertices. Among 43717 SRGs from dataset by Edward Spence, proposed vertex

Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann

Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have been tackled mainly via handcrafted heuristics in the past, advances in neural networks have motivated the development of general methods to learn heuristics from data. Many approaches utilize a neural network to directly construct a solution, but are limited in further im

Bashar Alhafni, Vivek Kulkarni, Dhruv Kumar, Vipul Raheja

As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to make it more personalized. However, most research on controllable text generation focuses on controlling the content or modeling specific high-level/coarse-grained attributes that r

Max McGinley, Michele Fava, S. A. Parameswaran

We study the behaviour of linear and nonlinear spectroscopic quantities in two-dimensional topologically ordered systems, which host anyonic excitations exhibiting fractional statistics. We highlight the role that braiding phases between anyons have on the dynamics of such quasiparticles, which as we show dictates the behaviour of both linear response coeffi

Yuan Xu, Chongwen Huang, Wei Li, Zhaohui Yang

In integrated ground-air-space (IGAS) wireless networks, numerous services require sensing knowledge including location, angle, distance information, etc., which usually can be acquired during the beam training stage. On the other hand, IGAS networks employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays g

Dingfan Chen, Marie Oestreich, Tejumade Afonja, Raouf Kerkouche

Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only for elementary metrics and relatively simple data distributions. In this paper, we

Will Penman, Joshua Babu, Abhinaya Raghunathan

We identify "values" as actions that classifiers take that speak to open questions of significant social concern. Investigating a classifier's values builds on studies of social bias that uncover how classifiers participate in social processes beyond their creators' forethought. In our case, this participation involves what counts as nutritious, what it mean

Pica Johansson, Jonathan Bright, Shyam Krishna, Claudia Fischer

The use of synthetic data provides an opportunity to accelerate online safety research and development efforts while showing potential for bias mitigation, facilitating data storage and sharing, preserving privacy and reducing exposure to harmful content. However, the responsible use of synthetic data requires caution regarding anticipated risks and challeng

Gianpietro Battocletti, Dimitris Boskos, Domagoj Tolić, Ivana Palunko

In this article we consider the problem of tether entanglement for tethered mobile robots. One of the main risks of using a tethered connection between a mobile robot and an anchor point is that the tether may get entangled with the obstacles present in the environment or with itself. To avoid these situations, a non-entanglement constraint can be considered

Jonathan Jenvrin

Amoroso and Masser proved that for every real $\epsilon > 0$, there exists a constant $c(\epsilon)>0$, such that for every algebraic number $\alpha$ with $\mathbb{Q}(\alpha)/\mathbb{Q}$ being a Galois extension, the height of $\alpha$ is either 0 or at least $c(\epsilon) [\mathbb{Q}(\alpha):\mathbb{Q}]^{-\epsilon}$. In this article we establish an explicit v

Brigt Håvardstun, Jan Kratochvíl, Joakim Sunde, Jan Arne Telle

We study a model of machine teaching where the teacher mapping is constructed from a size function on both concepts and examples. The main question in machine teaching is the minimum number of examples needed for any concept, the so-called teaching dimension. A recent paper [7] conjectured that the worst case for this model, as a function of the size of the

Jef Jonkers, Jarne Verhaeghe, Glenn Van Wallendael, Luc Duchateau

Generating probabilistic forecasts of potential outcomes and individual treatment effects (ITE) is essential for risk-aware decision-making in domains such as healthcare, policy, marketing, and finance. We propose two novel methods: the conformal convolution T-learner (CCT) and the conformal Monte Carlo (CMC) meta-learner, that generate full predictive distr

Felix Hermsen, Tobias Isken, David Thoma, Matthias F. M. Lutz

We consider the axial-vector together with its induced pseudo-scalar form factor of the nucleon as computed from the chiral Lagrangian with nucleon and isobar degrees of freedom. The form factors are evaluated at the one-loop level, where particular emphasis is put on the use of on-shell masses in the loop expressions. Our results are presented in terms of a

Matthew J. Hopkins, Michele T. Bannister, Chris Lintott

The interstellar object population of the Milky Way is a product of its stars. However, what is in fact a complex structure in the Solar neighbourhood has traditionally in ISO studies been described as smoothly distributed. Using a debiased stellar population derived from the Gaia DR3 stellar sample, we predict that the velocity distribution of ISOs is far m

S. Rohart, P. Campiglio, V. Repain, Y. Nahas

The magnetic susceptibility of self-organized two-dimensional Co nanodots on Au(111) has been measured as a function of their size in the 2-7~nm diameter range. We show that the activation energy for the thermal reversal displays a power law behavior with the dot volume. Atomic scale simulations based on the Heisenberg hamiltonian show that this behavior is

Hyesung Jeon, Yulhwa Kim, Jae-joon Kim

Due to the high memory and computational costs associated with large language models (LLMs), model compression techniques such as quantization, which reduces inference costs, and parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA), which reduce training costs, have gained significant popularity. This trend has spurred active resear

Chenyu Zhang, Xiangming Wen, Wei Zheng, Longdan Yu

With the development of mobile communication and industrial internet technologies, the demand for robust absolute time synchronization based on network for diverse scenarios is significantly growing. TAP is a novel network timing method that aims to achieve sub-microsecond synchronization over air interface. This paper investigates the improvement and end-to

Daniel A. Williams, Xuan Ji, Paul Corlies, Juan M. Lora

Using an idealised climate model incorporating seasonal forcing, we investigate the impact of rotation rate on the abundance of clouds on an Earth-like aquaplanet, and the resulting impacts upon albedo and seasonality. We show that the cloud distribution varies significantly with season, depending strongly on the rotation rate, and is well explained by the l

Luis Sanz-Lorenzo, Rafael Bravo de la Parra

In the Staged Progression (SP) epidemic models, infected individuals are classified into a suitable number of states. The goal of these models is to describe as closely as possible the effect of differences in infectiousness exhibited by individuals going through the different stages. The main objective of this work is to study, from the methodological point

Gregory Everett, Ryan Beal, Tim Matthews, Timothy J. Norman

In this paper, we present a novel sequential team selection model in soccer. Specifically, we model the stochastic process of player injury and unavailability using player-specific information learned from real-world soccer data. Monte-Carlo Tree Search is used to select teams for games that optimise long-term team performance across a soccer season by reaso

Hanxiao Wang, Chao Zhou

In this paper, we consider a dynamic coalition portfolio selection problem, with each agent's objective given by an Epstein--Zin recursive utility. To find a Pareto optimum, the coalition's problem is formulated as an optimization problem evolved by a multi-dimensional forward-backward SDE. Since the evolution system has a forward-backward structure, the pro

Apoorva Vashisth, Julius Rückin, Federico Magistri, Cyrill Stachniss

Autonomous robots are often employed for data collection due to their efficiency and low labour costs. A key task in robotic data acquisition is planning paths through an initially unknown environment to collect observations given platform-specific resource constraints, such as limited battery life. Adaptive online path planning in 3D environments is challen

Daniel Gratzer, Håkon Gylterud, Anders Mörtberg, Elisabeth Stenholm

When working in Homotopy Type Theory and Univalent Foundations, the traditional role of the category of sets, Set, is replaced by the category hSet of homotopy sets (h-sets); types with h-propositional identity types. Many of the properties of Set hold for hSet ((co)completeness, exactness, local cartesian closure, etc.). Notably, however, the univalence axi

Paolo Morettin, Andrea Passerini, Roberto Sebastiani

In machine learning (ML) verification, the majority of procedures are non-quantitative and therefore cannot be used for verifying probabilistic models, or be applied in domains where hard guarantees are practically unachievable. The probabilistic formal verification (PFV) of ML models is in its infancy, with the existing approaches limited to specific ML mod

Luis Costero, Francisco D. Igual, Katzalin Olcoz, Francisco Tirado

The coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which reso

Zhen Li, Xuan He, Xiaohu Tang

An insdel refers to a deletion or an insertion, and an edit refers to an insdel or a substitution. In this paper, we consider the segmented single-insdel (resp. single-edit) channel, where the channel's input bit stream is partitioned into segments of length $n$ and each segment can suffer from at most a single insdel (resp. edit) error. The value of $n$ is

Marlon Tobaben, Hibiki Ito, Joonas Jälkö, Yuan He

Membership inference attacks (MIAs) are used to test practical privacy of machine learning models. MIAs complement formal guarantees from differential privacy (DP) under a more realistic adversary model. We analyse MIA vulnerability of fine-tuned neural networks both empirically and theoretically, the latter using a simplified model of fine-tuning. We show t

Sebastian Schmidt, Ines Zelch, Janek Bevendorff, Benno Stein

Conversational search engines such as YouChat and Microsoft Copilot use large language models (LLMs) to generate responses to queries. It is only a small step to also let the same technology insert ads within the generated responses - instead of separately placing ads next to a response. Inserted ads would be reminiscent of native advertising and product pla

Alex Fontana, Ludovic Bellon

Optical beam deflection is a popular method to measure the deformation of micromechanical devices. As it measures mostly a local slope, its sensitivity depends on the location and size of the optical spot. We present a method to evaluate precisely these parameters, using the relative amplitude of the thermal noise induced vibrations. With a case study of a m

C. A. Samarahewa

The gas sensitivity of Mg/CuO nanocomposite films, characterized by varying mass ratios of Mg:CuO, was assessed under exposure to 1000 ppm of methanol vapor. Films were fabricated by the doctor blade method on conductive and nonconductive glass substrates. Structural and optical analyses were conducted using XRD and UV-Visible spectrums. The XRD patterns fac

Bruno Chaves Figueiredo, Maria Alexandra Oliveira, João Nuno Silva

The Alqueva Multi-Purpose Project (EFMA) is a massive abduction and storage infrastructure system in the Alentejo, which has a water quality monitoring network with almost thousands of water quality stations distributed across three subsystems: Alqueva, Pedrog\~ao, and Ardila. Identification of pollution sources in complex infrastructure systems, such as the

Chaoqun Wang, Yiran Qin, Zijian Kang, Ningning Ma

Recent camera-based 3D object detection is limited by the precision of transforming from image to 3D feature spaces, as well as the accuracy of object localization within the 3D space. This paper aims to address such a fundamental problem of camera-based 3D object detection: How to effectively learn depth information for accurate feature lifting and object l

Edvards Scukins, Markus Klein, Lars Kroon, Petter Ögren

As the effective range of air-to-air missiles increases, it becomes harder for human operators to maintain the situational awareness needed to keep a UAV safe. In this work, we propose a decision support tool to help UAV operators in Beyond Visual Range (BVR) air combat scenarios assess the risks of different options and make decisions based on those. Earlie

Dipankar Sarkar, Shubham Upadhyay

Blockchain technology has revolutionized media consumption and distribution in the digital age, allowing creators, consumers, and regulators to participate in a decentralized, fair, and engaging media environment. Epistral, an innovative media network that leverages blockchain technology, aims to be the world's first anti-mimetic media curation and consumpti

Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh

Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we im

Peter Hönig, Stefan Thalhammer, Jean-Baptiste Weibel, Matthias Hirschmanner

Recent advances in machine learning have greatly benefited object detection and 6D pose estimation. However, textureless and metallic objects still pose a significant challenge due to few visual cues and the texture bias of CNNs. To address his issue, we propose a strategy for inducing a shape bias to CNN training. In particular, by randomizing textures appl

Elona Agora, María J. Carro, Javier Soria

We characterize the weak-type boundedness of the Hilbert transform $H$ on weighted Lorentz spaces $\Lambda^p_u(w)$, with $p>0$, in terms of some geometric conditions on the weights $u$ and $w$ and the weak-type boundedness of the Hardy-Littlewood maximal operator on the same spaces. Our results recover simultaneously the theory of the boundedness of $H$ on w

Arthur Whyley, Scott W. Randall, Tracy E. Clarke, Reinout J. van Weeren

Ultra-steep spectrum (USS) radio sources with complex filamentary morphologies are a poorly understood subclass of diffuse radio source found in galaxy clusters. They are characterised by power law spectra with spectral indices less than -1.5, and are typically located in merging clusters. We present X-ray and radio observations of the galaxy cluster A272, c

Kartik Ahuja, Amin Mansouri

Out-of-distribution generalization capabilities of sequence-to-sequence models can be studied from the lens of two crucial forms of generalization: length generalization -- the ability to generalize to longer sequences than ones seen during training, and compositional generalization: the ability to generalize to token combinations not seen during training. I

Enrico Drigo, Stefano Baroni, Paolo Pegolo

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and u

Elena Stellino, Beatrice D'Alò, Elena Blundo, Paolo Postorino

We present a spectroscopic investigation into the vibrational and optoelectronic properties of WS2 domes in the 0-0.65 GPa range. The pressure evolution of the system morphology, deduced by the combined analysis of Raman and photoluminescence spectra, revealed a significant variation in the dome's aspect ratio. The modification of the dome shape caused major

Dingqun Deng, Lingda Xu

This paper studies the stability and large-time behavior of the three-dimensional (3-D) Boltzmann equation near shock profiles. We prove the nonlinear stability of the composite wave consisting of two shock profiles under general perturbations without the assumption of integral zero of macroscopic quantities. To address the challenge caused by the compressib

Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen

As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space. However, there is still a considerable gap in discovering and incorporating causality into RL, which hinders

Yongchen Zhou, Richard Jiang

The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches, and delves into their applications in diverse domains, includ

Mateo Cámara, César Díaz, Juan Casal, Jorge Ruano

We present the description, results, and analysis of the experiments conducted to find the equivalent resolution associated with handheld devices. That is, the resolution from which users stop perceiving quality improvements if better resolutions are presented to them in such devices. Thus, it is the maximum resolution that it is worth considering for genera

Zheng Wang, Bingzheng Gan, Wei Shi

In the rapidly evolving landscape of information retrieval, search engines strive to provide more personalized and relevant results to users. Query suggestion systems play a crucial role in achieving this goal by assisting users in formulating effective queries. However, existing query suggestion systems mainly rely on textual inputs, potentially limiting us

Yang Cao, Shao-Yu Lien, Ying-Chang Liang, Dusit Niyato

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities

Zilong Yuan, Zhiming Xu, He Li, Xinle Cheng

Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural networ

Xiaoqi Li, Yingjie Mao, Zexin Lu, Wenkai Li

Smart contract code summarization is crucial for efficient maintenance and vulnerability mitigation. While many studies use Large Language Models (LLMs) for summarization, their performance still falls short compared to fine-tuned models like CodeT5+ and CodeBERT. Some approaches combine LLMs with data flow analysis but fail to fully capture the hierarchy an

Cem Bilaloglu, Tobias Löw, Sylvain Calinon

In this article, we present a feedback control method for tactile coverage tasks, such as cleaning or surface inspection. These tasks are challenging to plan due to complex continuous physical interactions. In these tasks, the coverage target and progress can be easily measured using a camera and encoded in a point cloud. We propose an ergodic coverage metho

XueGuang Zhang

In this letter, motivated by double-peaked broad Balmer emission lines probably related to tidal disruption events (TDEs), a potential TDE candidate is reported in SDSS J160536+134838 (=SDSS J1605) at $z\sim0.44$ having quasar-like spectrum but with double-peaked broad H$\beta$. The long-term CSS light curve can be naturally described by a main-sequence star

Luis Inclán-Sánchez

The possibility of making compact stopband filters using coplanar-coupled EBG resonators in inverted microstrip gap waveguide technology is studied in this work. To do this, the filtering characteristics of different configurations of mushroom-type elements are shown in which the short-circuit element is placed on the edge of the resonator of the patch. The

Vladimir Vovk

A very simple example demonstrates that Fisher's application of the conditionality principle to regression ("fixed-$x$ regression"), endorsed by Sprott and many other followers, makes prediction impossible in the context of statistical learning theory. On the other hand, relaxing the requirement of conditionality makes it possible via, e.g., conformal predic

Natasha Butt, Blazej Manczak, Auke Wiggers, Corrado Rainone

Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchmarks of general intelligence such as the Abstraction and Reasoning Corpus (ARC). In this paper, we approach ARC as a programming-by-examples problem, and introduce a novel and scala

Liyun Zhu, Lei Wang, Arjun Raj, Tom Gedeon

Video Anomaly Detection (VAD) finds widespread applications in security surveillance, traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts, there remains a lack of concise reviews that provide insightful guidance for researchers. Such reviews would serve as quick references to grasp current challenges, research trends

Jan Wehner, Frans Oliehoek, Luciano Cavalcante Siebert

Learning rewards from human behaviour or feedback is a promising approach to aligning AI systems with human values but fails to consistently extract correct reward functions. Interpretability tools could enable users to understand and evaluate possible flaws in learned reward functions. We propose Counterfactual Trajectory Explanations (CTEs) to interpret re

Yuhong He, Aiwen Jiang, Lingfang Jiang, Zhifeng Wang

Transformers have recently emerged as a significant force in the field of image deraining. Existing image deraining methods utilize extensive research on self-attention. Though showcasing impressive results, they tend to neglect critical frequency information, as self-attention is generally less adept at capturing high-frequency details. To overcome this sho

Jinghong Li, Huy Phan, Wen Gu, Koichi Ota

Research surveys have always posed a challenge for beginner researchers who lack of research training. These researchers struggle to understand the directions within their research topic, and the discovery of new research findings within a short time. One way to provide intuitive assistance to beginner researchers is by offering relevant knowledge graphs(KG)

Ekaterina Khramtsova, Shengyao Zhuang, Mahsa Baktashmotlagh, Guido Zuccon

In this paper we present Large Language Model Assisted Retrieval Model Ranking (LARMOR), an effective unsupervised approach that leverages LLMs for selecting which dense retriever to use on a test corpus (target). Dense retriever selection is crucial for many IR applications that rely on using dense retrievers trained on public corpora to encode or search a

Jinwei Zeng, Yu Liu, Jingtao Ding, Jian Yuan

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby

Yuxuan Bian, Xuan Ju, Jiangtong Li, Zhijian Xu

In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning. Central to our approach is that we reconceive time-series forecasting as a self-supervised, multi-patch prediction task, which, compared to traditional contrastive learning or mask-and-reconstruction methods, captures t

Jean-Luc Baril, Nathanaël Hassler, Sergey Kirgizov, José L. Ramírez

We study the enumeration of different classes of grand knight's paths in the plane. In particular, we focus on the subsets of zigzag knight's paths that are subject to constraints. These constraints include ending at $y$-coordinate 0, bounded by a horizontal line, confined within a tube, among other considerations. We present our results using generating fun

Seong-Sik Kim, Hyun Min Lee, Sung-Bo Sim

We consider the interplay of the muon $g-2$ anomaly and the proton decay in the SUSY SU(5) GUTs with generation-independent scalar soft masses. In these scenarios, we introduce a number of $\bf 5+{\bar 5}$ messenger fields with doublet-triplet splitting in general gauge mediation to transmit SUSY breaking to the visible sector by gauge loops. As a result, sq

Pengcheng Lu, Junpeng Cao, Wen-Li Yang, Ian Marquette

We study the thermodynamics of the antiperiodic XXZ chain with anisotropy parameter {\eta}=i{\pi}/3 by means of the t-W method. We parameterize the eigenvalues of both the transfer matrix and the corresponding fused transfer matrix by their zero points instead of Bethe roots. Based on the patterns of the zero points distribution and the reconstructed entropy

Sahitya Yarragolla, Torben Hemke, Fares Jalled, Tobias Gergs

Nonlinearity is a crucial characteristic for implementing hardware security primitives or neuromorphic computing systems. The main feature of all memristive devices is this nonlinear behavior observed in their current-voltage characteristics. To comprehend the nonlinear behavior, we have to understand the coexistence of resistive, capacitive, and inertia (vi

J. M. Campillo-Robles, E. Ogando, F. Plazaola

Positron lifetimes have been calculated in bulk and monovacancies for most of the elements of the periodic table. Self-consistent and non-self-consistent schemes have been used for the calculation of the electronic structure in the solid, as well as different parameterizations for the positron enhancement factor and correlation energy. The ratio between the

Sukru Cavdar, Pinar Oruc, Serkan Eymur, Nihat Tugluoglu

n-TPA-IFA organic material was synthesized and deposited on p-Si by spin coating method to produce n-TPA-IFA/p-Si heterojunction diode. We determined that the dielectric constant and energy band gap of TPA-IFA organic material were 3.91 and 3.37 eV by DFT/B3LYP/6-311G(d,p) method using on Gaussian 09 W, respectively and the carrier type of TPA-IFA organic se

Bowen Jing, Bonnie Berger, Tommi Jaakkola

The biological functions of proteins often depend on dynamic structural ensembles. In this work, we develop a flow-based generative modeling approach for learning and sampling the conformational landscapes of proteins. We repurpose highly accurate single-state predictors such as AlphaFold and ESMFold and fine-tune them under a custom flow matching framework

Hamed Radpour, Markus Hofer, David Loschenbrand, Lukas Walter Mayer

Reconfigurable intelligent surfaces (RISs) enable reliable low-latency millimeter wave (mmWave) communication links in cases of a blocked line-of-sight (LoS) between the base station (BS) and the user equipment (UE), i.e. a RIS mounted on a wall or the ceiling provides a bypass for the radio communication link. We present an active RIS with 127 patch antenna

Nikita Doikov, Sebastian U. Stich, Martin Jaggi

The performance of optimization methods is often tied to the spectrum of the objective Hessian. Yet, conventional assumptions, such as smoothness, do often not enable us to make finely-grained convergence statements -- particularly not for non-convex problems. Striving for a more intricate characterization of complexity, we introduce a unique concept termed

Martin Huesmann, Bastian Müller

We derive a Benamou-Brenier type dynamical formulation for the Wasserstein metric $\mathsf W_p$ between stationary random measures recently introduced in [EHJM23]. A key step is a reformulation of the metric $\mathsf W_p$ using Palm probabilities.

Shawn Berry

Price perception by consumers represents a challenge to the ability of a business to correctly and profitably price and sell their products or services in a given market and any new target market. Complicating the perception of prices is the dynamics of price and income elasticity, both of which are key for estimating demand. This article proposes a novel an

Jianyuan Guo, Zhiwei Hao, Chengcheng Wang, Yehui Tang

Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models w

Nikita P. Kalinin, Lukas Steinberger

In this paper we study the problem of estimating the unknown mean $\theta$ of a unit variance Gaussian distribution in a locally differentially private (LDP) way. In the high-privacy regime ($\epsilon\le 1$), we identify an optimal privacy mechanism that minimizes the variance of the estimator asymptotically. Our main technical contribution is the maximizati

Mohammad Ali Dadgostarnia, Ramin Mousa, Saba Hesaraki

Along with factors such as cancer, blood pressure, street accidents and stroke, suicide has been one of Iran main causes of death. One of the main reasons for suicide is psychological stressors. Identifying psychological stressors in an at risk population can help in the early prevention of suicidal and suicidal behaviours. In recent years, the widespread po

Jinghui Lu, Ziwei Yang, Yanjie Wang, Xuejing Liu

In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs). The main cause of high latency in LLMs is the sequential decoding process, which autoregressively generates all labels and mentions for NER, significantly increase the sequence length. To this end, we introduce Parallel Decoding in LLM for

Arash Amini, Yigit Ege Bayiz, Ashwin Ram, Radu Marculescu

In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation. We present the CREDiBERT (CREDibility assessment using Bi-directional Encoder Representations from Transformers), a source credibility assessment model fine-tuned for Reddit submissions focusing on political discourse as the main contribut

Pierre Boldrini, Clotilde Laigle

In the context of future large surveys like the Euclid mission, extracting the cosmic web from galaxies at higher redshifts with more statistical power will become feasible, particularly within the group-cluster mass regime. Therefore, it is imperative to enlarge the number of metrics that can used to constrain our cosmological models at these large scales.

Luca Beurer-Kellner, Marc Fischer, Martin Vechev

To ensure that text generated by large language models (LLMs) is in an expected format, constrained decoding proposes to enforce strict formal language constraints during generation. However, as we show in this work, not only do such methods incur performance overhead during generation, but many of them also significantly impair task accuracy, if they do not

Michele Azzone, Roberto Baviera, Pietro Manzoni

A growing number of contributions in the literature have identified a puzzle in the European carbon allowance (EUA) market. Specifically, a persistent cost-of-carry spread (C-spread) over the risk-free rate has been observed. We are the first to explain the anomalous C-spread with the credit spread of the corporates involved in the emission trading scheme. W

Zian Li, Xiyuan Wang, Shijia Kang, Muhan Zhang

Invariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features in point clouds. These models are characterized by their simplicity, good experimental results and computational efficiency. However, their theoretical expressive power still rema

Darshana Saravanan, Naresh Manwani, Vineet Gandhi

We motivate weakly supervised learning as an effective learning paradigm for problems where curating perfectly annotated datasets is expensive and may require domain expertise such as fine-grained classification. We focus on Partial Label Learning (PLL), a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labe

Aviad Kaufmann, Itai Arad

We present a new decoder for the surface code, which combines the accuracy of the tensor-network decoders with the efficiency and parallelism of the belief-propagation algorithm. Our main idea is to replace the expensive tensor-network contraction step in the tensor-network decoders with the blockBP algorithm - a recent approximate contraction algorithm, bas

Hao Zhao, Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion

There is a consensus that instruction fine-tuning of LLMs requires high-quality data, but what are they? LIMA (NeurIPS 2023) and AlpaGasus (ICLR 2024) are state-of-the-art methods for selecting such high-quality examples, either via manual curation or using GPT-3.5-Turbo as a quality scorer. We show that the extremely simple baseline of selecting the 1,000 i

Harry Vinall-Smeeth

Both structured d-DNNF and SDD can be exponentially more succinct than OBDD. Moreover, SDD is essentially as tractable as OBDD. But this has left two important open questions. Firstly, does OBDD support more tractable transformations than structured d-DNNF? And secondly, is structured d-DNNF more succinct than SDD? In this paper, we answer both questions in

V. A. Pulido, F. Cabrera-Almeida, P. Quintana-Morales, E. Mendieta-Otero

This paper presents a new procedure for phase detector measurements that allows the use of generators that share a 10 MHz reference oscillator but do not synchronize in phase, in other words, quasi-synchronized RF generators. The objectives are taking advantage of the benefits of using two generators but recovering lower-cost generators that have worse synch

Giacomo Acciarini, Atılım Güneş Baydin, Dario Izzo

The Simplified General Perturbations 4 (SGP4) orbital propagation method is widely used for predicting the positions and velocities of Earth-orbiting objects rapidly and reliably. Despite continuous refinement, SGP models still lack the precision of numerical propagators, which offer significantly smaller errors. This study presents dSGP4, a novel differenti

Jingwang Ling, Ruihan Yu, Feng Xu, Chun Du

Physics-based inverse rendering enables joint optimization of shape, material, and lighting based on captured 2D images. To ensure accurate reconstruction, using a light model that closely resembles the captured environment is essential. Although the widely adopted distant environmental lighting model is adequate in many cases, we demonstrate that its inabil

Andrea Bastianin, Elisabetta Mirto, Yan Qin, Luca Rossini

Putting a price on carbon -- with taxes or developing carbon markets -- is a widely used policy measure to achieve the target of net-zero emissions by 2050. This paper tackles the issue of producing point, direction-of-change, and density forecasts for the monthly real price of carbon within the EU Emissions Trading Scheme (EU ETS). We aim to uncover supply-

Élie Aïdékon, William Da Silva, Xingjian Hu

We study the volume of rigid loop-$O(n)$ quadrangulations with a boundary of length $2p$ in the non-generic critical regime. We prove that, as the half-perimeter $p$ goes to infinity, the volume scales in distribution to an explicit random variable. This limiting random variable is described in terms of the multiplicative cascades of Chen, Curien and Maillar