February 2024 arXiv papers — page 52

M. A. Yurischev, Saeed Haddadi, Mehrdad Ghominejad

We derive the compact closed forms of local quantum uncertainty (LQU) and local quantum Fisher information (LQFI) for hybrid qubit-qutrit axially symmetric (AS) states. This allows us to study the quantum correlations in detail and present some essentially novel results for spin-(1/2, 1) systems, the Hamiltonian of which contains ten independent types of phy

Ruby A. Shi, Bai Yang Wang, Yusuke Iguchi, Motoki Osada

Infinite-layer nickelates $R_{1-x}$Sr$_{x}$NiO$_{2}$ ($R$ = La, Pr, Nd) are a class of superconductors with structural similarities to cuprates. Although long-range antiferromagnetic order has not been observed for these materials, magnetic effects such as antiferromagnetic spin fluctuations and spin-glass behavior have been reported. Different experiments h

Ashok Urlana, Charaka Vinayak Kumar, Ajeet Kumar Singh, Bala Mallikarjunarao Garlapati

Large language models (LLMs) have become the secret ingredient driving numerous industrial applications, showcasing their remarkable versatility across a diverse spectrum of tasks. From natural language processing and sentiment analysis to content generation and personalized recommendations, their unparalleled adaptability has facilitated widespread adoption

Jiri Adamek

Following ideas of Lawvere and Linton we prove that classical varieties are precisely the exact categories with a varietal generator. This means a strong generator which is abstractly finite and regularly projective. An analogous characterization of varieties of ordered algebras is also presented. We work with order-enriched categories, and introduce the con

Joern Ploennigs, Markus Berger, Martin Mevissen, Kay Smarsly

Quantum computing is a new computational paradigm with the potential to solve certain computationally challenging problems much faster than traditional approaches. Civil engineering encompasses many computationally challenging problems, which leads to the question of how well quantum computing is suitable for solving civil engineering problems and how much i

Moshe A. Milevsky, Thomas S. Salisbury

This paper presents a new type of modern accumulation-based tontine, called the Riccati tontine, named after two Italians: mathematician Jacobo Riccati (b. 1676, d. 1754) and financier Lorenzo di Tonti (b. 1602, d. 1684). The Riccati tontine is yet another way of pooling and sharing longevity risk, but is different from competing designs in two key ways. The

Paolo De Donato

In this work we prove the Stepanov differentiation theorem for multiple-valued functions. This theorem is proved in the wide generality of metric-space-multiple-valued functions without relying on a Lipschitz extension result. General definitions of differentiability and approximate differentiability for functions between suitable metric spaces are also intr

Jiangchao Qian, Yi Li, Zhihao Jiang, Robert Busch

We investigate the structural and electronic origin of antiferromagnetic (AFM) coupling in the Yttrium iron garnet (YIG) and permalloy (Py) bilayer system at the atomic level. Ferromagnetic Resonance (FMR) reveal unique hybrid modes in samples prepared with surface ion milling, indicative of antiferromagnetic exchange coupling at the YIG/Py interface. Using

Julia Katheder, Philipp Kindermann, Fabian Klute, Irene Parada

We introduce the $k$-Plane Insertion into Plane drawing ($k$-PIP) problem: given a plane drawing of a planar graph $G$ and a set $F$ of edges, insert the edges in $F$ into the drawing such that the resulting drawing is $k$-plane. In this paper, we show that the problem is NP-complete for every $k\ge 1$, even when $G$ is biconnected and the set $F$ of edges f

Zijun Long, George Killick, Lipeng Zhuang, Gerardo Aragon-Camarasa

State-of-the-art pre-trained image models predominantly adopt a two-stage approach: initial unsupervised pre-training on large-scale datasets followed by task-specific fine-tuning using Cross-Entropy loss~(CE). However, it has been demonstrated that CE can compromise model generalization and stability. While recent works employing contrastive learning addres

Panagiotis Charalampopoulos, Solon P. Pissis, Jakub Radoszewski, Wojciech Rytter

In the $k$-Edit Circular Pattern Matching ($k$-Edit CPM) problem, we are given a length-$n$ text $T$, a length-$m$ pattern $P$, and a positive integer threshold $k$, and we are to report all starting positions of the substrings of $T$ that are at edit distance at most $k$ from some cyclic rotation of $P$. In the decision version of the problem, we are to che

A. V. Ivanov

The paper studies the quantum action for the four-dimensional real $\phi^4$-theory in the case of a general formulation using the background field method. The three-loop renormalization is performed with the usage of a cutoff regularization in the coordinate representation. The absence of non-local singular contributions and the correctness of the renormaliz

Fabian Hahne, Vignesh Prasad, Alap Kshirsagar, Dorothea Koert

Hidden Markov Models with an underlying Mixture of Gaussian structure have proven effective in learning Human-Robot Interactions from demonstrations for various interactive tasks via Gaussian Mixture Regression. However, a mismatch occurs when segmenting the interaction using only the observed state of the human compared to the joint state of the human and t

Xingyou Song, Oscar Li, Chansoo Lee, Bangding Yang

Regression is a powerful tool to accurately predict the outcome metric of a system given a set of parameters, but has traditionally been restricted to methods which are only applicable to a specific task. In this paper, we propose OmniPred, a framework for training language models as universal end-to-end regressors over $(x,y)$ data from arbitrary formats. U

Jeehoon Park, Junyeong Park, Philsang Yoo

We describe complex conjugation on the primitive middle-dimensional algebraic de Rham cohomology of a smooth projective hypersurface defined over a number field that admits a real embedding. We use Griffiths' description of the cohomology in terms of a Jacobian ring. The resulting description is algebraic up to transcendental factors explicitly given by cert

Zihao Yue, Liang Zhang, Qin Jin

Large Multimodal Models (LMMs) often suffer from multimodal hallucinations, wherein they may create content that is not present in the visual inputs. In this paper, we explore a new angle of this issue: overly detailed training data hinders the model's ability to timely terminate generation, leading to continued outputs beyond visual perception limits. By in

Shidong Pan, Zhen Tao, Thong Hoang, Dawen Zhang

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within

Fangzhou Zhao, Tianhua Zhu, Zejie Li, Xiongfei Wang

Grid-forming voltage-source converter (GFM-VSC) may experience low-frequency resonances, such as synchronous resonance (SR) and sub-synchronous resonance (SSR), in the output power. This paper offers a comprehensive study on the root causes of low-frequency resonances with GFM-VSC systems and the damping control methods. The typical GFM control structures ar

Gerold Jäger, Marcel Turkensteen

Optimal solutions of combinatorial optimization problems can be sensitive to changes in the cost of one or more elements of the ground set E. Single and set tolerances measure the supremum / infimum possible change such that the current solution remains optimal for cost changes in one or more elements. The current definition does not apply to all elements of

Haozheng Li, Chunjiang Shi, Ying Chen, Ming Gong

We study the $S$-wave $D\bar{D}^*(I=0)$ scattering at four different pion masses $m_\pi$ ranging from 250 MeV to 417 MeV from $N_f=2$ lattice QCD. Three energy levels $E_{2,3,4}$ are extracted at each $m_\pi$. The analysis of $E_{2,3}$ using the effective range expansion (ERE) comes out with a shallow bound state below the $D\bar{D}^*$ threshold, and the pha

Liang Shan, Shuo Zhang, Jie Zhang, Zihe Wang

In this research, we study the problem that a collector acquires items from the owner based on the item qualities the owner declares and an independent appraiser's assessments. The owner is interested in maximizing the probability that the collector acquires the items and is the only one who knows the items' factual quality. The appraiser performs her duties

Teddy Lazebnik

Pandemics, with their profound societal and economic impacts, pose significant threats to global health, mortality rates, economic stability, and political landscapes. In response to these challenges, numerous studies have employed spatio-temporal models to enhance our understanding and management of these complex phenomena. These spatio-temporal models can

Lars Roemheld, Justin Rao

It is standard practice in online retail to run pricing experiments by randomizing at the article-level, i.e. by changing prices of different products to identify treatment effects. Due to customers' cross-price substitution behavior, such experiments suffer from interference bias: the observed difference between treatment groups in the experiment is typical

Amparo Gil, Javier Segura, Nico M. Temme

We derive asymptotic expansions of the large zeros of the Coulomb wave functions and for those of their derivatives. The new expansions have the same form as the McMahon expansions of the zeros of the Bessel functions and reduce to them when a parameter is equal to zero. Numerical tests are provided to demonstrate the accuracy of the expansions.

Siyin Wang, Jie Zhou, Qin Chen, Qi Zhang

Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain. Whereas, most methods are proposed under the assumption that the target (test) domain is known, making them fail to generalize well on unknown test data that is not always available in practice. In this paper, we focu

Maria Zamyatina, Duncan A. Christie, Eric Hébrard, Nathan J. Mayne

Transport-induced quenching in hot Jupiter atmospheres is a process that determines the boundary between the part of the atmosphere at chemical equilibrium and the part of the atmosphere at thermochemical (but not photothermochemical) disequilibrium. The location of this boundary, the quench level, depends on the interplay between the dynamical and chemical

King Yau Yip, Lingfei Wang, Tsz Fung Poon, Kai Ham Yu

Two-dimensional (2D) materials have gained increasing prominence not only in fundamental research but also in daily applications. However, to fully harness their potential, it is crucial to optimize their properties with an external parameter and track the electronic structure simultaneously. Magnetotransport over a wide magnetic field range is a powerful me

Ariel Rosenfeld, Teddy Lazebnik

Large Language Models (LLMs) are capable of generating text that is similar to or surpasses human quality. However, it is unclear whether LLMs tend to exhibit distinctive linguistic styles akin to how human authors do. Through a comprehensive linguistic analysis, we compare the vocabulary, Part-Of-Speech (POS) distribution, dependency distribution, and senti

David J. Schodt, Ryan Brown, Michael Merritt, Samuel Park

Obtaining heteroscedastic predictive uncertainties from a Bayesian Neural Network (BNN) is vital to many applications. Often, heteroscedastic aleatoric uncertainties are learned as outputs of the BNN in addition to the predictive means, however doing so may necessitate adding more learnable parameters to the network. In this work, we demonstrate that both th

Ziqi Yin, Hao Wang, Kaito Horio, Daisuke Kawahara

We investigate the impact of politeness levels in prompts on the performance of large language models (LLMs). Polite language in human communications often garners more compliance and effectiveness, while rudeness can cause aversion, impacting response quality. We consider that LLMs mirror human communication traits, suggesting they align with human cultural

J. M. Sánchez Velázquez, A. Steiner, R. Freund, M. Guevara-Bertsch

Noise is both ubiquitous and generally deleterious in settings where precision is required. This is especially true in the quantum technology sector where system utility typically decays rapidly under its influence. Understanding the noise in quantum devices is thus a prerequisite for efficient strategies to mitigate or even eliminate its harmful effects. Ho

Marco Fusari, Andrea Previtali, Pablo Spiga

Garonzi and Lucchini~\cite{GL} explored finite groups $G$ possessing a normal $2$-covering, where no proper quotient of $G$ exhibits such a covering. Their investigation offered a comprehensive overview of these groups, delineating that such groups fall into distinct categories: almost simple, affine, product action, or diagonal. In this paper, we focus on t

Tianying Ji, Yongyuan Liang, Yan Zeng, Yu Luo

The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explore the causal relationship between different action dimensions and rewards to evaluate the significance of various primitive behaviors during training. We introduce a causality-awa

Anika Hannemann, Jan Ewald, Leo Seeger, Erik Buchmann

Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. For example, precision medicine tailors medical treatments to patients on the basis of individual biomarkers, cellular and molecular states, etc. However, the data required is sensitive, voluminous, heterogeneous, and typically distributed across l

Yunfan Shao, Linyang Li, Zhaoye Fei, Hang Yan

Data plays a fundamental role in the training of Large Language Models (LLMs). While attention has been paid to the collection and composition of datasets, determining the data sampling strategy in training remains an open question. Most LLMs are trained with a simple strategy, random sampling. However, this sampling strategy ignores the unbalanced nature of

Yasemin Göksu, Antonio De Almeida Correia, Vignesh Prasad, Alap Kshirsagar

Bimanual handovers are crucial for transferring large, deformable or delicate objects. This paper proposes a framework for generating kinematically constrained human-like bimanual robot motions to ensure seamless and natural robot-to-human object handovers. We use a Hidden Semi-Markov Model (HSMM) to reactively generate suitable response trajectories for a r

Adriana Simancas, Justus Braach, Eric Buschmann, Ankur Chauhan

Monolithic active pixel sensors (MAPS) produced in a 65 nm CMOS imaging technology are being investigated for applications in particle physics. The MAPS design has a small collection electrode characterized by an input capacitance of ~fF, granting a high signal-to-noise ratio and low power consumption. Additionally, the 65 nm CMOS imaging technology brings a

Rendi Chevi, Alham Fikri Aji

We often verbally express emotions in a multifaceted manner, they may vary in their intensities and may be expressed not just as a single but as a mixture of emotions. This wide spectrum of emotions is well-studied in the structural model of emotions, which represents variety of emotions as derivative products of primary emotions with varying degrees of inte

Xinyu Wang, Hainiu Xu, Lin Gui, Yulan He

Task embedding, a meta-learning technique that captures task-specific information, has gained popularity, especially in areas such as multi-task learning, model editing, and interpretability. However, it faces challenges with the emergence of prompt-guided Large Language Models (LLMs) operating in a gradient-free manner. Existing task embedding methods rely

Dominick S. Scaletta, Albert F. Rigosi

A previous mathematical approach adopted for optimizing the number of total device elements required for obtaining high effective quantized resistances in graphene-based quantum Hall array devices (QHARS) has been further explored with partial recursion patterns. Designs would assume the use of epitaxial graphene elements, whose quantized Hall resistance at

Kasi Bandla, Dipankar Pal

Strong-ARM Dynamic Latch Comparators are widely used in high-speed analog-to-digital converters (ADCs), sense amplifiers in memory, RFID applications, and data receivers. This paper presents different methods to improve the performance of Strong-Arm latch-based comparators. The comparator's significant features such as power dissipation, propagation delay, o

Cong Li, Yang Wang, Jianfeng Zhang, Hongxiong Liu

Non-Hermitian physics, studying systems described by non-Hermitian Hamiltonians, reveals unique phenomena not present in Hermitian systems. Unlike Hermitian systems, non-Hermitian systems have complex eigenvalues, making their effects less directly observable. Recently, significant efforts have been devoted to incorporating the non-Hermitian effects into con

Zaijiu Shang, Yang Xu

In this paper we apply symplectic algorithms to nearly integrable Hamiltonian system, and prove it can maintain lots of elliptic lower dimensional invariant tori. We are committed to consider the elliptic lower dimensional invariant tori for symplectic mapping with a small twist under the R\"{u}ssmann's non-degenerate condition, and focus on its measure esti

Songbo Ling, Xin Lü

Let $S$ be a minimal irregular surface of general type, whose Albanese map induces a hyperelliptic fibration $f:\,S \to B$ of genus $g$.We prove a quadratic upper bound on the genus $g$, i.e., $g\leq h\big(\chi(\mathcal{O}_S)\big)$, where $h$ is a quadratic function. We also construct examples showing that the quadratic upper bounds can not be improved to th

Patrick Holzer, Ivica Turkalj

We analyze the frequency spectrum of Quantum Neural Networks (QNNs) using Minkowski sums, which yields a compact algebraic description and permits explicit computation. Using this description, we prove several maximality results for broad classes of QNN architectures. Under some mild technical conditions we establish a bijection between classes of models wit

Yu Cheng, Jie Sheng, Tsutomu T. Yanagida

A new fifth force between particles carrying $B-L$ charges is well-motivated by the intriguing $U(1)_{B-L}$ extension of the standard model. The gauge boson mediator, F\'eeton, also serves as a dark matter candidate. In this letter, we propose a novel experimental design to detect the quantum phase difference caused by this fifth force using a superconductin

Nick E. Mavromatos, Sarben Sarkar

This paper considers the possibility that, starting from a relativistic Hermitian quantum field theory in the ultraviolet (UV) regime, and applying a non-perturbative renormalization-group (RG) flow, we arrive at a situation where there are infrared (IR) singularities in the RG flow of couplings. The latter can be resolved by assuming that the theory can hav

Irina Ya. Aref'eva, Ali Hajilou, Pavel Slepov, Marina Usova

We consider the running coupling constant in holographic models supported by Einstein-dilaton-Maxwell action for heavy and light quarks. To obtain the dependence of the running coupling constant $\alpha$ on temperature and chemical potential we impose boundary conditions on the dilaton field that depend on the position of the horizon. We use two types of bou

Isak Silander, Johan Zakrisson, Ove Axner, Martin Zelan

Based on a recent experimental determination of the static polarizability and a first-principles calculation of the frequency-dependent dipole polarizability of argon, this work presents, by use of a Fabry-P\'erot refractometer operated at 1550 nm, a realization of the SI unit of pressure, the pascal, for pressures up to 100 kPa, with an uncertainty of [(0.9

Shouguo Zhu, Hailong Lin, Wenkai Zhu, Weihao Li

The magnetic tunnel junctions (MTJ) based on van der Waals (vdW) materials possess atomically smooth interfaces with minimal element intermixing. This characteristic ensures that spin polarization is well maintained during transport, leading to the emergence of richer magnetoresistance behaviors. Here, using all 2D vdW MTJs based on magnetic metal Fe3GeTe2 a

Guillaume Garret, Antoine Vacavant, Carole Frindel

Vascular segmentation represents a crucial clinical task, yet its automation remains challenging. Because of the recent strides in deep learning, vesselness filters, which can significantly aid the learning process, have been overlooked. This study introduces an innovative filter fusion method crafted to amplify the effectiveness of vessel segmentation model

Laurent Bartholdi, Ville Salo

We prove that the lamplighter group admits strongly aperiodic SFTs, has undecidable tiling problem, and the entropies of its SFTs are exactly the upper semicomputable nonnegative real numbers, and some other results. These results follow from two relatively general simulation theorems, which show that for a large class of effective subshifts on the sea-level

Pierre-Emmanuel Caprace, Timothée Marquis

We provide sufficient conditions for a free amalgamated product of torsionfree nilpotent groups to be residually nilpotent. We also characterise the residual nilpotence of certain higher-dimensional amalgams of unipotent groups over the rationals (known as KMS groups) in terms of their defining Cartan matrix. As an application, we give a normal form for the

Manuel Schlenkrich, Wolfgang Seiringer, Klaus Altendorfer, Sophie N. Parragh

Production planning must account for uncertainty in a production system, arising from fluctuating demand forecasts and execution-level friction. This article integrates scenario-based stochastic programming into a rolling horizon framework for capacitated lot sizing, evaluated via discrete-event simulation. We compare this stochastic approach against determi

Feng Lu, Lijun Zhang, Xiangyuan Lan, Shuting Dong

Recent studies show that vision models pre-trained in generic visual learning tasks with large-scale data can provide useful feature representations for a wide range of visual perception problems. However, few attempts have been made to exploit pre-trained foundation models in visual place recognition (VPR). Due to the inherent difference in training objecti

Xiaobo Liu, Chongyu Wang

Buryak and Shadrin conjectured a tautological relation on moduli spaces of curves $\overline{\mathcal{M}}_{g,n}$ which has the form $B^m_{g, \textbf{d}}=0$ for certain tautological classes $B^m_{g, \textbf{d}}$ where $m \geq 2, n \geq 1$ and $|\textbf{d}| \geq 2g+m-1$. In this paper we prove that this conjecture holds if it is true for the $m=2$ and $|\textb

Jingjing Sun, Jingyi Yang, Guyue Zhou, Yucheng Jin

The rapid development of musical AI technologies has expanded the creative potential of various musical activities, ranging from music style transformation to music generation. However, little research has investigated how musical AIs can support music therapists, who urgently need new technology support. This study used a mixed method, including semi-struct

Athulya Muraleedharan, Kevin Co, Maxime Vallet, Abdelali Zaki

Ferroelectric materials display exotic polarization textures at the nanoscale that could be used to improve the energetic efficiency of electronic components. The vast majority of studies were conducted in two dimensions on thin films, that can be further nanostructured, but very few studies address the situation of individual isolated nanocrystals synthesiz

Ian Le, Emine Yıldırım

The homogeneous coordinate ring of the Grassmannian $\rm{Gr}(k,n)$ has a well-known cluster structure. There is a categorification of this cluster structure via a category of modules for a ring $A_{k,n}$ due to Jensen-King-Su, building on work of Geiss-Leclerc-Schr\"oer, in which cluster variables correspond to indecomposable rigid modules. We give a combina

Aafko Boonstra, Charlene Kalle

Let $L=(L_d)_{d \in \mathbb N}$ be any ordered probability sequence, i.e., satisfying $0 < L_{d+1} \le L_d$ for each $d \in \mathbb N$ and $\sum_{d \in \mathbb N} L_d =1$. We construct sequences $A = (a_i)_{i \in \mathbb N}$ on the countably infinite alphabet $\mathbb N$ in which each possible block of digits $\alpha_1, \ldots, \alpha_k \in \mathbb N$, $k \i

Xinpeng Wang, Bolei Ma, Chengzhi Hu, Leon Weber-Genzel

The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging. One common evaluation approach uses multiple-choice questions (MCQ) to limit the response space. The model is then evaluated by ranking the candidate answers by the log probability of the first token prediction. However, first-tokens

Lei Zhang, Kaixin Bai, Qiang Li, Zhaopeng Chen

We introduce a Cable Grasping-Convolutional Neural Network designed to facilitate robust cable grasping in cluttered environments. Utilizing physics simulations, we generate an extensive dataset that mimics the intricacies of cable grasping, factoring in potential collisions between cables and robotic grippers. We employ the Approximate Convex Decomposition

Soohaeng Yoo Willow, Gyung Su Kim, Miran Ha, Amir Hajibabaei

Accurate and scalable universal interatomic potentials are key for understanding material properties at the atomic level, a task often hindered by the steep computational scaling. Although recent developments of machine learning potential has made significant progress, the flexibility and expansion to a wide range of compounds within a single model seems sti

Yun Yen, Gian Parusa, Michael Schüler

Angle-resolved photoemission spectroscopy (ARPES) is one of the most powerful techniques to study the electronic structure of materials. To go beyond the paradigm of band mapping and extract aspects of the Bloch wave-functions, the intricate interplay of experimental geometry, crystal structure, and photon polarization needs to be understood. In this work we

Valentin Gebhart, Manuel Gessner, Augusto Smerzi

Bounding the optimal precision in parameter estimation tasks is of central importance for technological applications. In the regime of a small number of measurements, or that of low signal-to-noise ratios, the meaning of common frequentist bounds such as the Cram\'er-Rao bound (CRB) become questionable. Here, we discuss different linear (Barankin-like) condi

Jinxu Zhao, Guanting Dong, Yueyan Qiu, Tingfeng Hui

In a realistic dialogue system, the input information from users is often subject to various types of input perturbations, which affects the slot-filling task. Although rule-based data augmentation methods have achieved satisfactory results, they fail to exhibit the desired generalization when faced with unknown noise disturbances. In this study, we address

Lin Chen, Jiayi Lian, Yuchen Mao, Guochuan Zhang

We investigate pseudo-polynomial time algorithms for Subset Sum. Given a multi-set $X$ of $n$ positive integers and a target $t$, Subset Sum asks whether some subset of $X$ sums to $t$. Bringmann proposes an $\tilde{O}(n + t)$-time algorithm [Bringmann SODA'17], and an open question has naturally arisen: can Subset Sum be solved in $O(n + w)$ time? Here $w$

Bin Zhu, Munan Ning, Peng Jin, Bin Lin

Despite recent progress in Multi-Modal Large Language Models (MLLMs), it remains challenging to integrate diverse tasks ranging from pixel-level perception to high-fidelity generation. Existing approaches often suffer from either restricted task extensibility or severe performance degradation due to modality interference. n this paper, we present LLMBind, an

Wei Han, Hui Chen, Soujanya Poria

Fine-tuning large language models (LLMs) on multi-task instruction-following data has been proven to be a powerful learning paradigm for improving their zero-shot capabilities on new tasks. Recent works about high-quality instruction-following data generation and selection require amounts of human labor to conceive model-understandable instructions for the g

CMS Collaboration

An inclusive search for long-lived exotic particles (LLPs) decaying to final states with a pair of muons is presented. The search uses data corresponding to an integrated luminosity of 36.6 fb$^{-1}$ collected by the CMS experiment from the proton-proton collisions at $\sqrt{s}$ = 13.6 TeV in 2022, the first year of Run 3 of the CERN LHC. The experimental si

Yudong He

Centroid-based clustering algorithms, such as hard K-means (HKM) and fuzzy K-means (FKM), have suffered from learning bias towards large clusters. Their centroids tend to be crowded in large clusters, compromising performance when the true underlying data groups vary in size (i.e., imbalanced data). To address this, we propose a new clustering objective func

Rolando Kindelan Nuñez, Mircea Petrache, Mauricio Cerda, Nancy Hitschfeld

Persistence diagrams (PD)s play a central role in topological data analysis, and are used in an ever increasing variety of applications. The comparison of PD data requires computing comparison metrics among large sets of PDs, with metrics which are accurate, theoretically sound, and fast to compute. Especially for denser multi-dimensional PDs, such compariso

Xinshuo Hu, Baotian Hu, Dongfang Li, Xiaoguang Li

The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context. Previous research has predominantly focused on examining hallucinations stemming from static input, such as in the domains of summarization or machine tr

Achraf Zinihi, Moulay Rchid Sidi Ammi, Matthias Ehrhardt

This paper investigates a novel nonlinear singular fractional SI model with the $\Phi_p$ operator and the Mittag-Leffler kernel. The initial investigation includes the existence, uniqueness, boundedness, and non-negativity of the solution. We then establish Hyers-Ulam stability for the proposed model in Banach space. Optimal control analysis is performed to

Yurong Chen, Zhaohua Chen, Xiaotie Deng, Zhiyi Huang

This paper considers the hidden-action model of the principal-agent problem, in which a principal incentivizes an agent to work on a project using a contract. We investigate whether contracts with bounded payments are learnable and approximately optimal. Our main results are two learning algorithms that can find a nearly optimal bounded contract using a poly

Benoît Guillemet, Assia Mahboubi, Matthieu Piquerez

This paper describes a formal proof library, developed using the Coq proof assistant, designed to assist users in writing correct diagrammatic proofs, for 1-categories. This library proposes a deep-embedded, domain-specific formal language, which features dedicated proof commands to automate the synthesis, and the verification, of the technical parts often e

Takehiro Takayanagi, Masahiro Suzuki, Ryotaro Kobayashi, Hiroki Sakaji

Causality is fundamental in human cognition and has drawn attention in diverse research fields. With growing volumes of textual data, discerning causalities within text data is crucial, and causal text mining plays a pivotal role in extracting meaningful patterns. This study conducts comprehensive evaluations of ChatGPT's causal text mining capabilities. Fir

Marco Borghesi, Alessandro Bosso, Giuseppe Notarstefano

This article introduces a novel framework for data-driven linear quadratic regulator (LQR) design. First, we introduce a reinforcement learning paradigm for on-policy data-driven LQR, where exploration and exploitation are simultaneously performed while guaranteeing robust stability of the whole closed-loop system encompassing the plant and the control/learn

Alfred Ferrer Florensa, Jose Juan Almagro Armenteros, Henrik Nielsen, Frank Møller Aarestrup

The use of deep learning models in computational biology has increased massively in recent years, and it is expected to continue with the current advances in the fields such as Natural Language Processing. These models, although able to draw complex relations between input and target, are also inclined to learn noisy deviations from the pool of data used dur

Konstantina Biza, Antonios Ntroumpogiannis, Sofia Triantafillou, Ioannis Tsamardinos

We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods. AutoCD's goal is to deliver all causal information that an expert human analyst would and answer a user's causal queries. We describe the architecture of such a platform, and illu

Guanyu Wang, Yuekang Li, Yi Liu, Gelei Deng

Augmented generation techniques such as Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) have revolutionized the field by enhancing large language model (LLM) outputs with external knowledge and cached information. However, the integration of vector databases, which serve as a backbone for these augmentations, introduces critical cha

Bin Yang, Yuming Qin, Alain Miranville, Ke Wang

In this paper, we shall investigate the existence and upper semicontinuity of pullback attractors for non-autonomous Kirchhoff wave equations with a strong damping in the time-dependent space $X_t$. After deriving the existence and uniqueness of solutions by the Faedo-Galerkin approximation method, we establish the existence of pullback attractors. Later on,

Zaijiu Shang, Yang Xu

In this paper we prove a KAM-like theorem of symplectic algorithms for nearly integrable Hamiltonian systems which generalises the result of \cite{r1} and \cite{r6} for the case of integrable systems.

Zhun Li, Xinrun Mi, Xinming Wang, Jian Lyu

In a magnetic skyrmion phase, magnetic moments form vortex-like topological textures which are of both fundamental and industrial interests. In $\beta$-Mn-type Co-Zn-Mn alloys, chrial magnetic skyrmions emerge above room temperature, providing a unique system for studying the skrymion physics and exploring spintronics applications. However, the magnetic skyr

Steven Y. K. Wong, Jennifer S. K. Chan, Lamiae Azizi

Time-series with volatility clustering pose a unique challenge to uncertainty quantification (UQ) for returns forecasts. Methods for UQ such as Deep Evidential regression offer a simple way of quantifying return forecast uncertainty without the costs of a full Bayesian treatment. However, the Normal-Inverse-Gamma (NIG) prior adopted by Deep Evidential regres

Aiqing Zhu, Qianxiao Li

Learning unknown stochastic differential equations (SDEs) from observed data is a significant and challenging task with applications in various fields. Current approaches often use neural networks to represent drift and diffusion functions, and construct likelihood-based loss by approximating the transition density to train these networks. However, these met

Sebastian Bordt, Ben Lengerich, Harsha Nori, Rich Caruana

Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans. In this work, we show that large language models (LLMs) are remarkably good at working with interpretable models, too. In particular, we show that LLMs can describe, interpret, and debug Generalized A

Jiajie Su, Chaochao Chen, Zibin Lin, Xi Li

Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interaction data encounter performance degradation when the sequences are short. To tackle this problem, we focus on Multi-Behavior Sequential Recommendation (MBSR) in this paper, which aim

Xu-Liang Fan

Narrow-line Seyfert 1 galaxies (NLS1s), a subclass of active galactic nuclei (AGNs) at early stage of accretion process, are also found to host relativistic jets. However, currently known jetted NLS1s are rare. The majority of NLS1s are undetected at radio band. The radio detection rate of NLS1s raises with the LOFAR Two-metre Sky Survey (LoTSS), which gives

Maxim K. Surkov, Ivan P. Yamshchikov

Evaluation plays a significant role in modern natural language processing. Most modern NLP benchmarks consist of arbitrary sets of tasks that neither guarantee any generalization potential for the model once applied outside the test set nor try to minimize the resource consumption needed for model evaluation. This paper presents a theoretical instrument and

Bertrand Meyer, Viktoryia Kananchuk, Li Huang

Techniques of Automatic Program Repair (APR) have the potential of thoroughly facilitating the task of producing quality software. After a promising start, however, progress in making APR practical has been hindered by the lack of a common framework to support the multiplicity of APR ideas and tools, and of target programming languages and environments. In t

Andrius Grigutis, Artur Nakliuda

Let $X_1,\,X_2,\,\ldots,\,X_N$, $N\in\mathbb{N}$ be independent but not necessarily identically distributed discrete and integer-valued random variables. Assume that $X_1\geqslant m_1$, $X_2\geqslant m_2$, $\ldots$, $X_N\geqslant m_N$ almost surely, where $m_1,\,m_2,\ldots,\,m_N$ are some integer numbers such that $m_1+m_2+\ldots+m_N<0$, and $X_k$ is identic

Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Puyu Wang

Deep learning-based methods have achieved a breakthrough in image anomaly detection, but their complexity introduces a considerable challenge to understanding why an instance is predicted to be anomalous. We introduce a novel explanation method that generates multiple alternative modifications for each anomaly, capturing diverse concepts of anomalousness. Ea

Miguel Vanvlasselaer

In this talk, we study the impact of first order phase transitions with fast bubble walls on mechanisms of leptogenesis and baryogenesis. We begin our exploration with the usual leptogenesis where the breaking of $B-L$ occurs via a PT with fast walls. Then we move to a more exotic case where the $B-L$ breaking phase transition creates heavy particles in the

Qi Wu, Dian-Yong Chen

In the present work, we investigate the production of the newly observed $P^\Lambda_{\psi s}(4338)$ state in $\Xi_b^-$ decay, where the $P^\Lambda_{\psi s}(4338)$ is assigned as a $\Xi_c \bar{D}$ molecular state. By using an effective Lagrangian approach, we evaluate the branching fractions of $\Xi_b^-\rightarrow P^\Lambda_{\psi s}(4338)K^-$ via the triangle

Yasuhiko Asao, Sergei O. Ivanov

We prove that the magnitude (co)homology of an enriched category can, under some technical assumptions, be described in terms of derived functors between certain abelian categories. We show how this statement is specified for the cases of quasimetric spaces, finite quasimetric spaces, and finite digraphs. For quasimetric spaces, we define the notion of a dis

Andreas Gustavsson

We study 6d $(1,0)$ superconformal theories. These have a natural lightlike conformal Killing vector, the Dirac current. We perform a conformal dimensional reduction along the Dirac current down to five-dimensions in such a way that we always preserve at least two real supercharges

Chenxi Huang, Yuenan Hou, Weicai Ye, Di Huang

NeRF-Det has achieved impressive performance in indoor multi-view 3D detection by innovatively utilizing NeRF to enhance representation learning. Despite its notable performance, we uncover three decisive shortcomings in its current design, including semantic ambiguity, inappropriate sampling, and insufficient utilization of depth supervision. To combat the

Rixin Li, Yi-Xian Chen, Douglas N. C. Lin

Dust trapping in the global pressure bump induced by magnetospheric truncation offers a promising formation mechanism for close-in super-Earths/sub-Neptunes. These planets likely form in evolved protoplanetary discs, where the gas temperature at the expanding truncation radius become amiable to refractory solids. However, dust accumulation may alter the disc

Ioan Baldea

Choosing self-assembled monolayers (SAM) of fluorine terminated oligophenylenes adsorbed on gold as illustration, we show that a single level (molecular orbital, MO) model can excellently reproduce full I-V curves measured for large area junctions fabricated with top EGaIn contact. In addition, this model unravels a surprising dichotomy between MO coupling t