March 2024 arXiv papers — page 9

Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, Blair Chen

We present Gecko, a compact and versatile text embedding model. Gecko achieves strong retrieval performance by leveraging a key idea: distilling knowledge from large language models (LLMs) into a retriever. Our two-step distillation process begins with generating diverse, synthetic paired data using an LLM. Next, we further refine the data quality by retriev

Ce Jin, Yinzhan Xu

In sparse convolution-type problems, a common technique is to hash the input integers modulo a random prime $p\in [Q/2,Q]$ for some parameter $Q$, which reduces the range of the input integers while preserving their additive structure. However, this hash family suffers from two drawbacks, which led to bottlenecks in many state-of-the-art algorithms: (1) The

Christian Kuehn, Carlos Pulido

Many natural phenomena are effectively described by interacting particle systems, which can be modeled using either deterministic or stochastic differential equations (SDEs). In this study, we specifically investigate particle systems modeled by SDEs, wherein the mean field limit converges to a Vlasov-Fokker-Planck-type equation. Departing from conventional

Jamie Norris, Aswin Chari, Dorien van Blooijs, Gerald Cooray

Epilepsy is one of the most common neurological disorders, often requiring surgical intervention when medication fails to control seizures. For effective surgical outcomes, precise localisation of the epileptogenic focus - often approximated through the Seizure Onset Zone (SOZ) - is critical yet remains a challenge. Active probing through electrical stimulat

Ryo Terasawa, Xiangchong Li, Masahiro Takada, Takahiro Nishimichi

The baryonic feedback effect is considered as a possible solution to the so-called $S_8$ tension indicated in cosmic shear cosmology. The baryonic effect is more significant on smaller scales, and affects the cosmic shear two-point correlation functions (2PCFs) with different scale- and redshift-dependencies from those of the cosmological parameters. In this

Neema Kotonya, Francesca Toni

As deep neural models in NLP become more complex, and as a consequence opaque, the necessity to interpret them becomes greater. A burgeoning interest has emerged in rationalizing explanations to provide short and coherent justifications for predictions. In this position paper, we advocate for a formal framework for key concepts and properties about rationali

V. Valenzuela-Villaseca, L. G. Suttle, F. Suzuki-Vidal, J. W. D. Halliday

We present a detailed characterization of the structure and evolution of differentially rotating plasmas driven on the MAGPIE pulsed-power generator (1.4 MA peak current, 240 ns rise-time). The experiments were designed to simulate physics relevant to accretion discs and jets on laboratory scales. A cylindrical aluminium wire array Z pinch enclosed by return

Ahmed Agiza, Marina Neseem, Sherief Reda

Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning. Consequently, parameter-efficient fine-tuning methods have emerged as a promising way to adapt pre-trained models to different tasks while training only a minimal number of parameters. While most of these methods are designed for single

Tianhao Wang, Robin Thunig, Horst Schirmeier

Soft errors in memories and logic circuits are known to disturb program execution. In this context, the research community has been proposing a plethora of fault-tolerance (FT) solutions over the last decades, as well as fault-injection (FI) approaches to test, measure and compare them. However, there is no agreed-upon benchmarking suite for demonstrating FT

Abhinav Kumar, Yuliang Guo, Xinyu Huang, Liu Ren

Monocular 3D detectors achieve remarkable performance on cars and smaller objects. However, their performance drops on larger objects, leading to fatal accidents. Some attribute the failures to training data scarcity or their receptive field requirements of large objects. In this paper, we highlight this understudied problem of generalization to large object

Anurag Roy, Riddhiman Moulick, Vinay K. Verma, Saptarshi Ghosh

Continual Learning (CL) enables machine learning models to learn from continuously shifting new training data in absence of data from old tasks. Recently, pretrained vision transformers combined with prompt tuning have shown promise for overcoming catastrophic forgetting in CL. These approaches rely on a pool of learnable prompts which can be inefficient in

Christian Duval, Malte Henkel, Peter Horvathy, Shain Rouhani

This paper reviews the history of the conformal extension of Galilean symmetry, now called Schr\"odinger symmetry. In the physics literature, its discovery is commonly attributed to Jackiw, Niederer and Hagen (1972). However, Schr\"odinger symmetry has a much older ancestry: the associated conserved quantities were known to Jacobi in 1842/43 and its euclidea

Federico Silvetti

With the ongoing Run 3 of the LHC and its upcoming High-Luminosity upgrade, there is a growing need to study observables with high precision both experimentally and theoretically. To increase precision on the theory side, improvements of fixed-order perturbative predictions, resummation of logarithmic enhancements and accurate determination of proton structu

PASTA Collaboration, N. Borghi, E. Ceccarelli, A. Della Croce

We aim at facilitating the visualization of astrophysical data for several tasks, such as uncovering patterns, presenting results to the community, and facilitating the understanding of complex physical relationships to the public. We present pastamarkers, a customized Python package fully compatible with matplotlib, that contains unique pasta-shaped markers

Nicolas Chopin, Francesca R. Crucinio, Sumeetpal S. Singh

Given a smooth function $f$, we develop a general approach to turn Monte Carlo samples with expectation $m$ into an unbiased estimate of $f(m)$. Specifically, we develop estimators that are based on randomly truncating the Taylor series expansion of $f$ and estimating the coefficients of the truncated series. We derive their properties and propose a strategy

Jaisidh Singh, Ishaan Shrivastava, Mayank Vatsa, Richa Singh

Existing vision-language models (VLMs) treat text descriptions as a unit, confusing individual concepts in a prompt and impairing visual semantic matching and reasoning. An important aspect of reasoning in logic and language is negations. This paper highlights the limitations of popular VLMs such as CLIP, at understanding the implications of negations, i.e.,

Bahman Moraffah

Bayesian nonparametric models offer a flexible and powerful framework for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets. This survey intends to delve into the significance of Bayesian nonparametrics, particularly in addressing complex challenges across various domains such as statistics, compu

Roman Berens, Trevor Gravely, Alexandru Lupsasca

The gravitational perturbations of a rotating Kerr black hole are notoriously complicated, even at the linear level. In 1973, Teukolsky showed that their physical degrees of freedom are encoded in two gauge-invariant Weyl curvature scalars that obey a separable wave equation. Determining these scalars is sufficient for many purposes, such as the computation

Soheila Khajoui, Saeid Dehyadegari, Sayyed Abdolmajid Jalaee

This study aims at predicting the impact of e-commerce indicators on international trade of the selected OECD countries and Iran, by using the artificial intelligence approach and P-VAR. According to the nature of export, import, GDP, and ICT functions, and the characteristics of nonlinearity, this analysis is performed by using the MPL neural network. The e

Zhiwen Fan, Wenyan Cong, Kairun Wen, Kevin Wang

While neural 3D reconstruction has advanced substantially, its performance significantly degrades with sparse-view data, which limits its broader applicability, since SfM is often unreliable in sparse-view scenarios where feature matches are scarce. In this paper, we introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction

Rowan Hall Maudslay, Simone Teufel, Francis Bond, James Pustejovsky

The senses of a word exhibit rich internal structure. In a typical lexicon, this structure is overlooked: a word's senses are encoded as a list without inter-sense relations. We present ChainNet, a lexical resource which for the first time explicitly identifies these structures. ChainNet expresses how senses in the Open English Wordnet are derived from one a

Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff

In the coordinator model of communication with $s$ servers, given an arbitrary non-negative function $f$, we study the problem of approximating the sum $\sum_{i \in [n]}f(x_i)$ up to a $1 \pm \varepsilon$ factor. Here the vector $x \in R^n$ is defined to be $x = x(1) + \cdots + x(s)$, where $x(j) \ge 0$ denotes the non-negative vector held by the $j$-th serv

Jovan Stojkovic, Esha Choukse, Chaojie Zhang, Inigo Goiri

With the ubiquitous use of modern large language models (LLMs) across industries, the inference serving for these models is ever expanding. Given the high compute and memory requirements of modern LLMs, more and more top-of-the-line GPUs are being deployed to serve these models. Energy availability has come to the forefront as the biggest challenge for data

Prashanth Amireddy, Amik Raj Behera, Manaswi Paraashar, Srikanth Srinivasan

We consider the task of locally correcting, and locally list-correcting, multivariate linear functions over the domain $\{0,1\}^n$ over arbitrary fields and more generally Abelian groups. Such functions form error-correcting codes of relative distance $1/2$ and we give local-correction algorithms correcting up to nearly $1/4$-fraction errors making $\widetil

Chai Wah Wu

Pandigital and penholodigital numbers are numbers that contain every digit or nonzero digit respectively. We study properties of pandigital or penholodigital numbers that are also square, oblong or prime.

Lipika Kolay, Soumitra Nandi

This study focuses on a combined analysis of various available inputs to constrain the parameter spaces of a simplified dark matter (SDM) model featuring a spin-0 mediator and fermionic dark matter (DM). The spin-0 mediator interacts with standard model (SM) fermions, SM gauge bosons, and DM. We constrain the parameter spaces of different relevant couplings,

Andrew D. Gow, Peter Clark, Dan Rycanowski

We present a new standard acronym for Active Galactic Nuclei, finally settling the argument of AGN vs. AGNs. Our new standard is not only etymologically superior (following the consensus set by SNe), but also boasts other linguistic opportunities, connecting strongly with relevant theology and streamlining descriptions of AGN properties.

M. Makuch, S. Kovacevic, M. R. Wenman, E. Martínez-Pañeda

An electro-chemo-mechanical phase-field formulation is developed to simulate pitting and stress corrosion in polycrystalline materials. The formulation incorporates dependencies of mechanical properties and corrosion potential on crystallographic orientation. The model considers the formation and charging dynamics of an electric double layer through a new ge

Rishi Veerapaneni, Qian Wang, Kevin Ren, Arthur Jakobsson

Multi-agent path finding (MAPF) is the problem of finding collision-free paths for a team of agents to reach their goal locations. State-of-the-art classical MAPF solvers typically employ heuristic search to find solutions for hundreds of agents but are typically centralized and can struggle to scale when run with short timeouts. Machine learning (ML) approa

Stefaan Vaes

We prove that every locally compact second countable group $G$ arises as the outer automorphism group Out $M$ of a II$_1$ factor, which was so far only known for totally disconnected groups, compact groups and a few isolated examples. We obtain this result by proving that every locally compact second countable group is a centralizer group, a class of Polish

Yoonhyuk Choi, Jiho Choi, Taewook Ko, Chong-Kwon Kim

The issue of data sparsity poses a significant challenge to recommender systems. In response to this, algorithms that leverage side information such as review texts have been proposed. Furthermore, Cross-Domain Recommendation (CDR), which captures domain-shareable knowledge and transfers it from a richer domain (source) to a sparser one (target), has receive

Mohamed Assem Ibrahim, Mahzabeen Islam, Shaizeen Aga

With unprecedented demand for generative AI (GenAI) inference, acceleration of primitives that dominate GenAI such as general matrix-vector multiplication (GEMV) is receiving considerable attention. A challenge with GEMVs is the high memory bandwidth this primitive demands. Multiple memory vendors have proposed commercially viable processing-in-memory (PIM)

Hanyu Li, Weizhi Ma, Peijie Sun, Jiayu Li

Cross-domain recommender (CDR) systems aim to enhance the performance of the target domain by utilizing data from other related domains. However, irrelevant information from the source domain may instead degrade target domain performance, which is known as the negative transfer problem. There have been some attempts to address this problem, mostly by designi

Nathan L. Tenhundfeld, Ryan Weber, William I. MacKenzie, Hannah M. Barr

Deepfake videos create dangerous possibilities for public misinformation. In this experiment (N=204), we investigated whether labeling videos as containing actual or deepfake statements from US President Biden helps participants later differentiate between true and fake information. People accurately recalled 93.8% of deepfake videos and 84.2% of actual vide

Bernat Font, Francisco Alcántara-Ávila, Jean Rabault, Ricardo Vinuesa

The control efficacy of classical periodic forcing and deep reinforcement learning (DRL) is assessed for a turbulent separation bubble (TSB) at $Re_\tau=180$ on the upstream region before separation occurs. The TSB can resemble a separation phenomenon naturally arising in wings, and a successful reduction of the TSB can have practical implications in the red

Kyle Poe, Enrique Mallada, Rene Vidal

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptio

Ijaz Ahmed, Abdul Quddus, Jamil Muhammad, Muhammad Shoaib

The current study explores the production of charged Higgs particles through photon-photon collisions within the Two Higgs Doublet Model context, including one-loop-level scattering amplitude of Electroweak and QED radiation. The cross-section has been scanned for plane ($m_{\phi^{0}}, \sqrt{s}$) investigating the process of $\gamma\gamma \rightarrow H^{+}H^

Yi Zhang, Xinran Zheng

In this note, based on the recent remarkable results of Dufour and Prieto-Rumeau, we deduce that for an absorbing MDP with a given initial state, under a standard compactness-continuity condition, the space of occupation measures has the same convergent sequences, when it is endowed with the weak topology and with the weak-strong topology. We provided two ex

Thibault Boulet

Context. Understanding the Milky Way's formation and evolution across cosmic epochs necessitates precise stellar age determination across all Galactic components. Recent advancements in asteroseismology, spectroscopy, stellar modelling, and machine learning, coupled with all-sky surveys, now offer highly reliable stellar age estimates. Aims. This study aims

Levon Nurbekyan, Siting Liu, Yat Tin Chow

We propose a monotone splitting algorithm for solving a class of second-order non-potential mean-field games. Following [Achdou, Capuzzo-Dolcetta, "Mean Field Games: Numerical Methods," SINUM (2010)], we introduce a finite-difference scheme and observe that the scheme represents first-order optimality conditions for a primal-dual pair of monotone inclusions.

Lucas Amoudruz, Sergey Litvinov, Petros Koumoutsakos

Biomedical applications such as targeted drug delivery, microsurgery, and sensing rely on reaching precise areas within the body in a minimally invasive way. Artificial bacterial flagella (ABFs) have emerged as potential tools for this task by navigating through the circulatory system with the help of external magnetic fields. While their swimming characteri

Fangxu Yu, Junjie Guo, Zhen Wu, Xinyu Dai

Emotion Recognition in Conversation (ERC) involves detecting the underlying emotion behind each utterance within a conversation. Effectively generating representations for utterances remains a significant challenge in this task. Recent works propose various models to address this issue, but they still struggle with differentiating similar emotions such as ex

Burcu Sayin, Pasquale Minervini, Jacopo Staiano, Andrea Passerini

We explore the potential of Large Language Models (LLMs) to assist and potentially correct physicians in medical decision-making tasks. We evaluate several LLMs, including Meditron, Llama2, and Mistral, to analyze the ability of these models to interact effectively with physicians across different scenarios. We consider questions from PubMedQA and several ta

Thomas Melistas, Nikos Spyrou, Nefeli Gkouti, Pedro Sanchez

Generative AI has revolutionised visual content editing, empowering users to effortlessly modify images and videos. However, not all edits are equal. To perform realistic edits in domains such as natural image or medical imaging, modifications must respect causal relationships inherent to the data generation process. Such image editing falls into the counter

Tomasz Przybyłowski

Consider a Boolean function f on the n-dimensional hypercube, and a set of variables (indexed by) $S \subset \{1,2,\ldots,n\}.$ The coalition influence of the variables S on a function f is the probability that after a random assignment of variables not in S, the value of f is undetermined. In this paper, we study a complementary notion, which we call the jo

A. Shchagin, G. Kube, A. Potylitsyn, S. Strokov

The set of frequencies and angular properties of radiation emitted from a solid-state crystalline undulator based on the channeling effect are considered. High-frequency and low-frequency branches of the undulator radiation and the angular distribution of the emitted radiation are analyzed. The ranges of frequencies and angles of radiation emitted from the s

Enrique Artal Bartolo, Santiago López de Medrano, María Teresa Lozano

The goal of this work is to study the smoothings of singular coaxial intersections of ellipsoids (where coaxial includes concentric) with generic singularities, with special attention to the 3-dimensional case.

Alfredo Biagini, Gianluca Cracchiolo, Antonino Petralia, Jesús Maldonado

The super-Earth LHS 1140 b is an interesting target for atmospheric observations since it is close to the habitable zone of its star and falls in the gap of the radius distribution of small exoplanets, in the region thought to correspond to the transition between planets with and without atmospheres. Observations of the primary transit with WFC3 on board of

Taha ValizadehAslani, Hualou Liang

Fine-tuning a pre-trained model, such as Bidirectional Encoder Representations from Transformers (BERT), has been proven to be an effective method for solving many natural language processing (NLP) tasks. However, due to the large number of parameters in many state-of-the-art NLP models, including BERT, the process of fine-tuning is computationally expensive

Mark Braverman, Sumegha Garg, Qian Li, Shuo Wang

We introduce a new notion of information complexity for multi-pass streaming problems and use it to resolve several important questions in data streams. In the coin problem, one sees a stream of $n$ i.i.d. uniform bits and one would like to compute the majority with constant advantage. We show that any constant pass algorithm must use $\Omega(\log n)$ bits o

Johannes Branahl

We provide novel, metatheoretical arguments strengthening the position that the naturalness problem of the light Higgs mass is a pseudo-problem: No physics beyond the standard model of particle physics is needed to explain the small value of the Higgs boson. By evaluating previous successes of the guiding principle of technical naturalness, we restrict its a

Michael B. Lund

The number of planets in the solar system over the last three centuries has, perhaps surprisingly, been less of a fixed value than one would think it should be. In this paper, we look at the specific case of Vulcan, which was both a planet before Pluto was a planet and discarded from being a planet before Pluto was downgraded. We examine the historical conte

Josiah Bjorgaard

Multimodal data fusion is essential for applications requiring the integration of diverse data sources, especially in the presence of incomplete or sparsely available modalities. This paper presents a comparative study of three multimodal embedding techniques, Modal Channel Attention (MCA), Zorro, and Everything at Once (EAO), to evaluate their performance o

Caiqi Zhang, Fangyu Liu, Marco Basaldella, Nigel Collier

Large Language Models (LLMs) have demonstrated remarkable capability in a variety of NLP tasks. However, LLMs are also prone to generate nonfactual content. Uncertainty Quantification (UQ) is pivotal in enhancing our understanding of a model's confidence on its generation, thereby aiding in the mitigation of nonfactual outputs. Existing research on UQ predom

K. S. Babu, Borut Bajc, Vasja Susič

We propose a non-supersymmetric $\mathrm{E}_{6}$ GUT with the scalar sector consisting of $\mathbf{650}\oplus \mathbf{351'} \oplus \mathbf{27}$. Making use of the first representation for the initial symmetry breaking to an intermediate stage, and the latter two representations for second-stage breaking to the Standard Model and a realistic Yukawa sector, th

T. Thebault, D. Braithwaite, G. Lapertot, D. Aoki

A structural orthorhombic-to-tetragonal phase transition was recently discovered in the heavy-fermion compound UTe$_2$ at a pressure $p^*\simeq3-8$~GPa [Honda \textit{et al.}, J. Phys. Soc. Jpn. \textbf{92}, 044702 (2023); Huston \textit{et al.}, Phys. Rev. Mat. \textbf{6}, 114801 (2022)]. In the high-pressure tetragonal phase, a phase transition at $T_x=235

Fengge Zhang, Jia-Xi Feng, Xian Gao

We investigate the scalar induced gravitational waves (SIGWs) in metric teleparallel gravity with the Nieh-Yan (NY) term, which results in parity violation during the radiation-dominated era. By solving the equations of motion of linear scalar perturbations from both the metric and the tetrad fields, we obtain the corresponding analytic expressions. Then, we

R. Xu, L. T. Yang, Q. Yue, K. J. Kang

We report new constraints on light dark matter (DM) boosted by blazars using the 205.4 kg day data from the CDEX-10 experiment located at the China Jinping Underground Laboratory. Two representative blazars, TXS 0506+56 and BL Lacertae are studied. The results derived from TXS 0506+56 exclude DM-nucleon elastic scattering cross sections from $4.6\times 10^{-

Mauro Comi, Alessio Tonioni, Max Yang, Jonathan Tremblay

Touch and vision go hand in hand, mutually enhancing our ability to understand the world. From a research perspective, the problem of mixing touch and vision is underexplored and presents interesting challenges. To this end, we propose Tactile-Informed 3DGS, a novel approach that incorporates touch data (local depth maps) with multi-view vision data to achie

Lia Bronsard, Dean Louizos, Dominik Stantejsky

We study minimizers of the Landau-de Gennes energy in $\mathbb{R}^3\setminus B_1(0)$ with external magnetic field in the large particle limit. We impose strong tangential anchoring and uniaxiality of the $Q-$tensor on the boundary. We derive a lower bound for the energy in terms of the boundary condition and show in the extreme cases of strong and weak magne

Wenyu Yang, Sergio Vitale, Hossein Aghababaei, Giampaolo Ferraioli

Tropical forests are a key component of the global carbon cycle. With plans for upcoming space-borne missions like BIOMASS to monitor forestry, several airborne missions, including TropiSAR and AfriSAR campaigns, have been successfully launched and experimented. Typical Synthetic Aperture Radar Tomography (TomoSAR) methods involve complex models with low acc

Cyrus Bjurlin, Theresa Chmiel

Hong-Ou-Mandel (HOM) interference is a quantum optics laboratory experiment that has recently become more accessible to undergraduate students. The experiment consists of two identical photons simultaneously entering a non-polarizing beam splitter. The wavefunctions destructively interfere and the photon pairs bunch (both exit the same output) at the outputs

Weifeng Lin, Xinyu Wei, Ruichuan An, Peng Gao

In this paper, we present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs). Visual prompts allow users to interact through multi-modal instructions, enhancing the models' interactivity and fine-grained image comprehension. In this framework, we propose a ge

Blaise Boissonneau, Aris Papadopoulos, Pierre Touchard

Mekler's construction is a powerful technique for building purely algebraic structures from combinatorial ones. Its power lies in the fact that it allows various model-theoretic tameness properties of the combinatorial structure to transfer to the algebraic one. In this paper, we push this ideology much further, describing a broad class of properties that tr

Jeffrey J. Early, Gerardo Hernández-Dueñas, Leslie M. Smith, M. -Pascale Lelong

We consider a rotating non-hydrostatic flow with arbitrary stratification and argue that 1) the appropriate form of potential vorticity (PV) for this system is in terms of isopycnal deviation and 2) the decomposition into energetically orthogonal solutions is fundamentally a PV-inversion. The new closed-form expression for available potential vorticity (APV)

J. Fowler, Ruth Murray-Clay

In the seven years that the starship Voyager spent in the Delta Quadrant, it used many questionable techniques to engage with alien civilizations and ultimately find its way home. From detailed studies of their logs and opening credits, we simulate Voyager's practice of orbiting a planet, to examine the effect on planetary rings. We outline a feasible planet

Ieva Čepaitė

Adiabatic protocols are employed across a variety of quantum technologies, from implementing state preparation and individual operations that are building blocks of larger devices, to higher-level protocols in quantum annealing and adiabatic quantum computation. The main drawback of adiabatic processes, however, is that they require prohibitively long timesc

Julen Etxaniz, Oscar Sainz, Naiara Perez, Itziar Aldabe

We introduce Latxa, a family of large language models for Basque ranging from 7 to 70 billion parameters. Latxa is based on Llama 2, which we continue pretraining on a new Basque corpus comprising 4.3M documents and 4.2B tokens. Addressing the scarcity of high-quality benchmarks for Basque, we further introduce 4 multiple choice evaluation datasets: EusProfi

Bruce Kleiner, Stefan Müller, László Székelyhidi, Xiangdong Xie

We develop a general toolbox to study $W^{1,p}$ solutions of differential inclusions $\nabla u \in K$ for unbounded sets $K$. A key notion is the concept that a subset $K$ of the space $\mathbb{R}^{d \times m}$ of $d \times m$ matrices can be reduced to another set $K'$. We then use this framework to show that the product rigidity for Sobolev maps fails for

Nir Gadish, Aydin Ozbek, Dev Sinha, Ben Walter

We show that Hopf invariants, defined by evaluation in Harrison cohomology of the commutative cochains of a space, calculate the logarithm map from a fundamental group to its Malcev Lie algebra. They thus present the zeroth Harrison cohomology as a universal dual object to the Malcev Lie algebra. This structural theorem supports explicit calculations in alge

Z. H. Zhang, L. T. Yang, Q. Yue, K. J. Kang

Dark matter (DM) is a major constituent of the Universe. However, no definite evidence of DM particles (denoted as ``$\chi$") has been found in DM direct detection (DD) experiments to date. There is a novel concept of detecting $\chi$ from evaporating primordial black holes (PBHs). We search for $\chi$ emitted from PBHs by investigating their interaction wit

Thibaut Thonet, Jos Rozen, Laurent Besacier

Research on Large Language Models (LLMs) has recently witnessed an increasing interest in extending the models' context size to better capture dependencies within long documents. While benchmarks have been proposed to assess long-range abilities, existing efforts primarily considered generic tasks that are not necessarily aligned with real-world applications

Kaiyuan Gao, Qizhi Pei, Gongbo Zhang, Jinhua Zhu

Molecular docking is a pivotal process in drug discovery. While traditional techniques rely on extensive sampling and simulation governed by physical principles, these methods are often slow and costly. The advent of deep learning-based approaches has shown significant promise, offering increases in both accuracy and efficiency. Building upon the foundationa

Shreyasi Pathak, Jörg Schlötterer, Jeroen Veltman, Jeroen Geerdink

Deep learning models have achieved high performance in medical applications, however, their adoption in clinical practice is hindered due to their black-box nature. Self-explainable models, like prototype-based models, can be especially beneficial as they are interpretable by design. However, if the learnt prototypes are of low quality then the prototype-bas

Aleksejus Kononovicius, Justas Kvedaravicius

Demographic heterogeneity is often studied through the geographical lens. Therefore it is considered at a predetermined spatial resolution, which is a suitable choice to understand scalefull phenomena. Spatial autocorrelation indices are well established for this purpose. Yet complex systems are often scale-free, and thus studying the scaling behavior of dem

Mitchell Black, Georgios Fainekos, Bardh Hoxha, Dimitra Panagou

This paper addresses the problem of risk-aware fixed-time stabilization of a class of uncertain, output-feedback nonlinear systems modeled via stochastic differential equations. First, novel classes of certificate functions, namely risk-aware fixed-time- and risk-aware path-integral-control Lyapunov functions, are introduced. Then, it is shown how the use of

Aleksejus Kononovicius, Bronislovas Kaulakys

Previously we have shown that pure 1/f noise arises from the trapping-detrapping process when traps are heterogeneous. Namely, the trapping-detrapping process relies on the assumption that detrapping rates of individual trapping centers in the condensed matter are random and uniformly distributed. Another assumption underlying the trapping-detrapping process

Adriano Meligrana, Adriano Fazzone

In this work, we present a new random sampling method for data streams where the probability of an element's inclusion in the sample is proportional to a weight associated with that element. Our method is based on sampling with replacement, although most of the literature on this topic has focused on sampling without replacement. Our algorithm generates a we

Facundo Pérez Paolino, Jeffrey S. Bary, Lynne A. Hillenbrand, Madison Markham

Accurate age and mass determinations for young pre-main sequence stars are made challenging by the presence of large-scale starspots. We present results from a near-infrared spectroscopic survey of ten T-Tauri Stars in Taurus-Auriga that characterize spot filling factors and temperatures, the resulting effects on temperature and luminosity determinations, an

Runhao Zeng, Xiaoyong Chen, Jiaming Liang, Huisi Wu

Temporal action detection (TAD) aims to locate action positions and recognize action categories in long-term untrimmed videos. Although many methods have achieved promising results, their robustness has not been thoroughly studied. In practice, we observe that temporal information in videos can be occasionally corrupted, such as missing or blurred frames. In

Taha Koleilat, Hojat Asgariandehkordi, Hassan Rivaz, Yiming Xiao

Medical image segmentation of anatomical structures and pathology is crucial in modern clinical diagnosis, disease study, and treatment planning. To date, great progress has been made in deep learning-based segmentation techniques, but most methods still lack data efficiency, generalizability, and interactability. Consequently, the development of new, precis

Keiichi Namikoshi, Alex Filipowicz, David A. Shamma, Rumen Iliev

We consider the problem of aligning a large language model (LLM) to model the preferences of a human population. Modeling the beliefs, preferences, and behaviors of a specific population can be useful for a variety of different applications, such as conducting simulated focus groups for new products, conducting virtual surveys, and testing behavioral interve

José Celestino, Manuel Marques, Jacinto C. Nascimento

Head pose estimation has become a crucial area of research in computer vision given its usefulness in a wide range of applications, including robotics, surveillance, or driver attention monitoring. One of the most difficult challenges in this field is managing head occlusions that frequently take place in real-world scenarios. In this paper, we propose a nov

Giovanni Cerulli

This paper deals with optimal policy learning (OPL) with observational data, i.e. data-driven optimal decision-making, in multi-action (or multi-arm) settings, where a finite set of decision options is available. It is organized in three parts, where I discuss respectively: estimation, risk preference, and potential failures. The first part provides a brief

Yinwei Wu, Xingyi Yang, Xinchao Wang

Despite their exceptional generative abilities, large text-to-image diffusion models, much like skilled but careless artists, often struggle with accurately depicting visual relationships between objects. This issue, as we uncover through careful analysis, arises from a misaligned text encoder that struggles to interpret specific relationships and differenti

Yicheng Mou, Haonan Chen, Jiaqi Liu, Qing Lan

Transport probes the motion of quasiparticles in response to external excitations. Apart from the well-known electric and thermoelectric transport, acoustoelectric transport induced by traveling acoustic waves has been rarely explored. Here, by adopting a hybrid nanodevices integrated with piezoelectric substrates, we establish a simple design of acoustoelec

Christian R. Ast, Piotr Kot, Maneesha Ismail, Sebastián de-la-Peña

Electron spin resonance (ESR) spectroscopy in scanning tunneling microscopy (STM) has enabled probing the electronic structure of single magnetic atoms and molecules on surfaces with unprecedented energy resolution, as well as demonstrating coherent manipulation of single spins. Despite this remarkable success, the field could still be greatly advanced by a

Daniel B. Hier, Tayo Obafemi-Ajayi, Gayla R. Olbricht, Devin M. Burns

Dimension reduction is increasingly applied to high-dimensional biomedical data to improve its interpretability. When datasets are reduced to two dimensions, each observation is assigned an x and y coordinates and is represented as a point on a scatter plot. A significant challenge lies in interpreting the meaning of the x and y axes due to the complexities

Giovanni Bellettini, Shokhrukh Yu. Kholmatov

Motivated by a conjecture of De Giorgi, we consider the Almgren-Taylor-Wang scheme for mean curvature flow, where the volume penalization is replaced by a term of the form \[ \int_{E\Delta F} f\Big(\frac{ {\rm d}_F }{\tau}\Big)~dx \] for $f$ ranging in a large class of strictly increasing continuous functions. In particular, our analysis covers the case \[ f

Giovanni Peccati, Michele Stecconi

We characterize the absolute continuity of the law and the Malliavin-Sobolev regularity of random nodal volumes associated with smooth Gaussian fields on generic $\mathcal{C}^2$ manifolds with arbitrary dimension. Our results extend and generalize the seminal contribution by Angst and Poly (2020) about stationary fields on Euclidean spaces and cover, in part

Yassin Chandran, Tommaso Cremaschi

In this work we show two results about approximating, with respect to the compact-open topology, mapping classes on surfaces of infinite-type by quasi-conformal maps, in particular we are interested in density results. The first result is that given any infinite-type surface $S$ there exists a hyperbolic structure $X$ on $S$ such that $\text{PMCG}(S)\subsete

CMS Collaboration

A search for ZZ and ZH production in the $\mathrm{b\bar{b}b\bar{b}}$ final state is presented, where H is the standard model (SM) Higgs boson. The search uses an event sample of proton-proton collisions corresponding to an integrated luminosity of 133 fb$^{-1}$ collected at a center-of-mass energy of 13 TeV with the CMS detector at the CERN LHC. The analysis

Wei Li, Jing-Yi Wu, Kilar Zhang

Neutron stars are among the main targets for gravitational wave observatories, however, their equation of state is still not well established. Mainly phenomenological models with many parameters are widely used by far, while theoretical models are not so practical. In arXiv:1902.08477, a theoretical equation of state with only one parameter is derived from W

Marc Fiammante, Anne-Isabelle Vermersch, Marie Vidailhet, Mario Chavez

Objective Accurate identification of hypoxic-ischemic brain injury in the early neonatal period is essential for initiating therapeutic hypothermia (TH) within 6 hours of birth to optimize neurodevelopmental outcomes. We aimed to develop a simple decision-making tool for identifying term neonates with hypoxic-ischemic encephalopathy (HIE) based on features o

Joshua Zoen-Git Hiew, Luca Nenna, Brendan Pass

We characterize the solution to the entropically regularized optimal transport problem by a well-posed ordinary differential equation (ODE). Our approach works for discrete marginals and general cost functions, and in addition to two marginal problems, applies to multi-marginal problems and those with additional linear constraints. Solving the ODE gives a ne

Shunpu Tang, Qianqian Yang, Deniz Gündüz, Zhaoyang Zhang

Recently, learning-based semantic communication (SemCom) has emerged as a promising approach in the upcoming 6G network and researchers have made remarkable efforts in this field. However, existing works have yet to fully explore the advantages of the evolving nature of learning-based systems, where knowledge accumulates during transmission have the potentia

JooHyun Park, YuJin Jeon, HuiYong Kim, SeungHwan Baek

Holography stands at the forefront of visual technology, offering immersive, three-dimensional visualizations through the manipulation of light wave amplitude and phase. Although generative models have been extensively explored in the image domain, their application to holograms remains relatively underexplored due to the inherent complexity of phase learnin

Chih-Hui Ho, Kuan-Chuan Peng, Nuno Vasconcelos

Anomaly detection (AD) aims to identify defective images and localize their defects (if any). Ideally, AD models should be able to detect defects over many image classes; without relying on hard-coded class names that can be uninformative or inconsistent across datasets; learn without anomaly supervision; and be robust to the long-tailed distributions of rea

Dieter Suess, Claas Abert, Sebastian Zeilinger, Florian Bruckner

We propose a field-free switching mechanism that utilizes two spatially orthogonal spin-orbit torque (SOT) currents. Initially applied simultaneously, one of the currents is subsequently switched off. The superposition of these two currents results in an in-plane magnetization, which is not orthogonal to the remaining SOT current after the second one is deac

Antonio Coviello, Francesco Linsalata, Umberto Spagnolini, Maurizio Magarini

Neuropathies are gaining higher relevance in clinical settings, as they risk permanently jeopardizing a person's life. To support the recovery of patients, the use of fully implanted devices is emerging as one of the most promising solutions. However, these devices, even if becoming an integral part of a fully complex neural nanonetwork system, pose numerous