May 2024 arXiv papers — page 15

Riccardo Renzulli

Capsule networks (CapsNets) were introduced to address convolutional neural networks limitations, learning object-centric representations that are more robust, pose-aware, and interpretable. They organize neurons into groups called capsules, where each capsule encodes the instantiation parameters of an object or one of its parts. Moreover, a routing algorith

Alan G. Goodman, Pavlos Xanthopoulos, Gabriel G. Plunk, Håkan Smith

The stellarator is a type of fusion energy device that - if properly designed - could provide clean, safe, and abundant energy to the grid. To generate this energy, a stellarator must keep a hot mixture of charged particles (known as a plasma) sufficiently confined by using a fully shaped magnetic field. If this is achieved, the heat from fusion reactions wi

Alok Goswami, B. V. Rao

This note takes forward a comment made in Dunford and Schwartz (LInear operators, Part 1 and describes dual of $L_1$ for general measure spaces.

G. Bastien, D. Repček, A. Eliáš, A. Kancko

The study of magnetic frustration in classical spin systems was motivated by the prediction and discovery of classical spin liquid states. These uncommon magnetic phases are characterized by a massive degeneracy of their ground state implying a finite magnetic entropy at zero temperature. While the classical spin liquid state was originally predicted in the

Lorenzo Pucci, Andrea Giorgetti

Only the chairs can edit This paper investigates the fundamental limits of target position estimation accuracy of joint sensing and communication (JSC) networks comprising several monostatic base stations (BSs) that cooperate to localize targets. Specifically, each BS adopts a multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (

Carrie Andrew, Sharif Islam, Claus Weiland, Dag Endresen

Biodiversity data are substantially increasing, spurred by technological advances and community (citizen) science initiatives. To integrate data is, likewise, becoming more commonplace. Open science promotes open sharing and data usage. Data standardization is an instrument for the organization and integration of biodiversity data, which is required for comp

Jia Li, Ge Li, Yunfei Zhao, Yongmin Li

How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs. To address the knowledge gap, we propose a new benchmark named DevEval, which has three advances. (1) DevEval aligns

ALICE Collaboration

First measurements of hadron(h)$-\Lambda$ azimuthal angular correlations in p$-$Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV using the ALICE detector at the Large Hadron Collider are presented. These correlations are used to separate the production of associated $\Lambda$ baryons into three different kinematic regions, namely those produced in the directi

Fangyi Chen, Han Zhang, Zhantao Yang, Hao Chen

Open-vocabulary object detection (OVD) requires solid modeling of the region-semantic relationship, which could be learned from massive region-text pairs. However, such data is limited in practice due to significant annotation costs. In this work, we propose RTGen to generate scalable open-vocabulary region-text pairs and demonstrate its capability to boost

X. Du, Y. D. Li, Y. T. Cao, C. Y. Pei

The discovery of pressurized superconductivity at 80 K in La3Ni2O7 officially brings nickelates into the family of high-temperature superconductors, which gives rise to not only new insights but also mysteries in the strongly correlated superconductivity. More recently, the sibling compound La4Ni3O10 was also shown to be superconducting below about 25 K unde

Zhi-Gang Wang, Xiao-Yuan Wang, Antti Rasila, Jia-Le Qiu

We first construct the harmonic K-quasiconformal Koebe functions, filling a long-standing foundational gap in geometric function theory. This construction provides a unified parametric candidate extremal function framework for conformal mappings, quasiconformal mappings, and harmonic mappings, and we formulate related conjectures for the extremal theory of h

Yevheniya Nosyk, Maciej Korczyński, Andrzej Duda

DNS Security Extensions (DNSSEC) provide the most effective way to fight DNS cache poisoning attacks. Yet, very few DNS resolvers perform DNSSEC validation. Identifying such systems is non-trivial and the existing methods are not suitable for Internet-scale measurements. In this paper, we propose a novel remote technique for identifying DNSSEC-validating res

Yuxiao Luo, Zhongcai Cao, Xin Jin, Kang Liu

Understanding human mobility patterns is essential for various applications, from urban planning to public safety. The individual trajectory such as mobile phone location data, while rich in spatio-temporal information, often lacks semantic detail, limiting its utility for in-depth mobility analysis. Existing methods can infer basic routine activity sequence

Seulki Chung

This paper presents a comparative analysis of univariate and multivariate GARCH-family models and machine learning algorithms in modeling and forecasting the volatility of major energy commodities: crude oil, gasoline, heating oil, and natural gas. It uses a comprehensive dataset incorporating financial, macroeconomic, and environmental variables to assess p

Federica Galluzzi, Bert van Geemen

An element in the Brauer group of a general complex projective $K3$ surface $S$ defines a sublattice of the transcendental lattice of $S$. We consider those elements of prime order for which this sublattice is Hodge-isometric to the transcendental lattice of another K3 surface $X$. We recall that this defines a finite map between moduli spaces of polarized K

Jacqueline Hidalgo-Jimenez, Taner Akbay, Tatsumi Ishihara, Kaveh Edalati

High-entropy oxides (HEOs), containing at least five principal cations, have recently emerged as promising photocatalysts for hydrogen production via water splitting. Despite their high potential, the impact of the cation mixtures on photocatalytic activity remains poorly understood. This study investigates the high-entropy photocatalyst TiZrHfNbTaO11 using

Chaochen Gao, Xing Wu, Qi Fu, Songlin Hu

Recent advancements in large language models (LLMs) have highlighted the importance of extending context lengths for handling complex tasks. While traditional methods for training on long contexts often use filtered long documents, these approaches lead to domain imbalances, limiting model performance. To address this, techniques like random document concate

Jan Peper, David Kröger, Jonathan Kipp, Florian Ziel

The future energy system will largely depend on volatile renewable energy sources and temperature-dependent loads, which makes the weather a central influencing factor. This article presents a novel approach for simulating weather scenarios for robust large-scale power system analysis. By applying different signal analysis methods, historical weather data is

Antoine Benoit, Jean-François Coulombel

We study the stability of a two-dimensional Lax-Wendroff scheme in a quarter-plane. Following our previous work, we aim here at adapting the energy method in order to study second order extrapolation boundary conditions. We first show on the one-dimensional problem why modifying the energy is a necessity in order to obtain stability estimates. We then study

Zhihuan Huang, Yuxuan Lu, Yongkang Guo, Yuqing Kong

Many battling games utilize a special item (e.g. Roshan in Defense of the Ancients 2 (DOTA 2), Baron Nashor in League of Legends (LOL), Golden Snitch in Quidditch) as a potential ``Game Changer''. The reward of this item can enable the underdog to make a comeback. However, if the reward is excessively high, the whole game may devolve into a chase for the ``G

Chengwei Dai, Kun Li, Wei Zhou, Songlin Hu

Large language models (LLMs) exhibit enhanced reasoning at larger scales, driving efforts to distill these capabilities into smaller models via teacher-student learning. Previous works simply fine-tune student models on teachers' generated Chain-of-Thoughts (CoTs) data. Although these methods enhance in-domain (IND) reasoning performance, they struggle to ge

Qiyuan Cheng, Jianping XIong, Xu Ding, Kaifan Ji

Low mass-ratio (q) contact binary systems are progenitors of stellar mergers such as blue straggles (BS) or fast-rotating FK Com stars. In this study, we present the first light curve analysis of two newly identified low mass-ratio contact binary systems, TIC 55007847 and TIC 63597006, that are identified from TESS. Both stars are classified as A-subtype con

Shreeya S. Shetye, Giordano Viviani, Richard I. Anderson, Nami Mowlavi

Classical Cepheids provide valuable insights into the evolution of stellar multiplicity among intermediate-mass stars. Here, we present a systematic investigation of single-lined spectroscopic binaries (SB1) based on high-precision velocities measured by the VELOcities of CEpheids (VELOCE) project. We detected 76 (29%) SB1 systems among the 258 Milky Way Cep

ALICE Collaboration

The first measurement of $_{\Lambda}^{3}\mathrm{H}$ and $^3_ {\overline{\Lambda}}\overline{\mathrm{H}}$ differential production with respect to transverse momentum and centrality in Pb$-$Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.02$~TeV is presented. The $_{\Lambda}^{3}\mathrm{H}$ has been reconstructed via its two-charged-body decay channel, i.e., $_{\Lamb

Shivam Dangwal, Yuji Ikeda, Blazej Grabowski, Kaveh Edalati

Safe and high-density storage of hydrogen, for a clean-fuel economy, can be realized by hydride-forming materials, but these materials should be able to store hydrogen at room temperature. Some high-entropy alloys (HEAs) have recently been shown to reversibly store hydrogen at room temperature, but the design of HEAs with appropriate thermodynamics is still

Margarida Romero

The rapid advancement of artificial intelligence (AI) has brought significant challenges to the education and workforce skills required to take advantage of AI for human-AI collaboration in the workplace. As AI continues to reshape industries and job markets, the need to define how AI literacy can be considered in lifelong learning has become increasingly cr

Nikolas Kirschstein, Yixuan Sun

Climate change exacerbates riverine floods, which occur with higher frequency and intensity than ever. The much-needed forecasting systems typically rely on accurate river discharge predictions. To this end, the SOTA data-driven approaches treat forecasting at spatially distributed gauge stations as isolated problems, even within the same river network. Howe

Yufeng Wang, Bo Sun

Diamond has the known highest thermal conductivity of around \SI{2000}{\watt\per\meter\per\kelvin} and is therefore widely used for heat dissipation. In practical applications, synthetic diamond microparticles are usually assumed to have similar thermal conductivity to that of bulk diamond because the particle size is larger than theoretical phonon mean free

Florian Mannel, Hari Om Aggrawal

We devise an L-BFGS method for optimization problems in which the objective is the sum of two functions, where the Hessian of the first function is computationally unavailable while the Hessian of the second function has a computationally available approximation that allows for cheap matrix-vector products. This is a prototypical setting for many inverse pro

Fengyuan Yang, Kerui Gu, Angela Yao

2D keypoints are commonly used as an additional cue to refine estimated 3D human meshes. Current methods optimize the pose and shape parameters with a reprojection loss on the provided 2D keypoints. Such an approach, while simple and intuitive, has limited effectiveness because the optimal solution is hard to find in ambiguous parameter space and may sacrifi

Herman Cappelen, Josh Dever, John Hawthorne

This paper presents an argument that certain AI safety measures, rather than mitigating existential risk, may instead exacerbate it. Under certain key assumptions - the inevitability of AI failure, the expected correlation between an AI system's power at the point of failure and the severity of the resulting harm, and the tendency of safety measures to enabl

Stephen Meisenbacher, Florian Matthes

The study of Differential Privacy (DP) in Natural Language Processing often views the task of text privatization as a $\textit{rewriting}$ task, in which sensitive input texts are rewritten to hide explicit or implicit private information. In order to evaluate the privacy-preserving capabilities of a DP text rewriting mechanism, $\textit{empirical privacy}$

Adrián Portillo Fernández

We define the continuous modeling property for first-order structures and show that a first-order structure has the continuous modelling property if and only if its age has the embedding Ramsey property. We use generalized indiscernible sequences in continuous logic to study and characterize $n$-dependence for continuous theories and first-order hyperdefinab

Vladimir V. Ulyanov

De Moivre (1733), investigating the limit distribution of the binomial distribution, was the first to discover the existence of the normal distribution and the central limit theorem. In this review article, we briefly recall the history of classical central limit theorem and martingale central limit theorem, and introduce a new direction of central limit the

J. van den Eijnden, D. Robins, R. Sharma, C. Sánchez-Fernández

The Rapid Burster is a unique neutron star low-mass X-ray binary system, showing both thermonuclear Type-I and accretion-driven Type-II X-ray bursts. Recent studies have demonstrated how coordinated observations of X-ray and radio variability can constrain jet properties of accreting neutron stars - particularly when the X-ray variability is dominated by dis

ALICE Collaboration

Measurements of (anti)deuteron and (anti)$^3$He production in the rapidity range $ |y| < $ 0.5 as a function of the transverse momentum and event multiplicity in Xe$-$Xe collisions at a center-of-mass energy per nucleon$-$nucleon pair of $\sqrt{s_{\rm NN}}$ = 5.44 TeV are presented. The coalescence parameters $B_2$ and $B_3$ are measured as a function of the

Antoine Picard-Weibel, Gabriel Capson-Tojo, Benjamin Guedj, Roman Moscoviz

Uncertainty quantification is critical for ensuring adequate predictive power of computational models used in biology. Focusing on two anaerobic digestion models, this article introduces a novel generalized Bayesian procedure, called VarBUQ, ensuring a correct tradeoff between flexibility and computational cost. A benchmark against three existing methods (Fi

Junqi Chen, Xu Tan, Sylwan Rahardja, Jiawei Yang

Deep learning-based sequence models are extensively employed in Time Series Anomaly Detection (TSAD) tasks due to their effective sequential modeling capabilities. However, the ability of TSAD is limited by two key challenges: (i) the ability to model long-range dependency and (ii) the generalization issue in the presence of non-stationary data. To tackle th

Frank A. Ruis, Alma M. Liezenga, Friso G. Heslinga, Luca Ballan

Collecting and annotating real-world data for the development of object detection models is a time-consuming and expensive process. In the military domain in particular, data collection can also be dangerous or infeasible. Training models on synthetic data may provide a solution for cases where access to real-world training data is restricted. However, bridg

Pengji Li, Leon Biesterfeld, Lars Klepzig, Jingzhong Yang

Colloidal semiconductor nanocrystals are promising materials for classical and quantum light sources due to their versatile chemistry and efficient photoluminescence (PL) properties. While visible emitters are well-established, the pursuit of excellent (near-)infrared sources continues. One notable candidate in this regard are photoluminescent two-dimensiona

Jean Kieffer

We describe how isogenies between principally polarized abelian surfaces (PPAS) with real multiplication (RM) can be decomposed into elementary isogeny types. This leads to a strategy for enumerating the isogeny class of a given PPAS with RM. This note is part of an ongoing work with R. van Bommel, S. Chidambaram, and E. Costa and is not indended for separat

Andrea Ramazzina, Stefanie Walz, Pragyan Dahal, Mario Bijelic

Reconstructing outdoor 3D scenes from temporal observations is a challenge that recent work on neural fields has offered a new avenue for. However, existing methods that recover scene properties, such as geometry, appearance, or radiance, solely from RGB captures often fail when handling poorly-lit or texture-deficient regions. Similarly, recovering scenes w

Chunhui Zhang, Li Liu, Guanjie Huang, Hao Wen

Underwater object tracking (UOT) is a foundational task for identifying and tracing submerged entities in underwater video sequences. However, current UOT datasets suffer from limitations in scale, diversity of target categories and scenarios covered, hindering the training and evaluation of modern tracking algorithms. To bridge this gap, we take the first s

Suzana Veljanovska, Hans Dermot Doran

Symbolic Aggregate approXimation (SAX) is a common dimensionality reduction approach for time-series data which has been employed in a variety of domains, including classification and anomaly detection in time-series data. Domains also include shape recognition where the shape outline is converted into time-series data forinstance epoch classification of arc

Manon Verbockhaven, Sylvain Chevallier, Guillaume Charpiat, Théo Rudkiewicz

Machine learning tasks are generally formulated as optimization problems, where one searches for an optimal function within a certain functional space. In practice, parameterized functional spaces are considered, in order to be able to perform gradient descent. Typically, a neural network architecture is chosen and fixed, and its parameters (connection weigh

Bingyan Xie, Yongpeng Wu, Yuxuan Shi, Wenjun Zhang

Though achieving marvelous progress in various scenarios, existing semantic communication frameworks mainly consider single-input single-output Gaussian channels or Rayleigh fading channels, neglecting the widely-used multiple-input multiple-output (MIMO) channels, which hinders the application into practical systems. One common solution to combat MIMO fadin

Deepak Narayan Gadde, Thomas Nalapat, Aman Kumar, Djones Lettnin

The increasing design complexity of System-on-Chips (SoCs) has led to significant verification challenges, particularly in meeting coverage targets within a timely manner. At present, coverage closure is heavily dependent on constrained random and coverage driven verification methodologies where the randomized stimuli are bounded to verify certain scenarios

Ryosuke Nakahama

We prove that the non-commutative harmonic oscillator on $L^2(\mathbb{R})\otimes\mathbb{C}^2$ introduced by Parmeggiani and Wakayama is equivalent to the two-photon quantum Rabi model, and they are also equivalent to a holomorphic differential equation on the unit disk. The confluence process of this differential equation and the relation with the one-photon

Yan-Hong Yao, Xin-He Meng

In this paper, we interpret the dark energy as an effect caused by small scale inhomogeneities of the universe with the use of the spatial averaged approach of Buchert. The model considered here adopts the Chevallier-Polarski-Linder(CPL) parameterizations of the equation of state of the effective perfect fluid from the backreaction effect. Thanks to the effe

Jiang Wang, Yuanzheng He, Daobilige Su, Katsutoshi Itoyama

Robot audition systems with multiple microphone arrays have many applications in practice. However, accurate calibration of multiple microphone arrays remains challenging because there are many unknown parameters to be identified, including the relative transforms (i.e., orientation, translation) and asynchronous factors (i.e., initial time offset and sampli

Yair Caro, Zsolt Tuza

We consider coloring problems inspired by the theory of anti-Ramsey / rainbow colorings that we generalize to a far extent. Let $\mathcal{F}$ be a hereditary family of graphs; i.e., if $H\in \mathcal{F}$ and $H'\subset H$ then also $H'\subset \mathcal{F}$. For a graph $G$ and any integer $n \geq |G|$, let $f(n,G|\mathcal{F})$ denote the smallest number $k$ o

Ruiyang Jin, Zaiwei Chen, Yiheng Lin, Jie Song

Independent learning (IL), despite being a popular approach in practice to achieve scalability in large-scale multi-agent systems, usually lacks global convergence guarantees. In this paper, we study two representative algorithms, independent $Q$-learning and independent natural actor-critic, within value-based and policy-based frameworks, and provide the fi

Alessio Moscariello, Alessio Sammartano

We consider the classical problem of determining the largest possible cardinality of a minimal presentation of a numerical monoid with given embedding dimension and multiplicity. Very few values of this cardinality are known. In addressing this problem, we apply tools from Hilbert functions and free resolutions of artinian standard graded algebras. This appr

Herman Cappelen, Josh Dever

AlphaGo plays chess and Go in a creative and novel way. It is natural for us to attribute contents to it, such as that it doesn't view being several pawns behind, if it has more board space, as bad. The framework introduced in Cappelen and Dever (2021) provides a way of thinking about the semantics and the metasemantics of AI content: does AlphaGo entertain

Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard, Nadia Oudjane

We explore online learning in episodic loop-free Markov decision processes on non-stationary environments (changing losses and probability transitions). Our focus is on the Concave Utility Reinforcement Learning problem (CURL), an extension of classical RL for handling convex performance criteria in state-action distributions induced by agent policies. While

Minu Kim, Yongsik Lee, Sehyeok Kang, Jihwan Oh

We present Preference Flow Matching (PFM), a new framework for preference-based reinforcement learning (PbRL) that streamlines the integration of preferences into an arbitrary class of pre-trained models. Existing PbRL methods require fine-tuning pre-trained models, which presents challenges such as scalability, inefficiency, and the need for model modificat

Vincent Froese, Moritz Grillo, Martin Skutella

Neural networks with ReLU activation play a key role in modern machine learning. Understanding the functions represented by ReLU networks is a major topic in current research as this enables a better interpretability of learning processes. Injectivity of a function computed by a ReLU network, that is, the question if different inputs to the network always le

Tianyi Chen, Hua Wang, Yutong Cai, Maohan Liang

Factor analysis acts a pivotal role in enhancing maritime safety. Most previous studies conduct factor analysis within the framework of incident-related label prediction, where the developed models can be categorized into short-term and long-term prediction models. The long-term models offer a more strategic approach, enabling more proactive risk management,

Fangyi Chen, Yunxiao Chen, Zhiliang Ying, Kangjie Zhou

Recurrent event time data arise in many studies, including biomedicine, public health, marketing, and social media analysis. High-dimensional recurrent event data involving many event types and observations have become prevalent with advances in information technology. This paper proposes a semiparametric dynamic factor model for the dimension reduction of h

Shuyuan Liu, Jiawei Chen, Shouwei Ruan, Hang Su

Embodied intelligence empowers agents with a profound sense of perception, enabling them to respond in a manner closely aligned with real-world situations. Large Language Models (LLMs) delve into language instructions with depth, serving a crucial role in generating plans for intricate tasks. Thus, LLM-based embodied models further enhance the agent's capaci

Dayakrishna Nailwal, MV Sunil Krishna, Alok Kumar Ranjan, Jia Yue

Nitric Oxide (NO) significantly impacts energy distribution and chemical processes in the mesosphere and lower thermosphere (MLT). During geomagnetic storms, a substantial influx of energy in the thermosphere leads to an increase in NO infrared emissions. Accurately predicting the radiative flux of Nitric Oxide is crucial for understanding the thermospheric

Filip Talimdjioski

Let $T$ be a compact, metrisable and strongly countable-dimensional topological space. Let $\mathcal{M}^T$ be the set of all metrics $d$ on $T$ compatible with its topology, and equip $\mathcal{M}^T$ with the topology of uniform convergence, where the metrics are regarded as functions on $T^2$. We prove that the set $\mathcal{A}^{T,1}$ of metrics $d\in\mathc

Jiahui Xu, Feng Jiang, Anningzhe Gao, Luis Fernando D'Haro

In dialogue systems, discourse plays a crucial role in managing conversational focus and coordinating interactions. It consists of two key structures: rhetorical structure and topic structure. The former captures the logical flow of conversations, while the latter detects transitions between topics. Together, they improve the ability of a dialogue system to

Damien Simon

Mixed radix bases in numeration is a very old notion but it is rarely studied on its own or in relation with concrete problems related to number theory. Starting from the natural question of the conversion of a basis to another for integers as well as polynomials, we use mixed radix bases to introduce two-dimensional arrays with suitable filling rules. These

S. I. Ipatov

The evolution of the orbits of bodies ejected from the Earth has been studied at the stage of its accumulation and early evolution after impacts of large planetesimals. In the considered variants of calculations of the motion of bodies ejected from the Earth, most of the bodies left the Hill sphere of the Earth and moved in heliocentric orbits. Their dynamic

Xiaoliang Wu, Chau Luu, Peter Bell, Ajitha Rajan

This paper proposes a fully explainable approach to speaker verification (SV), a task that fundamentally relies on individual speaker characteristics. The opaque use of speaker attributes in current SV systems raises concerns of trust. Addressing this, we propose an attribute-based explainable SV system that identifies speakers by comparing personal attribut

Ohjoon Kwon, Donghyeon Jeon, Nayoung Choi, Gyu-Hwung Cho

Most prior safety research of large language models (LLMs) has focused on enhancing the alignment of LLMs to better suit the safety requirements of humans. However, internalizing such safeguard features into larger models brought challenges of higher training cost and unintended degradation of helpfulness. To overcome such challenges, a modular approach empl

Inpyo Song, Minjun Joo, Joonhyung Kwon, Jangwon Lee

This paper addresses the daily challenges encountered by visually impaired individuals, such as limited access to information, navigation difficulties, and barriers to social interaction. To alleviate these challenges, we introduce a novel visual question answering dataset. Our dataset offers two significant advancements over previous datasets: Firstly, it f

Li Zhang, Peter Jansen, Tianyi Zhang, Peter Clark

Planning in textual environments have been shown to be a long-standing challenge even for current models. A recent, promising line of work uses LLMs to generate a formal representation of the environment that can be solved by a symbolic planner. However, existing methods rely on a fully-observed environment where all entity states are initially known, so a o

S. Niyonzima, N. Pop, F. Iacob, Å. Larson

Multichannel quantum defect theory is applied in the treatment of the dissociative recombination and vibrational excitation processes for the BeD$^+$ ion in the twenty four vibrational levels of its ground electronic state ($\textrm{X}\,{^{1}\Sigma^{+}},v_{i}^{+}=0\ldots 23$). Three electronic symmetries of BeD$^{**}$ states (\ensuremath{^{2}\Pi}, \ensuremat

Wen Wen, Jie Liang, Huawen Xu, Feng Jin

We report the experimental emulation of trembling quantum motion, or Zitterbewegung, of exciton polaritons in a perovskite microcavity at room temperature. By introducing liquid crystal molecules into the microcavity, we achieve spinor states with synthetic Rashba-Dresselhaus spin-orbit coupling and tunable energy splitting. Under a resonant excitation, the

Yinqin Li, Dachun Yang, Wen Yuan, Yangyang Zhang

In this article, we establish a quantitative weighted variant of a far-reaching inequality obtained by A. Cohen, W. Dahmen, I. Daubechies, and R. DeVore in 2003, whose dependence on the $A_p$-weight constant for any $p\in[1,\infty)$ is sharp. As applications, we obtain the almost characterization of the critical weighted Sobolev space in terms of wavelets, a

Guogang Zhu, Xuefeng Liu, Xinghao Wu, Shaojie Tang

Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model.In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model's prediction to skew towards some certain classes. Existing FSSL methods primarily tackle this issue by enhancing consistency in model p

Cui Ding, Wenfeng Dong, Xiaotong Jiao, Zhiyu Zhang

The discovery of high-mobility two-dimensional electron gas and low carrier density superconductivity in multiple SrTiO$_3$-based heterostructures has stimulated intense interest in the surface properties of SrTiO$_3$. The recent discovery of high-T$_c$ superconductivity in the monolayer FeSe/SrTiO$_3$ aroused the upsurge and underscored the atomic precision

Cristina-Maria Valcu, Richard A. Scheltema, Ralf M. Schweiggert, Mihai Valcu

Maternal investment directly shapes early developmental conditions and therefore has longterm fitness consequences for the offspring. In oviparous species prenatal maternal investment is fixed at the time of laying. To ensure the best survival chances for most of their offspring, females must equip their eggs with the resources required to perform well under

Dylan Zhang, Justin Wang, Francois Charton

Instruction tuning -- tuning large language models on instruction-output pairs -- is a promising technique for making models better adapted to the real world. Yet, the key factors driving the model's capability to understand and follow instructions not seen during training remain under-explored. Our investigation begins with a series of synthetic experiments

Francesco Bozzola, Lorenzo Brasco

We prove a two-sided estimate on the sharp $L^p$ Poincar\'e constant of a general open set, in terms of a capacitary variant of its inradius. This extends a result by Maz'ya and Shubin, originally devised for the case $p=2$, in the subconformal regime. We cover the whole range of $p$, by allowing in particular the extremal cases $p=1$ (Cheeger's constant) an

Nicolò Botteghi, Paolo Motta, Andrea Manzoni, Paolo Zunino

Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. However, constructing ROMs from noisy high-dimensional data is challenging. In this work, we propose a data-driven, non-intrusive method that utilizes stochastic variational deep kernel learning (SVDKL) to discover low

Haoqiong Bian, Dongyang Geng, Haoyang Li, Yunpeng Chai

Serverless query processing has become increasingly popular due to its advantages, including automated resource management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users t

Jinliang Zheng, Jianxiong Li, Sijie Cheng, Yinan Zheng

Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment between specific textual instruction and targeted local region of an image. To achieve more accurate and nuanced multimodal instruction following, we introduce Instruction-guided Visual Masking (IVM), a new versatile visual g

Wei Cheng, Yuhan Wu, Wei Hu

Recent years have witnessed the deployment of code language models (LMs) in various code intelligence tasks such as code completion. Yet, it is challenging for pre-trained LMs to generate correct completions in private repositories. Previous studies retrieve cross-file context based on import relations or text similarity, which is insufficiently relevant to

G. M. Minkov, O. E. Rut, A. A. Sherstobitov, A. V. Germanenko

Shubnikov-de Haas (SdH) oscillations and magneto-intersubband oscillations of magnetoresistance of structures with single HgTe quantum wells with a width of (10-18) nm have been experimentally studied. The spectrum of the conduction band in these structures is split by the spin-orbit interaction. This leads to beats of the SdH oscillations and the appearance

Freddy Delbaen, Chitro Majumdar

For a square integrable $m$-dimensional random variable $X$ on a probability space $(\Omega,\Fc,\Pr)$ and a sub sigma algebra $\Ac$, we show that there is a constructive way to represent $X-\Er[X\mid\Ac]$ as the sum of a series of variables that are independent of $\Ac$.

Chao Wang, Jiaxuan Zhao, Lingling Li, Licheng Jiao

Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities. However, manually determining the suitable Transformer architecture for a specific graph dataset or task requires extensive expert knowledge and laborious trials. This paper proposes

Jeiyoon Park, Chanjun Park, Heuiseok Lim

The recent introduction of the Assistants API highlights its potential for large language models (LLMs) in role-playing agents (RPA). However, maintaining consistent character personas remains a significant challenge due to variability in information extraction, which frequently omits critical elements such as backstory or interpersonal relationships. To add

Chong Li, Peiyuan Huang, Chen-Guang Wang, Haojie Li

Controlling the size and distribution of potential barriers within a medium of interacting particles can unveil unique collective behaviors and innovative functionalities. In this study, we introduce a unique superconducting hybrid device using a novel artificial spin ice structure composed of asymmetric nanomagnets. This structure forms a distinctive superc

Xiang-You Chen, Yu-Yu Zhang, Qing-Hu Chen, Hai-Qing Lin

The superradiant phase transition (SRPT) is forbidden in the standard isotropic Dicke model due to the so-called no-go theorem induced by A-square term. In the framework of the Dicke model, we demonstrate that SRPTs can occur at both zero and finite temperatures if we intrinsically tune the rotating wave and count-rotating atom-cavity coupling independently,

Sizhe Zheng, Pan Gao, Peng Zhou, Jie Qin

Style transfer aims to render an image with the artistic features of a style image, while maintaining the original structure. Various methods have been put forward for this task, but some challenges still exist. For instance, it is difficult for CNN-based methods to handle global information and long-range dependencies between input images, for which transfo

Ken-ichi Hikasa

After reviewing charge conjugation and the CPT theorem, we define Majorana fermions and clarify the relationship of Majorana, Weyl, and Dirac fields. Appearance of Majorana fermions in various scenarios of physics beyond the Standard Model is discussed, including neutrino masses, baryon asymmetry of the universe, grand unified theories, and supersymmetry.

Tautvydas Misiunas, Hassan Mansoor, Jasper Uijlings, Oriana Riva

Large-language models and large-vision models are increasingly capable of solving compositional reasoning tasks, as measured by breakthroughs in visual-question answering benchmarks. However, state-of-the-art solutions often involve careful construction of large pre-training and fine-tuning datasets, which can be expensive. The use of external tools, whether

Vijay Gupta, Anjali

The present study offers a general exponential operator connected with a^2+x^2; for positive real "a". We estimate the asymptotic formula for simultaneous and ordinary approximation of the constructed operator. In the last section, we graphically interpret the created operator's convergence to two periodic functions "x sin(x)" and "-x/2*cos(pi*x)". We also c

Nway Nway Ei, Kitae Kim, Yan Kyaw Tun, Zhu Han

Integrating terrestrial and non-terrestrial networks has emerged as a promising paradigm to fulfill the constantly growing demand for connectivity, low transmission delay, and quality of services (QoS). This integration brings together the strengths of the reliability of terrestrial networks, broad coverage and service continuity of non-terrestrial networks

Alexander Hoen, Dominik Kamp, Ambros Gleixner

The recent performance improvements in mixed-integer programming (MIP) have been accompanied by a significantly increased complexity of the codes of MIP solvers, which poses challenges in fixing implementation errors. In this paper, we introduce MIP-DD, a solver-independent tool, which to the best of our knowledge is the first open-source delta debugger for

Zhiwen Yang, Haowei Chen, Ziniu Qian, Yang Yi

Although single-task medical image restoration (MedIR) has witnessed remarkable success, the limited generalizability of these methods poses a substantial obstacle to wider application. In this paper, we focus on the task of all-in-one medical image restoration, aiming to address multiple distinct MedIR tasks with a single universal model. Nonetheless, due t

Kazuki Yokomizo, Yuto Ashida

The competition between quantum many-particle dynamics and continuous monitoring can lead to measurement-induced phase transitions (MIPTs). So far, MIPTs have been extensively explored in fermionic or spin systems. To examine the possibility of an MIPT in bosonic systems, we study the entanglement structure in continuously monitored free bosons with long-ran

Wenshuo Yu, Shiqi Dong, Shaoping Lu, Xintong Dong

Post-stack seismic profiles are images reflecting containing geological structures which provides a critical foundation for understanding the distribution of oil and gas resources. However, due to the limitations of seismic acquisition equipment and data collecting geometry, the post-stack profiles suffer from low resolution and strong noise issues, which se

Renjie Shen, Yuehui Ma, Hongchi Wang, Suziye He

In this work, we present the data from the Milky Way Imaging Scroll Painting (MWISP) project for the Maddalena giant molecular cloud (GMC). We decompose the 13CO emission datacube of the observed region into hierarchical substructures using a modified Dendrogram algorithm. We investigate the statistical properties of these substructures and examine the role

Xingyu Wan, Chengquan Zhang, Pengyuan Lyu, Sen Fan

Existing OCR engines or document image analysis systems typically rely on training separate models for text detection in varying scenarios and granularities, leading to significant computational complexity and resource demands. In this paper, we introduce "Detect Any Text" (DAT), an advanced paradigm that seamlessly unifies scene text detection, layout analy

Jean-Baptiste Vincent, Erik Asphaug, Olivier Barnouin, Joel Beccarelli

Morphological mapping is a fundamental step in studying the processes that shaped an asteroid surface. Yet, it is challenging and often requires multiple independent assessments by trained experts. Here, we present fast methods to detect and characterize meaningful terrains from the topographic roughness: entropy of information, and local mean surface orient

Kuo Liao, Shuang Li, Meng Zhao, Liqun Liu

Recent strides in large language models (LLMs) have yielded remarkable performance, leveraging reinforcement learning from human feedback (RLHF) to significantly enhance generation and alignment capabilities. However, RLHF encounters numerous challenges, including the objective mismatch issue, leading to suboptimal performance in Natural Language Understandi