December 2024 arXiv papers — page 189

Xuanlin Li, Tong Zhao, Xinghao Zhu, Jiuguang Wang

Contact-rich bimanual manipulation involves precise coordination of two arms to change object states through strategically selected contacts and motions. Due to the inherent complexity of these tasks, acquiring sufficient demonstration data and training policies that generalize to unseen scenarios remain a largely unresolved challenge. Building on recent adv

Rohail Asim, Arjuna Sathiaseelan, Arko Chatterjee, Mukund Lal

Despite increasing mobile Internet penetration in developing regions, mobile users continue to experience a poor web experience due to two key factors: (i) lack of locally relevant content; (ii) poor web performance due to complex web pages and poor network conditions. In this paper, we describe our design, implementation and deployment experiences of GAIUS,

Cai Dieball, Aljaž Godec

A broken time-reversal symmetry, i.e. broken detailed balance, is central to non-equilibrium physics and is a prerequisite for life. However, it turns out to be quite challenging to unambiguously define and quantify time-reversal symmetry (and violations thereof) in practice, that is, from observations. Measurements on complex systems have a finite resolutio

Yuda Song, Hanlin Zhang, Carson Eisenach, Sham Kakade

Self-improvement is a mechanism in Large Language Model (LLM) pre-training, post-training and test-time inference. We explore a framework where the model verifies its own outputs, filters or reweights data based on this verification, and distills the filtered data. Despite several empirical successes, a fundamental understanding is still lacking. In this wor

Shantanav Chakraborty, Siddhartha Das, Arnab Ghorui, Soumyabrata Hazra

Extracting work from a physical system is one of the cornerstones of quantum thermodynamics. The extractable work, as quantified by ergotropy, necessitates a complete description of the quantum system. This is significantly more challenging when the state of the underlying system is unknown, as quantum tomography is extremely inefficient. In this article, we

Peter Christiansen, Pierre Van Mechelen

The Large Hadron Collider (LHC) at CERN in Switzerland became operational in 2009 and has since then produced a plethora of results for proton-proton (pp) collisions. This short review covers results that relates to soft QCD focusing on non-diffractive physics at mid-rapidity. Most of the presented results comes from transverse momentum ($p_{\rm T}$) spectra

Shant Baghram

Are we alone? It is a compelling question that human beings have confronted for centuries. The search for extraterrestrial life is a broad range of quests for finding simple forms of life up to intelligent beings in the Universe. The plausible assumption is that there is a chance that intelligent life will be followed by advanced civilization equipped or eve

Gautam Kamath

The last decade has seen a number of advances in computationally efficient algorithms for statistical methods subject to robustness constraints. An estimator may be robust in a number of different ways: to contamination of the dataset, to heavy-tailed data, or in the sense that it preserves privacy of the dataset. We survey recent results in these areas with

Irina Bobkova, Jack Carlisle, Emmett Fitz, Mattie Ji

Let $\operatorname{E}_2$ be the Morava E-theory of height 2 at the prime 2. In this paper, we compute the homotopy groups of $\operatorname{E}_2^{hC_6} \wedge \mathbb{R}P^2$ and $\operatorname{E}_2^{hC_6} \wedge \mathbb{R}P^2 \wedge \mathbb{C}P^2$ using the homotopy fixed point spectral sequences.

Leon Tran, Fang Han

Azadkia and Chatterjee (Azadkia and Chatterjee, 2021) recently introduced a graph-based correlation coefficient that has garnered significant attention. The method relies on a nearest neighbor graph (NNG) constructed from the data. While appealing in many respects, NNGs typically lack the desirable property of scale invariance; that is, changing the scales o

Qiang Xi, Ning-Chen Sun, Yi-Han Zhao, Justyn R. Maund

Metallicity plays a crucial role in the evolution of massive stars and their final core-collapse supernova (CCSN) explosions. Integral-field-unit (IFU) spectroscopy can provide a spatially resolved view of SN host galaxies and serve as a powerful tool to study SN metallicities. While early transient surveys targeted on high star formation rate and metallicit

Emmanuel Kammerer

We establish the scaling limit of the geodesics to the root for the first passage percolation distance on random planar maps. We first describe the scaling limit of the number of faces along the geodesics. This result enables us to compare the metric balls for the first passage percolation and the dual graph distance. It also enables us to give an upper boun

Baike She, Matthew Hale

The study of spreading processes often analyzes networks at different resolutions, e.g., at the level of individuals or countries, but it is not always clear how properties at one resolution can carry over to another. Accordingly, in this work we use dissipativity theory from control system analysis to characterize composite spreading networks that are compr

Diego R. Amancio, Jeaneth Machicao, Laura V. C. Quispe

Recent studies have explored the addition of virtual edges to word co-occurrence networks using word embeddings to enhance graph representations, particularly for short texts. While these enriched networks have demonstrated some success, the impact of incorporating semantic edges into traditional co-occurrence networks remains uncertain. This study investiga

Huei Sears, Ryan Chornock, Peter Blanchard, Raffaella Margutti

GRB 221009A is one of the brightest transients ever observed with the highest peak gamma-ray flux for a gamma-ray burst (GRB). A type Ic-BL supernova (SN), SN 2022xiw, was definitively detected in late-time JWST spectroscopy (t = 195 days, observer-frame). However, photometric studies have found SN 2022xiw to be less luminous (10-70%) than the canonical GRB-

A. C. Cem Say, M. Utkan Gezer

A proof of quantumness is a protocol through which a classical machine can test whether a purportedly quantum device, with comparable time and memory resources, is performing a computation that is impossible for classical computers. Existing approaches to provide proofs of quantumness depend on unproven assumptions about some task being impossible for machin

Shuwen Chai, Miklós Z. Rácz

We study learning problems on correlated stochastic block models with two balanced communities. Our main result gives the first efficient algorithm for graph matching in this setting. In the most interesting regime where the average degree is logarithmic in the number of vertices, this algorithm correctly matches all but a vanishing fraction of vertices with

Kasper Johansson, Thomas Schmelzer, Stephen Boyd

We consider the problem of managing a portfolio of moving-band statistical arbitrages (MBSAs), inspired by the Markowitz optimization framework. We show how to manage a dynamic basket of MBSAs, and illustrate the method on recent historical data, showing that it can perform very well in terms of risk-adjusted return, essentially uncorrelated with the market.

Vaishnavi Bhargava, Rajat Ghosh, Debojyoti Dutta

We introduce CPP-UT-Bench, a benchmark dataset to measure C++ unit test generation capability of a large language model (LLM). CPP-UT-Bench aims to reflect a broad and diverse set of C++ codebases found in the real world. The dataset includes 2,653 {code, unit test} pairs drawn from 14 different opensource C++ codebases spanned across nine diverse domains in

Zeynab Kaseb, Stavros Orfanoudakis, Pedro P. Vergara, Peter Palensky

This study introduces PINN4PF, an end-to-end deep learning architecture for power flow (PF) analysis that effectively captures the nonlinear dynamics of large-scale modern power systems. The proposed neural network (NN) architecture consists of two important advancements in the training pipeline: (A) a double-head feed-forward NN that aligns with PF analysis

Ayush Jaiswal

We have studied irreducible real (respectively, quaternionic) Lie algebroid connections and prove that the Gauge theoretic moduli space has Hausdorff Hilbert manifold structure. This work generalises some known results about simple semi-connections for complex vector bundle on compact complex manifold in real algebraic geometry.

Filipe Kelmer

We consider a class of third-order evolution equations of the form \begin{equation*} \left\{ \begin{array}{l} \displaystyle u_{t}=F\left(x,t,u,u_x,u_{xx},u_{xxx},v,v_x,v_{xx},v_{xxx}\right), \displaystyle v_{t}=G\left(x,t,u,u_x,u_{xx},u_{xxx},v,v_x,v_{xx},v_{xxx}\right), \end{array} \right. \end{equation*} describing pseudos-pherical (\textbf{pss}) or spheri

Yugo Onishi, Alexander Avdoshkin, Liang Fu

We show that a quadratic form of quantum geometric tensor in $k$-space sets a bound on the $q^4$ term in the static structure factor $S(q)$ at small $\vec{q}$. Bands that saturate this bound satisfy a condition similar to Laplace's equation, leading us to refer to them as $\textit{harmonic bands}$. We provide examples of harmonic bands in one- and two-dimens

Pranav Doma, Aliasghar Arab, Xuesu Xiao

Autonomous navigation guided by natural language instructions is essential for improving human-robot interaction and enabling complex operations in dynamic environments. While large language models (LLMs) are not inherently designed for planning, they can significantly enhance planning efficiency by providing guidance and informing constraints to ensure safe

Kasper Johansson, Stephen Boyd

We consider the problem of constructing a portfolio that combines traditional financial assets with crypto assets. We show that despite the documented attributes of crypto assets, such as high volatility, heavy tails, excess kurtosis, and skewness, a simple extension of traditional risk allocation provides robust solutions for integrating these emerging asse

Jinyuan Wu

This document reports the design, implementation and testing of a small silicon resource usage, very flexible arbitrary percentile finding scheme called the Tiny Median Filter. It can be used not only as a median filter in image processing with square filtering windows, but also for applications of any percentile filter or maximum or minimum finder with any

Karen D. Wang, Zhangyang Wu, L'Nard Tufts, Carl Wieman

Developing problem-solving competency is central to Science, Technology, Engineering, and Mathematics (STEM) education, yet translating this priority into effective approaches to problem-solving instruction and assessment remain a significant challenge. The recent proliferation of generative artificial intelligence (genAI) tools like ChatGPT in higher educat

Cristina Flaut, Andreea Baias

In this paper we count the number of k-potent elements over HZp , the quaternion algebra over Zp, and we give a descriptive formula for the general case. For k in {3, 4, 5}, we give an explicit formula for these values. Moreover, as an application, we count the number of solutions of the equation xk = 1 over HZp .

Qian Hu, John Veitch

Bayesian inference with stochastic sampling has been widely used to obtain the properties of gravitational wave (GW) sources. Although computationally intensive, its cost remains manageable for current second-generation GW detectors because of the relatively low event rate and signal-to-noise ratio (SNR). The third-generation (3G) GW detectors are expected t

Molly Carton, Jakub F. Kowalewski, Jiani Guo, Jacob F. Alpert

Torque and continuous rotation are fundamental methods of actuation and manipulation in rigid robots. Soft robot arms use soft materials and structures to mimic the passive compliance of biological arms that bend and extend. This use of compliance prevents soft arms from continuously transmitting and exerting torques to interact with their environment. Here,

Weixian Yan, Ozan Alp Topal, Zinat Behdad, Ozlem Tugfe Demir

Integrated sensing and communication (ISAC) is a promising technology for future mobile networks, enabling sensing applications to be performed by existing communication networks, consequently improving the system efficiency. Millimeter wave (mmWave) signals provide high sensing resolution and high data rate but suffer from sensitivity to blockage. Cell-free

Luca Biasco, Livia Corsi, Guido Gentile, Michela Procesi

We study the dynamics of solutions for a family of nonlinear Schroedinger equations on the circle, with a smooth convolution potential and Gevrey regular initial data. Our main result is the construction of an asymptotically full measure set of small-amplitude time almost-periodic solutions, which are dense on invariant tori. In regions corresponding to posi

P. Vijay Kumar, Sugandh Mishra, Dileep Dharmappa

In the first part of this two-part paper, we construct a family MFD$_2$ of low-correlation quaternary spreading codes having period $2046$. By quaternary, we mean that the spreading code symbols are drawn from $Z_4$ and are designed to be used in conjunction with QPSK modulation. Apart from low auto and crosscorrelation properties, we also require in additio

Hayden McTavish, Jon Donnelly, Margo Seltzer, Cynthia Rudin

Many important datasets contain samples that are missing one or more feature values. Maintaining the interpretability of machine learning models in the presence of such missing data is challenging. Singly or multiply imputing missing values complicates the model's mapping from features to labels. On the other hand, reasoning on indicator variables that repre

João Gonçalves, Danny Marfatia, António P. Morais, Roman Pasechnik

We study supercooled first-order phase transitions above the QCD scale in a wide class of conformal Majoron-like U(1)' models that explain the totality of active neutrino oscillation data and produce a detectable stochastic gravitational wave background (SGWB) at LIGO, LISA and ET. We place constraints on the U(1)' breaking scale and gauge coupling using cur

Zhaofeng Hu, Sifan Zhou, Zhihang Yuan, Dawei Yang

3D single object tracking is essential in autonomous driving and robotics. Existing methods often struggle with sparse and incomplete point cloud scenarios. To address these limitations, we propose a Multimodal-guided Virtual Cues Projection (MVCP) scheme that generates virtual cues to enrich sparse point clouds. Additionally, we introduce an enhanced tracke

Gabriele Giudici, Aramis Augusto Bonzini, Claudio Coppola, Kaspar Althoefer

Dexterous robotic manipulator teleoperation is widely used in many applications, either where it is convenient to keep the human inside the control loop, or to train advanced robot agents. So far, this technology has been used in combination with camera systems with remarkable success. On the other hand, only a limited number of studies have focused on lever

R. R. Samani, A. Nunez, B. De Schutter

The growing volume of available infrastructural monitoring data enables the development of powerful datadriven approaches to estimate infrastructure health conditions using direct measurements. This paper proposes a deep learning methodology to estimate infrastructure physical parameters, such as railway track stiffness, using drive-by vibration response sig

Jiale Feng, Samuel W. Blair, Timilehin Ayanlade, Aditya Balu

We present a novel method for soybean (Glycine max (L.) Merr.) yield estimation leveraging high throughput seed counting via computer vision and deep learning techniques. Traditional methods for collecting yield data are labor-intensive, costly, prone to equipment failures at critical data collection times, and require transportation of equipment across fiel

Goki Muramoto, Yuri Yasui, Hirosuke Asahi

When language is utilized as a medium to store and communicate sensory information, there arises a kind of radical virtual reality, namely "the realities that are reduced into the same sentence are virtual/equivalent." In the current era, in which artificial intelligence engages in the linguistic mediation of sensory information, it is imperative to re-exami

Eugenio Clerico

We consider the problem of testing the mean of a bounded real random variable. We introduce a notion of optimal classes for e-variables and e-processes, and establish the optimality of the coin-betting formulation among e-variable-based algorithmic frameworks for testing and estimating the (conditional) mean. As a consequence, we provide a direct and explici

Gregory Dexter, Petros Drineas, Rajiv Khanna

We provide space complexity lower bounds for data structures that approximate logistic loss up to $\epsilon$-relative error on a logistic regression problem with data $\mathbf{X} \in \mathbb{R}^{n \times d}$ and labels $\mathbf{y} \in \{-1,1\}^d$. The space complexity of existing coreset constructions depend on a natural complexity measure $\mu_\mathbf{y}(\m

Ramesh Manuvinakurike, Elizabeth Watkins, Celal Savur, Anthony Rhodes

In this work we explore utilizing LLMs for data augmentation for manufacturing task guidance system. The dataset consists of representative samples of interactions with technicians working in an advanced manufacturing setting. The purpose of this work to explore the task, data augmentation for the supported tasks and evaluating the performance of the existin

Dani Roytburg, Deborah Olorunisola, Sandeep Soni, Lauren Klein

In this project, we describe a method of modeling semantic leadership across a set of communities associated with the #BlackLivesMatter movement, which has been informed by qualitative research on the structure of social media and Black Twitter in particular. We describe our bespoke approaches to time-binning, community clustering, and connecting communities

Brett Min, Yuxuan Zhang, Yuxuan Guo, Dvira Segal

Understanding coupled electron-phonon systems is one of the fundamental issues in strongly correlated systems. In this work, we aim to extend the notion of mixed-state phases to the realm of coupled electron/spinphonon systems. Specifically, we consider a two-dimensional cluster Hamiltonian locally coupled to a set of single bosonic modes with arbitrary coup

L. A. Ferreira, L. R. Livramento

We propose a generalization of the so-called rational map ansatz on the Euclidean space $\mathbb{R}^3$, for any compact simple Lie group $G$ such that $G/{\widehat K}\otimes U(1)$ is an Hermitian symmetric space, for some subgroup ${\widehat K}$ of $G$. It generalizes the rational maps on the two-sphere $SU(2)/U(1)$, and also on $CP^N=SU(N+1)/SU(N)\otimes U(

Tianyu Wang, Jianming Zhang, Haitian Zheng, Zhihong Ding

Shadows are often under-considered or even ignored in image editing applications, limiting the realism of the edited results. In this paper, we introduce MetaShadow, a three-in-one versatile framework that enables detection, removal, and controllable synthesis of shadows in natural images in an object-centered fashion. MetaShadow combines the strengths of tw

James Austgen, Andrés Fábrega, Mahimna Kelkar, Dani Vilardell

Inherent in the world of cryptocurrency systems and their security models is the notion that private keys, and thus assets, are controlled by individuals or individual entities. We present Liquefaction, a wallet platform that demonstrates the dangerous fragility of this foundational assumption by systemically breaking it. Liquefaction uses trusted execution

J. Antonio García, Rafael Cruz-Alvarez

A relation between the Dirac bracket (DB) and Nambu bracket (NB) is presented. The Nambu bracket can be related with Dirac bracket if we can write the DB as a generalized Poisson structure. The NB associated with DB have all the standard properties of the original DB. When the dimension of the phase space is $s+2$ where $s$ is the number of second class cons

Jiangtao Wang, Zhen Qin, Yifan Zhang, Vincent Tao Hu

Vision tokenizers have gained a lot of attraction due to their scalability and compactness; previous works depend on old-school GAN-based hyperparameters, biased comparisons, and a lack of comprehensive analysis of the scaling behaviours. To tackle those issues, we introduce Grouped Spherical Quantization (GSQ), featuring spherical codebook initialization an

Daniela Szwarcman, Sujit Roy, Paolo Fraccaro, Þorsteinn Elí Gíslason

This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time series samples from NASA's Harmonized Landsat and Sentinel-2 data archive at 30-m resolution, the new model incorporates temporal and location embeddings for enhanced performance a

Yiftach Edelstein, Or Patashnik, Dana Cohen-Bar, Lihi Zelnik-Manor

Advancements in text-to-image diffusion models have led to significant progress in fast 3D content creation. One common approach is to generate a set of multi-view images of an object, and then reconstruct it into a 3D model. However, this approach bypasses the use of a native 3D representation of the object and is hence prone to geometric artifacts and limi

Nissan Itzhaki, Uri Peleg

Instant Folded Strings (IFSs) are unconventional light strings that emerge when the string coupling increases with time. A particularly intriguing property of IFSs, especially relevant to cosmology, is that they violate the Null Energy Condition (NEC). In this paper, we begin to explore their cosmological effects. We find that NEC violation by IFSs is signif

Ugo Giuseppe Aglietti, Giancarlo Ferrera

We consider heavy quark mass ($m$) effects in the Energy-Energy Correlation function in $e^+e^- \to hadrons$ at high energy $Q$, in the back-to-back (two-jet) region. In the ultra-relativistic limit, $Q \gg m$, the QCD Sudakov form factor $S(b)$ in impact parameter ($b$-)space reads: \begin{eqnarray} \log S(b) &=& - \int_{m^2}^{Q^2} \frac{dk^2}{k^2} \left\{

L. K. Dewangan, N. K. Bhadari, A. K. Maity, O. R. Jadhav

A multi-wavelength, multi-scale study of the Mon R2 hub-filament system (HFS) reveals a spiral structure, with the central hub containing more mass than its filaments. ALMA C$^{18}$O(1-0) emission reveals several accreting filaments connected to a molecular ring (size $\sim$0.18 pc $\times$ 0.26 pc). The molecular ring surrounds the infrared (IR) ring (size

Annie N. Wang, Luchao Qi, Roni Sengupta

We introduce a novel continual learning problem: how to sequentially update the weights of a personalized 2D and 3D generative face model as new batches of photos in different appearances, styles, poses, and lighting are captured regularly. We observe that naive sequential fine-tuning of the model leads to catastrophic forgetting of past representations of t

Yerram Varun, Rahul Madhavan, Sravanti Addepalli, Arun Suggala

Large Language Models (LLMs) are typically trained to predict in the forward direction of time. However, recent works have shown that prompting these models to look back and critique their own generations can produce useful feedback. Motivated by this, we explore the question of whether LLMs can be empowered to think (predict and score) backwards to provide

Tongyu He, Jiao Li, Jiangdan Li, Jianping Xiong

Triple star systems are critical for understanding stellar dynamics and compact objects in astrophysics, yet confirmed hierarchical triples identified via spectroscopy remain limited. In this study, we identified 23 triple systems by cross-matching the Gaia DR3 non-single star catalog with LAMOST DR10 spectroscopic data; 18 of them are new discoveries. For t

Paolo Baglioni, Francesco Di Renzo

The Non-Linear Sigma Model (NLSM) is an example of a field theory on a target space exhibiting intricate geometry. One remarkable characteristic of the NLSM is asymptotic freedom, which triggers interest in perturbative calculations. In the lattice formulation of NLSM, one would naturally rely on Numerical Stochastic Perturbation Theory (NSPT) to conduct hig

Gianluca Manzan, Daniele Tantari

Restricted Boltzmann Machines (RBMs) are generative models designed to learn from data with a rich underlying structure. In this work, we explore a teacher-student setting where a student RBM learns from examples generated by a teacher RBM, with a focus on the effect of the unit priors on learning efficiency. We consider a parametric class of priors that int

R. K. Cochrane, H. Katz, R. Begley, C. C. Hayward

Robust inference of galaxy stellar masses from photometry is crucial for constraints on galaxy assembly across cosmic time. Here, we test a commonly-used Spectral Energy Distribution (SED) fitting code, using simulated galaxies from the SPHINX20 cosmological radiation hydrodynamics simulation, with JWST NIRCam photometry forward-modelled with radiative trans

Kai Sun, Siyan Xue, Fuchun Sun, Haoran Sun

Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated

Gheorghe Craciun, Abhishek Deshpande, Jiaxin Jin

Under mass-action kinetics, complex-balanced systems emerge from biochemical reaction networks and exhibit stable and predictable dynamics. For a reaction network $G$, the associated dynamical system is called $\textit{disguised toric}$ if it can yield a complex-balanced realization on a possibly different network $G_1$. This concept extends the robust prope

Hartmut Schmidt, Andreas Grübl, José Montes, Eric Müller

As numerical simulations grow in size and complexity, they become increasingly resource-intensive in terms of time and energy. While specialized hardware accelerators often provide order-of-magnitude gains and are state of the art in other scientific fields, their availability and applicability in computational neuroscience is still limited. In this field, n

Olga Martynova, Alexander Okhotin

It is proved that the family of tree languages recognized by nondeterministic tree-walking automata is not closed under complementation, solving a problem raised by Boja\'nczyk and Colcombet ("Tree-walking automata do not recognize all regular languages", SIAM J. Comp. 38 (2008) 658--701). In addition, it is shown that nondeterministic tree-walking automata

Hiroki Furuta, Heiga Zen, Dale Schuurmans, Aleksandra Faust

Large text-to-video models hold immense potential for a wide range of downstream applications. However, they struggle to accurately depict dynamic object interactions, often resulting in unrealistic movements and frequent violations of real-world physics. One solution inspired by large language models is to align generated outputs with desired outcomes using

Francesco Grandi, Antonio Picano, Ronny Thomale, Dante M. Kennes

The quench dynamics of systems exhibiting cooperative or almost competitive orders in equilibrium are explored using Ginzburg-Landau theory plus fluctuations. We show that when the renormalization of the free energy by fluctuations is taken into account, anisotropic stiffnesses and relaxation rates of the order parameters can lead to a stabilization of order

Giuseppe Canonaco, Alberto Pozanco, Daniel Borrajo

The concept of abstraction has been independently developed both in the context of AI Planning and discounted Markov Decision Processes (MDPs). However, the way abstractions are built and used in the context of Planning and MDPs is different even though lots of commonalities can be highlighted. To this day there is no work trying to relate and unify the two

Devin Brown, Balazs Elek, Iva Halacheva

The cactus group acts combinatorially on crystals via partial Sch\"utzenberger involutions. This action has been studied extensively in type $A$ and described via Bender-Knuth involutions. We prove an analogous result for the family of crystals $B(n\varpi_1)$ in type $D$. Our main tools are combinatorial toggles acting on reverse plane partitions of height $

Gabriele Giudici, Claudio Coppola, Kaspar Althoefer, Ildar Farkhatdinov

Robotic telemanipulation - the human-guided manipulation of remote objects - plays a pivotal role in several applications, from healthcare to operations in harsh environments. While visual feedback from cameras can provide valuable information to the human operator, haptic feedback is essential for accessing specific object properties that are difficult to b

Aohan Zeng, Zhengxiao Du, Mingdao Liu, Kedong Wang

We introduce GLM-4-Voice, an intelligent and human-like end-to-end spoken chatbot. It supports both Chinese and English, engages in real-time voice conversations, and varies vocal nuances such as emotion, intonation, speech rate, and dialect according to user instructions. GLM-4-Voice uses an ultra-low bitrate (175bps), single-codebook speech tokenizer with

Kaixiong Gong, Kaituo Feng, Bohao Li, Yibing Wang

Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While these models demonstrate impressive performance across a wide range of audio-visual applications, our proposed DeafTest reveals that MLLMs often struggle with simple tasks humans fin

Biman Barua, M. Shamim Kaiser

The increasing demand for scalable, efficient resource management in hybrid cloud environments has led to the exploration of AI-driven approaches for dynamic resource allocation. This paper presents an AI-driven framework for resource allocation among microservices in hybrid cloud platforms. The framework employs reinforcement learning (RL)-based resource ut

Marius F. W. Trollmann, Paolo Rossetti, Rainer A. Böckmann

Comment to article published in Proc. Natl. Acad. Sci. U. S. A.: Garaizar, A. et al. 'Toward understanding lipid reorganization in RNA lipid nanoparticles in acidic environments.' Proc. Natl. Acad. Sci. U. S. A. 121, e2404555121 (2024)

Saurab Chhachhi, Fei Teng

Data is an increasingly vital component of decision making processes across industries. However, data access raises privacy concerns motivating the need for privacy-preserving techniques such as differential privacy. Data markets provide a means to enable wider access as well as determine the appropriate privacy-utility trade-off. Existing data market framew

Jie Ren, Zhe Yu

We express holographic thermal correlators using a recurrence relation of $\{a_n\}$ at $n\to\infty$, building on recent advances in the connection formula for the Heun equation. We consider two gravitational solutions that correspond to distinct states in different subsectors of $\mathcal{N}=4$ super-Yang-Mills theory at finite temperature and density. The f

Partha Kumar Paul, Sujit Kumar Sahoo, Narendra Sahu

The singlet-doublet vector-like fermion dark matter model has been extensively studied in the literature over the past decade. An important parameter in this model is the singlet-doublet mixing angle ($\sin\theta$). All the previous studies have primarily focused on annihilation and co-annihilation processes for obtaining the correct dark matter relic densit

Nacer Eddine Belaloui, Abdellah Tounsi, Rabah Abdelmouheymen Khamadja, Mohamed Messaoud Louamri

While numerical simulations are presented in most papers introducing new methods to enhance the VQE performance, comprehensive, comparative, and applied studies remain relatively rare. We present a comprehensive, yet concise guide for the implementation of the VQE for molecular problems on NISQ devices, specifically applied to estimate the ground state energ

Marco Molinari, Victor Shao, Luca Imeneo, Mateusz Mikolajczak

Determining company similarity is a vital task in finance, underpinning risk management, hedging, and portfolio diversification. Practitioners often rely on sector and industry classifications such as SIC and GICS codes to gauge similarity, the former being used by the U.S. Securities and Exchange Commission (SEC), and the latter widely used by the investmen

Emmanouil Kariotakis, Nicholas D. Sidiropoulos, Aritra Konar

Dense subgraph discovery (DSD) is a key graph mining primitive with myriad applications including finding densely connected communities which are diverse in their vertex composition. In such a context, it is desirable to extract a dense subgraph that provides fair representation of the diverse subgroups that constitute the vertex set while incurring a small

Abhas Kumar, Kapil Pathak, Rajesh Kavuru, Prabhakar Srinivasan

This paper analyzes the performance of Small Language Models (SLMs) and Vision Language Models (VLMs) and evaluates the trade-off between model performance and carbon emissions across 4 essential tasks: Image Captioning, Visual Question Answering (VQA), Dialogue Summarization and Text-to-SQL conversion. Various SLMs and VLMs belonging to the Qwen and LLaMA a

Aniruddha Ganguly, Debolina Chatterjee, Wentao Huang, Jie Zhang

Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized gene expression. However, existing metho

Christopher L. Rom, Matthew Jankousky, Maxwell Q. Phan, Shaun O'Donnell

The synthesis of ternary nitrides is uniquely difficult, in large part because elemental N$_2$ is relatively inert. However, lithium reacts readily with other metals and N$_2$, making Li-M-N the most numerous sub-set of ternary nitrides. Here, we use Li$_2$ZrN$_2$, a ternary with a simple synthesis recipe, as a precursor for ion exchange reactions towards AZ

Felix Kurtz, Gevin von Witte, Lukas Jehn, Alp Akbiyik

The discovery of the kagome metal CsV$_3$Sb$_5$ sparked broad interest, due to the coexistence of a charge density wave (CDW) phase and possible unconventional superconductivity in the material. In this study, we use low-energy electron diffraction (LEED) with a $\mu$m-sized electron beam to explore the periodic lattice distortion at the antimony-terminated

Jacob Marks, Brent A. Griffin, Jason J. Corso

We introduce a new framework for analyzing classification datasets based on the ratios of reconstruction errors between autoencoders trained on individual classes. This analysis framework enables efficient characterization of datasets on the sample, class, and entire dataset levels. We define reconstruction error ratios (RERs) that probe classification diffi

Dan Su, Kezhi Kong, Ying Lin, Joseph Jennings

Recent English Common Crawl datasets like FineWeb-Edu and DCLM achieved significant benchmark gains via aggressive model-based filtering, but at the cost of removing 90% of data. This limits their suitability for long token horizon training, such as 15T tokens for Llama 3.1. In this paper, we show how to achieve better trade-offs between accuracy and data qu

Weihua Xiao, Venkata Sai Charan Putrevu, Raghu Vamshi Hemadri, Siddharth Garg

Prefix circuits are fundamental components in digital adders, widely used in digital systems due to their efficiency in calculating carry signals. Synthesizing prefix circuits with minimized area and delay is crucial for enhancing the performance of modern computing systems. Recently, large language models (LLMs) have demonstrated a surprising ability to per

Jørgen Olsen Lye, Boris Vertman, Mannaim Gennaro Vitti

In this work we introduce a family of conformal flows generalizing the classical Yamabe flow. We prove that for a large class of such flows long-time existence holds, and the arguments are in fact simpler than in the classical case. Moreover, we establish convergence for the case of negative scalar curvature and expect a similar statement for the positive an

Junyuan Zhang, Qintong Zhang, Bin Wang, Linke Ouyang

Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge to reduce hallucinations and incorporate up-to-date information without retraining. As an essential part of RAG, external knowledge bases are commonly built by extracting structured data from unstructured PDF documents using Optical Character Recognit

Dmitriy Morozov, Primoz Skraba

Most algorithms for computing persistent homology do so by tracking cycles that represent homology classes. There are many choices of such cycles, and specific choices have found different uses in applications. Although it is known that persistence diagrams can be computed in matrix multiplication time [8] for the more general case of zigzag persistent homol

Alice Portal, Andrea Angelidou, Raphael Rousseau-Rizzi, Shira Raveh-Rubin

Understanding convective processes leading to severe weather hazards within Mediterranean cyclones is relevant for operational forecasters, insurance industry, and enhancing societal preparedness. In this work we examine the climatological link between Mediterranean cyclones and atmospheric conditions conducive to the formation of severe convection and conve

Yihong Chen, Jiancheng Yang, Deniz Sayin Mercadier, Hieu Le

We present a novel approach to reconstruction of 3D cardiac motion from sparse intraoperative data. While existing methods can accurately reconstruct 3D organ geometries from full 3D volumetric imaging, they cannot be used during surgical interventions where usually limited observed data, such as a few 2D frames or 1D signals, is available in real-time. We p

Xiaohan Yu, Li Zhang, Chong Chen

Recommendation Systems have become integral to modern user experiences, but lack transparency in their decision-making processes. Existing explainable recommendation methods are hindered by reliance on a post-hoc paradigm, wherein explanation generators are trained independently of the underlying recommender models. This paradigm necessitates substantial hum

Manon Ramel, Cyrille Doux, Marine Kuna

Upcoming deep optical surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will scan the sky to unprecedented depths, detecting billions of galaxies. However, this amount of detections will lead to the apparent superposition of galaxies in the images, a phenomenon known as blending, that can affect the accurate measurement o

Zhongshuo Lin, Yifan Wang, Hehu Xie

Based on neural network and adaptive subspace approximation method, we propose a new machine learning method for solving partial differential equations. The neural network is adopted to build the basis of the finite dimensional subspace. Then the discrete solution is obtained by using the subspace approximation. Especially, based on the subspace approximatio

Oleksandr Kurbatov, Kyrylo Riabov, Mykhailo Velykodnyi

This document proposes a combination of several techniques to construct anonymous and untraceable payment systems. The proposed system supports arbitrary transfer amounts and enables the simultaneous transfer of multiple assets.

Nastaran Behrooznia, Sofia Brenner, Arturo Merino, Torsten Mütze

This paper investigates the problem of listing faces of polytopes that represent combinatorial objects, such as hypercubes, permutahedra, associahedra, and their generalizations. Firstly, we consider the face lattice, which is the inclusion order of all faces of a polytope, and we seek a Hamiltonian cycle in its cover graph, i.e., for any two consecutive fac

Abhirath Anand, Lina Nikolaidou, Christian Poelma, Angeliki Laskari

The turbulent boundary layer (TBL) development over an air cavity is experimentally studied using planar particle image velocimetry. The present flow, representative of those typically encountered in ship air lubrication, resembles the geometrical characteristics of flows over solid bumps studied in literature. However, unlike solid bumps, the cavity has a v

Xinyang Zhao, Yichao Li, Wenxiu Yang, Furen Deng

Neutral hydrogen (HI) intensity mapping (IM) presents great promise for future cosmological large-scale structure surveys. However, a major challenge for HIIM cosmological studies is to accurately subtract the foreground contamination. An accurate beam model is crucial for improving the quality of foreground subtraction. In this work, we develop a stacking-b

Ziheng Liu, Jiayi Zhang, Yiyang Zhu, Enyu Shi

Cell-free massive multiple-input multiple-output (mMIMO) offers significant advantages in mobility scenarios, mainly due to the elimination of cell boundaries and strong macro diversity. In this paper, we examine the downlink performance of cell-free mMIMO systems equipped with mobile-APs utilizing the concept of unmanned aerial vehicles, where mobility and